Reviewerly/exHarmony huggingface dataset:
Envisioning Reflective and Relaxing Design with ASMR

In this paper, we aim to acquaint the sensory phenomenon of Autonomous Sensory Meridian Response (ASMR) with the DIS community and investigate its relevance via research through design. We inquire if the aesthetics of ASMR media can inspire the design of technology that is reflective and relaxing. In order to do so, we created a sonic toolkit that encourages users to explore their environments and make recordings inspired by the detail-oriented, intimate, and mundane nature of ASMR media. Over the course of a self-directed user study, we invited five participants to engage the toolkit and interviewed them about their experience. We present a description of ASMR and its connection to ongoing conversations within DIS as well as our findings from the user study. We conclude with a provocation which suggests that fostering relaxing designs may be as simple as providing people with a microphone.
A Novel Method of Multi-user Redirected Walking for Large-Scale Virtual Environments

The wireless virtual reality (VR) solution for Head-Mounted Display (HMD) expands the support to multi-user VR system, so multiple users or participants can collaborate in the same physical space for a large-scale virtual environment. The foremost technical obstacle of multi-user VR system is how to accommodate multiple users within the same physical space. It needs address the problem of potential collisions among these users who are moving both virtually and physically. By analyzing the challenges of multiple users, this paper presents a novel method of multi-user redirected walking for large-scale virtual environments. In this method, an improved approach is presented to calculate the rotation gains, and a user clustering algorithm is adopted to divide the users into groups that are automatically adjusted based on the distance between the boundaries or the size of remaining space. Then a heuristic algorithm for three-user or two-user redirected walking is designed to solve the problem of multi-user roaming in a faster and more efficient way than traditional methods. To verify the validity of our method, we used a computer simulation framework to statistically analyze the influence of different factors, such as the physical space size and the number of users. The results show that our multi-user redirected walking algorithm for large-scale virtual environments is effective and practical.
n-gram Cache Performance in Statistical Extraction of Relevant Terms in Large Corpora

Statistical extraction of relevant n-grams in natural language corpora is important for text indexing and classification since it can be language independent. We show how a theoretical model identifies the distribution properties of the distinct n-grams and singletons appearing in large corpora and how this knowledge contributes to understanding the performance of an n-gram cache system used for extraction of relevant terms. We show how this approach allowed us to evaluate the benefits from using Bloom filters for excluding singletons and from using static prefetching of nonsingletons in an n-gram cache. In the context of the distributed and parallel implementation of the LocalMaxs extraction method, we analyze the performance of the cache miss ratio and size, and the efficiency of n-gram cohesion calculation with LocalMaxs.
Augmented Reality in Children’s Education in the Republic of Macedonia

The aim of the research presented in this paper is to support the hypothesis that augmented reality is a good educational tool compared to the traditional ways of learning. In order to understand the level of knowledge, attitude and preferences towards the use of augmented reality (AR) in education in the Republic of Macedonia, we have conducted a survey where we designed several questions and examples that helped us to collect initial insights on the topic. The second part of the study was a practical experiment showing us that regardless of the environment, children prefer this visual way of studying compared to the traditional one. The AR applications used in this experiment were Skyview, Augment and Anatomy 4D, introduced to two classes of 6–8 and 10–12 years old. Additionally, AR applications were used by two children with dyslexia, 9 and 12 years old.
On the Implementation of Evolving Dynamic Cognitive Maps

Fuzzy Cognitive Maps (FCM) and other Dynamic Cognitive Maps (DCM) allow simulation of the evolution of complex qualitative dynamic systems through time. However, the DCM model is static by itself in the sense that its cognitive configuration, i.e., the concepts’ definitions, the relations among the concepts and the structure of the map, do not change with time. This paper introduces DCM meta-states, a simple but versatile Finite State Machine based mechanism that can be used to implement Evolving FCM and generic Evolving Dynamic Cognitive Maps (Ev-DCM).
LWE from non-commutative group rings

The Learning-With-Errors (LWE) problem (and its variants including Ring-LWE and Module-LWE), whose security are based on hard ideal lattice problems, has proven to be a promising primitive with diverse applications in cryptography. For the sake of expanding sources for constructing LWE, we study the LWE problem on group rings in this work. One can regard the Ring-LWE on cyclotomic integers as a special case when the underlying group is cyclic, while our proposal utilizes non-commutative groups. In particular, we show how to build public key encryption schemes from dihedral group rings, while maintaining the efficiency of the Ring-LWE. We prove that the PKC system is semantically secure, by providing a reduction from the SIVP problem of group ring ideal lattice to the decisional group ring LWE problem. It turns out that irreducible representations of groups play important roles here. We believe that the introduction of the representation view point enriches the tool set for studying the Ring-LWE problem.
Grazing incidence interferometry for testing rough asperics

In the past grazing incidence interferometry has been applied for plane, cylindrical, acylindrical and general rod-like surfaces using diffractive beam splitters. Here, we demonstrate that also rough convex steep rotational symmetric aspherics can be measured along one meridian in a single step using diffractive beam splitters and phase shifting techniques. Especially, rough surfaces can be measured with this method due to the large effective wavelength (here about 10&mu;m) of the test. The extension to the whole surface can be attained by successive meridional measurements of the surface under test by azimuthal adjustments. The principle of the method is given, first simulated and experimental results are presented. The occurrence of the interferogram is discussed and the experimental evaluation on a single meridian including the unwrapped phase are shown. Furthermore the simulated results of aberrations caused by object misalignment and the first results for the elimination of adjustment aberrations are presented.
Privacy Protection for Context-Aware Services: A Two-Layer Three-Party Game Model

In the era of context-aware services, users are enjoying remarkable services based on data collected from a multitude of users. However, in order to benefit from these services, users are enduring the risk of leaking private information. Game theory is a powerful method that is utilized to balance such tradeoff problems. The drawback is that most schemes consider the tradeoff problem from the aspect of the users, while the platform is the party that dominates the interaction in reality. There is also an oversight to formulate the interaction occurring between multiple users, as well as the mutual influence between any two parties involved, including the user, platform and adversary. In this paper, we propose a platform-centric two-layer three-party game model to protect the users’ privacy and provide quality of service. One layer focuses on the interactions among the multiple asymmetric users and the second layer considers the influence between any two of the three parties (user, platform, and adversary). We prove that the Nash Equilibrium exists in the proposed game and find the optimal strategy for the platform to provide quality service, while protecting private data, along with interactions with the adversary. Using real datasets, we present simulations to validate our theoretical analysis.
Double pulse LED illumination for phase detection in RGB-interferometry

This contribution considers the application of pulsed LED illumination, synchronized with the exposure gap of a CMOS Bayer camera and an oscillating reference mirror, to record two &pi;/2 phase shifted interferograms for quadrature based phase retrieval. Measurements of a superfine roughness standard in lateral motion are presented, demonstrating the capability of the setup to record the surface topography of moving objects. The application of the interferometric measurement data to calibrate a topography measuring wave front sensor, which is employed to support surface unwrapping is also discussed.
A Reconstruction of Quantum Mechanics

We show that exactly the same intuitively plausible definitions of state, observable, symmetry, dynamics, and compound systems of the classical Boolean structure of intrinsic properties of systems lead, when applied to the structure of extrinsic, relational quantum properties, to the standard quantum formalism, including the Schrödinger equation and the von Neumann–Lüders Projection Rule. This approach is then applied to resolving the paradoxes and difficulties of the orthodox interpretation.
Optimizing Customer-Agent Interactions with Natural Language Processing and Machine Learning

Efficient and successful customer service is an integral aspect of all businesses. In 2017, U.S. businesses lost $75 billion through poor customer service, where customers encountered unhelpful staff or spent too much time on unresolved issues. Customer experience management software companies analyze call center customer-agent transcriptions using methods such as sentiment analysis and topic modeling to improve their clients' customer service. However, these approaches are not optimized to account for the sequential nature of these customer-agent interactions. For example, while it is important to know how many customers cancel a service, businesses also need to understand how agents respond to a cancellation request and how certain actions may lead to a positive or negative outcome. To analyze the progression of conversations and understand actions that maximize positive outcomes, our research represents each contact center dialogue as a Markov decision process (MDP). For each conversation, we annotated whether the problem was resolved and whether the outcome was good or bad from a business perspective. We employed natural language processing (NLP) to extract the customer states and agent actions from call transcriptions. Our results identify and visualize the most frequent transcription sequences from successful conversations and estimate the expected probability of an outcome when an agent takes an action given a certain customer state. Such an approach may be used to develop programs to train agents for improved customer service in call centers.
Feature-Weighted Fuzzy K-Modes Clustering

Fuzzy k-modes (FKM) are variants of fuzzy c-means used for categorical data. The FKM algorithms generally treat feature components with equal importance. However, in clustering process, different feature weights need to be assigned for feature components because some irrelevant features may degrade the performance of the FKM algorithms. In this paper, we propose a novel algorithm, called feature-weighted fuzzy k-modes (FW-FKM), to improve FKM with a feature-weight entropy term such that it can automatically compute different feature weights for categorical data. Some numerical and real data sets are used to compare FW-FKM with some existing methods in the literature. Experimental results and comparisons actually demonstrate these good aspects of the proposed FW-FKM with its effectiveness and usefulness in practice.
Optimization of a geometrical calibration procedure for stereoscopic endoscopy systems

Stereoscopic video endoscopes are widely used for remote visual inspection and precise three-dimensional (3D) measurements in industrial and biomedical applications. The reconstruction of 3D points from the corresponding image points requires calibration procedure which accuracy affects the measurement uncertainty. We propose to perform an optimal choice of the calibration technique and the calibration target parameters using the computer simulation at the design stage. The effectiveness of this approach is demonstrated via the design of self-developed miniature prism-based stereoscopic system. We simulated acquisition of the calibration and measurement data using optical design software. The conventional calibration technique requiring many positions of the flat target with arbitrary displacements and rotations was compared with another one, which uses the translation stage to provide pure translation of the target. We analyzed the impact of the translation uncertainty, the number of positions, the number of targets and the uncertainty of image point coordinates on the uncertainty of calibration parameters and 3D measurements. We have shown that the second technique could provide the same calibration accuracy as the first one with less number of images. The results of computer simulation were confirmed experimentally using the prototype of the self-developed stereoscopic endoscope. The proposed approach may be used to optimize calibration techniques and reduce a cost of calibration equipment for various stereoscopic measurement systems.
Convolutional neural network for Iceberg Classifier

Abstract Image classification has been a popular research field in computer version, and we employ convolutional neural network to solve a practical problem that is iceberg classifier. Iceberg classifier is critical import to keep the ship safe in the ocean, because the ships can program another routine after they detect the iceberg. When radar detects an object, it cannot separate it from ship or any other solid object. The radar images contain the shape, size and brightness features. There are several ways to solve the iceberg classification problem: (1) Hough transform [1] (2) convolution neural network [2]. Convolutional neural network can obtain image features that are invisible for human beings. Convolution neural network has achieved expected performance in many image classification problems. Our experiment shows that convolution neural network can classify the iceberg from any other object and we get accuracy 96% and F1 score 96%. Support vector machine [3] achieves accuracy 79% and F1 score 78%.
CXNet-m2: A Deep Model with Visual and Clinical Contexts for Image-Based Detection of Multiple Lesions

Diagnosing multiple lesions on images is facing with challenges of incomplete and incorrect disease detection. In this paper, we propose a deep model called CXNet-m2 for the detection of multiple lesions on chest X-ray images. In our model, there is a convolutional neural network (CNN) for encoding the images, a recurrent neural network (RNN) for generating the next word (the name of lesion) and an attention mechanism to align the visual contexts with the prediction of words. There are two main contributions of CXNet-m2 to improve the work efficiency and increase the diagnosis accuracy. (1) Inspired by image captioning, CXNet-m2 adapts the classification system to a language model, where Bi-LSTM is used to learn the clinical relationship between lesions. (2) Inspired by attention mechanism, the prediction of possible lesions is guided by visual contexts, where the visual contexts are selected by the previously generated words and chosen visual regions. The experimental results on Chestx-ray14 show that CXNet-m2 achieves better AUC and the different versions of CXNet-m2 illustrate the importance of pre-training and clinical contexts.
Ateb-Modeling Method Application for High Quality Video Traffic Modeling

This paper presents the procedure of implementing Ateb-modeling method, used for high definition video traffic samples. The subject of the study were high definition video traffic samples with different characteristics. It was shown, that those traffic samples have a self-similar nature, so the developed method could be used for modeling of future traffic values. Simulation tests were prepared to show the adequacy of used Ateb-modeling method for chosen high definition video samples. The obtained research results indicate the need to adapt the proposed method of modeling traffic values for 8K high definition video samples.
Human Body Tracking Method Based on Deep Learning Object Detection

Aiming at the problem of poor robustness of human detector based on artificial extraction feature, This paper proposes a visual tracking method based on deep learning object detection, which draws on the research results of target detection. The method utilizes the advantage of deep learning in feature representation, and uses the regression-based depth detection model YOLO to extract candidate targets. We re-clustered the data set for human targets, which improved the network performance of YOLO. For the extracted candidate frame position, the region is clipped. The HOG features of the candidate regions are extracted for target screening to achieve target tracking. Compared with pedestrian detection methods such as KCF and so on, this method reduces the miss detection rate and false detection rate, improves the robustness of the algorithm, and the detection speed meets the real-time requirements.
EXTREME shearography: high-speed shearography instrument for in-plane surface strain measurements during an impact event

This work presents the design and the latest experimental results on the surface strain measurements during an impact event obtained with the EXTREME high-speed shearography instrument. The shearography technique is used in this project to provide a quantitative measurement of the surface strain development at the first moments of the impact event (μs time scale) which may reveal the initiation of the failure mechanisms in composite materials. Experimentally measured surface strain components over the field of view will be used as input and validation data for new numerical and analytical models of the impact response of composites. The new configuration of the shearography instrument realises measurements of the in- and out-of-plane surface strain components to improve coupling with the numerical models. Two viewing directions (shearing interferometers) with a double-frame approach are used to capture the interferograms during the impact. The interferometers realise a double-imaging Mach-Zehnder scheme for the spatial phase-shifting with independent control of the shearing amount and the carrier frequency. The set of technical parameters of the developed shearography instrument makes it one of the most extreme applications of shearography for material characterisation. The framework for this work is the “EXTREME Dynamic Loading – Pushing the Boundaries of Aerospace Composite Material Structures” Horizon 2020 project.
A Model for Inebriation Recognition in Humans Using Computer Vision

The cost of substance use regarding lives lost, medical and psychiatric morbidity and social disruptions by far surpasses the economic costs. Alcohol abuse and dependence has been a social issue in need of addressing for centuries now. Methods exist that attempt to solve this problem by recognizing inebriation in humans. These methods include the use of blood tests, breathalyzers, urine tests, ECGs and wearables devices. Although effective, these methods are very inconvenient for the user, and the required equipment is expensive. We propose a method that provides a faster and convenient way to recognize inebriation. Our method uses Viola-Jones-based face-detection for the region of interest. The face images become input to a Convolutional Neural Network (CNN) which attempts to classify inebriation. In order to test our model's performance against other methods, we implemented Local Binary Patterns (LBP) for feature extraction, and Support Vector Machines (SVM), Gaussian Naive Bayes (GNB) and k-Nearest Neighbor (kNN) classifiers. Our model had an accuracy rate of 84.31% and easily outperformed the other methods.
Macro-Fiscal Gains from Anti-Corruption Reforms in the Republic of Congo

This paper argues that oil revenue management and public investment in Congo are vulnerable to corruption as a result of limited transparency and accountability.Corruption has potentially contributed to poor macro-fiscal outcomes.The paper acknowledges the authorities' anti-corruption efforts made so far and proposes further critical reforms to reduce remaining vulnerabilities.Using a dynamic stochastic general equilibrium model results show that, depending on the reforms adopted, the potential additional growth can range between 0.8 to 1.8 percent per year over the next 10 years, and debt can decline by 2.25 to 3 percent of GDP per year over the same period.These results suggest that macrofiscal gains from anti-corruption reforms could be substantial even under conservative reform scenarios.
Measurement of the refractive index of a transparent film using interferometry

We propose the method of measuring the refractive indices (RI) of transparent films using White Light Scanning Interferometry(WLSI). Fourier-transformed interferogram is represented as a DC-term and the sinusoidal term whose amplitude is the product of the sample reflectance and the energy term which is determined by the reference mirror and a light source. Once we get the interferogram data of the reference sample whose RI is known, we can obtain the energy term of the WLSI system. After obtaining the energy term of the WLSI system, we measure the interferogram data of the sample whose RI is unknown under the same experimental conditions. Combining these energy terms and the interferogram, we get the RI of the sample for each wavelength.
MANAGER’S COMPETENCIES OF THE DIGITAL REVOLUTION ERA

The society is steadily moving towards digitalization. To implement the transformations planned for most sectors of the economy, the well-functioning of the already formulated managerial competencies is required. At the same time, it is difficult to imagine a “step into the future” without additional efforts in this direction. According to the authors, there is an urgent need to develop new “profiles of success” − cross-professional complexes of knowledge and skills, and then – their optimal implementation in the daily activities of specialists. Competencies evolve quickly, therefore, each manager is required additional responsibility-to monitor the effectiveness of their competencies and update them in accordance with the requirements of time.
Ternary Search Trie based Algorithms for Recognizing the Names of Power Devices

Abstract With the development of the smart grid technology and the advent of the big data, it is highly desired to develop efficient computational methods for mining the values behind of huge volumes of data on the grid. However non-unified naming of the electronic devices from the de-centralized and heterogeneous power systems makes the procedure a hard problem. In the present work, we proposed a Ternary Search Trie based algorithm for recognizing the devices from the huge volumes of transaction data in the power grid. On the base of well-defined professional vocabulary of electronic devices, the algorithm scans and segments records having names of devices using the Ternary Search Trie method. An integrative scoring schema combing semantic and word order similarity is designed to evaluate the segments against the target device names. Experimental results indicate that the proposed method achieves only 3.13% error in parsing stage, and higher recognition rate. Moreover, the algorithm lists top-n matches for professional judgments to make the work more useful and rigorous.