Reviewerly/exHarmony huggingface dataset:
Hyper Spectral Image Compression Using Fractal Compression with Arithmetic and Huffman Coding

The contemporary world is marked by technological developments, especially in communication.The cultural revolution in linguistic caused by the development of the digital communication technologies brought in new codes changing the way we humans relate to each other, accordingly the way we think and the way we live. The end of Grand Narratives announced a post-historical period where the boundaries between reality and fiction, history and story disappeared. The liquidation of the meaning, on one hand, has been celebrated by capitalist logic in form of endlessly expanding markets, on the other hand, it has caused an intellectual crisis within the history of the Western thought. In the contemporary world, the ever increasing human activities haven’t been only undermining the planet unprecedentedly but also opening up new horizons. Vilem Flusser points both the creative potentials of the human in the im-mediate future and the dangers the human creativity is about to face therein. Similarly, Gilles Deleuze points out that creativity has the capacity to de-territorialize the transformative energies of the world and re-territorialize them in order to re-design planet’s and human kind’s future. This work, in a period of time where the boundaries between history and story have been melting down, specu-lates on the potentials and the meaning of storytelling in the immediate future of humanity
Sequential Sentence Embeddings for Semantic Similarity

Sentence embeddings are distributed representations of sentences intended to be general features to be effectively used as input for deep learning models across different natural language processing tasks. State-of-the-art sentence embeddings for semantic similarity are computed with a weighted average of pretrained word embeddings, hence completely ignoring the contribution of word ordering within a sentence in defining its semantics. We propose a novel approach to compute sentence embeddings for semantic similarity that exploits a linear autoencoder for sequences. The method can be trained in closed form and it is easy to fit on unlabeled sentences. Our method provides a grounded approach to identify and subtract common discourse from a sentence and its embedding, to remove associated uninformative features. Unlike similar methods in the literature (e.g. the popular Smooth Inverse Frequency approach), our method is able to account for word order. We show that our estimate of the common discourse vector improves the results on two different semantic similarity benchmarks when compared to related approaches from the literature.
Efficient Learning of Model Weights via Changing Features During Training.

In this paper, we propose a machine learning model, which dynamically changes the features during training. Our main motivation is to update the model in a small content during the training process with replacing less descriptive features to new ones from a large pool. The main benefit is coming from the fact that opposite to the common practice we do not start training a new model from the scratch, but can keep the already learned weights. This procedure allows the scan of a large feature pool which together with keeping the complexity of the model leads to an increase of the model accuracy within the same training time. The efficiency of our approach is demonstrated in several classic machine learning scenarios including linear regression and neural network-based training. As a specific analysis towards signal processing, we have successfully tested our approach on the database MNIST for digit classification considering single pixel and pixel-pairs intensities as possible features.
Basic Geophysics - Introduction to Seismic Exploration

Can we take a look inside the Earth?Difference between seismology and seismics, representation of different active and passive seismic sources, seismic receivers, applications in seismology and seismics.
A Minimal Social Weight Wearable Device for Thermal Regulation

Temperature changes can be severe during mountain-trekking based on the sun location and trail path. The task of keeping the body warm and comfortable to avoid hypothermia can be mitigated by a wearable device which records real-time data and analyzes it. In this paper, we propose a wearable device for body temperature regulation called warmUp which has minimal social weight. This device can predict, trained on the previously collected users’ data, whether or not it is cold for that person. The wearable device is compact and can be hidden inside a vest. A simple binary classifier indicated that the user is getting cold by using present temporal temperature variations. We train the neural network by first collecting the data using the input sensors hidden inside the vest and asking the user to classify the data. Future applications of our device are: automatically warming the vest which the user is wearing; fire fighting applications, and wilderness survival.
Using Mathematical Induction

In this chapter we learn how to use mathematical induction in various proofs. The method of proving different claims, identities, and inequalities, which is called the mathematical induction, can be formulated as follows. Assume a certain thesis is to be demonstrated for all $$n\in \mathbb {N}$$ . Then the inductive proof is composed of two steps: The implementation of these two steps means that the inductive proof is accomplished and it allows for the conclusion that the thesis is true for every natural n.
Universal Communication Efficient Quantum Threshold Secret Sharing Schemes

Quantum secret sharing (QSS) is a cryptographic protocol in which a quantum secret is distributed among a number of parties where some subsets of the parties are able to recover the secret while some subsets are unable to recover the secret. In the standard $((k,n))$ quantum threshold secret sharing scheme, any subset of $k$ or more parties out of the total $n$ parties can recover the secret while other subsets have no information about the secret. But recovery of the secret incurs a communication cost of at least $k$ qudits for every qudit in the secret. Recently, a class of communication efficient QSS schemes were proposed which can improve this communication cost to $\frac{d}{d-k+1}$ by contacting $d\geq k$ parties where $d$ is fixed prior to the distribution of shares. In this paper, we propose a more general class of $((k,n))$ quantum secret sharing schemes with low communication complexity. Our schemes are universal in the sense that the combiner can contact any number of parties to recover the secret with communication efficiency i.e. any $d$ in the range $k\leq d\leq n$ can be chosen by the combiner. This is the first such class of universal communication efficient quantum threshold schemes.
Enhancement of health-care services using blockchain with data authentication and protection

Historical details of a patient leave a very critical challenge for treatment in health-care industry. As the population is exponentially growing across the world, people may need to consult multiple places for their treatment or consultation with different health-care professionals. A secure platform is required on which patient&apos;s data can be shared among the authorised people with necessary details only. In this context, blockchain is one of the upcoming technologies that ensure the requirement of secure data sharing.
On a complete and sufficient statistic for the correlated Bernoulli random graph model

Inference on vertex-aligned graphs is of wide theoretical and practical importance.There are, however, few flexible and tractable statistical models for correlated graphs, and even fewer comprehensive approaches to parametric inference on data arising from such graphs. In this paper, we consider the correlated Bernoulli random graph model (allowing different Bernoulli coefficients and edge correlations for different pairs of vertices), and we introduce a new variance-reducing technique -- called \emph{balancing} -- that can refine estimators for model parameters. Specifically, we construct a disagreement statistic and show that it is complete and sufficient; balancing can be interpreted as Rao-Blackwellization with this disagreement statistic. We show that for unbiased estimators of functions of model parameters, balancing generates uniformly minimum variance unbiased estimators (UMVUEs). However, even when unbiased estimators for model parameters do {\em not} exist -- which, as we prove, is the case with both the heterogeneity correlation and the total correlation parameters -- balancing is still useful, and lowers mean squared error. In particular, we demonstrate how balancing can improve the efficiency of the alignment strength estimator for the total correlation, a parameter that plays a critical role in graph matchability and graph matching runtime complexity.
Time-multiplexed photonic reservoir computing

Reservoir computing (RC) has reinvigorated neuromorphic computing activities in photonics. RC radically reduces the required complexity for a hardware implementation in photonics as compared to earlier efforts in the nineties. Currently, multiple photonic RC systems show great promise for providing a practical yet powerful hardware substrate for neuromorphic computing. Among those, delay-based systems offer through a time-multiplexing technique a simple technological route to implement photonic neuromorphic computation. We will review the state of the art on delay-based RC and discuss our advances in substrates implemented as passive coherent fibre-ring cavities and semiconductor lasers with delayed optical feedback. Passive coherent reservoirs built using fiber loops have achieved record performances, but are still aided by nonlinear electro-optical transformations at the input and output. Nevertheless, when targeting all-optical reservoirs, these nonlinearities will be absent. We have found that optical nonlinearities in the fibre itself can be sufficient to enhance the task solving capabilities of a passive reservoir. Also, delay-based optical substrates for RC tend to be quite bulky employing long fiber loops or free-space optics. As a result, the processing speeds are limited in the range of kSa/s to tens of MSa/s. We have studied and developed substrates using external cavities which are far shorter than what has been realized before in experiment. Specifically, by integrating a semiconductor laser together with a 10.8 cm delay line on an active/passive InP photonic chip using the Jeppix platform, we can increase the processing speed to GSa/s.
The Concept of Humanization of Professional Activities of University Teachers as a Basis for Improving the Quality of Its Results

Research subject. In this paper, a study is made of the principles of humanization and their impact on the management of labor quality of higher education faculty in modern conditions. In the context of the digital economy, labor is transformed into creativity. This will especially affect the field of activity of scientists of the teaching staff of the Higher School. Goals and objectives. The goal is to discuss the results of the study of the parameters of the complex phenomenon “management of the work quality of teachers in the Higher School, based on the principles of humanization.” To achieve the designated goal, the following tasks were completed: a comparative assessment of various concepts on the principles of humanization of labor in the digital economy; discussion of the content of the quality indicator “the degree of humanization of labor”; to assess the above process, the possibility and expediency of applying the generalizing characteristics (degree of humanization), by the indicators of which it is possible to judge the quality of work of the faculty and researchers. Methodology. The traditional methodology of scientific research is used, namely, the formulation of a scientific hypothesis with subsequent argumentation. Conclusions. In the scientific literature, there is the concept of the quality of work or the working life of teachers, which is interpreted as the degree of satisfaction of the teacher’s personal needs and interests through the implementation of all types of labor activity, which is transformed into creative activity in the digital economy. It is obvious that the quality of work is directly related to self-actualization, self-development, self-education, and, therefore, to the degree of humanization of the teacher’s work. In our opinion, the principles of humanization of labor proposed above allow us, if observed, to conclude the degree of humanization of the teacher’s labor, and thus, it is possible to judge the quality of labor activity of the teaching staff. The same principles apply to the transformation of labor into creativity. Scientific novelty of the work consists in the development of the principles of humanization of labor with its transformation into creativity in a digital economy.
Saliency Detection for Semantic Segmentation of Videos

Vasudev, H. Supreeth, Y. S. Patel, Zeba Srikar, H. I. Yadavannavar, Smita Jadhav, Yashaswini Mudenagudi, UmaThere has been remarkable progress in the field of Semantic segmentation in recent years. Yet, it remains a challenging problem to apply segmentation to the video-based applications. Videos usually involve significantly larger volume of data compared to images. Particularly, a video contains around 30 frames per second. Segmentation of the similar frames unnecessarily adds to the time required for segmentation of complete video. In this paper, we propose a contour detection-based approach for detection of salient frames for faster semantic segmentation of videos. We propose to detect the salient frames of the video and pass only the salient frames through the segmentation block. Then, the segmented labels of the salient frames are mapped to the non-salient frames. The salient frame is defined by the variation in the pixel values of the background subtracted frames. The background subtraction is done using MOG2 background subtractor algorithm for background subtraction in various lighting conditions. We demonstrate the results using the Pytorch model for semantic segmentation of images. We propose to concatenate the semantic segmentation model to our proposed framework. We evaluate our result by comparing the time taken and the mean Intersection over Union (mIoU) for segmentation of the video with and without passing the video input through our proposed framework. We evaluate the results of Saliency Detection Block using Retention and Condensation ratio as the quality metrics.
IMAGE ENCRYPTION FOR SECURE NETWORK TRANSFER

Now-a-days, most of our data is travelled over internet so the security of that data is most important. Cryptography and Steganography are two important branches of information security. Cryptography provides encryption techniques for a secure communication. Many cryptographic algorithms are available and almost all are suitable for text encryption. Communicating only through text is not possible now-a–days and hence images, audios and videos are used for communication. Steganography involves hiding information in images. Apart from steganography, visual cryptography is widely used for securing transfer of images or visual secrets. One of the efficient methods for visual cryptography is Colour share generation in which the image is divided into shares and in decryption process all shares are stacked together to reveal the secret image. But implementing only Colour share generation will not be that much secured. Still there are some patterns in the Colour shares generated using many of the existing methods. This work proposes a new scheme for visual cryptography using both AES and Colour share generation. In this scheme R, G and B component is extracted from Colour image then apply Visual-AES algorithm on all components and generate an encrypted image. The encrypted image is then divided into Colour shares. At decryption side all shares are combined using Colour share combination and is passed as input to the Visual-AES decryption module to reveal the secret image.
PSYCHOLOGICAL FEATURES OF IMAGE DEVELOPMENT FUTURE PRACTICAL PSYCHOLOGISTS

The article deals with the problem of psychological features of the development the image future practical psychologists. Theoretical approaches to understanding the concept of image analised, its role in the structure of the profession of a practical psychologist is presented. Much attention is paid to comparing the definition of the concept of image in terms of synonymous, classification, functional, communicative, projective approaches. The psychological features that underlie the development of the image future practical psychologists, such as communicative abilities, level of self-realization are considered. Also, the importance of building a professional image and using it to be a competitive professional and a topical specialist in the labor market is substantiated. The article describes the results of the empirical study of the level of communication, the level of self-realization and level of self-confidence from students of the direction "Practical psychology", for which was initiated the diagnostics of the level of communicative abilities by the method of V.V. Sinyavsky and B.O. Fedoryshyn, the level of self-realization of personality by the method of S.I. Kudinov and the level of self-esteem by the method of Dembo-Rubinstein. The results obtained indicate that practical psychologists have high level of communicative abilities, harmonious level of self-realization, mainly high and medium level of self-esteem of dominant individual-psychological characteristics: communicative abilities, self-esteem, self-realization . The article presents the analysis of psychological features of forming the image of practical psychologists from the point of view of psychological substantiation of the level of their formation and concludes the prevailing level
Goldie Vance: The Hotel Whodunit by Lilliam Rivera

Reviewed by: Goldie Vance: The Hotel Whodunit by Lilliam Rivera Kiri Palm Rivera, Lilliam Goldie Vance: The Hotel Whodunit; illus. by Elle Power. Little, 2020 [264p] Trade ed. ISBN 978-0-316-45664-7 $14.99 E-book ed. ISBN 978-0-316-45663-0 $9.99 Reviewed from galleys R Gr. 4-7 Sixteen-year-old Floridian Goldie Vance loves working and living at the Crossed Palms Resort, parking fancy cars and reveling in the who’s-who of famous hotel guests in early 1960s Florida, but she dreams of eventually taking over the position of in-house detective. Goldie is thrilled to find out that the entire cast and crew of a movie blockbuster, including world-famous star Delphine Lucerne, will be staying at the Crossed Palms, and the hotel’s safe will be housing the crown jewel of the picture: a diamond-encrusted swimming cap to be worn by Ms. Lucerne. Unfortunately, the swimming cap disappears right under the watchful eyes of Goldie and her mentor, current in-house detective Walter Tooey; will Goldie be able to find the cap and save the reputations (and jobs) of her friends and family? Based on the graphic novel series by Hope Larson, this middle-grade novel is kind to new series readers, offering a standalone adventure for Goldie and the gang. Goldie is appealing as a headstrong, confident, absurdly good-natured narrator who worms her way into the heart of the surprisingly normal star Lucerne. Any reader familiar with detective novels will predict the swimming cap’s disappearance (and perhaps the identity of the burglar) far before the case truly begins, but Rivera unravels the mystery in an enjoyable romp with a large cast of quirky, lovable characters. Final copy will include full-color illustrations. [End Page 319] Copyright © 2020 The Board of Trustees of the University of Illinois
DIGITALIZATION, INFORMATION AND COMMUNICATION SERVICES OF THE NETWORK OF LIBRARIES IN UKRAINE FOR YOUTH

The purpose of the article is to study information and communication services and to provide servicesof libraries of Ukraine for youth, young people in the period of digitalization of social communications. Thepractical application of digitalization of resources of libraries of Ukraine for youth, young people, and provisionof information and communication services to users, as well as defining communication channels between thelibrarian and the user are considered. The methodology of the research is to apply the theoretical principlesof analytical and synthetic processing of information, which made it possible to characterize information andcommunication services of libraries of Ukraine for youth, young people, and library services in the age ofdigitalization. The scientific novelty is to identify the transitional directions for the preparation and provisionof library service information and communication facilities of libraries of Ukraine for youth, young peoplethrough the formation of a single space of transformational and innovative changes in the library networkfor youth, young people in the digital age. Conclusions. The main goals of further digital modernization oflibraries of Ukraine should be: introduction of the latest digital technologies, optimization of technologicalprocesses of library service in digitalization of social communications; prioritizing the provision of digitalproduction services for libraries of Ukraine for young people; combining and using library and informationactivities in the digital dimension; developing and implementing a strategy for promoting libraries for youth,young people in the digital socio-communication space; updating the role of the library in new models ofinformation and knowledge management in the digital media space; establishing a sustainable and effectivepartnership of library institutions in the digital area; development of competitive advantages of library projectsin the digital socio-cultural space.
A Collaborative Model for Sentiment Analysis and Summarizing User Reviews Using Machine Learning and Data Mining Techniques

The classification of emotions is a hot topic of research in the industrial and academic field. Themain methods of classifying emotions are based on machine learning and treat the classification ofemotions as a text classification problem. However, the classification of emotions is widelyrecognized as a highly domain-dependent task. The mood classifier trained in one domain may notwork properly in another domain. A simple solution to this problem is to train a domain-specificemotion classifier for each domain. However, it is difficult to identify enough data for each domainbecause they are in a large amount. In addition, this method omits mood information in otherdomains. In this document, we propose to collaboratively train multi-domain sentiment classifiersbased on learning multiple tasks. In particular, we divide the mood classifier into two domains ineach domain, one generic and one domain specific. The general opinion classifier can capture theglobal opinion information and is trained in various areas to obtain a better generalizability. Thedomain-specific opinion classifier is trained using data labeled in a domain to capture the domainspecificopinion information. In addition, we examine two types of relationships between domains,one based on the text content and the other based on the distribution of mood words. We create adomain similarity diagram using domain relationships and encode it in our approach asregularization for domain- specific opinion classifiers.
Transparent media thickness measurement employing low-coherence interferometry and a multi-element array

In this communication, we present a method to measure the thickness of transparent media employing a low coherence interferometer and a multi-element array as a photo-detector. The multi-element array employed is part of a consumer-grade digital camera (&lt; 0.5 k$). A two-beam interferometer is created by inserting the slab of a transparent material half-way through into the measurement beam. The method is evaluated on a thin microscope cover slip and on a thicker microscope glass slide.