Reviewerly/exHarmony huggingface dataset:
Communicative Functions in Human-Computer Interface Design: A Taxonomy of Functional Animation

Whenever a user performs a task or communicates via their computer or device, they are guided by visual cues to interact successfully with the interface. This human-computer interaction is, therefore, mediated by the communication established between designer and user through the texts, graphic elements, and animations that make up the visual design of the interface. Animation is an element of visual language of the graphical elements of an interface. This study aims to establish the functions of animation. We reviewed the literature and discussed the shortcomings identified in the existing taxonomies of functional animation. We then proposed an updated classification, partly inspired by the functions presented in Jakobson’s communication model. Based on a content analysis of the design guidelines from the leading mobile phone developers and comparing these sources, we propose the following list of categories: Identifying, Structural, Guide, Feedback, Didactic, Esthetic, and Emotive. This new taxonomy aims to contribute to the theoretical frameworks used in visual communication when studying interface design. It will be useful, for example, to help detect, classify, and assess the appropriateness of animations based on the functions they provide to an interface.
Natural Language Processing Systems for Diagnosing and Determining Level of Lung Cancer: A Systematic Review

Introduction: Lung cancer is the second most common cancer for men and women. Using natural language processing to automatically extract information from text, lead to decrease labor of manual extraction from large volume of text material and save time. The aim of this study is to systematically review of studies which reviewed NLP methods in diagnosing and staging lung cancer.Material and Methods: PubMed, Scopus, Web of science, Embase was searched for English language articles that reported diagnosing and staging methods in lung cancer Using NLP until DEC 2019. Two reviewers independently assessed original papers to determine eligibility for inclusion in the review.Results: Of 119 studies, 7 studies were included. Three studies developed a NLP algorithm to scan radiology notes and determine the presence or absence of nodules to identify patients with incident lung nodules for treatment or follow-up. Two studies used NLP to transform the report text, including identification of UMLS terms and detection of negated findings to classifying reports, also one of them used an SVM-based text classification system for staging lung cancer patients. All studies reported various performance measures based on the difference between combination of methods. Most of studies have reported sensitivity and specificity of the NLP algorithm for identifying the presence of lung nodules.Conclusion: Evaluation of studies in diagnosing and staging methods in lung cancer using NLP shows there is a number of studies on diagnosing lung cancer but there are a few works on staging that. In some studies, combination of methods was considered and NLP isolated was not sufficient for capturing satisfying results. There are potentials to improve studies by adding other data sources, further refinement and subsequent validation.
Particle Swarm Optimization for Load Balancing in Distributed Computing Systems – A Survey

Development of technology like Cloud Computing and its widespread usage has given rise to exponential increase in the volume of traffic. With this increase in huge traffic the resources in the network would either be insufficient to handle the traffic or the situation may cause some of the resources to be over utilized or underutilized. This condition leads to reduced performance of the system. To improve the performance of the system the traffic requires to be regulated such that all the resources are utilized conferring to their capacity which is known as load balancing. Load balancing has been one of the concerns in the distributed computing systems where the computing nodes do not have a global view of the network. There have been constant efforts to provide an efficient solution for load balancing through the approaches like game theory, fuzzy logic, heuristics and metaheuristics. Even though various solutions exist for balancing the load, the issue is challenging as there does not exist one best fit solution. The paper aims at the study of how Particle Swarm Optimization approach is used to achieve an optimal solution for load balancing in distributed computing system.
Creative Reconstruction of the Technological Society

Contemporary society is on a clearly unsustainable path, and faces multiple disaster scenarios in the coming decades unless transformative action is taken in the very near future. Among the prime root causes of our present dilemma is modern technology. It has allowed the emergence of modern-day miracles of our technological society, but it has also brought an exploding global population and widespread assaults on the natural environment. In fact, all major social problems are ultimately technological problems. Furthermore, technology is expanding exponentially on several fronts, and threatens to exceed human control. The thesis of ‘technological determinism’ has a long history, but only in recent years has its effects become manifest. Under such conditions, one promising long-term solution is a slow but steady retraction of modern technology. Such a ‘creative reconstruction’ of society will allow us to maintain that which is truly valuable in life, while putting humanity and the planet on a path to real sustainability.
Accelerating Genomic Data Analytics With Composable Hardware Acceleration Framework

This article presents a framework, Genesis (genome analysis), to efficiently and flexibly accelerate generic data manipulation operations that have become performance bottlenecks in the genomic data processing pipeline utilizing FPGAs-as-a-service. Genesis conceptualizes genomic data as a very large relational database and uses extended SQL as a domain-specific language to construct data manipulation queries. To accelerate the queries, we designed a Genesis hardware library of efficient coarse-grained primitives that can be composed into a specialized dataflow architecture. This approach explores a systematic and scalable methodology to expedite domain-specific end-to-end accelerated system development and deployment.
Tutorial Proposal: End-to-End Speech Translation

Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation.Speech translation has attracted interest for many years, but the recent successful applications of deep learning to both individual tasks have enabled new opportunities through joint modeling, in what we today call 'end-to-end speech translation.'In this tutorial we will introduce the techniques used in cutting-edge research on speech translation.Starting from the traditional cascaded approach, we will given an overview on data sources and model architectures to achieve state-of-the art performance with endto-end speech translation for both high-and low-resource languages.In addition, we will discuss methods to evaluate analyze the proposed solutions, as well as the challenges faced when applying speech translation models for real-world applications.
Designing Soundscapes for Presence in Virtual Reality Exhibitions: A Study of Visitor Experiences

In museum exhibition design, the experiential aspects of virtual reality and other immersive technologies are increasingly being explored. This study contributes to these explorations, focusing on the role of hearing and sound in visitors' experiences of a hybrid virtual environment designed for an architecture museum exhibition. Physically, the environment consisted of a full-scale, multi-level structure installed in a large gallery space. Virtually, visitors 'switched' between being in a contemporary villa and a natural shoreline biotope while moving in the physical installation, experiencing and comparing nature and architecture as "parallel realities." This study investigates visitors' experiences of realism in the soundscape and how this contributed to the immersive experience. Exit interviews with randomly selected visitors on the sound experience (N = 82) are primary data for this study. Visitor responses to questions related to sound (N = 320) and data collected from interviews and observations of recruited visitor pairs (N = 16) are complementary data. The study finds that visitors considered sound essential to the high degree of realism they experienced in the hybrid virtual environment, in the sense of "being there," and that this was dependent on signal types that were appropriate in type and variation. Additionally, relevant to exhibition design practice, issues of quality and delivery methods had minimal impact on the visitor experience.
Secure E-Voting System Using Visual Cryptography &amp; Block Chain Ledger

Expanding advanced innovation has revolutionized the life of individuals. In this digital world many countries are trying to initiate an E-voting system in regular election process. Researchers are looking forward for the innovative ideas for secure and user friendly system. Block chain is one of the novel concepts that come with number of features to develop E-services. By embracing block chain in the circulation of databases on e-casting ballot frameworks, one can decrease the duping wellsprings of database control. This venture intends to execute casting a ballot result utilizing block chain calculation from each place of decision. In proposed system we have chosen Block chain for its decentralized framework and Here in security and data integrity is mainly achieved by making use of Visual Cryptography (VC) concept. This VC technique comes in when user casts vote on an E-voting portal.
Description method with automatically configurable Gaussian radial basis function for complex freeform surface

The description of deformable mirror (DM) surface, which is usually a complex freeform surface, affects the measurement speed and accuracy in a real-time interferometric measurement system with a DM as the dynamic compensator. We propose an accurate and fast description method with automatically configurable Gaussian radial basis function. The distribution and shape factors of GRBFs are related to the complexity of the surface with sufficient flexibility to improve the accuracy, and the fitting results are automatically obtained using a traversal optimization algorithm, which can improve the fitting speed by reducing the number of time-consuming calculations. The feasibility is verified by numerical and practical experiment.
A Hybrid Technique based on RSA and Data Hiding for Securing Handwritten Signature

Data exchange has been significantly encouraged by the development of communication technology and the wide use of social media over the Internet.Therefore, it is important to hide the data transmitted, especially the data that requires a person's signature.Where the signature is an increasingly needed item that is used in our daily life to achieve some paperbased authentication in departments, the individual himself needs the signature.Cryptography and steganography are commonly considered to be the most important data hiding methodologies.Steganography is used to hide the secret message in the carrier media, such as text, audio, video, and image files, without the carrier media being distorted, and cryptography is used to conceal the purpose of the secret message.A hybrid data hiding (image steganography) and encryption technique is implemented in this research on the time domain.The secret handwritten signature image is first encrypted using the public key algorithm (RSA) in the proposed technique, then randomly inserted using Embedding data process to be concealed in one of the last three bits of that pixel(1 st Least Significant Bit, 2 nd LSB, and 3 rd LSB) based on mathematical randomized formula over all pixels of the carrier media (image).It is assumed that the process of randomization will increase the protection provided by the technique.The suggested technique is implemented on gray level cover images.As a consequence of the random scattering of bits and using encryption, it is noted that the proposed technique achieves enhanced data hiding results in terms of performance, protection, and imperceptibility properties and the histogram of the proposed technique is better and provides more protection and security than the ordinary sequential Least Significant Bit (LSB).
Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems

This chapter aims to aid the development of Cyber-Physical Systems (CPS) in automated understanding of events and activities in various applications of video-surveillance. These events are mostly captured by drones, CCTVs or novice and unskilled individuals on low-end devices. Being unconstrained, these videos are immensely challenging due to a number of quality factors. We present an extensive account of the various approaches taken to solve the problem over the years. This ranges from methods as early as Structure from Motion (SFM) based approaches to recent solution frameworks involving deep neural networks. We show that the long-term motion patterns alone play a pivotal role in the task of recognizing an event. Consequently each video is significantly represented by a fixed number of key-frames using a graph-based approach. Only the temporal features are exploited using a hybrid Convolutional Neural Network (CNN) + Recurrent Neural Network (RNN) architecture. The results we obtain are encouraging as they outperform standard temporal CNNs and are at par with those using spatial information along with motion cues. Further exploring multistream models, we conceive a multi-tier fusion strategy for the spatial and temporal wings of a network. A consolidated representation of the respective individual prediction vectors on video and frame levels is obtained using a biased conflation technique. The fusion strategy endows us with greater rise in precision on each stage as compared to the state-of-the-art methods, and thus a powerful consensus is achieved in classification. Results are recorded on four benchmark datasets widely used in the domain of action recognition, namely CCV, HMDB, UCF-101 and KCV. It is inferable that focusing on better classification of the video sequences certainly leads to robust actuation of a system designed for event surveillance and object cum activity tracking.
A relational shape abstract domain

Static analyses aim at inferring semantic properties of programs. We distinguish two important classes of static analyses: state analyses and relational analyses. While state analyses aim at computing an over-approximation of reachable states of programs, relational analyses aim at computing functional properties over the input–output states of programs. Several advantages of relational analyses are their ability to analyze incomplete programs, such as libraries or classes, but also to make the analysis modular, using input–output relations as composable summaries for procedures. In the case of numerical programs, several analyses have been proposed that utilize relational numerical abstract domains to describe relations. On the other hand, designing abstractions for relations over input–output memory states and taking shapes into account is challenging. In this paper, we propose a set of novel logical connectives to describe such relations, which are inspired by separation logic. This logic can express that certain memory areas are unchanged, freshly allocated, or freed, or that only part of the memory was modified. Using these connectives, we build an abstract domain and design a static analysis that over-approximates relations over memory states containing inductive structures. We implement this analysis and report on the analysis of basic libraries of programs manipulating lists and trees.
Dynamic Development of Information Technologies, Organization of Library Services Using Digital Space and Through Social Networks

Starting with the creation of the global information space, there are two forces that can really influence information processes. The first side is the technological side, the other side is the state structures based on social and humanitarian issues.The creators of global networks and the owners of key technologies advocate that everything remains as it is, based on freedom of speech, and in humanitarian terms, states are trying to control this process based on the implementation of legislation.The first side is based on free speech, controls the advertising market and dodges taxes, the other side wants to deal with “concerned groups” and also fight social ailments.In this confrontation, libraries, as institutions influencing the information environment, stand next to the state and try not to lose their attractiveness. In this respect, libraries can act as a bridge between local and regional as well as global networks.&#x0D; Libraries, academic and university libraries currently operating in the world and meeting modern challenges are the main participants in the authoritative information cycle. Coordinating the information activities of these agencies can lead to a significant improvement in the information environment.&#x0D; The main goal of the study is to identify the optimal forms and methods of using social networks in libraries. It is also exploring the reasons why libraries use social media to ensure an effective and widespread information service.
A Modified Particle Swarm Optimization for Task Scheduling in Cloud Computing

Cloud computing emerges as a powerful platform to deliver IT services through internet. Due to rapid development of cloud computing the user’s dependencies on cloud have increased therefore scheduling of task is the key challenge. This research paper focused on scheduling user’s requests to best suitable resources in optimal way. We propose a modified particle swarm optimization (MPSO) task scheduling algorithm in order to optimize execution time, transmission time, makespan, transmission cost and load balancing of virtual machines. The MPSO algorithm maintains inertia weight from 0.4 to 0.9 and it plays an important role to achieve best cost. The implementation result of MPSO on CloudSim produces best cost as compared to original PSO.
Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review

This short article describes the method of digital cytopathology using Z-stack scanning with or without extended focusing. This technology is suitable to observe such thick clusters as adenocarcinoma on cytologic specimens. Artificial intelligence (AI) has been applied to histological images, but its application on cytologic images is still limited. This article describes our attempt to apply AI technology to cytologic digital images. For molecular analysis, cytologic materials, such as smear, LBC, and cell blocks, have been successfully used for targeted single gene detection and multiplex gene analysis with next-generation sequencing. As a future perspective, the system can be connected to full automation by combining digital cytopathology with AI application to detect target cancer cells and to perform molecular analysis. The literature review is updated according to the subjects.
Influence of Standardized Nursing Management of Hospital Based on Smart Electronic Medical Blockchain on Nursing Quality of Digestive Endoscopy Room

With the increase of the incidence rate of digestive system diseases, digestive endoscopy has become an essential measure. The nursing quality of digestive endoscopy room will have a direct impact on the examination results and treatment effect, so improving the nursing quality of digestive endoscopy room has always been the focus of attention. In this paper, the smart electronic medical blockchain technology and hospital standardized nursing management mode are combined to develop the smart medical standardized nursing management mode. Firstly, 88 patients with digestive endoscopy in a hospital from October 2018 to October 2019 were selected as the experimental subjects: 44 cases in the control group and 44 cases in the experimental group. The control group implemented the traditional management mode of digestive endoscopy room, and the experimental group implemented the standardized nursing management mode of smart medicine. The blood pressure level before and after nursing and the probability of adverse reactions in the process of nursing were counted. After nursing, the patients were asked to fill in the nursing satisfaction questionnaire, evaluate the nursing quality of nursing staff, and make statistics and comparison on the occurrence of safety events in digestive endoscopy room. After nursing, the systolic pressure drop of the experimental group was 125.36 ± 7.27 mmhg, diastolic pressure drop was 73.24 ± 4.21 mmhg, and the incidence of adverse reactions was 11.36%. The satisfaction rate of the experimental group was 15%, 29.55%, and 22.73% higher than that of the control group. The average scores of nursing skills, operation level, and examination results of nursing staff in the experimental group were 94.49, 95.12, and 95.89, respectively; the qualified rate of disinfection of digestive endoscopy room in the experimental group was 100%, the degree of cooperation between doctors and nurses was 100%, the timely rate of emergency measures was 95.45%, and the incidence of accidents was 0%. This shows that, under the standardized nursing of smart medicine, the blood pressure of patients is more stable, the incidence of adverse reactions is lower, the nursing satisfaction is higher, and the nursing quality of nursing staff and the safety of digestive endoscopy room are also improved.
GAttANet: Global Attention Agreement for Convolutional Neural Networks

Transformer attention architectures, similar to those developed for natural language processing, have recently proved efficient also in vision, either in conjunction with or as a replacement for convolutional layers. Typically, visual attention is inserted in the network architecture as a (series of) feedforward self-attention module(s), with mutual key-query agreement as the main selection and routing operation. However efficient, this strategy is only vaguely compatible with the way that attention is implemented in biological brains: as a separate and unified network of attentional selection regions, receiving inputs from and exerting modulatory influence on the entire hierarchy of visual regions. Here, we report experiments with a simple such attention system that can improve the performance of standard convolutional networks, with relatively few additional parameters. Each spatial position in each layer of the network produces a key-query vector pair; all queries are then pooled into a global attention query. On the next iteration, the match between each key and the global attention query modulates the network's activations -- emphasizing or silencing the locations that agree or disagree (respectively) with the global attention system. We demonstrate the usefulness of this brain-inspired Global Attention Agreement network (GAttANet) for various convolutional backbones (from a simple 5-layer toy model to a standard ResNet50 architecture) and datasets (CIFAR10, CIFAR100, Imagenet-1k). Each time, our global attention system improves accuracy over the corresponding baseline.