Reviewerly/exHarmony huggingface dataset:
Development of the quality of additional professional programs through the introduction of the principle of transformability

Development and implementation of the transformability principle have been preceded by insufficient satisfaction of trainees, federal and municipal officers in particular, with the quality of conventional supplemental professional programs of further training, as well as their low motivation to learning and improvement of professional competences. This article is aimed at analysis of efficiency of implementation of transformability principle into supplemental professional programs of further training. Focused interview and focus groups method by R. Merton were the main research method. This procedure was applied during training of two focus groups according to transformable supplemental professional program titled Manage by word, aimed at development of communication competences of government representatives (309 civil officers of Tambov oblast) and officers and heads of multifunctional centers of state and municipal services (58 persons). The term “transformability” implies structuring of education route by trainees themselves as well as existence of mandatory and variable elements allowing to adapt the program content to various targeted audiences. The main results of the work have been confirmation of satisfaction of trainees with the quality of transformable supplemental professional program and improved motivation to learning.
Comparative Study of Feature Extraction Using Different Transform Techniques in Frequency Domain

The compressed sensing is a mathematical approach of reconstructing a signal that is acquired from the dimensionally reduced data coefficients/less number of samples, i.e., less than the Niquist rate. The data coefficients are high-frequency components and low-frequency components. The high-frequency components are due to the rapid changes in the images (edges) and low-frequency correspond are due to slow varying information (continuous surface). The idea is to retain only low-frequency components, i.e., the significant components that constitute the compressed signal. This compressed signal is the sparse signal which is so helpful during medical scenario. During the Medical Resonance Imaging (MRI) scans, the patient undergoes many kinds difficulties like uncomfortness, patients are afraid of the scanning devices, he/she cannot be stable or changing his body positions slightly. Due to all these reasons, there can be a chance of acquiring only the less number of samples during the process of MRI scan. Even though the numbers of samples is less than the Nyquist rate, the reconstruction is possible by using the compressed sensing technique. The work has been carried out in the frequency domain to achieve the sparsity. The comparative study is done on percentage of different levels of sparsity of the signal. This can be verified by using peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity (SSIM) methods which are calculated between the reference image and the reconstructed image.
Towards Interpretable Multi-task Learning Using Bilevel Programming

Interpretable Multi-Task Learning can be expressed as learning a sparse graph of the task relationship based on the prediction performance of the learned models. Since many natural phenomenon exhibit sparse structures, enforcing sparsity on learned models reveals the underlying task relationship. Moreover, different sparsification degrees from a fully connected graph uncover various types of structures, like cliques, trees, lines, clusters or fully disconnected graphs. In this paper, we propose a bilevel formulation of multi-task learning that induces sparse graphs, thus, revealing the underlying task relationships, and an efficient method for its computation. We show empirically how the induced sparse graph improves the interpretability of the learned models and their relationship on synthetic and real data, without sacrificing generalization performance. Code at https://bit.ly/GraphGuidedMTL.
Design and Realization of Multiple_Platform Digital Museum Interaction System Based on VR&amp;AR

The museum is a place for collecting, storing, displaying and researching physical objects representing natural and human cultural heritage, and rationally classifying them, providing visitors with learning, education, and entertainment services. Providing high-quality information services is the core value of the museum. The museum invisibly reflects the historical background of the cultural relics displayed and the cultural connotation of the displayed area. It cannot be classified, arranged, and juxtaposed in a single manner, but should be the expression of internal cultural spirit. Create a vivid, contextual, and convenient online digital museum content ecology and information service framework through the combination of museums and VR technology. With the help of realistic content scenes and excellent modeling tools and ecosystems, it provides distinctions for museum users Offline, with a richer content ecosystem and a more convenient content ecosystem, will greatly enhance the competitiveness of digital museums and increase the number of museum users. This article takes "Hunan Changsha Museum" as the research object, researches education for ordinary people, focuses on getting cultural relics out of the museum, and comprehensively utilizes cultural relics resources. Let the cultural heritage be deeply rooted in the hearts of the people through a variety of expressions, use Internet technology, big data processing, cloud data integration, high-fidelity modeling technology, and use cultural relics and the history behind them as content to build PC terminals, mobile terminals, and integrated display machines, A cross-platform and multi-dimensional intelligent interaction platform for paper books. Let the collection of cultural relics go out of the exhibition hall and into the lives of the people.
Few-Shot Knowledge Reasoning: An Attention Mechanism Based Method

As a core issue of knowledge graph research, knowledge graph reasoning and completion technology have always been a hot topic of current research. Existing knowledge graph reasoning techniques usually require a large amount of training for each relationship; in addition, training each relationship requires a large number of training samples. Inspired by meta-learning [1], this chapter combines the idea of meta-learning and the attention mechanism [2] for knowledge reasoning. We introduce the few-shot reasoning method based on the attention mechanism, which has superior performance. The novel method greatly reduces the number of samples required for each relationship training, which reduces the scale of the reasoning problem. Second, with the attention mechanism, the proposed method could achieve higher accuracy since historical information could be utilized for reasoning. Third, the method could greatly improve the extensibility of knowledge. When dealing with newly added relationship, the method could easily learn the pattern of the relationship and there is no need to retrain the model.
Towards Accurate Seismic Events Detection Using Motion Sensors on Smartphones

Smartphones equipped with motion sensors can be manipulated as a Community Seismic Network for earthquake detection. But, there still have many challenges such as the limited sensing capability of the off-the-shelf sensors and unpredictable diversity of daily operations by phone users in current smartphones, which yield poor monitoring quality. So we present a suite of algorithms towards detecting anomalous seismic events from sampling data contaminated by users operations, including a lightweight signal preprocessing method, a two-phase events picking and timing scheme on local smartphones, and a decision fusion scheme to maximize anomaly detection performance at the fusion center while meeting the requirements on system false alarm rate. We experimentally evaluate the proposed approach on networked smartphones and shake tables. The results verify the effectiveness of our approach in distinguishing anomalous seismic events from noises due to normal daily operation.
Probabilistic Graph Attention for Relation Extraction for Domain of Geography

In view of the lack of labeled corpus and the difficulty of extracting multiple relations in the extraction of entity relation in the geographic domain, a method based on probabilistic graph is proposed for extracting the entity relation in the geographic domain. This method uses the semantic information in the knowledge base to enhance the representation of the geographic domain corpus to alleviate the problem of insufficient labeled corpus. It uses character-word hybrid vectors that can be effectively integrated into the semantic information as the feature vectors. The vectors are transmitted to Bi-LSTM and self-attention for global deep feature extraction. Finally drawing on the idea of probabilistic graph, the "semi pointer-semi annotation" method is utilized to extract the head entities, traverse the head entity, and then uses the same method to extract tail entities and relations. By comparing the experimental results on the geographic domain corpus and ACE05 corpus with other advanced methods, the probabilistic graph-based extraction method effectively improves the geographic domain entity relation extraction effect.
A Survey of Advanced Encryption for Database Security: Primitives, Schemes, and Attacks

The use of traditional encryption techniques in Database Management Systems is limited, as encrypting data within the database can prevent basic functionalities such as ordering and searching. Advanced encryption techniques and trusted hardware, however, can enable standard functionalities to be achieved on encrypted databases, and a number of such schemes have been proposed in the recent literature. In this survey, different approaches towards database security through software/hardware components are explored and compared based on performance and security, and relevant attacks are discussed.
Physics Enhanced Data-Driven Models With Variational Gaussian Processes

Centuries of development in natural sciences and mathematical modeling provide valuable domain expert knowledge that has yet to be explored for the development of machine learning models. When modeling complex physical systems, both domain knowledge and data provide necessary information about the system. In this paper, we present a data-driven model that takes advantage of partial domain knowledge in order to improve generalization and interpretability. The presented approach, which we call EVGP (Explicit Variational Gaussian Process), has the following advantages: 1) using available domain knowledge to improve the assumptions (inductive bias) of the model, 2) scalability to large datasets, 3) improved interpretability. We show how the EVGP model can be used to learn system dynamics using basic Newtonian mechanics as prior knowledge. We demonstrate how the addition of prior domain-knowledge to data-driven models outperforms purely data-driven models.
Technosphere Thinking in the Transformations of Earth Sciences

Abstract The paper focuses on identifying factors of influence of technosphere consciousness on the state of Earth sciences. The comparative analysis of current philosophical and scientific advances showed changes in a status of technosphere consciousness in culture. The authors formulated a hypothesis on a fusion of scientific rationality of Earth sciences with an axiological area of culture. A special methodology was developed to trace and demonstrate a determination influence of technosphere consciousness on the logic of development of Earth sciences and the state of communication processes in society. The authors described consequences of risks occurred as a result of strengthening the independence of technosphere consciousness and modeled opportunities of their minimization. It was proved that in the information communication society technosphere consciousness could form a new paradigm foundation for Earth sciences. The authors described a special character of the correlation between the state of technosphere consciousness, a level of development of technosphere and the status of Earth sciences. They determined key areas for negative consequences of blurring semantics of technosphere consciousness.
A Trademark Image Retrieval Tool Based on Deep Learning Features

In this work, we present the advances in the construction of a trademark image retrieval system using machine learning techniques. This application will be used as a tool by a government entity to identify potential similar trademark images. The similarity is computed via two different types of features: color-based and deep-based features. Furthermore, we included the Pantone color scale as a proxy to identify relevant colors in the image. The algorithm was evaluated using a subset of 5000 images from LLD—Large Logo Dataset (Sage et al., LLD - large logo dataset - version 0.1 [1]), and a ground truth obtained from two surveys applied to users. Based on the NDCG metric, promising results were obtained that validate the set of proposed features.
Security Threats to Business Information Systems Using NFC Read/Write Mode

Radio Frequency IDentification (RFID) and related technologies such as Near Field Communication (NFC) are becoming essential in industrial contexts thanks to their ability to perform contactless data exchange, either device-to-device or tag-to-device.One of the three main operation modes of NFC, called read/write mode, makes use of the latter type of interaction.It is extensively used in business information systems that make use of NFC tags to provide the end-user with augmented information in one of several available NFC data exchange formats, such as plain text, simple URLs or enriched URLs.Using a wide variety of physical form factors, NFC-compatible tags (wireless transponders) are currently available in many locations with applications going from smart posters, contactless tokens, tap-and-go payments or transport ticketing to automated device configuration, patient identification at hospitals or inventory management within supply chains.Most of these applications handle sensitive processes or data.This paper proposes a complete security threat model for the read/write operation mode of NFC used in Next Generation Industrial IoT (Nx-IIoT) contexts.This model, based on a wellknown methodology, STRIDE, allows developers and users to identify NFC applications vulnerabilities or weaknesses, analyze potential threats, propose risk management strategies, and design mitigation mechanisms to mention only some significant examples.
Designing Quantum Bodies: A New Calculus of Responsibility

Critical Design has embraced Karen Barad's theory of agential realism for rethinking design as a discipline, an artifact, and a profession. This article meditates on an exploratory question: what if we consider Barad to be in some way ‘doing design’? The article draws attention to the fact that Barad's 2007 book, Meeting the Universe Halfway, does not begin with physics or feminism, but rather, with a close reading of a play. I suggest that Barad situates literature and fiction as central features in her analysis of historical experiments in physics, that she doesn't treat these experiments as historical events or even conceptual questions, but entangles them with a type of ‘experimental’ physics. Barad confronts the unsatisfactory ethical conclusions in the play, and thereby isolates a pressing design problem: how to loosen the hold of individualism on feminist ethics? This article proposes a provocation: that Barad engages in a project to ‘design away’ the classical body that stands in the way of more ethical quantum futures. The article highlights the need for speculative design methods because they transcend disciplinary divisions and help to overcome design problems as enduring as individualism. Engaging with fictional worlds that touch upon our contemporary technoscientific present, Barad's science fictional story, I argue, asks us to ponder our relations, imagine new ones, and open pathways to more ethical and equitable futures.
Bangla Broadcast Speech Recognition Using Support Vector Machine

Over the past few decades, incredible growth has been revealed in the recognition of modern language at speech technologies and in its related fields alike signal processing, pattern recognition, machine learning, cognitive science, and so on. Yet the significant working progress of Bangla speech processing is not visible enough. This paper introduces the identification of Bangla broadcast speech by the use of support vector machine (SVM). This method is based on linear kernel SVM applied on MATLAB platform. Four types of noisy broadcast speech data are used in this experiment and SVM shows a fairly good outcome.
UNICODE For Hiding Information In A Text Document

Steganography is a unique approach for developing tools and methods to hide the fact of transmitting a secret message. The first traces of steganographic methods are lost in ancient times. From detective works, various methods of secret writing between the lines of ordinary text are well known: from milk to complex chemical reagents with subsequent processing. Digital steganography is based on hiding or embedding additional information in digital objects, while causing some distortion of these objects. In this case, text, images, audio, video, network packets, and so on can be used as objects or containers. To embed a secret message, steganographic methods rely on redundant container information or properties that the human perception system cannot distinguish. Recently, there has been a lot of research in the field of hiding information in a text container, since text documents are used in many organizations. Based on this, here the MS Word document is considered as a data carrier, which has various parameters, changing these parameters can achieve data integration. In the same article, we present steganography using invisible Unicode characters of the Space type, but with different encoding. A combined approach for encoding Latin characters is proposed for the effectiveness of the method.
Digital Transformation and Strategic Management of Frontline Services With Robotic Technologies

Robots and artificial intelligence have been touted as vehicles for generating operational efficiency and cost savings for a business enterprise. Specifically, automated, digitalized frontline services have the potential to streamline an enterprise's service efficiency, alleviate burdens from its human employees, and enhances its relationships with the customers. Besides these cost and functional benefits, this chapter posits that robotic services can garner customers' preferences and satisfaction, that is, there are situations in which robotic services are not only a cost-saving option, but they are even preferred by an enterprise's customers. Specifically, two important drivers—social distancing and utilitarian expectations of services—can lead to customers' choosing robots over humans. This chapter concludes that deploying and managing digital and automation technologies, such as service robots, in frontline services can not only transform an enterprise's operations but also enhance its customer management and service expectations.
HCI for Elderly, Measuring Visual Complexity of Webpages Based on Machine Learning

The increasing number of elderly persons, aged 65 and over, highlights the problem of improving their experience with computers and the web considering their preferences and needs. Elderlies' skills like cognitive, haptic, visual, and motor skills are reduced by age. The visual complexity of web pages has a major influence on the quality of user experience of elderly users according to their reduced abilities. Therefore, it is quite beneficial if the visual complexity of web pages could be measured and reduced in applications and websites which are designed for them. In this way a personalized less complex version of the website could be provided for older users. In this article, a new approach for measuring the visual complexity is proposed by using both Human-Computer Interaction (HCI) and machine learning methods. Six features are considered for complexity measurements. Experimental results demonstrated that the trained proposed machine learning approach increases the accuracy of classification of applications and websites based on their visual complexity up to 82% which is more than its competitors. Besides, a feature selection algorithm indicates that features such as clutter and equilibrium were selected to have the most influence on the classification of webpages based on their visual complexity.
Modular AR Framework for Vision-Language Tasks

Mixed / augmented reality systems have become more and more sophisticated in recent years. However, they still lack any ability to reason about the surrounding world. On the other hand, computer vision research has made many advancements towards a more human-like reasoning process. This paper aims to bridge these 2 research areas by implementing a modular framework which interconnects an AR application with a deep learning based vision model. Finally, a few potential use cases of the proposed system are showcased. The developed framework allows the application to utilize a variety of Vision-Language (V+L) models, to gain additional understanding about the surrounding environment. The system is designed to be modular and expandable. It is able to connect any number of Python processes of the V+L models to Unity apps using AR technology. The system was evaluated in our university's smart home lab based on daily life use cases. With a further extension of the framework by additional downstream tasks provided by V+L models and other computer vision systems, this framework should find wider adoption in AR applications. The increasing ability of applications to comprehend visual common sense and natural conversations would enable more intuitive interactions with the user, who could perceive his device more as a virtual assistant and companion.
Abnormal Activity Detection in Healthcare

Detecting abnormal activity is crucial in healthcare, especially for elderly people. Real time and early detection will prevent severe injuries and save lives. Time series data analysis can help to timely identify any abnormal behaviour outlier from daily routines. In this paper, we studied abnormal activity detection in healthcare applying machine learning and time series forecasting models and technology. A novel approach is proposed to detect abnormality in real time in consideration of risk factors in healthcare of elderly people. The approach is tested on real data set of a sensor hits and the locations of the sensor as well as descriptions outlining the types of sensors and the placements of the sensors. Experiment results show the effectiveness of the approach.
Hypergraph Induced Graph Convolutional Network for Multi-Label Image Recognition

It is well known that exploiting label connections is important for multi-label image recognition. However, many of the existing methods utilize the connections between label pairs, ignoring high-order connections, thus may result in the degradation of recognition performance. A few methods can capture high-order label connections but have high complexity and low scalability. Inspired by the nature of hypergraph coding high-order connections, we propose a Hypergraph induced Graph Convolutional Network (HI-GCN), which can capture high-order label connections with high adaptivity and scalability. Specifically, we first build an adaptive hypergraph on labels in a data-driven manner, which allows the high-order label connections to be exploited adaptively and scalability. Then, by updating label embeddings via using Hypergraph induced Graph Convolutional network (HI-GCN), the label embeddings are mapped to label classifiers with high-order connections. Experimental results on MS-COCO, SUN and ESP datasets validate the effectiveness of our approach.