Reviewerly/exHarmony huggingface dataset:
Research on the classification of lymphoma pathological images based on deep residual neural network

BACKGROUND: Malignant lymphoma is a type of tumor that originated from the lymphohematopoietic system, with complex etiology, diverse pathological morphology, and classification. It takes a lot of time and energy for doctors to accurately determine the type of lymphoma by observing pathological images. OBJECTIVE: At present, an automatic classification technology is urgently needed to assist doctors in analyzing the type of lymphoma. METHODS: In this paper, by comparing the training results of the BP neural network and BP neural network optimized by genetic algorithm (GA-BP), adopts a deep residual neural network model (ResNet-50), with 374 lymphoma pathology images as the experimental data set. After preprocessing the dataset by image flipping, color transformation, and other data enhancement methods, the data set is input into the ResNet-50 network model, and finally classified by the softmax layer. RESULTS: The training results showed that the classification accuracy was 98.63%. By comparing the classification effect of GA-BP and BP neural network, the accuracy of the network model proposed in this paper is improved. CONCLUSIONS: The network model can provide an objective basis for doctors to diagnose lymphoma types.
A class of two or three weights linear codes and their complete weight enumerators

In the past few years, linear codes with few weights constructed from defining-sets have been extensively studied. In this paper, we further investigate a class of two-weight or three-weight linear codes from defining sets and determine their weight and complete weight enumerators by application of the theory of quadratic forms and some special Weil sums over finite fields. This paper generalizes some results in Jian et al. (2019), Yang (2020) and Zhu and Xu (2017). The parameters and weight distributions of the punctured codes of the discussed linear codes are also determined, and some optimal codes with respect to the Griesmer bound and some strongly regular graphs from two-weight projective codes are obtained.
Adaptive resource planning for cloud-based services using machine learning

The problem of using cloud computing resources for services is related to planning the amount of resources needed and their subsequent reservation. This problem occurs both on the side of the customer who tries to minimize the cost of the service and on the side of the cloud provider who wants to make the best use of existing infrastructure without introducing any modifications. In our article, we want to show how the problem of overestimating the utilization of resources for services which use cloud computing can be handled. Solving this problem will allow significant savings to be made by both the customer and the cloud infrastructure provider. The system we have developed demonstrates the considerable utility of machine learning methods when planning cloud resource reservation for network services. The models proposed, which use a multilayer perceptron, have yielded good results for both short- and long-term reservations.
Test, Trace, and Put on the Blockchain?: A Viewpoint Evaluating the Use of Decentralized Systems for Algorithmic Contact Tracing to Combat a Global Pandemic

The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing.
Privacy-Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario

The arrival of cloud computing age makes data outsourcing an important and convenient application. More and more individuals and organizations outsource large amounts of graph data to the cloud computing platform (CCP) for the sake of saving cost. As the server on CCP is not completely honest and trustworthy, the outsourcing graph data are usually encrypted before they are sent to CCP. The optimal route finding on graph data is a popular operation which is frequently used in many fields. The optimal route finding with support for semantic search has stronger query capabilities, and a consumer can use similar words of graph vertices as query terms to implement optimal route finding. Due to encrypting the outsourcing graph data before they are sent to CCP, it is not easy for data customers to manipulate and further use the encrypted graph data. In this paper, we present a solution to execute privacy-guarding optimal route finding with support for semantic search on the encrypted graph in the cloud computing scenario (PORF). We designed a scheme by building secure query index to implement optimal route finding with support for semantic search based on searchable encryption idea and stemmer mechanism. We give formal security analysis for our scheme. We also analyze the efficiency of our scheme through the experimental evaluation.
Which OCR toolset is good and why? A comparative study

Optical Character Recognition (OCR) is a very active research area in many challenging fields like pattern recognition, natural language processing (NLP), computer vision, biomedical informatics, machine learning (ML), and artificial intelligence (AI). This computational technology extracts the text in an editable format (MS Word/Excel, text files, etc.) from PDF files, scanned or hand-written documents, images (photographs, advertisements, and alike), etc. for further processing and has been utilized in many real-world applications including banking, education, insurance, finance, healthcare and keyword-based search in documents, etc. Many OCR toolsets are available under various categories, including open-source, proprietary, and online services. This research paper provides a comparative study of various OCR toolsets considering a variety of parameters.
Site Experience Enhancement and Perspective in Cultural Heritage Fruition—A Survey on New Technologies and Methodologies Based on a “Four-Pillars” Approach

This paper deals with innovative fruition modalities of cultural heritage sites. Based on two ongoing experiments, four pillars are considered, that is, User Localization, Multimodal Interaction, User Understanding and Gamification. A survey of the existing literature regarding one or more issues related to the four pillars is proposed. It aims to put in evidence the exploitation of these contributions to cultural heritage. It is discussed how a cultural site can be enriched, extended and transformed into an intelligent multimodal environment in this perspective. This new augmented environment can focus on the visitor, analyze his activity and behavior, and make his experience more satisfying, fulfilling and unique. After an in-depth overview of the existing technologies and methodologies for the fruition of cultural interest sites, the two experiments are described in detail and the authors’ vision of the future is proposed.
Researching the Link Between the Geometric and R鑞yi Discord for Special Canonical Initial States Based on Neural Network Method

Quantum correlation which is different to the entanglement and classical correlation plays important role in quantum information field. In our setup, neural network method is adopted to simulate the link between the Rènyi di... | Find, read and cite all the research you need on Tech Science Press
Breaking out: the turning point in learning using mobile technology

Despite considerable research on YouTube as a digital media platform, little research to date has quantified the device-type used to access that online media. Analyzing access-device data for videos on one YouTube video channel—Scientific Animations Without Borders (SAWBO), which produces educational content specifically accessible to low- or non-literate, poor, or geographically isolated learners in less developed areas of the world—the results identify the historical moments between 2015 and 2017 when mobile/smartphones, both globally and by region, crossed a tipping point to surpass all other ICT devices (including desktop PCs, laptops, and other Internet-accessing technologies) as the primary device-type for accessing SAWBO videos. Specifically, data from January 2013 to June 2018 obtained for SAWBO's YouTube channel were sampled to capture and distinguish the access device-type used and then summarized in broad global and regional categories. The tipping point, as the date where the percentage of views from mobile phones was equivalent to the percentage of views from computers, were also calculated globally and by region. Besides documenting this critical global-historical moment, the results also have implications for mass digital-messaging generally and mobile-based public service learning specifically.
Extractive Text Summarization Using Recent Approaches: A Survey

In this era of growing digital media, the volume of text data increases day by day from various sources and may contain entire documents, books, articles, etc. This amount of text is a source of information that may be insignificant, redundant, and sometimes may not carry any meaningful representation. Therefore, we require some techniques and tools that can automatically summarize the enormous amounts of text data and help us to decide whether they are useful or not. Text summarization is a process that generates a brief version of the document in the form of a meaningful summary. It can be classified into abstractive text summarization and extractive text summarization. Abstractive text summarization generates an abstract type of summary from the given document. In extractive text summarization, a summary is created from the given document that contains crucial sentences of the document. Many authors proposed various techniques for both types of text summarization. This paper presents a survey of extractive text summarization on graphical-based techniques. Specifically, it focuses on unsupervised and supervised techniques. This paper shows the recent works and advances on them and focuses on the strength and weaknesses of surveys of previous works in tabular form. At last, it concentrates on the evaluation measure techniques of summary.
Efficient $\ell ^0$ Gradient-Based Super-Resolution for Simplified Image Segmentation

We consider a variational model for single-image super-resolution based on the assumption that the gradient of the target image is sparse. We enforce this assumption by considering both an isotropic and an anisotropic <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> regularisation on the image gradient combined with a quadratic data fidelity, similarly as studied for signal recovery problems. For the numerical realisation of the model, we propose a novel efficient ADMM splitting algorithm whose substeps solutions are computed efficiently by means of hard-thresholding and standard conjugate-gradient solvers. We test our model on highly-degraded synthetic and real-world data and quantitatively compare our results with several sparsity-promoting variational approaches as well as with state-of-the-art deep-learning techniques. Our experiments show that thanks to the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> smoothing on the gradient, the super-resolved images can be used to improve the accuracy of standard segmentation algorithms for applications like QR codes and cell detection and land-cover classification problems.
Reward Shaping Based Federated Reinforcement Learning

Federated reinforcement learning aims to promote training efficiency or improve policy quality through information interaction with privacy protection. Existing federated reinforcement learning methods rarely utilize the structure of reinforcement learning algorithms while are limiting to specific scenarios or algorithms. We propose a general federated reinforcement learning framework FRS, which employs reward shaping as the federated information shared among different clients with different tasks to promote each client’s training speed and policy quality. The federated reward shaping is implicitly learned by average state value information of all clients to protect each client’s task privacy as the real trajectory is anonymous. Experiments on the GridWorld environment show that FRS can algorithm-independently improve the policy quality and promote training speed with protecting each client’s privacy.
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata

In this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases.CHOLAN consists of a pipeline of two transformerbased models integrated sequentially to accomplish the EL task.The first transformer model identifies surface forms (entity mentions) in a given text.For each mention, a second transformer model is employed to classify the target entity among a predefined candidates list.The latter transformer is fed by an enriched context captured from the sentence (i.e.local context), and entity description gained from Wikipedia.Such external contexts have not been used in state of the art EL approaches.Our empirical study was conducted on two well-known knowledge bases (i.e., Wikidata and Wikipedia).The empirical results suggest that CHOLAN outperforms state-of-the-art approaches on standard datasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.
Supporting School Aged Children to Train Their Vision by Using Serious Games

Several children struggle with vision difficulties caused by problematic coordination between their left and right eye muscles, i.e., oculomotor dysfunction (OMD). Many OMDs can be improved by training the eyes via physical exercises defined and supervised by vision experts. The aim of this paper is to investigate the feasibility of utilizing Serious Games (SGs) and eye-tracking technologies (ETs) for training the eyes of children having OMD. Via these activities, a trainee can, with her eye gaze, follow objects which are moving, change their directions and speed, or pop up on the screen. The results present mapping the current physical training goals to activities for SGs using input from ETs, and illustrate this correspondence for designing and developing six games. The games’ feasibility evaluation is done via semistructured interviews and evaluating user experiences. Three vision teachers (VTs) were involved in design and development, ensuring achievement of training goals, and five VT students in evaluations. The findings demonstrate the potential of using SGs and ETs to train OMD and point to future needs for improvements.
Classification of Thyroid Carcinoma in Whole Slide Images Using Cascaded CNN

The objective of this research is to build a “Whole Slide Images” classification system using Convolutional Neural Network (CNN). This system is capable of classifying Thyroid tumors into three types: Follicular adenoma, follicular carcinoma, and papillary carcinoma. Furthermore, the cascaded CNN technique is additionally employed to classify the classified follicular carcinoma into four subclasses: follicular carcinoma, papillary follicular variant, well-differentiated follicular carcinoma, and Poorly-differentiated follicular carcinoma. Results of the proposed CNN architecture showed effective classification of Thyroid carcinoma in the whole slide images with an overall accuracy of 94.69%. In the first classification stage, the images are classified into either one of three main types with an overall accuracy of 98.74%, while in the second classification stage, using the cascaded CNN, accuracy was 95.90% for further sub-classification into four sub-classes. Our cascaded CNN outperformed the accuracy of other studies due to splitting classification process of the thyroid into two stages which reduces the number of classes in each stage.
Anomaly Detection in Resource Constrained Environments With Streaming Data

Isolation Forest (or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iForest</i> ) is a well-known technique for anomaly detection. It is, however, a bulky approach that assumes the luxury of large storage space and is also ineffective with dynamic streaming data so common nowadays in varied application domains. In this work, we present the Preprocessed Isolation Forest ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PiForest</i> ) approach for anomaly detection that works well in resource constrained environments and is also effective on streaming data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PiForest</i> is largely based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iForest</i> algorithm and to effectively handle the streaming data includes a pre-processing stage. In the pre-processing stage, Principal Component Analysis (PCA) is first harnessed to significantly reduce the dimension and bulk of the data. Subsequently, the streaming characteristic of the data is handled through a sliding window mechanism that creates sequential blocks of data for systematic processing. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PiForest</i> is able to identify anomalies as effectively as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iForest</i> and other state-of-the-art anomaly detection techniques but has substantially low storage and prediction complexity. We conduct empirical evaluation of the proposed approach with standard data sets and show that it performs comparably with standard techniques in terms of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and is able to work with high-dimensional, streaming data. Subsequently, we do a real-world hardware implementation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PiForest</i> and demonstrate that the approach is realistic and practicable in resource-constrained environments.
Improved YOLOv3 model for vehicle detection in high-resolution remote sensing images

Vehicle detection is an important method for understanding high-resolution remote sensing images. Deep convolutional neural network (DCNN)-based methods have improved many computer vision tasks and have achieved state-of-the-art results in many object detection datasets. Object detection of remote sensing images has been radically changed by the introduction of DCNN. Considering correlation between the scale distribution of objects and spatial resolution of remote sensing images, we propose an improved vehicle detection method based on a YOLOv3 model. A multi-scale clustering anchor box generation algorithm is proposed to obtain the anchor box parameters that match the resolution of each layer of the feature pyramid of model. This allows us to get more accurate anchor parameters. Focal loss is introduced into the default loss function to reduce the weight of negative samples, which were easily classified, that focus the model training process on samples that are difficult to classify. For the imbalance problem of positive and negative samples in the detection method based on the prior anchor box, focal loss is used to focus the model training process on samples that are difficult to classify. The experiment is performed on a dataset consisting of remote sensing images obtained from Worldview-3, and the results show that compared with the basic YOLOv3 algorithm, the average accuracy of vehicle detection is improved by 8.44%. The accuracy of vehicle detection of high-resolution remote sensing images is significantly improved while maintaining the speed of single-stage target detection. This approach is tested on an xView dataset consisting of remote sensing images obtained from Worldview-3. In addition, through using the proposed method, the average precision of vehicle detection increased by 8.44%. The experimental results show that the proposed method can be used for object detection in high-resolution remote sensing images effectively, and this method can significantly improve the performance of the model without sacrificing inference speed.
Tahoe

Decision trees are widely used and often assembled as a forest to boost prediction accuracy. However, using decision trees for inference on GPU is challenging, because of irregular memory access patterns and imbalance workloads across threads. This paper proposes Tahoe, a tree structure-aware high performance inference engine for decision tree ensemble. Tahoe rearranges tree nodes to enable efficient and coalesced memory accesses; Tahoe also rearranges trees, such that trees with similar structures are grouped together in memory and assigned to threads in a balanced way. Besides memory access efficiency, we introduce a set of inference strategies, each of which uses shared memory differently and has different implications on reduction overhead. We introduce performance models to guide the selection of the inference strategies for arbitrary forests and data set. Tahoe consistently outperforms the state-of-the-art industry-quality library FIL by 3.82x, 2.59x, and 2.75x on three generations of NVIDIA GPUs (Kepler, Pascal, and Volta), respectively.
SSProve: A Foundational Framework for Modular Cryptographic Proofs in Coq

State-separating proofs (SSP) is a recent methodology for structuring game-based cryptographic proofs in a modular way. While very promising, this methodology was previously not fully formalized and came with little tool support. We address this by introducing SSProve, the first general verification framework for machine-checked state-separating proofs. SSProve combines high-level modular proofs about composed protocols, as proposed in SSP, with a probabilistic relational program logic for formalizing the lower-level details, which together enable constructing fully machine-checked crypto proofs in the Coq proof assistant. Moreover, SSProve is itself formalized in Coq, including the algebraic laws of SSP, the soundness of the program logic, and the connection between these two verification styles.