Reviewerly/exHarmony huggingface dataset:
Classification via local manifold approximation

Classifiers label data as belonging to one of a set of groups based on input features. It is challenging to obtain accurate classification performance when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports. This is particularly true when training data are limited. To address this problem, this article proposes a new type of classifier based on obtaining a local approximation to the support of the data within each class in a neighborhood of the feature to be classified, and assigning the feature to the class having the closest support. This general algorithm is referred to as LOcal Manifold Approximation (LOMA) classification. As a simple and theoretically supported special case having excellent performance in a broad variety of examples, we use spheres for local approximation, obtaining a SPherical Approximation (SPA) classifier. We illustrate substantial gains for SPA over competitors on a variety of challenging simulated and real data examples.
A Model to Predict Crosscut Stress Based on an Improved Extreme Learning Machine Algorithm

The analysis of crosscut stability is an indispensable task in underground mining activities. Crosscut instabilities usually cause geological disasters and delay of the project. On site, mining engineers analyze and predict the crosscut condition by monitoring its convergence and stress; however, stress monitoring is time-consuming and expensive. In this study, we propose an improved extreme learning machine (ELM) algorithm to predict crosscut’s stress based on convergence data, for the first time in literature. The performance of the proposed technique is validated using a crosscut response by means of the FLAC3D finite difference program. It is found that the improved ELM algorithm performs higher generalization performance compared to traditional ELM, as it eliminates the random selection for input weights. Furthermore, a crosscut construction project in an underground mine, Yanqianshan iron mine, located in Liaoning Province (China), is selected as the case study. The accuracy and efficiency of the improved ELM algorithm has been demonstrated by comparing predicted stress data to measured data on site. Additionally, a comparison is conducted between the improved ELM algorithm and other commonly used artificial neural network algorithms.
Stimulating Photo Curation on Smartphones

Personal photo collections have grown due to digital photography and the introduction of smartphones, and photo collections have become harder to manage. Deleting photos appears to be difficult and the task of curation is often perceived as not enjoyable. The lack of curation can make it harder to retrieve photos when people need them for various reasons, such as individual reminiscing, shared remembering or self-presentation. In this study we investigate how we can stimulate people to organise their photo collections on their smartphones. Ten participants evaluated and qualitatively compared four applications with different characteristics regarding voting on and deleting photos. We found that voting on photos is easier and more enjoyable in comparison to deleting photos, that participants showed reminiscence while organising, that deleting can be frustrating, that participants have different preferences for sorting and viewing photos and that voting could make deleting and retrieving easier.
Optimized Feed Forward Neural Network for Microscopic White Blood Cell Images Classification

Solving the slow convergence of the traditional neural network and searching for weights in Feed Forward Neural Network (FNN) is important to achieve the minimum training error. This paper presents an Optimized Feed Forward Neural Network (OFNN) for Microscopic white blood cell (WBC) images classification. Particle swarm optimization (PSO) and Gravitational Search Algorithm (GSA) are used to train feed forward neural network and to search for the weights of the FFN to achieve minimum error and high classification rate. The OFNN is used to classify the white blood cells into Agranulocytes that contains lymphocytes and monocytes cells and Granulocytes that contains neutrophils, eosinophils and basophils accurately. The OFNNs is trained using cells shape features of the segmented cells. The experimental results show that the obtained results are promising with classification accuracy being greater than 93% for all types.
Interaction Narratives for Responsive Architecture

In this position paper, we present the results of an ongoing theoretical investigation into the phenomenon of interactive architecture. Interaction in architecture deals with the meaningful exchange of information and physical acts between building and person. This goes beyond responsive systems like automated doors, shading systems, and so on. Most examples of interactive architecture are technological explorations that probe possibilities and the potential for interaction. In this paper we claim that this is not enough. The notion of interactive architecture is explored through social aspects, user experience, situatedness, and agent-based theory. From this we argue that interactive buildings need comprehensive and consistent styles of interaction rather than a series of isolated and unrelated interaction events. Different people in various contexts require different sets of behavior from an interactive building. These sets are conceptualized as interaction narratives, following the work of Maria Lehman. We argue that such narratives can provide a better fit of the interactive building with the user, and lead to a more profound understanding of such systems.
GENERALIZED METHOD OF DESIGNING UNMANNED REMOTELY OPERATED COMPLEXES BASED ON THE SYSTEM APPROACH

Self-propelled underwater systems belong to the effective means of marine robotics. The advantages of their use include the ability to perform underwater work in real time with high quality and without risk to the life of a human operator. At present, the design of such complexes is not formalized and is carried out separately for each of the components – a remotely operated vehicle, a tether-cable and cable winch, a cargo device and a control and energy device. As a result, the time spent on design increases and its quality decreases. The system approach to the design of remotely operated complexes ensures that the features of the interaction of the components of the complex are taken into account when performing its main operating modes. In this paper, the system interaction between the components of the complex is proposed to take into account in the form of decomposition of “underwater tasks (mission) – underwater technology of its implementation – underwater work on the selected technology – task for the executive mechanism of the complex” operations. With this approach, an information base is formed for the formation of a list of mechanisms of the complex, the technical appearance of its components is being formed, which is important for the early design stages. Operative, creative and engineering phases of the design of the complex are proposed. For each phase, a set of works has been formulated that cover all the components of the complex and use the author's existence equations for these components as a tool for system analysis of technical solutions. The perspective of the scientific task of the creative phase to create accurate information models of the functioning of the components of the complex and models to support the adoption of design decisions based on a systematic approach is shown. The obtained results form the theoretical basis for finding effective technical solutions in the early stages of designing remotely operated complexes and for automating the design with the assistance of modern applied computer research and design packages.
The implication of spatial temporal changes on facial micro-expression analysis

Facial micro-expression datasets lack consistency and standardisation, with different research groups using various experimental settings, in particular, where the datasets are varied in resolution and frame rates. To provide new insights into the roles of frame rate and resolution, we conduct an investigation into the use of different frame rates and resolution on current benchmark datasets (SMIC and CASME II). By using Temporal Interpolation Model, we subsample SMIC (original frame rate is 100 fps) to 50 fps and CASME II (original frame rate is 200 fps) into 100 fps and 50 fps. In addition, the resolution settings are adjusted to three scaling factors: 100% (original resolution), 75% and 50%. Three feature types are used to test the performance of these settings, which are Local Binary Patterns in Three Orthogonal Planes, 3D Histograms of Oriented Gradient and Histogram of Oriented Optical Flow. The results showed that the frame rate and resolution could affect the performance of micro-expression recognition, which behave distinctively dependent on feature types. This work provides new guidelines for future research in selecting frame rate, resolution and feature descriptors in micro-expressions recognition.
Semi-automated Reasoning About Non-determinism in C Expressions

Research into C verification often ignores that the C standard leaves the evaluation order of expressions unspecified, and assigns undefined behavior to write-write or read-write conflicts in subexpressions—so called “sequence point violations”. These aspects should be accounted for in verification because C compilers exploit them. We present a verification condition generator (vcgen) that enables one to semi-automatically prove the absence of undefined behavior in a given C program for any evaluation order. The key novelty of our approach is a symbolic execution algorithm that computes a frame at the same time as a postcondition. The frame is used to automatically determine how resources should be distributed among subexpressions. We prove correctness of our vcgen with respect to a new monadic definitional semantics of a subset of C. This semantics is modular and gives a concise account of non-determinism in C. We have implemented our vcgen as a tactic in the Coq interactive theorem prover, and have proved correctness of it using a separation logic for the new monadic definitional semantics of a subset of C.
Study on learning resource authentication in MOOCs based on blockchain

In MOOCs, the learning resources authentication is a matter of great concern for the learners and teachers. Its construction is faced with the challenges of information security and privacy protection. Considering that the blockchain has the advantages of decentralisation, autonomous and non-tampering, this paper provides a solution to implement the construction based on blockchain technology, which includes the system architecture, experimental validation and key technologies such as decentralised transaction and tamper-resistance. The results prove the technical feasibility and safety reliability of blockchain to learning resource management in MOOCs.
Communication Cost for Non-Markovianity of Tripartite Quantum States: A Resource Theoretic Approach

To quantify non-Markovianity of tripartite quantum states from an operational viewpoint, we introduce a class $\Omega^*$ of operations performed by three distant parties. A tripartite quantum state is a free state under $\Omega^*$ if and only if it is a quantum Markov chain. We introduce a function of tripartite quantum states that we call the non-Markovianity of formation, and prove that it is a faithful measure of non-Markovianity, which is continuous and monotonically nonincreasing under a subclass $\Omega$ of $\Omega^*$. We consider a task in which the three parties generate a non-Markov state from scratch by operations in $\Omega$, assisted with quantum communication from the third party to the others, which does not belong to $\Omega$. We prove that the minimum cost of quantum communication required therein is asymptotically equal to the regularized non-Markovianity of formation. Based on this result, we provide a direct operational meaning to a measure of bipartite entanglement called the c-squashed entanglement.
Adversarial image generation by combining content and style

Images can be considered as the combination of two parts: the content and the style. The authors’ approach can leverage this property by extracting a certain unique style from the reference images and combining it to generate images with new contents. With a well-defined style feature extraction module, they propose a novel framework to generate images with various styles and the same content. To train the style specific image generation model efficiently, a double-cycle training strategy is proposed: they input two natural-content pairs simultaneously, extract their style features, and exchange them twice to obtain the reconstruction of the input natural images. What is more, they apply the triplet margin loss to the style feature extracted from the images before and after style exchange and an adversarial discriminator to force the style-exchanged images to be real. They perform experiments on licence-plate image, Chinese characters, and shoes or handbags images generating, obtain photo-realistic results and remarkably improve the corresponding supervised recognition task.
Street Lamp Fault Diagnosis System Based on Extreme Learning Machine

In view of the construction of urban lighting system needs a lot of manpower deployment, especially for its fault diagnosis problem management. This paper proposes a fault model detection and diagnosis subsystem based on the extreme learning machine for street lamps system. The subsystem is part of the event rule response system which is based on the complex event processing technology framework. The system can handle a large amount of sensor data, perform filtering, carry out complex data processing and decision making. The experimental results show that the proposed street lamp fault diagnosis system based on extreme learning machine can diagnose the street lamp fault effectively and respond to it automatically.
Patriotic education of Russian youth: modern realities

The article deals with the problem of patriotic education of the Russian youth. In the face of contemporary external and internal threats patriotic education of citizens becomes a priority in ensuring national security of the country. History of Russia shows that the Patriotic idea was one of the significant factors in ensuring the consolidation of society. Underestimation of patriotism can lead to the reduction of socio-economic, spiritual and cultural foundations for the development of society and the state. One of the main objectives of the current domestic policy of the Russian state is the preservation of cultural and historical heritage and traditional system of values. The basis for these values should be patriotism.
История библиотечного дела сквозь призму отечественных диссертационных исследований: библиометрический анализ

The aim of the article is to present an overview of generation, formation and development of the history of domestic librarianship, since the late 1930s until 2018, basing on the study of materials of dissertations. In the course of analysis, the author revealed 1064 theses in the field of library science and related areas, 270 of which are devoted to the history of librarianship. The article shows that dissertations research on the history of librarianship in the initial period from 1930 to 1950 occupied up to the half of all library dissertation theses; about a third of them were created in the period from 1951 to 1970 and a quarter — since 1971 to the present. This indicates that interest to the history of librarianship is stable. The article reflects the peak of historical research that fell on the period from 2001 to 2010. The author suggests that 25% indicator level of historical studies of the total number of research works on library science is a necessary and sufficient level to ensure the development of library science in general. The article notes multidisciplinary nature of dissertations research: 60% of studies defended in pedagogical sciences, 37% — in historical sciences and slightly more than 3% — in other branches of science. The article reveals the gradual increase in the number of prepared and defended dissertation theses in historical sciences, which contributes to the expansion of research space in the history of library construction. The author carried out periodization of dissertations research. It is shown that the main attention was paid to the research studies on the history of Soviet period in library construction.
High throughput automatic muscle image segmentation using parallel framework

Fast and accurate automatic segmentation of skeletal muscle cell image is crucial for the diagnosis of muscle related diseases, which extremely reduces the labor-intensive manual annotation. Recently, several methods have been presented for automatic muscle cell segmentation. However, most methods exhibit high model complexity and time cost, and they are not adaptive to large-scale images such as whole-slide scanned specimens. In this paper, we propose a novel distributed computing approach, which adopts both data and model parallel, for fast muscle cell segmentation. With a master-worker parallelism manner, the image data in the master is distributed onto multiple workers based on the Spark cloud computing platform. On each worker node, we first detect cell contours using a structured random forest (SRF) contour detector with fast parallel prediction and generate region candidates using a superpixel technique. Next, we propose a novel hierarchical tree based region selection algorithm for cell segmentation based on the conditional random field (CRF) algorithm. We divide the region selection algorithm into multiple sub-problems, which can be further parallelized using multi-core programming. We test the performance of the proposed method on a large-scale haematoxylin and eosin (H &E) stained skeletal muscle image dataset. Compared with the standalone implementation, the proposed method achieves more than 10 times speed improvement on very large-scale muscle images containing hundreds to thousands of cells. Meanwhile, our proposed method produces high-quality segmentation results compared with several state-of-the-art methods. This paper presents a parallel muscle image segmentation method with both data and model parallelism on multiple machines. The parallel strategy exhibits high compatibility to our muscle segmentation framework. The proposed method achieves high-throughput effective cell segmentation on large-scale muscle images.
Analysis of Smart Applications using Internet of Things

Internet of Things which is a network of physical objects plays a vital role in the near future.The idea behind Internet of Things is that Things instead of humans can make use of Internet to communicate.IoT can be used to ease the life style of people with better facilities such as smart cities, homes, hospitals that give a better service than the traditional one.Analysis of applications that use IoT is very much needed to improve the quality of life style of people.
Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images

We present here a new method to predict cloud concentration five minutes in advance from all-sky images using the Artificial Neural Networks (ANN). An autoregressive neural network with backpropagation (Ar-BP) was created and trained with four years of all-sky images as inputs. The pictures were taken with a hemispheric sky imager fixed on the roof at the Institute of Meteorology and Climatology (IMUK) of the Leibniz Universität Hannover, Hannover, Germany. Firstly, a statistical method is presented to obtain key information of the pictures. Secondly, a new image-processing algorithm is suggested to optimize the cloud detection process starting with the Haze Index. Finally, the cloud concentration five minutes in advance at the IMUK is forecasted using machine learning methods. A persistence model forecast to provide a reference for comparison was generated. The results are quantified in terms of the root mean square error (RMSE) and the mean absolute error (MAE). The new algorithm reduced both the RMSE and the MAE of the prediction by approximately 30% compared to the reference persistence model under diverse cloud conditions. The new algorithm could be used as a tool for the stable maintenance of the network for the transmission system operators, i.e., the primary control reserve (within 30 seconds) and the secondary control reserve (within 5 minutes).
A Study on Trust in Black Box Models and Post-hoc Explanations

Machine learning algorithms that construct complex prediction models are increasingly used for decision-making due to their high accuracy, e.g., to decide whether a bank customer should receive a loan or not. Due to the complexity, the models are perceived as black boxes. One approach is to augment the models with post-hoc explainability. In this work, we evaluate three different explanation approaches based on the users’ initial trust, the users’ trust in the provided explanation, and the established trust in the black box by a within-subject design study.
Material Sketching

Designing for digital or robotic fabrication typically involves a virtual model in order to determine and coordinate the required operations of its construction. As a result, its creative design space becomes constrained to material expressions that can be predicted through digital modeling. This paper describes our preliminary thinking and first empirical results when this digital modeling phase is skipped, and the designing occurs interactively 'with' the fabrication operations themselves. By analyzing the material responses of corrugated cardboard to simple linear cutting operations that are executed by a robotic arm, we demonstrate how emergent material effects can be discovered improvisationally. Such material effects cannot be virtually modeled, however, they can be recreated and controlled by the robotic manipulations. We believe this form of 'material sketching' broadens the advances in 'human-fabrication interaction' towards a novel and unforeseeable expressions of physical form that require a much more direct, yet still digital, relationship with materiality.
Route planning for cooperative air-ground robots with fuel constraints

Limited payload capacity on small unmanned aerial vehicles (UAVs) results in restricted flight time. In order to increase the operational range of UAVs, recent research has focused on the use of mobile ground charging stations. The cooperative route planning for both aerial and ground vehicles (GVs) is strongly coupled due to fuel constraints of the UAV, terrain constraints of the GV and the speed differential of the two vehicles. This problem is, in general, an NP-hard combinatorial optimization problem. Existing polynomial-time solution approaches make a trade-off in solution quality for large-scale scenarios and generate solutions with large relative gaps (up to 50%) from known lower bounds. In this work, we employ a hybrid metaheuristic known as Construct, Merge, Solve & Adapt (CMSA) in order to develop a scalable and computationally efficient solution approach. We discuss results for large scale scenarios and provide a comparative analysis with the current state-of-the-art.