Reviewerly/exHarmony huggingface dataset:
Counting mitoses: SI(ze) matters

The assessment of proliferative activity by counting mitoses is something that most if not all histopathologists practice, as it is required for diagnosis and/or grading of many tumours. In the past, mitoses were often counted ‘per high power field’, which was convenient, but was regarded as scientifically questionable even 40 years ago, as it ignored the fact that microscope fields may differ substantially, even at the same high power (×400) magnification.1 Other features such as vessels, cell numbers or apoptoses are also counted in some circumstances, and the same issues apply. The problem has been compounded by digital pathology, where fields are rectangular, and also vary substantially in the area displayed. There is considerable potential for error, and in some cases, this could be clinically significant – changing diagnosis or grade. However, the problem is solved very simply by the use of standardised international (SI) units. The recently published paper in Modern Pathology2 therefore recommends that features such as mitoses are always counted per mm2 with an indication of the area to be counted and the technique used for counting (usually ‘hotspot’ or ‘average’). Acknowledgements: I am grateful to Drs Puay Hoon Tan, William D. Travis, Pieter Wesseling, Yukako Yagi, Valerie A. White, Dilani Lokuhetty, and Richard A. Scolyer for their assistance with the work on which this talk is based. References 1. Ellis PS, Whitehead R. Mitosis counting - a need for reappraisal. Hum Pathol 1981; 12: 3–4. 2. Cree IA, Tan PH, Travis WD, et al. Counting mitoses: SI(ze) matters! Mod Pathol 2021; 34: 1651–7.
Privacy Preserved Based E-voting System Using Blockchain

Electronic balloting has been utilized in various forms since the Nineteen Seventies with basic edges. They need gained way more quality and are of growing interests. Blockchain is secure public ledgers for protective transaction information and also permits transparent transaction verification. To implement in a large number, it needs the addressing issues regarding the protection and responsibility of system. The Blockchain technology, introduced by Satoshi Nakamoto using the cryptologic currency Bitcoin in 2008 developing a secure, clear and redistributed system with the absence of a 3rd party for access and management, within the election procedure of casting and counting of votes .In modern world, elections are important however society doesn't trust their election system that may be a major concern for world. Even today, we still suffer from a blemished voting technique. This process is implemented to reduce major problems like hacking of EVM, recasting, etc. The blockchain is said as rising technology that guarantees for boosting totally different aspects of the many industries. Increasing e-voting into blockchain technology may be the answer to reduce the current issues in e-voting system. Smart contract makes blockchain a strong tool and lots of options that overcomes ancient systems. The system should be trustworthy for candidate. Because of its stability, extensive use, and facilities of smart contracts logic. Ethereum and its network is one in all the foremost appropriate ones.
Social Communications using Big Data Applications

Social computing is interactive and collaborative behavior between computer users. Personal computing is an individual user activity in that one user generally commands computing. In social computing, the Internet allows users to interact through many mediums, including: Social computing is basically the use of a computer for social purposes. Before the Internet, computers were largely used as tools for increasing productivity. The Internet introduced a social element where users could network, share interests, publish personal insights and use their computers for more than just doing a job faster. Social computing can still benefit businesses synergistically if it is used for business purposes. Social computing can be used to market products and promote customer relations. Online marketing and viral marketing are two types of promotional advertising that have grown out of social computing.
Generalizable Multi-Camera 3D Pedestrian Detection

We present a multi-camera 3D pedestrian detection method that does not need to train using data from the target scene. We estimate pedestrian location on the ground plane using a novel heuristic based on human body poses and person’s bounding boxes from an off-the-shelf monocular detector. We then project these locations onto the world ground plane and fuse them with a new formulation of a clique cover problem. We also propose an optional step for exploiting pedestrian appearance during fusion by using a domain-generalizable person re identification model. We evaluated the proposed approach on the challenging WILDTRACK dataset. It obtained a MODA of 0.569 and an F-score of 0.78, superior to state-of-the-art generalizable detection techniques.
Information Systems Strategic Planning Using the Ward and Peppard Method

The application of information systems that have not been maximized at BPR Centradana Kapuas is due to the unplanned development of an information system that refers to the information system strategic planning document. This study aims to produce a strategic plan of information technology and systems that are appropriate and sustainable, as well as analyzing business areas and making strategic information so that it is easier to determine strategic decisions and can overcome problems that exist in BPR Pancur Banua Khatulistiwa. The research method used in strategic planning of this information system uses the Ward and Peppard Model. The proposed information system strategy planning is based on internal and external business environment analysis as well as IS/IT internal and external environmental analysis through SWOT analysis, SWOT Matrix, Value Chain Analysis, PEST analysis and IS/IT analysis which produces a portfolio for future information system needs.
Sales System Using Apriori Algorithm to Analyze Consumer Purchase Patterns

This study aims to create a sales system to get order data on time, not too late to result in days, and the data becomes structured. As well as develop solutions to process sales transaction data which will increasingly use a priori algorithms to find out consumer buying patterns so that they can be output for decision making or knowledge. This study uses a qualitative method to deepen understanding of the phenomena currently happening as profoundly as possible. This shows the importance of depth and detail of the data studied. The system development uses the waterfall method because it fits perfectly with the needs of the system to be built. From the results of the study, calculating a sample of transaction data with a total of 12 data on August 7-8, 2021, using the Tanagra tools resulted in a rule association that if you buy a vortex, you will buy a Caraco with a support value of 58% and a confidence value of 100%, having a lift ratio value of 1.3 stated that the two products have a solid attachment to each other. Followed by if you buy Faraco, you will purchase a vortex. If you believe in a crystal, you will buy an arco that meets the specified parameter criteria with a minimum support value of 20% and minimum confidence of 50%.
Custom Cloud: An Efficient Model for Cloud Service Selection Based on Neural Network

Cloud Computing uses a remote web-hosting network for storing, handling, and processing data, instead of a local server or a personal computer. Due to the huge demand for cloud services, the number of cloud service providers (CSPs) is expanding day by day. Each CSP has its unique offering. The availability of multiple CSPs having different offerings has confused the users in selecting an efficient service provider. It becomes much more complex if cloud users change their requirements based on demands and the comfort level of the experienced operator. This paper would resolve this downside by establishing a Custom Cloud model for the collection of cloud services by defining a trend of shifting users' needs or priorities. A combination of various Multi-Criteria Decision-Making (MCDM) methods is used to train the neural network. The trained neural network is used to select the efficient CSP based on user preferences. The improvement in the results shows the acceptability and efficiency of the proposed model in the cloud environment.
A weakly supervised object detection approach using point annotation

一种基于点标注的弱监督目标检测方法 量数据下, 这种模式并不能适应目标检测算法需求.为平衡数据标注成本和模型性能, 弱监督目标检 测 (weakly supervised object detection, WSOD) [11∼20] 逐渐成为该领域中一个重要研究方向.大多数弱监督目标检测算法将训练过程建模为一个多实例学习 (multiple-instance learning, MIL) 问题 [15∼17, 21] .这些算法通常独立识别每个候选框区域特征, 选择弱监督检测器 (weakly supervised detector, WSD) 中最高得分框作为伪监督信息在线精细化实例分类网络 [22,23] , 并生成多个紧密围绕 在目标周围的重叠候选框.然而, 由于弱监督目标检测缺乏具体监督信息, 在弱监督学习过程中会产 生不确定问题, 一定程度上限制了现有弱监督目标检测性能. 弱监督目标检测中的不确定问题包括: 位置不确定 (location ambiguity)、语义不确定 (semantic ambiguity)、数量不确定 (count ambiguity).位置不确定是指预测结果突出判别性较大的部分目标, 高置信度预测框无法满足目标检测要求 (即 真值与预测框之间的 IoU > 0.5) (如图 1(a) 所示).产生的原因是监督信息只有图像级标签, 没有准 确的定位信息.语义不确定是指当图片存在多类别目标时, 某些类别目标漏检 (如图 1(b) 所示).产 生的原因是多标签图像中, 每个图像区域的语义有多种不同的可能性 [16,17] .数量不确定是指高置 信度预测框包含同一类别的多个目标, 测试时将多个同类目标识别为单个实例 (如图 1(c) 所示).产 生的原因是图像标注仅能反映相应目标是否出现, 而属同一类别的目标数量完全无法体现在图像标 注中.为解决上述问题, 本文利用点标注信息, 对空间、类别和实例间的丰富依赖关系建模. 模拟人类视 觉认知特点, 通过依赖关系推理弥补弱监督目标检测中监督信息的不足, 解决漏检、错检问题.本文为 推理候选框检测得分, 对标注点周围候选框建立空间依赖关系 (如图 1(d) 所示).标注点一般位于目标 上, 与标注点空间相邻的候选框至少包含部分目标或目标周围背景.本文通过聚合目标内部及周围背 景信息, 促进目标部分与整体 (如猫的头部与全身)、前景与背景 (如猫在窗台) 间信息传播, 推理出完 整目标位置 (如图 1(e) 所示
New Attacks from Old Distinguishers Improved Attacks on Serpent

Serpent was originally proposed in 1998 and is one of the most studied block ciphers. In this paper we improve knowledge of its security by providing the current best attack on this cipher, which is a 12-round differential-linear attack with lower data, time and memory complexities than the best previous attacks. Our improvements are based on an improved conditional key guessing technique that exploits the properties of the Sboxes.
Workload-aware storage policies for cloud object storage

Different applications have different access characteristics and various performance requirements. Thus, the shared cloud object store entails providing tenant-specific policies. However, the limited configurability of existing storage policies makes it difficult to provide efficient and flexible policies to meet tenants' evolving needs. First, existing policies that only control request forwarding cannot provide sufficient optimizations for workload performance. Second, those policies lack the flexibility to adapt to the possible workload changes during runtime. In this paper, we propose Mass, a programmable framework to provide the enhanced storage policies for diverse workloads based on their access characteristics. We also design its enhancements, C-Mass, extending Mass's capabilities through container-based policy deployment to efficiently handle workload changes. Compared with existing storage policies, the latency and throughput of workloads under Mass are improved by up to 81.6% and 231.5%, respectively. Further, the workload performance under C-Mass is optimized by up to 40%.
Data Enhancement Technology Based on I-GOS Algorithm

Although deep learning is developing rapidly and being applied on a large scale, its essence is still a black box model. When evaluating the credibility of the model, the judgment for the prediction of the model is important. Based on the I-GOS algorithm in the field of visual interpretation, we effectively extract the effective information of the sample and then use the sample to retrain a new model to improve the accuracy of the model. Thus, a new data enhancement method is proposed to train the algorithm by using the ImageNet dataset and retrain it by using the I-GOS algorithm to extract effective information. The experiment result shows that compared with the original model, the accuracy of the new model is improved by 5% in the top 1 and 3% in the top 5, which fully proves the effectiveness of this data enhancement method.
THE CHILD AS A SUBJECT AND OBJECT OF DIGITAL CULTURE

the article discusses the issues of digitalization of a child's life in changing conditions.
Efficient Face Re-Identification through PSO Based Adaptive Deep Learning Models

Face plays a vital role in the Recognition or Re-Identification of a person. Therefore, it is significant to identify and extract the facial visual features that automatically lead to face identification-based classification. Facial features comprise different ways of detection, for instance, they could be located at corners or midpoints of the facial features that rely on multiple components such as eyes, lips, nose with different emotions and expressions used in face recognition. This paper introduced a robust and efficient deep learning model with the use of a transfer learning approach for PSO for extraction and selection of the best facial features. Deep learning models “Openface via PSO and introduced customized Inception-V3 model via PSO is used and present detailed comparative accuracy of both models in terms of classification recognition. For this, the paper presents seven different algorithms to evaluate the efficiency of the model with four different face databases. It is evident from the result; neural network classifier shows a gradual hike to calculate accuracy with the proposed PSO-based OpenFace deep learning approach. On the other hand, random forest and AdaBoost algorithm were observed most compatible with the customized PSO-based Inception-V3 model.
Sociocultural Approaches For Modern Scientific And Educational Space Construction

The research is devoted to a problem of misunderstanding among people due to differences in outlook, life attitude, and culture-reasonable views.The collaboration of people from different countries with different cultural backgrounds and worldviews is of particular importance at the level of scientific communication.Unity, respect may allow us to concentrate on solving important scientific problems.In this regard, the article attempted to study the socio-cultural component of the modern scientific and educational space of Russian universities.The purpose of our research was to determine the main problems that both graduate students and teachers may face.Research is also devoted to reasons for their emergence in communication field of scientific and educational space of Russian universities, from the point of view of their significance, complexity and defining ways to overcome them.The study was conducted with postgraduate level students at universities of the Republic of Tatarstan.The study involved 60 postgraduate students, 15 professors and scientific advisers working with them.The result of the study was the identification of factors that represent risks as well as promising approaches for the development of the system of training of higher scientific qualification, specialists, the solution of which will be facilitated by updating the socio-cultural component of the formation of the scientific and educational space of the university.
Correction

This article refers to:Look away to listen: the interplay of emotional context and eye contact in video conversationsCan attitude similarity shape social inhibition of return?
Neighborhood Preserving Kernels for Attributed Graphs

We describe the design of a reproducing kernel suitable for attributed graphs, in which the similarity between two graphs is defined based on the neighborhood information of the graph nodes with the aid of a product graph formulation. Attributed graphs are those which contain a piece of vector information and a discrete label over the nodes and edges. We represent the proposed kernel as the weighted sum of two other kernels of which one is an R-convolution kernel that processes the attribute information of the graph and the other is an optimal assignment kernel that processes label information. They are formulated in such a way that the edges processed as part of the kernel computation have the same neighborhood properties and hence the kernel proposed makes a well-defined correspondence between regions processed in graphs. These concepts are also extended to the case of the shortest paths. We identified the state-of-the-art kernels that can be mapped to such a neighborhood preserving framework. We found that the kernel value of the argument graphs in each iteration of the Weisfeiler-Lehman color refinement algorithm can be obtained recursively from the product graph formulated in our method. By incorporating the proposed kernel on support vector machines we analyzed the real-world data sets and it showed superior performance in comparison with that of the other state-of-the-art graph kernels.
Parsing Arabic Verbal Sentence Using Grammar Ontology

We build a model to parse the Arabic verbal sentence based on Arabic grammar ontology. The ontology conceptualizes the Arabic verbal sentence through the representation of grammar parsing classes, verb properties, and conjunction checking. By populating the ontology with verbal sentences and adding grammar rules, we form a verbal sentence knowledge base. The parsing model is supported by morphological analysis for sentence syntactic analysis and supported by Arabic synonyms extractor for deriving synonyms. We have implemented the model and have provided it with a user interface where the user can enter a sentence to be parsed and obtains the parsing results. The interface has the options to partially or totally add diacritics to the words of the sentence and it has the possibility to remove ambiguity by choosing the most appropriate analysis from lexicon results. To evaluate the model, we have selected a representative set of Arabic verbal sentences from Arabic grammar books that represent all the possibilities of a verbal sentence. We have performed several parsing tests on these sentences with and without diacritics. The results prove the ability of the model to parse the various forms of the verbal sentence. The accuracy increases when the sentence is diacriticized while avoiding free word order and following the Arabic verbal sentence general form.
Improved Q Network Auto-Scaling in Microservice Architecture

Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service. A cloud provider requires flexible resource management to meet the continually changing demands, such as auto-scaling and provisioning. A common approach used in both commercial and open-source computing platforms is workload-based automatic scaling, which expands instances by increasing the number of incoming requests. Concurrency is a request-based policy that has recently been proposed in the evolving microservice framework; in this policy, the algorithm can expand its resources to the maximum number of configured requests to be processed in parallel per instance. However, it has proven difficult to identify the concurrency configuration that provides the best possible service quality, as various factors can affect the throughput and latency based on the workloads and complexity of the infrastructure characteristics. Therefore, this study aimed to investigate the applicability of an artificial intelligence approach to request-based auto-scaling in the microservice framework. Our results showed that the proposed model could learn an effective expansion policy within a limited number of pods, thereby showing an improved performance over the underlying auto expansion configuration.
Research on the Model of Distance Education Resource Integration from the Perspective of Comparative Education

In order to solve the problems of high data loss rate, long time-consuming resource integration, large amount of redundant data, and incomplete results of resource integration in traditional methods, a design method of distance education resource integration model from the perspective of comparative education is proposed. Association rules are used to mine resources and get effective resources in the context of comparative pedagogy, with the goal of increasing the impact of resource integration. Then, using a three-tier architecture, a remote education resource integration model is created, and the mining results are combined through the application layer, resource encryption layer, and multicast communication layer to achieve distance education resource integration. Simulation results show that the method of this article has low data loss rate, short resource integration time, less redundant data, and comprehensive integration results, which provides an effective technical support for the development of education.
Optimized allocation of water resources in the Jiulong River watershed under cooperative game based on linear programming

Optimized allocation of water resources in relatively water-scarce areas is the basis for rational development and utilization of regional water resources and the fundamental guarantee for sustainable utilization of water resources.In this paper, game theory and linear programming from the aspect of non-engineering measures are used to construct a cooperative game mode (maximizing economic benefits of urban agglomerations in the watershed) and a non-cooperative game mode (maximizing economic benefits of individual cities in the watershed), and it is concluded that the water consumption benefit of the cooperative game mode in the techno garden scenario (rationality of urban agglomerations in the Jiulong River Watershed) is 64.442 billion yuan higher than that of the non-cooperative game (rationality of each city).Moreover, under this scenario, water resource allocation is optimized and the water quality is also significantly improved.Therefore, the cooperative game is the optimum model for the development of urban agglomerations in the watershed, and more attention should be paid to the comprehensive management of water resources, ecological resources, and human activities from the aspect of the river catchment areas given the particularity of water resources.
Pre-service elementary teacher’s digital literacy with cognitive style and self-regulated learning

The Covid-19 pandemic has forced many educational institutions to implement online learning through distance learning. However, there are many obstacles that occur in its implementation, one of which is the low level of digital literacy as a mandatory ability in distance learning. This study aims to determine students' digital literacy skills based on factors within students, namely the ability of Cognitive Style and Self-Regulated Learning. This study uses a quantitative approach, with the type of expost facto research. The population in this study were all prospective elementary school teacher students at one of the universities in Majalengka Regency who carried out distance learning, with a sample of 33 respondents. Data were collected using a questionnaire in the form of a digital literacy ability questionnaire, cognitive style and self-regulated learning during distance learning. The results showed that students with cognitive style and self-regulated learning in the high category had better digital literacy skills than the medium and low categories. The conclusion obtained from this study is that Cognitive style and self-regulated learning affect digital literacy skills.