Reviewerly/exHarmony huggingface dataset:
On-board Data Handling

This chapter describes the operations of the spacecraft components that handle the distribution and processing of on-board data. This subsystem is referred to as on-board data handling (OBDH). Modern spacecraft are built around a powerful processing module that uses software to perform many functions. Complex data structures that need to be handled are defined to communicate with the ground stations, but also to handle on-board components. Using classic communications satellites and scientific satellites in low Earth orbit as examples, we present the basic components of OBDH in more detail, including typical operational tasks and considerations.
User models for recommendation systems

Currently, a user model or a representation of the user is used to recommend, personalize, predict, and manage inference in different recommender systems. Now, all the necessary user information is scattered. This document presents a systematic study to identify all the user information that recommendation systems use, represented or not by means of a model. This searching is important because it identifies the different data sources where user models are analyzed, and attributes and forms of user interaction are identified. The results presented are very useful for structuring a generalized user model.
Empirical Satisfaction Degree of Express Service on Big Data

With the development of online shopping in recent years, online shopping has gradually become a habit of people’s daily life, and has gradually driven the development of the logistics and express delivery industry. In order to meet consumers’ online shopping needs, some changes have also taken place in express delivery services, not only from the perspective of the enterprise, but more from the perspective of the consumer, Improve and improve the process of express delivery services, and develop value-added express delivery services to meet consumer demand for express delivery services. Therefore, it is of great practical significance to study and improve customer satisfaction. This paper analyzes the reliability, validity and correlation of the samples through big data obtained from questionnaire survey. It is concluded that the delivery, communication, responsiveness, economics and flexibility of the express service have a positive impact on customer satisfaction.
The Image of the Future and Scenarios for the Development of Science as a Cultural Phenomenon

Designing an image of the future is not amusement of futurologists and visionaries, but a necessary tool for organizing social life. Today, the construction of such an im­age is formed around the question of the future of science. The article deals with this issue through the analysis of the current state of science, which uses the concept of technoscience explicated not only as a modern form of scientific knowledge pro­duction, but as a new form of existence science as cultural phenomenon. Based on this analysis author generates and discusses four scenarios for the possible devel­opment of technoscience in the long-term (5–20 years) and strategic perspective. Two scenarios are negative, since they are fraught with the actual abolition of science as a cultural phenomenon. The first is associated with the strengthening of applied re­search and the formation of an artificial intellectual environment that radically re­duces the need for developed theoretical knowledge. The second scenario assumes the continuation of the trend of commercialization of science and the commodifica­tion of scientific knowledge, affecting both the field of applied and basic research. The analysis of the directions combined under the name “Megascience” allows to speak about the possibility of two positive scenarios, one of which is inertial and as­sumes the preservation of the ideal of pure science as an activity motivated by the search for truth, and the second demonstrates the possibility of humanizing oper­ationally oriented cognition and softening the contradiction between the discovery of truth and the mastery of new possibilities of action in the world.
A Genetic Model for Medical Images Reproduction

Reproducing images arise in many applications such as image compression, Image optimization, graphic art, and medical image processing. For medical images, image quality is crucial for proper diagnosis from an imaging study. The challenges in medical image processing occur due to poor image contrast and artifacts that outcome from missing organ boundaries. In this paper, a new variant of the Genetic Algorithm (GA) is proposed to provide an optimal solution to the medical image reproduction problem. The genetic operators have been adapted and the fitness function has been adjusted to produce good solutions. The implemented method, tested with different medical images, offers good results and agrees well with the complex medical images and even with low-quality images.
Dual Attention Mechanisms Based Auto-Encoder for Video Anomaly Detection

Video anomaly detection refers to the identification of abnormal behaviors that do not conform to normal patterns. Reconstruction of video frames based on auto-encoder is the current mainstream video anomaly detection method. If frames have higher reconstruction error than the threshold, these frames will be treated as the anomalous frames. However, auto-encoders lack attention to global information and channel dependence. The attention mechanism enables the neural network to accurately focus on input-related elements and becomes an important part of the neural network. In order to focus the feature of both channel and spatial dimensions, we propose dual attention mechanisms based auto-encoder (DAMAE) for video anomaly detection. After each down-sampling, the feature map is operated by two kinds of attention processing. The feature map is divided into specific groups. Every individual group can autonomously enhance its learnt expression and suppress possible noise. By fusing channel attention and spatial attention, DAMAE is able to capture the pixel-level pairwise relationship and channel dependence. Compared with traditional auto-encoder in the process of each up-sampling, the feature with channel attention and spatial attention can reconstruct the normal pattern of the video better. Experimental results show that our method is superior to other advanced methods, which proves the effectiveness of our method.
VisNow-Medical—A Visual Analysis Platform for Medical Data Processing

The VisNow Medical platform is a set of integrated algorithms for visual analysis of medical data and is an extension of the VisNow platform used for image processing and visualization. VisNow Medical platform emphasizes on the ease of development of case-specific, multi-platform medical applications, with the possibility of processing one-, two-, and three-dimensional data in terms of filtering, denoising, segmentation, mapping, classification, quantitative and qualitative visual processing. VisNow Medical facilitates the research and development process in medical field. This research presents several applications of the platform for magnetic resonance, computed tomography and image processing applications.
Packaging

One of the main strengths of Python is the ecosystem, the third-party packages on PyPI. There are packages to do anything from running computations in parallel on GPUs for machine learning to reducing the boilerplate needed for writing classes. This means that a lot of the practical work with Python is handling the third-party dependencies.
Beyond the Noise – Information Discovery in Bitcoin Revisited

The introduction of futures contracts on an asset is often heralded as an indicator of market maturity. In this paper, we revisit the currently, mixed empirical evidence relating to price discovery between Bitcoin spot and futures markets. Confirming previous findings, information share (IS) and component share (CS) metrics suggest that futures markets lead spot markets in price discovery. In sharp contrast, however, results for the information share leadership (ILS) metric, which is more resistant to Bitcoin's pervasive market microstructure noise, indicate that price discovery in the spot market dominates in the pre-COVID period. Some evidence is found for the increasing dominance of futures markets in the post-COVID period, pointing to an emerging maturity in Bitcoin markets.
Mitigating Position Bias in Review Search Results with Aspect Indicator for Loss Aversion

Conventional review websites display a list of item search results with average rating scores (i.e., star ratings). We propose a method of designing snippets that encourage users to search items on review websites more carefully. The proposed snippets include aspect indicators that identify negative aspects if the item has a good star rating and vice versa. We expect the aspect indicators will help mitigate biases due to ranking position and star ratings by making users feel a “loss” if they do not carefully examine items. Our user study showed that the proposed method of including aspect indicators for loss aversion made participants spend more time searching a list of search results and checking items with worse star ratings, especially when searching hospitals. In contrast, showing aspect indicators that conformed to star ratings caused shortsighted review searches.
Suggesting Hotels Through Reviews Using Sentiment Analysis

Recommendation helps people in finding out the best choice based on the preferences and options of their own. Starting from the e-commerce websites, these recommendation systems play a major role in online advertisements that cannot be avoided in the Internet era today. These recommendation systems are used in variety of applications and services, and also, these systems have a remarkable place in the recommendation of hotels. A majority of people who come from other places find it difficult to find a hotel which suits their requirements in terms of money, hospitality, quality, location, and others is tedious. Hotel recommendation is required for those who come from other places and come in search for best hotels that suits their needs and own interest. This recommendation system recommends the users on basis of the comments given to the hotel previously by other customers who have visited the hotels. The customers express all their feelings and other specialties, drawback and features in comments that are helpful for the upcoming customers to judge the hotel. This system is useful for those people who often visit new places. The information extracted from the previous customers is a valuable resource that is used by the system to analyze the hotel’s quality and standards and also help the new customers who have no idea about the place and the hotel.
Boosting Blockchain Mechanism Using Cryptographic Algorithm in WSN

The bonding and swapping of data within the smart artifices are done by the internet of things (IoT) nowadays. It is assumed that many dangers and attacks on WSN infrastructures are done by the increasing and fast-growing IoT’s. As far as the WSN systems are concerned, their most important necessities are observation, reliability, and liability. If such situations have not prevailed, the so-called WSN system could be used by malevolent and hostile users. In the case of the centralized WSN system, when data is conveyed between devices to be shared, problems and risks are quite common. The recent availability of the blockchain and cryptographic tools supports shielding data integrity. This work is mainly framed for studying the temperature and the humidity and various changes seen in the warehousing locations provided for the storage of crops. The data is collected by means of Iot sensor having a wireless sensor network. The blockchain mechanism is employed for the data got from the sensor. We have analysed existing algorithms and its performance. Among the algorithms taught, we have considered the AES to be better performance. So, we have transformed the AES to be a Tuned AES and we have employed it to study the performace in better manner.
An MR Image Segmentation Method Based on Dictionary Learning Preprocessing and Probability Statistics

Segmentation of brain MR images can help clinicians to extract regions of interest. At present, there are many studies on brain MR image segmentation, especially in deep learning methods. However, deep learning methods requires a very large quantity of datasets. With the popularity of deep learning, dictionary learning has been revived again. As a traditional machine learning segmentation method, dictionary learning requires less sample size, has high segmentation efficiency, and can describe the image well. This paper mainly proposes an MR image segmentation method based on posterior probability. The process of medical image acquisition is accompanied by the generation of noise which is limited by factors such as the equipment environment. We use the powerful classification function of the naive Bayes model to classify the noise data and sample label data in the images, and then provide the classification results to the dictionary learning sparse matrix for sparse expression. We achieve the effect of noise reduction and denoising through mathematical methods to improve the signal-to-noise ratio of medical image data. After getting the denoised image, we adopt region growth combined with the softmax function method to classify the pixels. The results of this paper provide technical support for the subsequent image segmentation, detection and other computer-assisted clinical diagnosis, so as to improve the efficiency of automated clinical diagnosis for clinical application.
Lightweight Authentication and Encryption for Online Monitoring in IIoT Environments

Emerging industrial technologies building upon lightweight, mobile and connected embedded devices increase the need for trust and enforcing access control in industrial environments. We propose an approach which combines Physical Unclonable Functions (PUFs), firmware fingerprinting and Attribute-Based Encryption (ABE) for enabling authentication and fine-grained access control of the data generated on the IoT end nodes. This approach is evaluated using an experimental setup and its feasibility for online monitoring in industrial environments is demonstrated. The proposed architecture adds a processing overhead of under 1% on a low-cost microcontroller and a communication latency of 144 ms over a long-range wireless link, while having a low power consumption and protecting against multiple cyber-threats.
Deep Reinforcement-Based Conversational AI Agent in Healthcare System

Conversational AI is a sub-domain of artificial intelligence that deals with speech-based or text-based AI agents that have the capability to simulate and automate conversations and verbal interactions. A Goal Oriented Conversational Agent (GOCA) is a conversational AI agent that attempts to solve a specific problem for the users as per their inputs. The development of Reinforcement Learning algorithms has opened up new opportunities in research related to conversational AI, due to the striking similarity the algorithm bears to the way a conversation takes place. This chapter aims to describe a novel, hybrid conversational AI architecture using Deep Reinforcement Learning that can give state-of-the-art results on the tasks of Intent Classification, Entity Recognition, Dialog Management, State Tracking, Information Retrieval and Natural Language Response Generation. The architecture also consists of external AI modules, focused on carrying out intelligent tasks pertaining to the healthcare sector. The AI tasks that the conversational agent is capable of performing are—Text-based Question Answering, Text Summarization and Visual Question Answering.
Identification of library information resources in the Semantic Web information space

The author discusses the modern approach to unique identification of library collections allocated in Internet-based distributed information systems. Identification issues are examined being harmonized with the semantic web principles as the priority vector for digital information; they are based on current international and national standards.General principles of the modern system of identification of library information resources are formulated. The article is prepared within the framework of the State Order No. 730000F.99.1.BV09AA00006.
Generating a Gesture Set Using the User-defined Method in Smart Home Contexts

Gesture interaction is a natural interaction method and it has been widely applied in various smart contexts. Smart home system is a promising area to integrate gesture interaction. Under this background, it is necessary to generate a set of gestures that can support users’ intuitive interaction with smart home devices. Gesture elicitation study (GES) is an effective method used for generating gestures. In this study, by following GES, we develop a gesture set for controlling a smart TV via a smart speaker, which was common in smart home contexts. Two studies were conducted. In study 1, we conducted a diary study to generate target tasks, resulting in fifteen most frequent tasks in domestic contexts. In study 2, GES was conducted to generate gestures for each command by involving twelve participants. The generated gestures were analyzed by combining frequency, match, ease of use, learnability, memorability and preference, resulting in a set of gestures for smart home contexts.Keywords: Gesture Interaction, Smart Home System, Gesture Elicitation Study
Analysis of educational demands of the faculty and librarians of vocational educational institutions for designing public open online-courses

The Republican Institute for Vocational Education has realized UNESCO-UNEVOC, the international experimental project and developed the open online-course “The Basics of Pedagogical Design of Online-course for Vocational Education”. At the first project stage, the demands of the faculty and librarians of vocational and extended educational institutions of the Republic of Belarus in training and learning supraprofessional (digital) competences were revealed. These studies are unique as the mass open online-courses in this area have not been designed never before. The method of questionnaire survey based on Google Forms online service was selected for the study. The analytical review of the survey findings is presented; 107 lecturers and librarians of the institutions of vocational and extended education were surveyed. The obtained data are partially visualized with the pictures to demonstrate, in particular: type of respondent institution; job experience in years; using digital educational resources and instructional media in classes; target audience of the public open online course “The Basics of Pedagogical Design of Online-course for Vocational Education”. The conclusions are made on the course impact on solving the modern problems of VUCA-world.
An Improved User Interface for Enabling Smart Access Using Low-Cost QR-Based Systems at Various Points

Organizations of today are scrutinized for neglecting safety and healthcare of their employees. The entry/exit points witness rigorous human contact and are hotspots of germ transmission by touch. In this paper, we propose an authentication method for physical access control by using a quick response (QR) code powered setup. This low-cost method facilitates quick implementation by only requiring the smartphones of its employees. The main motive is to help organizations in tracking and prioritizing the safety of its employees when entering the organization and facilitating seamless human resource management. This system uses an encryption algorithm that assures a secured flow of the programmed events, avoiding any external mal-hindrance. The QR code is transmitted through a carrier network, thus enabling faster operations. This part of the study tries to enhance the proposed system which is the next version of the system described in a previous works. This system is feasible for accessing gadget/tokens in semi-restricted public places and venues demanding a record of individuals using their services. QR code reading is unambiguous, as it only demands proximity of the reader device to the user’s screen, thereby trenching any chances of fallacy. This technology is proficient and expedient in ways such as higher data storage capacity, curtailed implementation cost, technical simplicity, extensive use and is globally available. Varied compatible sequencers and extraneous features can be accumulated in this ecosystem. The proposed system is demonstrated with a simple UI that can be upgraded in scale when implemented. The validation of the proposed system is measured with the SUS scale among a set of users.
A Novel Intelligent Ant Colony System Based on Blockchain

Swarm intelligence occurs when the collective behavior of low-level individuals and their local interactions form an overall pattern of uniform function. Incorporating swarm intelligence allows us to disregard global models when we explore collective cooperation systems that lack any central control. Blockchain is a key technology in the functioning of Bitcoin and combines network and cryptographic algorithms. A group of agents agrees on a particular status and records the protocol without controlling it. Blockchain and other distributed systems, such as ant colony systems, allow the building of “ants” that are more secure, flexible, and successful. We use the principle of blockchain technology and carry out ant colony research to solve three urgent problems. We use new security protocols, system implementations, and business models to generate ant swarm system scenarios. Finally we combine these two technologies to solve the problems of limitation and reduced future potential. Our work opens the door to new business models and approaches that allow ant colony technologies to be applied to a wide range of market applications.
Visualizing Data

Nothing tells a story about your data as powerfully as good plots. Graphics captures your data much better than summary statistics and often shows you features that you would not be able to glean from summaries alone.
Unsupervised Learning

For supervised learning, we have one or more targets we want to predict using a set of explanatory variables. But not all data analysis consists of making prediction models. Sometimes, we are just trying to find out what structure is actually in the data we analyze. There can be several reasons for this. Sometimes, unknown structures can tell us more about the data. Sometimes, we want to explicitly avoid unknown structures (if we have datasets that are supposed to be similar, we don’t want to discover later that there are systematic differences). Whatever the reason, unsupervised learning concerns finding unknown structures in data.