Reviewerly/exHarmony huggingface dataset:
Deep Learning Methods for Microscopic Medical Images

An image's hidden information is worth of thousand words. The proper interpretation of a medical image can aid in the early detection and diagnosis of a disease, improving patient survival rates. Image analysis is subjective and time-consuming when done manually. On the other hand, automated image analysis has a lot of obstacles due to the architecture and colors of the medical images. In this paper, deep learning techniques based on CNN and RNN in the field of medical image analysis are reviewed for three main tasks like retrieval, classification, and medical image captioning. These two techniques (CNN and RNN) are found highly accurate. It also presents the overview of the work done in this field.
Two Way Communicator between Blind-Deaf-Dumb Person and Normal People

Communication plays a major role in expressing one's feelings to another person. People with vision, hearing, and speech problems find it difficult to communicate with others. They have some special languages for those people, which include tactile signing, braille etc. Among those methods, braille is the most widely used communication language. It is a system that uses a group of dots that are arranged in a particular manner to form letters, numbers and some special characters. The main aim of this research is to provide a two-way communication device between blind-deaf-dumb person and normal people. This work converts the input data, either in voice or image into braille that can be understood by deaf, blind and dumb person and also converts braille input from blind-deaf-dumb person into text or voice that can be understood by normal people. Once the text from images or voice is detected, it starts converting the text into braille format. The braille characters are displayed one after another with the help of the braille display. The deaf-blind-dumb person can feel the letters by placing their fingers on the braille display and in case the blind-deaf-dumb person wants to communicate with the normal people, they give the braille information through a display converted into text displayed on a liquid crystal display and gives voice output from the speaker.
Hybrid Encryption through End to End in Messaging Service Applications

End-to-end cryptography has been incorporated by a number of well-known messaging apps in order to effectively secure the private of online communication. Yet, prior research suggests that many users struggle, in part because of false mental representations, to use encryption products accurately and confidently as well as to appreciate their security benefits. There are numerous end-to-end encryption techniques available for various communication patterns. An encrypted messaging protocol is required for a secured chat in order to prevent these types of vulnerabilities and threats of data attacks. It will be used for secure data transmission in the messenger's signal encryption protocol integrates several cryptographic methods, including with public-key encryption. Each individual must own two randomly generated keys, one of which must be kept a secret and the other must be made accessible to the whole public. The sender encrypts messages using the recipient's public key in this approach. On the other end, the recipient uses their secret key to decode it.
Penalty-Enabled Serverless Architecture for Cloud-based Startup Solutions

The step to the success of startups is through overcoming competitors by adopting software innovations that improve businesses. Serverless computing model, recently, has intrigued a sizable number of startup professionals belonging to various sectors, including financial or IoT-enabled application developers. One of the main flaws is its heavy dependency on cloud providers, which can still result in hefty pricing to startups and stalling functions in applications. This article proposes a penaltyenabled serverless architecture for startups. The architecture can boost the economy of startups and can analyze the serverlessoriented cost-saving options in applications. The penalty-oriented approach could enable cloud architects, developers, and startups, to rethink the utilization of serverless functions; to gleam of with future innovations.
Ocean data compression based on block SVD

Abstract Fast development of ocean observations and numerical modeling increases the need for data transmission, storage and extraction. This paper presented a new data compression method based on Singular Value Decomposition (SVD) with data matrix divided into different sub-matrices with consideration of odevity and remnant. An automatic matrix-dividing method is applied to divide smartly the data matrix into sub-matrices. These sub-matrices are then compressed based on an improved SVD, which enhances the compression performance by utilizing the orthogonal property of vectors generated by SVD. A dynamic optimization method which is capable of determining the proper scale of retained data under the accuracy requirement of ocean data is also established. Two indices are derived mathematically to search the best block pattern quickly. The performance and reliability of the block-based SVD compression is verified with the successful compression and recovery of the Hybrid Coordinate Ocean Model data.
Fast Track Smoothing Algorithm for Underwater Target Tracking Based on KD Tree

Abstract A fast-track smoothing algorithm based on the KD Tree is proposed to address the problem of state estimation in underwater multi-target tracking. For multi-target tracking, the multi-Bernoulli filter becomes intractable due to the high-dimensional nature of the posterior. This problem can be overcome by the particle filter (PF), which approximates the posterior by a set of weighted samples (“particles”). The probability distribution of the multi-Bernoulli algorithm based on this algorithm contains a series of particles with weights. Therefore, while smoothing it, the state transfer probability of each particle must be computed, and the state transfer function must be calculated, which is related to the difference in states between the k-1 moment and the k moment, we refer to this difference as the state distance. However, the KD Tree can be used to divide the particle set into different state spaces according to the particle states, and thus calculate the state distances more efficiently. The remarkable feature of this algorithm is that the time complexity is greatly reduced from that O(N 2 ) of the traditional algorithm to O(NlogN). This paper implements the proposed method using particles, the improved algorithm based on KD tree effectively mitigates the disadvantages of previous forward-backward smoothing algorithms, and significantly reduces the computation time while improving the system state estimation precision. Experimental results of simulation analysis and the optimal sub-patten assignment distance is employed as an assessment criterion to validate the algorithm’s performance.
A MODEL OF ORGANIZATIONAL CULTURE OF HIGHER EDUCATION INSTITUTIONS ON THE EXAMPLE OF UNIVERSITIES OF THE SZECHUAN PROVINCE OF THE PEOPLE’S REPUBLIC OF CHINA

In the article, the author described the model of organizational culture development. Using the example of the Southwest University of Finance and Economics, Chengdu, the author identified the elements of the organizational culture model. The basis for the development of organizational culture is the interdependence of factors, principles, and trends in the work of a higher education institution. The first level involves the development of a system of organizational culture management goals at the university in a real cultural space. Based on these goals, a unique strategy is formed. The second level of organizational culture development involves the integration of the content and technologies of its implementation at the internal university level, as well as at the level of inter-university interaction. The third level involves the structuring of managerial aspects of organizational culture, various methods of monitoring and current evaluation of organizational culture of the university. The article emphasizes that universities in Sichuan province are open to innovation and international cooperation. But the universities of this province have strong traditions and a classical hierarchy in management. For the organic combination of traditions and innovations in the management of organizational culture, the goals are to improve the quality of management subjects and obtain economic, social, scientific and technical, ecological and other effects. The combination of traditions and innovations in the development of the organizational culture of universities in Sichuan province involves successive elements of the content of management: initiation, marketing, release, implementation, monitoring, evaluation of effectiveness, diffusion of an integrated idea. The success of the organizational culture model of universities depends on the coordination and coordinated productive cooperation of all participants in the educational process, structures that are interested in the quality of education. Therefore, the author emphasized: the board should combine traditions and innovations; the university functions as self-sufficient, provides appropriate training of managers, coordinators and teachers; creation of conditions for self-realization of the individual, socially significant productive creative activity; favorable microclimate and support for young scientists.
Interacting Multiple Model Filtering for Aircraft Guidance Modes Identification from Surveillance Data

Accurate and reliable trajectory prediction (TP) is required in several air traffic management systems. Estimating the aircraft trajectory in a vertical plane typically requires the knowledge of a pair of operational instructions. A sequence of operational instructions specifies the aircraft intent, information that is seldom available, besides for the ownship trajectory planning system. In the execution of the trajectory, the aircraft is directed by the (auto)pilot through a series of sequential guidance modes that might override some of the planning instructions of the aircraft intent. Therefore, having reliable guidance mode information is fundamental for the next generation of air- or ground-based TP. The main goal of this contribution is to develop a methodology able to identify in real-time the active guidance modes for both vertical climb and descent profiles, using only Automatic Dependent Surveillance-Broadcast and Enhanced Mode-S Surveillance data. The proposed solution is based on an interacting multiple model (IMM) filtering approach, which uses a bank of filters, each one matched to a possible guidance mode. The guidance mode identification performance of the IMM-based solution is validated with i) a set of simulated representative trajectories and ii) real flight data obtained from flight data recorders.
Reprint of: Instilling the core tenets of hospitality in healthcare services: The role of service assurance and social presence

The rising elderly population around the globe together with an improved life expectancy is driving demand for effective healthcare. As healthcare technologies become increasingly innovative, various types of connected devices have been developed in order to provide high-quality healthcare services. The research question of this study is whether the presence of such devices and perceived assurance affect the satisfaction of the health service recipient. This study focuses on homecare services for elderly people and explores the effect of social presence by machines vs. humans and service assurance by machines vs. humans on customer satisfaction. This study found that using connected devices does not significantly impact customer satisfaction with the healthcare service provider. The findings of this study highlight the importance of perceived service assurance levels as well as social presence delivered by humans for homecare services.
Variants and satisfiability in the infinitary unification wonderland

So far, results about variants, the finite variant property (FVP), variant unification, and variant satisfiability have been developed for equational theories E∪B where B is a set of axioms having a finitary unification algorithm, and the equations E, oriented as rewrite rules E→, are convergent modulo B. The extension to the case when B has an infinitary unification algorithm, for example because of non-commutative symbols having associative axioms, was not developed. This paper develops such an extension. In particular, the relationships between the FVP and the boundedness (BP) properties, the identification of conditions on E∪B ensuring FVP, the effective computation of variants and variant unifiers, and criteria making possible the existence of variant satisfiability procedures for the initial algebras of theories E∪B that are either FVP or BP are all explored in detail. The extension from the finitary to the infinitary B-unification case includes some surprises. Furthermore, since all the results are extended beyond FVP theories to the wider class of BP theories, new opportunities are opened up to use these symbolic techniques in wider classes of theories and applications.
Multimodal Machine Learning in Prognostics and Health Management of Manufacturing Systems

Prognostics and health management (PHM) is a crucial enabler to reduce maintenance costs and enhance the availability and reliability of manufacturing systems. In the context of Industry 4.0, these systems become more complex and can be monitored by different types of sensors. The quality and completeness of data are crucial factors for the success of any PHM task in this paradigm. Here, we investigate the possibility of exploiting additional data sources in manufacturing besides monitoring sensors, e.g. production line cameras or maintenance reports. We first present the terminologies of multimodal learning and the potential it holds for industrial PHM. We then further explore the development and notable works in this field applied to other domains, look at the relevant works in PHM, and finally present a case study to demonstrate how multimodal learning can be performed to improve PHM processes.
Automatic image caption generation using deep learning

Image captioning is an interesting and challenging task with applications in diverse domains such as image retrieval, organizing and locating images of users’ interest, etc. It has huge potential for replacing manual caption generation for images and is especially suitable for large-scale image data. Recently, deep neural network based methods have achieved great success in the field of computer vision, machine translation, and language generation. In this paper, we propose an encoder-decoder based model that is capable of generating grammatically correct captions for images. This model makes use of VGG16 Hybrid Places 1365 as an encoder and LSTM as a decoder. To ensure the complete ground truth accuracy, the model is trained on the labeled Flickr8k and MS-COCO Captions datasets., Further, the model is evaluated using all popular standard metrics such as BLEU, METEOR, GLEU, and ROUGE_L. Experimental results indicate that the proposed model obtained a BLEU-1 score of 0.6666, METEOR score of 0.5060, and GLEU score of 0.2469 on the Flickr8k dataset and BLEU-1 score 0.7350, METEOR score of 0.4768 and GLEU score 0.2798 on MS-COCO Caption dataset. Thus, the proposed method achieved a significant performance as compared to the state-of-art approaches. To evaluate the efficacy of the model further, we also show the results of caption generation from live sample images that reinforce the validity of the proposed approach.
Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals

Automatic Modulation Recognition (AMR) can obtain the modulation mode of the received signal for subsequent processing without the assistance of the transmitter. Although the existing AMR methods have been mature for the orthogonal signals, these methods face challenges when deployed in non-orthogonal transmission systems due to the superimposed signals. In this paper, we aim to develop efficient AMR methods for both downlink and uplink non-orthogonal transmission signals using deep learning-based data-driven classification methodology. Specifically, for downlink non-orthogonal signals, we propose a Bi-directional Long Short-Term Memory (BiLSTM)-based AMR method that exploits long-term data dependence to automatically learn irregular signal constellation shapes. Transfer learning is further incorporated to improve recognition accuracy and robustness under varying transmission conditions. For uplink non-orthogonal signals, the combinatorial number of classification types explodes exponentially with the number of signal layers, which becomes the major obstacle to AMR. We develop a spatio-temporal fusion network based on the attention mechanism to efficiently extract spatio-temporal features, and network details are optimized according to the superposition characteristics of non-orthogonal signals. Experiments show that the proposed deep learning-based methods outperform their conventional counterparts in both downlink and uplink non-orthogonal systems. In a typical uplink scenario with three non-orthogonal signal layers, the recognition accuracy can approach 96.6% in the Gaussian channel, which is 19% higher than the vanilla Convolution Neural Network.
WHICH FACTORS CAN EXPLAIN THE CORRELATION BETWEEN CRYPTOMINING ACTIVITIES AND ENVIRONMENTAL IMPACT? A THEORETICAL ANALYSIS

In 2021, a massive relocation of cryptocurrency mining farms from China to Kazakhstan caused one of the largest energy crises in Asia and Europe. This work aims at an understanding of the current debate of cryptocurrency mining activities on the environment. The review highlights: 1. The existence of a positive relationship between cryptomining activities and the deterioration of environmental quality; 2. The contribution of renewable energies and blockchain algorithms to mitigate the above effects. Main results suggest the need for a supranational institution to monitor the current cryptocurrency production to complement current policies by the European Commission and United Nations.
Fully Parallel Homological Region Adjacency Graph via Frontier Recognition

Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-dimensional images is extracted in an efficient manner through the computation of a new structure called the HomDuRAG of an image; that is, the dual graph of the HomRAG (a topologically consistent extended version of the classical RAG). These representations are derived from the two traditional self-dual square grids (in which physical pixels play the role of 2-dimensional cells) and encapsulate the whole set of topological features and relations between the three types of objects embedded in a digital image: 2-dimensional (regions), 1-dimensional (contours) and 0-dimensional objects (crosses). Here, a first version of a fully parallel algorithm to compute this new representation is presented, whose timing complexity order (in the worst case and supposing one processing element per 0-cell) is O(log(M×N)) , M and N being the height and width of the image. Efficient implementations of this parallel algorithm would allow images to be processed in real time, as well as permit us to uncover fast algorithms for contour detection and segmentation, opening new perspectives within the image processing field.
Iterative Oblique Decision Trees Deliver Explainable RL Models

The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DTs) as simple and transparent surrogate models for opaque deep reinforcement learning (DRL) agents. We investigate three algorithms for generating training data for axis-parallel and oblique DTs with the help of DRL agents (“oracles”) and evaluate these methods on classic control problems from OpenAI Gym. The results show that one of our newly developed algorithms, the iterative training, outperforms traditional sampling algorithms, resulting in well-performing DTs that often even surpass the oracle from which they were trained. Even higher dimensional problems can be solved with surprisingly shallow DTs. We discuss the advantages and disadvantages of different sampling methods and insights into the decision-making process made possible by the transparent nature of DTs. Our work contributes to the development of not only powerful but also explainable RL agents and highlights the potential of DTs as a simple and effective alternative to complex DRL models.
Exploring agent-based chatbots: a systematic literature review

Abstract In the last decade, conversational agents have been developed and adopted in several application domains, including education, healthcare, finance, and tourism. Nevertheless, chatbots still need to address several limitations and challenges, especially regarding personalization, limited knowledge-sharing capabilities, multi-domain campaign support, real-time monitoring, or integration of chatbot communities. To cope with these limitations, many approaches based on multi-agent systems models and technologies have been proposed in the literature, opening new research directions in this context. To better understand the current panorama of the different chatbot technology solutions employing agent-based methods, this Systematic Literature Review investigates the different application domains, end-users, requirements, objectives, technology readiness levels, designs, strengths, limitations, and future challenges of the solutions found in this scope. The results of this review are intended to provide researchers, software engineers, and innovators with a complete overview of the current state of the art and a discussion of the open challenges.
Implications: Recommendations for Future Education and Labor Policies

A job in which there is a lack of time-dependence for the Judge’s score can be modeled as Judgement Game, while the existence of a time-dependence for the Judge’s score means that the job must be modeled as a Learning Game. Humans have a comparative learning advantage in Learning Games, while learning-enabled machines have a comparative learning advantage in Judgment Games. Humans should focus on jobs that can be modeled as Learning Games. Current education practices need revision because they focus on teaching knowledgebases/skillsets used in jobs that resemble Judgment Games. This contributes to the current mismatch in the labor market—in which many people are looking for work while, at the same time, many jobs are going unfilled. As a remedy, counterintuitive recommendations for changes in labor and education policies are provided. These include a renewed focus on liberal arts education rather than the current trend that emphasizes technical training, and that employers should screen job candidates for learning ability rather than specific skillsets. In addition, if AI is to work with humans, it must have knowledgebases/skillsets along the expertise and interpersonal dimensions. The framework presented provides a practical means for separating the interpersonal dimension from the expertise dimension.