Reviewerly/exHarmony huggingface dataset:
Interactive thyroid whole slide image diagnostic system using deep representation

Background and objectives: The vast size of the histopathology whole slide image poses formidable challenges to its automatic diagnosis. With the goal of computer-aided diagnosis and the insights that suspicious regions are generally easy to identify in thyroid whole slide images (WSIs), we develop an interactive whole slide diagnostic system for thyroid frozen sections based on the suspicious regions preselected by pathologists. Methods:We propose to generate feature representations for the suspicious regions via extracting and fusing patch features using deep neural networks. We then evaluate region classification and retrieval on four classifiers and three supervised hashing methods based on the feature representations. The code is released at https://github.com/PingjunChen/ThyroidInteractive. Results: We evaluate the proposed system on 345 thyroid frozen sections and achieve 96.1% cross-validated classification accuracy, and retrieval mean average precision (MAP) of 0.972. Conclusions: With the participation of pathologists, the system possesses the following four notable advantages compared to directly handling whole slide images: 1) Reduced interference of irrelevant regions; 2) Alleviated computation and memory cost. 3) Fine-grained and precise suspicious region retrieval. 4) Cooperative relationship between pathologists and the diagnostic system. Additionally, experimental results demonstrate the potential of the proposed system on the practical thyroid frozen section diagnosis.
Epilogue: Rethinking digital literacy: Media education in the age of digital capitalism

Advocates of digital education have increasingly recognized the need for young people to acquire digital media literacy. However, this idea is often seen in instrumental terms, and is rarely implemented in any coherent or comprehensive way. This paper suggests that we need to move beyond a binary view of digital media as offering risks and opportunities for young people, and the narrow ideas of digital skills and internet safety to which it gives rise. The article propose that we should take a broader and more critical approach to the rise of ‘digital capitalism’, and to the ubiquity of digital media in everyday life. In this sense, the paper argue that the well-established conceptual framework and pedagogical strategies of media education can and should be extended to meet the new challenges posed by digital and social media.This article presents some reflections as an epigraph of the special issue "Digital learning: distraction or default for the future", whose final result has allowed us to group a set of critical research and analysis on the inclusion of digital technologies in educational contexts. The points of view presented in this epigraph is also developed in more detail in the book "The Media Education Manifesto" (Buckingham, 2019).
Blockchain-Based Cyber Threat Intelligence System Architecture for Sustainable Computing

Nowadays, the designing of cyber-physical systems has a significant role and plays a substantial part in developing a sustainable computing ecosystem for secure and scalable network architecture. The introduction of Cyber Threat Intelligence (CTI) has emerged as a new security system to mitigate existing cyber terrorism for advanced applications. CTI demands a lot of requirements at every step. In particular, data collection is a critical source of information for analysis and sharing; it is highly dependent on the reliability of the data. Although many feeds provide information on threats recently, it is essential to collect reliable data, as the data may be of unknown origin and provide information on unverified threats. Additionally, effective resource management needs to be put in place due to the large volume and diversity of the data. In this paper, we propose a blockchain-based cyber threat intelligence system architecture for sustainable computing in order to address issues such as reliability, privacy, scalability, and sustainability. The proposed system model can cooperate with multiple feeds that collect CTI data, create a reliable dataset, reduce network load, and measure organizations’ contributions to motivate participation. To assess the proposed model’s effectiveness, we perform the experimental analysis, taking into account various measures, including reliability, privacy, scalability, and sustainability. Experimental results of evaluation using the IP of 10 open source intelligence (OSINT) CTI feeds show that the proposed model saves about 15% of storage space compared to total network resources in a limited test environment.
EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

Selecting the most relevant features from a high dimensional dataset is always a challenging task. In this regard, the feature selection (FS) method acts as a solution to this problem mainly in the domain of data mining and machine learning. It aims at improving the performance of a learning model greatly by choosing the relevant features and ignoring the redundant ones. Besides, this also helps to achieve efficient use of space and time by the learning model under consideration. Though over the years, many meta-heuristic algorithms have been proposed by the researchers to solve FS problem, still this is considered as the open research problem due to its enormous challenges. Particularly, these algorithms, at times, suffer from poor convergence because of the improper tuning of exploration and exploitation phases. Here lies the importance of the hybrid meta-heuristics which help to improve the searching capability and convergence rate of the parent algorithms. To this end, the present work introduces a new hybrid meta-heuristic FS model by combining two meta-heuristics - Harmony Search (HS) algorithm and Artificial Electric Field Algorithm (AEFA), which we have named as Electrical Harmony based Hybrid Meta-heurtistic (EHHM). The proposed hybrid meta-heuristic converges faster than its predecessors, thereby ensuring its capability to search efficiently. Usability of EHHM is examined by applying it on 18 standard UCI datasets. Moreover, to prove its supremacy, we have compared it with 10 state-of-the-art FS methods. Link to code implementation of proposed method: khalid0007/Metaheuristic-Algorithms/FS_AEFAhHS.
Application of evolutionary and swarm optimization in computer vision: a literature survey

Abstract Evolutionary algorithms (EAs) and swarm algorithms (SAs) have shown their usefulness in solving combinatorial and NP-hard optimization problems in various research fields. However, in the field of computer vision, related surveys have not been updated during the last decade. In this study, inspired by the recent development of deep neural networks in computer vision, which embed large-scale optimization problems, we first describe a literature survey conducted to compensate for the lack of relevant research in this area. Specifically, applications related to the genetic algorithm and differential evolution from EAs, as well as particle swarm optimization and ant colony optimization from SAs and their variants, are mainly considered in this survey.
Area and LCOE considerations in utility-scale, single-axis tracking PV power plant topology optimization

This paper deals with the optimization of utility-scale, single-axis tracking PV power plant design, and suggests the use of a new evaluation metric YAREA, represented by the annual energy yield normalized by the PV power plant area. This metric takes into account both the energy yield (MWh/MWp) and the total energy production efficiency of the PV plant (MWh/m2), performing an optimization of both yield and available area. To illustrate and validate the proposed method, historical solar irradiation data were treated and used as input data for the creation of a large number of PV power plant topology scenarios spread over the Brazilian 8.5 million km2 national territory, and a case study was presented to illustrate the proposed method. The new metric showed important results for the PV plant design decision-making process, indicating a technical optimization complemented by the evaluation of the LCOE, leading to an economic optimization. While the topology defined through YAREA presents an average of 12.4% reduction of the maximum LCOE found, it presents a 58.9% enhanced YAREA and 91.4% more energy generated per surface area, almost doubling the investment potential over an area.
Impact of the COVID-19 pandemic on online home learning: An explorative study of primary schools in Indonesia

The purpose of this study was to identify the constraints of the online teaching and learning process at home as a result of the unprecedented situation with the pandemic COVID-19 The study used an exploratory case study, and for the research approach, a qualitative case study method was used to obtain information about the constraints and consequences of the pandemic COVID-19 on teaching and learning activities in primary schools In this study, the respondents were 15 teachers and parents of two primary schools in Tangerang, Indonesia A list of semi-structured interview questions was developed based on the related literature and was used to collect in-depth information from the respondents The findings of this research revealed some challenges and constraints experienced by students, teachers, and parents in online learning The challenges related to students were: limited communication and socializing among students, a higher challenge for students with special education needs, and longer screen time Parents saw the problem was more related to a lack of learning discipline at home, more time spent to assist their children's learning at home-especially for children below Grade 4 in Primary School, a lack of technology skills, and higher internet bills Teachers identified more challenges and constraints, including some restrictions in the choices of teaching methods normally applicable in a regular face-to-face class, less coverage of curriculum content, lack of technology skills that hinder the potential of online learning, the lacks of e-resources in Indonesian language resulting in more time needed to develop e-contents, longer screen time as a result of e-content creating and giving feedback on students' work, more intense and time-consuming communication with parents, the challenge for better coordination with colleague teachers, principals, and a higher internet bill © 2020 SERSC
DICOM Format and Protocol Standardization—A Core Requirement for Digital Pathology Success

As the use of digital techniques in toxicologic pathology expands, challenges of scalability and interoperability come to the fore. Proprietary formats and closed single-vendor platforms prevail but depend on the availability and maintenance of multiformat conversion libraries. Expedient for small deployments, this is not sustainable at an industrial scale. Primarily known as a standard for radiology, the Digital Imaging and Communications in Medicine (DICOM) standard has been evolving to support other specialties since its inception, to become the single ubiquitous standard throughout medical imaging. The adoption of DICOM for whole slide imaging (WSI) has been sluggish. Prospects for widespread commercially viable clinical use of digital pathology change the incentives. Connectathons using DICOM have demonstrated its feasibility for WSI and virtual microscopy. Adoption of DICOM for digital and computational pathology will allow the reuse of enterprise-wide infrastructure for storage, security, and business continuity. The DICOM embedded metadata allows detached files to remain useful. Bright-field and multichannel fluorescence, Z-stacks, cytology, and sparse and fully tiled encoding are supported. External terminologies and standard compression schemes are supported. Color consistency is defined using International Color Consortium profiles. The DICOM files can be dual personality Tagged Image File Format (TIFF) for legacy support. Annotations for computational pathology results can be encoded.
GenAug: Data Augmentation for Finetuning Text Generators

In this paper, we investigate data augmentation for text generation, which we call GenAug.Text generation and language modeling are important tasks within natural language processing, and are especially challenging for lowdata regimes.We propose and evaluate various augmentation methods, including some that incorporate external knowledge, for finetuning GPT-2 on a subset of Yelp Reviews.We also examine the relationship between the amount of augmentation and the quality of the generated text.We utilize several metrics that evaluate important aspects of the generated text including its diversity and fluency.Our experiments demonstrate that insertion of character-level synthetic noise and keyword replacement with hypernyms are effective augmentation methods, and that the quality of generations improves to a peak at approximately three times the amount of original data.
Sparse Text Generation

Current state-of-the-art text generators build on powerful language models such as GPT-2, achieving impressive performance.However, to avoid degenerate text, they require sampling from a modified softmax, via temperature parameters or ad-hoc truncation techniques, as in top-k or nucleus sampling.This creates a mismatch between training and testing conditions.In this paper, we use the recently introduced entmax transformation to train and sample from a natively sparse language model, avoiding this mismatch.The result is a text generator with favorable performance in terms of fluency and consistency, fewer repetitions, and n-gram diversity closer to human text.In order to evaluate our model, we propose three new metrics for comparing sparse or truncated distributions: -perplexity, sparsemax score, and Jensen-Shannon divergence.Human-evaluated experiments in story completion and dialogue generation show that entmax sampling leads to more engaging and coherent stories and conversations.
Classification score approach for detecting adversarial example in deep neural network

Abstract Deep neural networks (DNNs) provide superior performance on machine learning tasks such as image recognition, speech recognition, pattern analysis, and intrusion detection. However, an adversarial example, created by adding a little noise to an original sample, can cause misclassification by a DNN. This is a serious threat to the DNN because the added noise is not detected by the human eye. For example, if an attacker modifies a right-turn sign so that it misleads to the left, autonomous vehicles with the DNN will incorrectly classify the modified sign as pointing to the left, but a person will correctly classify the modified sign as pointing to the right. Studies are under way to defend against such adversarial examples. The existing method of defense against adversarial examples requires an additional process such as changing the classifier or modifying input data. In this paper, we propose a new method for detecting adversarial examples that does not invoke any additional process. The proposed scheme can detect adversarial examples by using a pattern feature of the classification scores of adversarial examples. We used MNIST and CIFAR10 as experimental datasets and Tensorflow as a machine learning library. The experimental results show that the proposed method can detect adversarial examples with success rates: 99.05% and 99.9% for the untargeted and targeted cases in MNIST, respectively, and 94.7% and 95.8% for the untargeted and targeted cases in CIFAR10, respectively.
Graph-Based Social Relation Reasoning

Human beings are fundamentally sociable—that we generally organize our social lives in terms of relations with other people. Understanding social relations from an image has great potential for intelligent systems such as social chatbots and personal assistants. In this paper, we propose a simpler, faster, and more accurate method named graph relational reasoning network (GR $$^2$$ N) for social relation recognition. Different from existing methods which process all social relations on an image independently, our method considers the paradigm of jointly inferring the relations by constructing a social relation graph. Furthermore, the proposed GR $$^2$$ N constructs several virtual relation graphs to explicitly grasp the strong logical constraints among different types of social relations. Experimental results illustrate that our method generates a reasonable and consistent social relation graph and improves the performance in both accuracy and efficiency.
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification

Few-shot classification requires classifiers to adapt to new classes with only a few training instances.State-of-the-art meta-learning approaches such as MAML learn how to initialize and fast adapt parameters from limited instances, which have shown promising results in few-shot classification.However, existing meta-learning models solely rely on implicit instance-based statistics, and thus suffer from instance unreliability and weak interpretability.To solve this problem, we propose a novel meta-information guided meta-learning (MIML) framework, where semantic concepts of classes provide strong guidance for meta-learning in both initialization and adaptation.In effect, our model can establish connections between instance-based information and semantic-based information, which enables more effective initialization and faster adaptation.Comprehensive experimental results on few-shot relation classification demonstrate the effectiveness of the proposed framework.Notably, MIML achieves comparable or superior performance to humans with only one shot on FewRel evaluation.The source code and experiment details of this paper can be obtained from https://github.com/thunlp/MIML.
Multi-Head Self-Attention Transformation Networks for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to analyze the sentiment polarity of an input sentence in a certain aspect.Many existing methods of ABSA employ long short-term memory (LSTM) networks and attention mechanism.However, the attention mechanism only models the local certain dependencies of the input information, which fails to capture the global dependence of the inputs.Simply improving the attention mechanism fails to solve the issue of target-sensitive sentiment expression, which has been proven to degrade the prediction effectiveness.In this work, we propose the multi-head self-attention transformation (MSAT) networks for ABSA tasks, which conducts more effective sentiment analysis with target specific self-attention and dynamic target representation.Given a set of review sentences, MSAT applies multi-head target specific self-attention to better capture the global dependence and introduces target-sensitive transformation to effectively tackle the problem of target-sensitive sentiment at first.Second, the part-of-speech (POS) features are integrated into MSAT to capture the grammatical features of sentences.A series of experiments carried on the SemEval 2014 and Twitter datasets show that the proposed model achieves better effectiveness compared with several state-of-the-art methods.
End-to-End Super-Resolution for Remote-Sensing Images Using an Improved Multi-Scale Residual Network

Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.
The Digital Competence of Pre-Service Educators: The Influence of Personal Variables

Currently, 21st century students need competences that enable them to adapt to a new type of individual information and individual knowledge relationship, and, therefore, the education system should contemplate new ways for learners to develop in accordance with this so-called information and knowledge society. One of special importance is so-called digital competency. This article presents the results of a research study to determine the influence that the variables of gender, age, and academic degree have on the acquisition of digital competence by pre-service educators, with a sample of 370 students from different education degrees from the University of Salamanca (Spain). A quantitative methodology was used, employing a non-experimental method and the electronic survey technique to collect information on the dimensions of knowledge, as well as the management of and attitude towards information and communication technologies (ICTs). Data were analyzed inferentially from a comparison of means using nonparametric tests. This analysis was completed with the incorporation of Receiver Operating Characteristic (ROC) curves, which allowed us to graphically verify the differences between the subsamples and thus compare the different groups in relation to the proposed dimensions. The main conclusion was that the three variables studied can be considered as influential, though not determinant, in the acquisition of digital competency.
A decision model for blockchain applicability into knowledge-based conversation system

Conversation systems usually suffer from the challenge of knowledge management from multiple human experts. The current mechanism used in knowledge-based conversation system is always based on centralized servers, which may be problematic in terms of transparency and security. Blockchain solutions are currently being proposed improve the security and efficiency in different domains. However, there are various blockchain platforms with different characteristics, and conversation system implemented using blockchain platform is not in place yet. In this paper, we clearly identified the requirement analysis of knowledge-based conversation system and present a decision model for identify the best fitting blockchain platform for knowledge-based conversation system. In the proposed method, multiple measurements including Analytical Hierarchy Process (AHP), Fuzzy analytical hierarchy process (FAHP), and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) are utilized to analyze and create consistent result together, which can be used for the selection of blockchain platforms and improve the efficiency of the decision-making process.