Reviewerly/exHarmony huggingface dataset:
Advanced deep learning applications in diagnostic pathology

For many years, pathologists have performed histopathological diagnoses by observing tissue specimens under a microscope. Recently, however, it has become possible to scan whole slides of tissue specimens using a dedicated slide scanner and store the resultant high-resolution digital images, i.e., whole slide images. This has led to the emergence of digital pathology, a field in which whole slide images are used for histopathological diagnoses. This field is gradually expanding, especially in large hospitals such as university hospitals. In addition, dramatic advancements in image recognition technology have been made since 2012 when deep learning won the general image recognition competition ILSVRC with overwhelming accuracy. Subsequently, deep learning has been applied to various medical images, including X-ray, ocular fundus, and skin images, and is reported to have achieved generalist- or even professional-level diagnostic accuracy in each field. Similarly, the use of deep learning, directed towards digital histopathological images, for assistance with pathological diagnoses is gradually becoming practicable, especially for diseases in which many cases occur. Recently, advanced applications have been developed such as searching for similar cases, predicting genetic mutations from histological images, and generating special stained images from hematoxylin and eosin-stained images. These emerging applications have the potential to greatly expand the field of diagnostic pathology and contribute to the further development of medicine. In this review, we introduce the use of deep learning technology in the field, detail the current advanced applications, and speculate on future perspectives.
Pedestrian Detection in Robotic Response for the First Responders

The paper presents two object detector networks on USC Pedestrian database in order to detect and localize pedestrians in photos. The COCO metrics was used, because it was considered a very important problem, well addressed. All children in pictures were well localized and the models can be used on autonomous robots and cars. Both Faster R-CNN and SSD missed a single person in the testing C dataset. The research results lead to a precision of 0.733 on Faster R-CNN and 0.583 on SSD on COCO metrics. In future works this results will be used in augmenting data for search and rescue missions conducted by first responders.
Resolution Representation Based Person Re-Identification for Smart Cities Using Deep Neural Networks (DNNs)

The advancement of deep learning has facilitated rapid progress in person re-identification (re-id) task. Its applications in intelligent video surveillance made it a key component of today's smart cities infrastructure. Person re-id task is aimed to identify the person in distributed camera setup with non-overlapping views. The feature extraction process is an important part of person re-id technique. The present state of art methods mostly used ResNet as a backbone for feature extraction, which results in low geometric transformation modeling and low-resolution representation learning. We addressed these two major challenges by integrating deformable convolution module to enhance the transformation modeling capability and replaced the traditional ResNet backbone for person re-id with the novel feature extraction network named as HRNet, which is based on high-resolution representation learning without any additional supervision. The verification of our approach performance is done by conducting an experiment on a person re-id dataset named Market-1501. We achieved 90.57% Rank-1 accuracy and 75.43% mAP, outperforming the ResNet baseline results, which confirmed the effectiveness of our approach and will have a promising future in person re-id.
Two-stream deep convolutional neural network approach for RGB-D face recognition

Two-dimensional face recognition has been researched for the past few decades. With the recent development of Deep Convolutional Neural Network (DCNN) deep learning approaches, two-dimensional face recognition had achieved impressive recognition accuracy rate. However, there are still some challenges such as pose variation, scene illumination, facial emotions, facial occlusions exist in the two-dimensional face recognition. This problem can be solved by adding the depth images as input as it provides valuable information to help model facial boundaries and understand the global facial layout and provide low-frequency patterns. RGB-D images are more robust compared to RGB images. Unfortunately, the lack of sufficient RGB-D face databases to train the DCNN are the main reason for this research to remain undiscovered. So, in this research, new RGB-D face database is constructed using the Intel RealSense D435 Depth Camera which has 1280 x 720-pixel depth. Twin DCNN streams are developed and trained on RGB images at one stream and Depth images at another stream, and finally combined the output through fusion soft-max layers. The proposed DCNN model shows an accuracy of 95% on a newly constructed RGB-D database.
Development of User-Centred Interaction Design Patterns for the International Data Space

This paper describes a set of methods for the development of user-centred interaction design patterns (IDP) based on the International Data Space (IDS). The IDS is to be understood as a virtual market place where data and products can be bought and sold as well as related contracts concluded. Based on the existing reference model of the IDS, new types of user interfaces (UI) will be created for this purpose. It is important for the developers of the UI’s that the users and the associated core tasks are known, so that the interactions can be designed in a user-friendly interface. IDP’s are intended to support developers in developing applications. The patterns are design patterns and represent proven solutions for frequently occurring problems for the developers when designing the UI. They also provide design alternatives and can effectively support communication between potential end users and developers.
A Novel Data and Model Hybrid-Driven Method for Image Restoration Using Residual Dense Attention U-Net

As people's pursuit of large screen-to-body ratio screen experience continues to improve, neither the digging front camera nor the bangs front camera can meet people's requirements for the front camera of a mobile phone. Therefore, the research and development of full-screen equipment has become a new trend. A full-screen device requires the imaging device to be placed below the screen, which we call an under-display cameras. The under-display cameras will improve the user's interactive experience while expanding the screen-to-body ratio of the mobile phone. However, there are many problems in the development of under-display cameras. When the imaging device is installed under the screen, the lower light transmittance will cause serious image degradation. Therefore, a new U-Net, which we call residual dense attention UNet (RDAU-Net), is proposed in this paper. A residual dense attention module which we propose in RDAU-Net to replace the single-layer convolution in the U-Net network. Meanwhile, the introduction of channel attention can effectively enhance the interdependence between channels, thereby adaptively re-dividing channel features. Experiments show that our RDAU-Net has better accuracy and faster recovery efficiency than existing methods.
A Distributed Fewer Environment for the Use of Blockchain IoT Device Sharing

Abstract Due to the fact that the data markets become ubiquitous, it also becomes apparent that Internet of Things (IoT) applications produce data sources that are of benefit for future third party users. We are creating a marketplace for IoT data sources, allowing any pair of data providers and customers to participate in data transactions without any previous dependence on the shared trusts, to unlock those possible utility. We have a marketplace and architecture for the exchanging of streaming data, ranging from data publicity to legally binding exchange deals and payments. We prove that we can give participants an interchange of costs between the transactional data exchange and the possibility of loss of data when trading through unreliable third parties, through the use of blockchained technologies and smart contracts, in particular. On a test platform using Crypto currency Smart Contracts, we analyze these agreements experimentally.
The Behaviour of the Product T-Norm in Combination with Several Implications in Fuzzy PID Controller

Fuzzy control is an intelligent software performed to tune a process and make it react in a desirable way. Nowadays, many researchers are interested in the Fuzzy Proportional-Integral-Derivative (FPID) controller because of its performance and simple structure. FPID controller, as fuzzy controller, is based on the Compositional Rule of Inference (CRI) that allows to infer with fuzzy data. As defined by Zadeh, the CRI contains two parameters: t-norm (T) and fuzzy implication (I). Because of the singleton representation of crisp inputs in fuzzy controllers, the t-norm is no longer considered in the CRI, which gives results based only on the fuzzy implication. In this study, we use non-singleton representation of the inputs, and we apply several implications in a fuzzy PID controller combined with the product t-norm. We study the behaviour of the fuzzy PID controller according to each combination (T,I) to evaluate its efficiency in term of quality and time of convergence. We finally compare the obtained results with the theoretical inference results and we find that they are consistent.
Sensory sensemaking : a study of fitness bands and its quantified body representation of young Indians

&lt;i&gt;There has been an explosion of wearable fitness-tracking consumer devices in India. The body and its functions are now visually analysable using these bands. The notion of being mindful about the body is fused with a fitness culture. This culture is supported by a growing market for fitness commodities that work towards transforming the self into computable data. This paper attempts to explore the manner in which fitness tracking devices enable positive reinforcement in young Indians. The primary means of data collection for the study was in-depth interviews with young adults (18 to 25 years of age). The participants expressed an affective relationship with the devices, especially the impact of data visuals that was a motivational factor in improving user performance. The study uses the Self-Determination Theory to map user motivation around fitness devices. The qualitative findings revealed the usage of fitness trackers in young Indians to be a private endeavour. While participants felt a positive sense of agency with the devices, they were also averse to the idea of competing within virtual fitness community.&lt;/i&gt;
Initial Reflections on the Use of Augmented Cognition in Derailing the Kill Chain

Digital systems are now our new critical infrastructures. In contrast to traditional infrastructures, a digital system can be attacked remotely by software weapons. The so-called Advanced Persistent Threat (APT) actors are nation-state actors or advanced organized crime groups (ransomware and industrial espionage), and they pose a threat to corporations, critical infrastructures and even nation states. The “Kill Chain” represents a model of the attack pattern of an APT group. Augmented cognition (AC) is poised to become an important part of the tool chest of the defenders. In this paper we outline some areas where AC could make a difference.
The Interplay Between Emojis and Linguistic Text

This paper examines the interplay between emojis and the accompanying linguistic text when the two channels disagree with each other. In general, emojis are typically regarded as an explicit way to convey a specific emotion. However, it is observed that emojis, even of low ambiguity, may not explicitly express a corresponding emotion in a message. Based on the 831 comments extracted from Sina Weibo, we argue that the interplay between an emoji and its accompanying text may influence the expressed emotion. When the two channels mismatch, the overall emotion is often determined by the emotion expressed in the text regardless of the emotion type. It is observed that emojis expressing happiness occur most frequently in text expressing other emotions, as compared to emojis of other emotions. Moreover, emojis representing happiness are particularly common in text expressing anger. The overall emotion is mostly determined by the text (i.e. anger), meaning that the emojis representing happiness are frequently used ironically. We believe that this paper will lay the groundwork for the studies of emotion detection.
Quantum computing with CMOS technology

Quantum computing is poised to be the innovation driver of the next decade. Its information processing capabilities will radically accelerate drug discovery, improve online security, or even boost artificial intelligence [1]. Building a quantum computer promises to have a major positive impact in society, however building the hardware that will enable that paradigm change its one of the greatest technological challenges for humanity.
Contextualised Word Embeddings Based on Transfer Learning to Dialogue Response Generation: a Proposal and Comparisons

Contextualised word embeddings have recently become essential elements of Natural Language Processing (NLP) systems since these embedding models encode not only words but also their contexts to generate context-specific representations. Pre-trained models such as BERT, GPT, and derived architectures are increasingly present on NLP task benchmarks. Several comparative analyses of such models have been performed, but so far no one compares the most recent architectures in a dialogue generation dataset by considering multiple metrics relevant to the task. In this paper, we not only propose an encoder-decoder system that uses transfer learning with pre-trained word embeddings, but we also systematically compare various pretrained contextualised word embedding architectures on the DSTC-7 dataset, using metrics based on mutual information, dialogue length, and variety of answers. We use the word embeddings as a first layer of the encoder, making it possible to encode the texts in a latent space. As a decoder, we use an LSTM layer and a byte pair encoding tokenisation, aligned with state-of-the-art dialogue systems recently published. The networks are trained during the same amount of epochs, with the same optimisers and learning rates. Considering the quality of the dialogue, our results show that there is no superior technique on all metrics. However, there are relevant differences concerning the computational costs to encode the data.
Pre and Post Effects on Schooling Due to Covid-19

This project will show that how activities that done by the school is important for the new learners. This stored all the activities of school that enhance the development of each child towards their career after Covid impact. Involvement in cocurricular Activities helps student become tougher and avoid mental health issues. There is a lack of such activities which will enhance the knowledge of child with Covid precautions and if parents want to develop their child’s knowledge in technology and also for online activities, So there is not such platform where they can do all this without misusing the technology in unawareness also if parents wants to polish their interest, so that, they can think to make a career in it. So, how can they analyse that whatever the school will be able to do it or not, without visiting each school due to Covid 19. we will mention the facilities, activities, skills and international activities, events, to do using technology, etc. in the project. This project will have own interface/platform for online activities for students so that they could not misuse the technology. There is some online activities (Activities From Home) in Project that can be done while making some social - distance because of Covid. This project can increase efficiency of skill development and career opportunities from their interest in respective activity or skill. Managing the impact of COVID-19 on education systems without losing required knowledge for students via Incredible school activity as well.
Formation of Digital Competencies Among Students of Teacher Education

The article provides the typology of online educational materials, concretizes and clarifies digital competencies that are in demand in the modern information educational environment.&#x0D; In accordance with the principles of the system-activity approach, a methodology for the formation of digital competencies in future teachers in the context of the organization of project activities of students of pedagogical specialties is proposed. It is shown that this activity can be associated with the step-by-step methodological development of electronic educational materials in the environment of online services.
Human–object interaction detection with missing objects

Human–object interaction (HOI) detection is an important vision task that requires the detection of individual object instances and reasoning of their relations. Despite encouraging advancement in recent years, past methods are still limited to relatively simple images where the human and object instances can be detected without difficulties. HOI in the wild should work even when the objects that a person is interacting with are not visible or hard to detect in the image. In this paper, we formulate HOI with missing objects (HOI-MO) as a research problem, and show that it is indeed critical as many such instances can be found even in the commonly-used public HOI detection datasets. We label these to compose new test sets for the proposed method. To our knowledge, we introduce the first method for such challenging HOI detection that incorporates global scene information. The effectiveness and superiority of the proposed method are demonstrated through extensive experiments and comparisons.
Next Generation Cloud Computing: Security, Privacy and Trust Issues from the System View

Cloud Computing has become the key resource providing the computing needs. The inclusion of Internet of Things (IoT) along with the emergence of Artificial Intelligence (AI) including Machine Learning (ML) provide endless opportunities with a new extended reach into all facets of human life. This platform brings new and unprecedented challenges, particularly the combination of end user devices and service provided systems; as such, there is a need to develop an end-to-end architecture that is modular whilst addresses four key parameters being, system architecture, security, privacy, and trust. Within this work in progress article, we identify the new and emerging security, privacy, and trust issues along with the gaps within Next Generation Cloud Computing when viewed in entirety, the system view; propose a novel modular architecture for use when consuming these services that reduces identified security risks; and develop a new framework for mitigating issues utilising Machine Learning.
Practical Tips of English Expressions for Non-Native English-Speaking Peer Reviewers

Peer review is an essential part in ensuring the quality of papers published in scientific journals. Good reviews are beneficial for both authors and journal editors by providing authors with the opportunity to improve their manuscripts and editors with valuable comments to aid themselves to make their decisions. Despite a plethora of research articles on general guidelines for peer reviewing, it is difficult to find papers on English expressions peer reviewers can use, in particular for nonnative English-speaking peer reviewers. Therefore, this article provides downto-earth guidance for non-native English-speaking reviewers to construct better quality reviews. To this end, I suggest useful English expressions to help peer reviewers, whose mother tongue is not English, enhance the quality of their reviews.
Improving Determine Lexical Meanings for Mongolian Cyrillic Script and Its Used to Keyword Extraction

Improving determine lexical meanings for Mongolian Cyrillic scripts with agglutinative languages are essential tool to information retrieval. This method had integrated the following methods such as to tag part of speech through creating lexicon and to determine lexical meanings through parsing word structure according to orthography of Mongolian Cyrillic script. We evaluate the effectiveness of our method and its contribution the keyword extraction from text materials of Mongolian Cyrillic scripts.
Event Detection through Lexical Chain Based Semantic Similarity Algorithm

Abstract Twitter is a platform where millions of people tend to tweet about new events happening in their lives. Celebrities tweeting about the new product endorsement, Politicians tweeting about their views towards a policy or people, Natural calamities occurring are tweeted instantly. Studying this data can provide us with useful information. In this paper, we have proposed an Event detection using a lexical chain based semantic similarity algorithm, for detecting Events from Twitter streams. Lexical chains have been used to preserve the Lexical cohesion in a text. The Twitter data set was collected using “tweepy” API, then pre-processing was done, steps like tokenization, stop word removal, and stemming is carried out and stored the tweets in a text file. Then lexical chains were built using the tweets in the file. The formation of the key graph, with each node as a lexical chain, was carried out. Then the clustering algorithm ‘SCAN’ was used to cluster the lexical chains, the formed clusters represent the keywords of an event. The last summarization step was carried out for each cluster representing an event.
Tweeting While Pregnant: Is Online Health Communication About Physical Symptoms and Emotions About the Tweeter?

We analyze word usage patterns in posts on Twitter to learn about information and support seeking related to the common experience of food cravings in pregnancy. We apply information-theoretic measures of lexical diversity across tweets to understand information sharing about the body and its conditions (measured by use of embodiment verbs), and about emotions (measured by degree of positive or negative valence) conditioned on degree of self-disclosure (choice of pronoun). Descriptions of food cravings were consistent with face-to-face work and included frequent references to sweets and fruits and sometimes included face emojis signaling humor and self-deprecation. Tweet content varied with degree of self-disclosure such that messages formed around variants of the pronoun I included a wider variety of emotion-related words but a more limited variety of words referencing the body than did those without that pronoun. Patterns of reduced word redundancy suggest greater elaboration with respect to emotional affect in the presence of I pronouns while enhanced redundancy indicates shared endorsements and concerns pertaining to the body in the presence of I pronouns. Findings are discussed in the context of prior work on communication in online and face-to-face communities.