Reviewerly/exHarmony huggingface dataset:
Issues of musical and vocal education of theater students

The author considers the problem of training specialists in the field of Acting and shows that the need of theaters for actors of a "synthetic" kind (with universal voice capabilities, with a high degree of musicality) is dictated by the increasing role of musical elements in a modern performance and is also determined by the search in modern directing. Theatrical practice demonstrates that the musicality of an actor is a crucial quality of acting. Today, in general, as always, the issues of organizing and implementing vocal education for future actors are among the most complex and energy-intensive. First-year students who come to the theater program track at the higher educational institution either do not have any preliminary vocal training at all, or are trained at the elementary level. Nevertheless, it should be noted that some of them were trained to play musical instruments and have musicality. The article deals with the specifics of working with students, aimed mainly at the formation of basic singing skills, which are associated with the acoustic properties of sound, high singing position, vocal diction, breathing, voice science, i.e. all the things that make up the technology of singing professionalism. Meanwhile, the author shows that this work is not with students of the vocal department, but with future actors of the drama theater. And our task is to educate an artistic and creative person, a participant in the dramatic process, which is all theatrical activity.
An improved Pearson coefficient based literature recommendation system for small research teams

Recommendation technologies can help users to solve the problem of information overload. In the academic and educational fields, the application of intelligent recommendation technology has largely improved the effective use of academic resources, especially for researchers whose research tasks are constantly updated by research teams. In this paper, we propose a recommendation method that takes into account the changing stages of researchers’ tasks and the needs of researchers’ tasks. The recommendation model first considers user task and demand changes over time, introduces the forgetting function based on the Ebbinghaus forgetting curve into user features to create a dynamic user preference matrix, then uses the improved TF-IDF algorithm to extract article features to correlate user task demands with article features to avoid the problem of irrelevance of literature and user tasks, and finally by linear combination of Finally, the similarity between users and the similarity between users and documents is linked to generate a list of document recommendations. The experimental results show that the proposed method can better take into account the phase change of researchers’ tasks and the correlation between research tasks and articles, and it is confirmed through experiments that the method has higher recommendation quality than the traditional collaborative filtering method.
A Comparison of Search Functionalities in Several Tools Used for Searching within Digital Text Collections

Abstract The search functionalities that a search tool for a digital text collection offer, will determine how a user can interact with and search in such a collection. This poster considers the search functionalities in four tools that are used to search within digital text collections. Search functionalities that are well known in information science, namely basic word searching, phrase searching, wildcards, Boolean operators, proximity searching, field searching, case sensitive searching, filtering, as well as linguistic search functions will be considered. Knowledge about search functionalities can inform and improve the design of tools for digital text collections.
Reducing the Data Cost of Machine Learning with AI: A Case Study

The past several years have seen a strong push toward using Deep Learning systems–Neural Networks with multiple hidden layers. Deep Learning is now used in many machine learning applications and provides leading performance on numerous benchmark tasks. However, this increase in performance requires very large datasets for training. From a practitioner prospective, the model that performs best in benchmark tasks may be too data intensive to be adapted to practical application. We describe a behavior recognition problem that was solved using a sequence-based Deep Learning system and then reimplemented using a more knowledge-driven sequence matching approach due to data constraints. We contrast the two approaches and the data required to achieve sufficient performance and flexibility.
Relation-Aware Multi-hop Reasoning forVisual Dialog

Visual dialog is a multi-modal task that requires a dialog agent to answer a series of progressive questions grounded in an image. In this paper, we propose Relation-aware Multi-hop Reasoning Network (i.e. R2N for short) for visual dialog tasks, which can perform multi-hop reasoning during visual co-reference resolution process in a recurrent way. At each hop, in order to fully understand the visual scene in the image, a Relation-aware Graph Attention Network is used, which encodes each image into graphs with multi-type inter-object relations via a graph attention mechanism. Moreover, we find that the auxiliary clustering mechanism on answer candidates is conducive to model's performance. We evaluate R2N on VisDial v1.0 dataset. Experimental results on the VisDial v1.0 dataset demonstrate that the proposed model is effective and outperforms compared models.
Laser Projection System for Continuous Operation in Manufacturing and Educational Use Cases

The paper discusses the creation of a new laser projection system intended for continuous operation. Appropriate application scenarios are considered and some of the problems related to the design and implementation of the system are discussed. The paper proposes and tests an innovative technical approach to the control of the thermoelectric cooling module used in the system. A new electronic unit is designed, developed and tested. It is a major functional block of the system and its main tasks are the temperature regulation, the control of the laser diode brightness and the provision of communication capabilities for remote monitoring and management. Our initial experiments show that the temperature control works very well during long-term operation. The protocol for remote monitoring and management and the desktop software program developed by us provide nice insights into the performance of our temperature management algorithms in the firmware of the system.
Text Analysis of the Hybrid Digital Corpora

Digital means of communication have become the primary source of information for the majority of population. Given their availability through various platforms digital contents have an important role in shaping of the public interests and opinion. Every now and then a part of it becomes occupied with a new concept that resonates for a certain time, creating corpora of contents that persist on the platforms readily available for the consumers to reach them. It is critical to objectively determine what kind of discourse these text corpora created. In order to examine one such case, related to the use of the term "hibridni rat" (en. hybrid war), this research conducted a study that applied contemporary text analysis techniques. The process consisted of several stages: initially, having investigated the research phenomenon, the analytical problems were defined. This was followed by the identification of the digital contents posted under the national domain of the Republic of Croatia, their retrieval, structuring and pre-processing. Thus, the digital corpora suitable for analysis were created and they were subjected to processing by using the text mining techniques. The results of this process have provided an enhanced insight into the relatively large corpora of digital texts that are difficult for a reader to review, grasp on, and extract the useful information about the phenomenon being investigated just by using the traditional means of browsing the contents. The proposed approach for text fetching and analysis is a major contribution of this study. At the same time the described procedure is relatively easy to reproduce so it can be used in the analysis of texts available in digital format that are related to other phenomena.
Exploring the Effectiveness of Employing Limited Resources for Deep Neural Pairwise Evaluation of Machine Translation

In this paper, a light resource learning schema, i.e. a schema that depends on limited resources, is introduced, which aims to choose the better translation between two machine translation (MT) outputs, based on information regarding the source segments (SSE) and string-based features. A concatenation of vectors, including mathematically calculated embeddings from the SSE, statistical MT (SMT) and neural MT (NMT) segments (S1 and S2 respectively), are used as input to a neural network (NN). Experiments are run for two different forms of text structure (one that is a formal, well-structured corpus (C2) and one that is informal (C1)) for the English (EN) – Greek (EL) language pair. Instead of relying on high-level experts’ annotations, a novel automatic metric is proposed for determining the better translation, namely the quality estimation (QE) score. This score is based on string-based features derived from both the SSE and the MT segments. Experimental results demonstrate a quite good performance for the proposed feed-forward NN, comparable to the existing state of-the-art models for MT evaluation that require more sophisticated resources.
Shapes Determine Elections

When we elect representatives to the U.S. House of Representatives (or to state legislative bodies, or even the school board), we do so by dividing people into districts, and having each district elect one representative. The districts we draw as shapes on maps can affect the outcome of the elections. As a result, the process of creating or changing districts and the shapes we draw to create them are important. After every census, each state must construct a new district map. When they do that unfairly, it can give an unfair advantage to one of the political parties. This is called gerrymandering. By looking at the shapes of districts and examining their compactness, we can start to detect fair and unfair district maps.
GIS ANALYSIS OF DEM IN THE INTERESTS OF SPECIALIZED MAPPING OF MINING TERRAIN

Using the example of the Korshunovsky GOK quarry, the possibilities of processing publicly available SRTM C-SIR and ASTER GDEM DEM data and remote sensing materials obtained from LANDSAT and SENTINEL satellites in the SAGA GIS for specialized mapping of mountainous terrain are considered. The results of constructing derived geomorphometric maps based on DEM are presented. Restrictions on the use of the materials under consideration are justified.
PocketView: Through-Fabric Information Displays

People often have to remove their phone from an inaccessible location like a pocket to view things like notifications and directions. We explore the idea of viewing such information through the fabric of a pocket using low resolution bright LED matrix displays. A survey confirms viewing information on inaccessible phones is desirable, and establishes types of pockets in garments worn by respondents and what objects are typically put in pockets. A technical evaluation validates that LED light can shine through many common garment fabrics. Based on these results, functional hardware prototypes are constructed to demonstrate different form factors of through-fabric display devices, such as a phone, wallet, a key fob, a pen, and earbud headphone case. A simple interaction vocabulary for viewing key information on these devices is described, and the social and technical aspects of the approach are discussed.
Cybersecurity and Privacy in Smart Cities for Citizen Welfare

In smart cities, technologies and systems of various types, from manual sensors to data collection devices, cooperate to improve citizens' wellbeing. They take advantage of information technologies and the internet of things (IoT) to increase citizens' welfare through the implementation of services with distinct objectives, like reducing energy consumption and improving transport routes and health services. Due to their functionalities and characteristics, IoT devices work interconnectedly and collect large amounts of data. In this context, cybersecurity and privacy arise as topics of central interest, as security breaches can lead to personal data exposure and service interruptions and malfunctions, thus directly affecting citizens' welfare and the implementation of the Sustainable Development Goals. This chapter discusses how cybersecurity risks affect smart cities' operations and citizens' welfare. It presents some current cybersecurity techniques and how to apply them in the smart city context. It also reviews some open issues and future directions.
Robustness of Compressed Deep Neural Networks with Adversarial Training

Deep learning models are not applicable on edge computing devices. Consequently, compressed deep learning models gain momentum recently. Meanwhile, adversarial attacks targeting conventional deep neural networks (DNNs) and compressed DNNs are flouring nowadays. This paper firstly surveys the current compressing techniques, including pruning, distillation, quantization and weights sharing. Then, two iterative adversarial attacks, including I-FGSM (Iterative-Fast Gradient Sign Method) and PGD (Project Gradient Descent), are introduced. Three scenarios are built to test each DNN’s robustness against adversarial attacks. Besides, each DNN is trained with samples generated by different adversarial attacks and is then compressed under different pruning rate and tested under different attacks. The experimental results prove firstly that when a DNN is compressed with pruning rate lower than 70.0% is safe and with tiny accuracy decline. Second, iterative adversarial attacks are effective and cause dramatic performance degradation. Third, adversarial training helps to secure the compressed DNNs while lowering transferability of adversarial samples constructed by different attack algorithms.
Comparative features of distance learning in regional universities

The global online transition has become a real challenge to the traditional form of education, which has prompted universities to reconsider the system of providing education services. The authors conducted a sociological study based on the investigation of the relationships of the academic teaching staff, their assessment of the effectiveness of the measures taken by the universities in organizing the transition to a distance learning (DL) format of education. Lecturers of regional universities of Russia, Kyrgyzstan and Mongolia took part in the study. The results of the study made it possible to identify the most organized and optimistic category of lecturers of Mongolian universities and focused attention on the main problems in the implementation of DL at all universities. The normalization of the epidemiological situation in the future, the restoration of the economic level of development of each country will enable the universities to define their approaches to the use of distance learning technologies (DLT) and their role in the traditional face-to-face education system in accordance with the needs of the students and the peculiarities of social life.
Unsupervised virtual view synthesis from monocular video with generative adversarial warping

Virtual view synthesis from monocular video is challenging, as it aims to infer photorealistic views from single reference view. Previous work have achieved acceptable visual quality, however, are heavily relied on supervision information, such as depth or pristine virtual view, which are inadequate in practical application. In this paper, an unsupervised virtual view synthesis method is proposed to get rid of the supervision information. Firstly, it embed a spatiotemporal generative adversarial network into traditional depth-image-based rendering framework with no explicit depth information provided. Secondly, it utilized novel perceptual constraints without relying on pristine images, including the blind synthesized image quality metric and no-reference structure similarity. The entire framework is fully convolutional, producing hallucinated results in an end-to-end way. Particularly, the whole framework is independent of supervision information. Experimental results demonstrate that the proposed method produces pleasant virtual views in comparison with supervised methods, thereby can be beneficial to practical applications.
Real time 3D Hand Gesture Recognition by Weighted Depth Difference Motion History Image in Networked HCI

Hand gesture recognition applied in human-computer interaction (HCI) is a useful but challenging task because of the low inter-class and high intra-class variability. It is difficult to improve real-time performance while ensuring accuracy. Since simple but robust features are crucial for the performance of recognition, we proposed the weighted depth difference motion history image (WDD-MHI) to extract spatial-temporal information. Subsequently, a weighted random forest classifier with second training is employed for action classification. The developed method is shown to be computationally efficient allowing it to run in real-time. We designed a multimedia video-wall control system based on online gesture recognition, the recognition results indicate competitive performance of our method over the other methods.
Using Deep Learning to Support the User Interface Design of Mobile Applications with App Inventor

Transforming a user interface sketch into a wireframe using App Inventor has been a common need both in computing education and the development of applications by end users. As this part of mobile application development is challenging and time-consuming, we present an approach that automates this process through the prototyping of user interfaces. The Sketch2aia approach employs deep learning to detect the user interface components and their position in a hand-drawn sketch, creating an intermediate representation of the user interface and automatically generating the App Inventor code for the respective wireframe. The approach achieves an average classification accuracy of the components of the user interface of 87.72% and results of a preliminary evaluation with users indicate that it generates wireframes that resemble sketches in terms of visual similarity. The approach is available as a web tool and can be used to support the teaching of user interface design effectively and efficiently, as well as the development of mobile applications by end users.
PrivacyPrimer: Towards Privacy-Preserving Episodic Memory Support For Older Adults

Built-in pervasive cameras have become an integral part of mobile/wearable devices and enabled a wide range of ubiquitous applications with their ability to be "always-on". In particular, life-logging has been identified as a means to enhance the quality of life of older adults by allowing them to reminisce about their own life experiences. However, the sensitive images captured by the cameras threaten individuals' right to have private social lives and raise concerns about privacy and security in the physical world. This threat gets worse when image recognition technologies can link images to people, scenes, and objects, hence, implicitly and unexpectedly reveal more sensitive information such as social connections. In this paper, we first examine life-log images obtained from 54 older adults to extract (a) the artifacts or visual cues, and (b) the context of the image that influences an older life-logger's ability to recall the life events associated with a life-log image. We call these artifacts and contextual cues "stimuli". Using the set of stimuli extracted, we then propose a set of obfuscation strategies that naturally balances the trade-off between reminiscability and privacy (revealing social ties) while selectively obfuscating parts of the images. More specifically, our platform yields privacy-utility tradeoff by compromising, on average, modest 13.4% reminiscability scores while significantly improving privacy guarantees -- around 40% error in cloud estimation.
Quantised Siamese Tracker for 4K/UltraHD Video Stream – a demo

This demo presents a hardware architecture for an object tracker based on a quantised Siamese neural network. The system is designed to work with a 4K/UltraHD input video stream and to meet the real time and energy efficiency constraints. The network is designed using Brevitas and implemented with FINN tool from Xilinx. The Siamese tracker runs on the ZCU 104 board, with the Zynq UltraScale+ MPSoC device from Xilinx.
DIMSAN: Fast Exploration with the Synergy between Density-based Intrinsic Motivation and Self-adaptive Action Noise

Exploration in environments with sparse rewards remains a challenging problem in Deep Reinforcement Learning (DRL). For the off-policy method, it usually needs a large number of training samples. With the growing dimensions of state and action space, this method becomes more and more sample-inefficient. In this paper, we propose a novel fast exploration method for off-policy reinforcement learning, called Density-based Intrinsic Motivation and Self-adaptive Action Noise (DIMSAN). Our main contribution is twofold: (1) We propose a Density-based Intrinsic Motivation (DIM) method. It introduces a new intrinsic-reward generation mechanism based on samples&#x2019; density estimation during experience replay and encourages the agent to seek novel and unfamiliar states. (2) We propose a Self-adaptive Action Noise (SAN) to deal with the exploration-exploitation tradeoffs, which could automatically change the exploration step through adding adaptive action space noise. The synergy between DIM and SAN could guide the agent to search the state and action space with high efficiency. We evaluate our method on the benchmark manipulation tasks and the designed challenging ones. Empirical results show that our method outperforms the existing methods in terms of convergence speed and sample efficiency, especially in challenging tasks.
Introducing an Integrated VR Sensor Suite and Cloud Platform

Broken Colors is building the next generation of commercial cognitive science. Our assessment and training platform presents cognitive tasks, games, and scenarios inside an immersive VR environment while our hardware suite silently tracks eye-movement, facial expression, neurosignals (EEG) and voice. These signals are sent to our cloud platform where traditional neuroscience algorithms are combined with machine learning to create a full-spectrum analysis of the user's cognitive performance.
Out of the Past: The Challenges of B.C.E. Dates in Metadata

Abstract While well‐established standards for encoding date metadata exist, Before Common Era (BCE) dates, previously referred to as Before Christ (BC) dates, are a challenge. Libraries, archives, and museums have a rich variety of artifacts from this era. However, the lack of encoding or underuse of available standards hinders the ability to search and browse for this content. While several metadata standards support encoding of BCE dates, the full capability is often not exploited. By analyzing metadata in the Digital Public Library of America (DPLA, https://dp.la/ ), the authors examine the benefits and limitations of the Extended Date Time Format (EDTF) and related interoperability issues.