Reviewerly/exHarmony huggingface dataset:
Multi-floor laboratory transportation technologies based on intelligent mobile robots

Abstract Transportation technologies for mobile robots include indoor navigation, intelligent collision avoidance and target manipulation. This paper discusses the research process and development of these interrelated technologies. An efficient multi-floor laboratory transportation system for mobile robots developed by the group at the Center for Life Science Automation (CELISCA) is then introduced. This system is integrated with the multi-floor navigation and intelligent collision avoidance systems, as well as a labware manipulation system. A multi-floor navigation technology is proposed, comprising sub-systems for mapping and localization, path planning, door control and elevator operation. Based on human–robot interaction technology, a collision avoidance system is proposed that improves the navigation of the robots and ensures the safety of the transportation process. Grasping and placing operation technologies using the dual arms of the robots are investigated and integrated into the multi-floor transportation system. The proposed transportation system is installed on the H20 mobile robots and tested at the CELISCA laboratory. The results show that the proposed system can ensure the mobile robots are successful when performing multi-floor laboratory transportation tasks.
Spatio-Temporal Feature Extraction/Recognition in Videos Based on Energy Optimization

Videos are spatio-temporally rich in static to dynamic objects/scenes, sparse to dense, and periodic to non-periodic motions. Particularly, the dynamic texture (DT) exhibits complex appearance and motion changes that remain a challenge to deal with. This paper presents an energy optimization method for feature extraction and recognition in videos. For noise and background jitter, the Tikhonov regularization with eigen-vector and Frenet-Serret formula-based energy constraints is also proposed. The different periodicity of DT can be adapted by the time-varying number of learning temporal frames. The optimal duration of an image sequence is determined from the temporal property of its eigen-values. Unlike the state-of-the-art recognition methods, i.e., sparse coding and slow feature analysis, the proposed method can capture the physical property of objects and scenes: velocity, acceleration, and orientation. Also, the static and dynamic image regions can be locally classified. Owing to these spatio-temporal features, stability, robustness, and accuracy of feature extraction and recognition are enhanced. Using DT videos, the superiority of the proposed method compared to the state-of-the-art recognition methods is experimentally shown.
Effects of Virtual Agent Gender on User Performance and Preference in a VR Training Program

This paper presents findings of a small-scale exploratory study that investigated effects of virtual agent gender on user performance and preference in a virtual reality (VR) training program. During the study, twelve participants, predominantly young male adults (10 males, average age 24 years), took part in a customized Box and Blocks Test (BBT), where virtual female and male agents instructed them to quickly move specific color cubes from one side of the table to the other side of the table. Although, on average, the male-dominated sample performed better with the male agent than the female agent, with respect to task completion time, error rate, and error correction time, no significant difference was identified. There was also no significant effect of agent gender on preference, perceived helpfulness, and perceived professionalism. However, interestingly, significantly more participants found the female agent more attractive than the male agent, presumably due to the male-dominated sample.
Security predictions—A way to reduce uncertainty

Rapid changes in security threat landscapes cause uncertainty for IT operations and security professionals and may force changes to organizations' security strategy. Decision support data that help reduce ambiguity or even predict future developments in this regard can thus be of economic value. Based on over 200 security predictions published in 2015, we use a topic modeling approach to identify 17 underlying predicted threat developments. To verify the extent to which these predicted threat topics were realized in 2016, we solicited opinions from respondents with varying experience of IT and information security. In addition, we reviewed secondary sources to corroborate the survey results. Based on the presented findings, we conclude that the security predictions for 2016 did foresee notable developments in that year. The identified latent predictions were related to hacking political campaigns, large-scale data breaches of personal data and health records, increasing threats from various types of malware, specifically ransomware, and large-scale DDoS attacks. The findings of this research are relevant to IT executives and security professionals, who can apply this approach to improve the effectiveness of organizations' technology and cyber security strategy.
Non-Commutative Probability Models in Human Decision Making: Binary Hypothesis Testing

In this paper, we consider the binary hypothesis testing problem, as the simplest human decision making problem, using a von-Neumann non-commutative probability framework. We present two approaches to this decision making problem. In the first approach, we represent the available data as coming from measurements modeled via projection valued measures (PVM) and retrieve the results of the underlying detection problem solved using classical probability models. In the second approach, we represent the measurements using positive operator valued measures (POVM). We prove that the minimum probability of error achieved in the second approach is the same as in the first approach.
Prediction of Time Series of Photovoltaic Energy Production Using Artificial Neural Networks

An artificial neural network (ANN) model is used for forecasting the power provided by photovoltaic solar panels using feed forward neural network (FFNN) of a photovoltaic installation located in the city of Mohammedia (Morocco). One year of hourly data on solar irradiance, ambient temperature and output PV power were available for this study. For this, different combinations of inputs with different numbers of hidden neurons were considered. To evaluate this model several statistic parameters were used such, as the coefficient of determination (R2), the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE). The results of this model, tested on unknown data, showed that the model works well, with determination coefficients lying between 0.98 and 0.998 for sunny days and between 0.82 and 0.96 for cloudy days.
Modern Information Technology in Education

The modern period of development of society is subject to a strong infl uence on it of information technologies, which, in turn, penetrate into all spheres of human life, forming a global information space. An integral and important part of such processes is the informatization of education. The introduction of technology makes it possible to qualitatively change the methods and forms of the learning process, making it more comfortable and aff ordable. In our country, one of the top priorities has always been to get quality education and useful skills. However, for several reasons, there are certain obstacles to the full implementation of technology in the learning process. The relevance of the chosen topic is due to the transition of education to an innovative development path. The article discusses the experience of introducing information technology in the educational process in order to improve and move to a new level. A comparative analysis of existing approaches and achievements using information technologies is carried out. Attention is paid to Russian educational online projects and the costs of their implementation.
Flow-concurrence and bandwidth ratio on the Internet

The relevance of flow-based monitoring in tasks such as the detection of anomalies and denial of use attacks, traffic reporting, performance evaluation, software routing among others motivates the study of the Internet traffic in terms of flows. However, by the time a network manager or practitioner start any of these tasks, they face the challenge of planning probes' capacities, which depends on the number of concurrent flows. Unfortunately, while the bandwidth of a network or link, both in operation and in future deployments, can be known, or at least estimated in advance, nothing is known about the load in terms of concurrent flows. We aimed at filling this gap by studying the concurrence of flows with respect to the bandwidth normalized by factors such as protocol shares, timeouts, applications, time and years among others. As a result, we provide the research community with several models, based on lognormal distributions, and parameter estimates in such a way that any player in the Internet arena can estimate the number of concurrent flows in their own infrastructure. Moreover, such results emerge from a diverse set of network traces so making the extrapolation of conclusions viable.
Pharmacological Treatment of Motion Sickness

Practically everything has been tried at one time or another to treat motion sickness. There are a number of medications that are quite effective, although most have some unwanted side effects and some adversely affect performance. Scopolamine and promethazine are still considered to be the most effective anti-motion sickness medications. Perhaps the most disturbing feature of the anti-motion sickness drug story is that nothing new and effective has appeared in the last forty years or so. Recently, drugs that have effective anti-emetic properties in certain clinical settings have not been found to be effective in a motion environment. If you are treating passengers, make your choice of medication according to circumstances such as duration and severity of exposure and individual idiosyncrasies. When dealing with operators of potentially hazardous equipment or those performing certain skilled tasks, the choice is more difficult and it may be better to avoid drugs all together.
Manually-parcellated gyral data accounting for all known anatomical variability

Morphometric brain changes occur throughout the lifetime and are often investigated to understand healthy ageing and disease, to identify novel biomarkers, and to classify patient groups. Yet, to accurately characterise such changes, an accurate parcellation of the brain must be achieved. Here, we present a manually-parcellated dataset of the superior frontal, the supramarginal, and the cingulate gyri of 10 healthy middle-aged subjects along with a fully detailed protocol based on two anatomical atlases. Gyral parcels were hand-drawn then reviewed by specialists blinded from the protocol to ensure consistency. Importantly, we follow a procedure that allows accounting for anatomical variability beyond what is usually achieved by standard analysis packages and avoids mutually referring to neighbouring gyri when defining gyral edges. We also provide grey matter thickness, grey matter volume, and white matter surface area information for each parcel. This dataset and corresponding measurements are useful in assessing the accuracy of equivalent parcels and metrics generated by image analysis tools and their impact on morphometric studies.
Optimizing nontrivial quantum observables using coherence

In this paper we consider the quantum resources required to maximize the mean values of any nontrivial quantum observable. We show that the task of maximizing the mean value of an observable is equivalent to maximizing some form of coherence, up to the application of an incoherent operation. For any nontrivial observable, there always exists a set of preferred basis states where the superposition between such states is always useful for optimizing the mean value of a quantum observable. The usefulness of such states is expressed in terms of an infinitely large family of valid coherence measures which is then shown to be efficiently computable via a semidefinite program. We also show that these coherence measures respect a hierarchy that gives the robustness of coherence and the l1 norm of coherence additional operational significance in terms of such optimization tasks.
An Introduction to Quantum Computing and Its Application

This paper reviews the two major approaches to quantum computing and discusses early quantum applications.
Atlas

Graphs are everywhere, growing increasingly complex, and still lack scalable, interactive tools to support sensemaking. To address this problem, we present Atlas, an interactive graph exploration system that adapts scalable edge decomposition to enable a new paradigm for large graph exploration, generating explorable multi-layered representations. Atlas simultaneously reveals peculiar subgraph structures, (e.g., quasi-cliques) and possible vertex roles in connecting such subgraph patterns. Atlas decomposes million-edge graphs in seconds, scaling to graphs with up to 117 million edges. We present the results from a think-aloud user study with three graph experts and highlight discoveries made possible by Atlas when applied to graphs from multiple domains, including suspicious yelp reviews, insider trading, and word embeddings. Atlas runs in-browser and is open-sourced.
Probing attack of share‐serial threshold implementation of advanced encryption standard

The authors present the first security evaluation of the side-channel attack countermeasure called the share-serial threshold implementation proposed by Wegener and Moradi. In the share-serial threshold implementation, all the component functions are processed using a unified circuit thereby reducing total circuit area. They show that the share-serial threshold implementation is vulnerable to probing attack using a single probe. In the proposed attack, an attacker places a single probe on a wire in the unified circuit and read the target wire over multiple cycles. The attacker can recover unshared 1-bit intermediate value by combining the measured data. The attack highlights an interesting difference between side-channel analysis and probing attack. The authors also evaluate the number of cipher calls to attack the block cipher advanced encryption standard (AES).
Adaptive convolutional neural network for large change in video object segmentation

This study tackles the semi‐supervised segmentation task for the objects that have large motion or appearance change in a video sequence, which is very challenging to the existing methods of video object segmentation (VOS). In this study, a novel adaptive approach is presented, named adaptive convolutional neural network for large change VOS, which determines when and how to fine‐tune the convolutional neural network through the motion metric and the appearance metric among consecutive video frames. Additionally, a lightweight optimisation algorithm for the predictive binary mask is introduced which is effective for pixel prediction by eliminating the discrete points cluster. To illustrate the advantages of this approach, experiments have been performed on four VOS datasets, which demonstrate that the proposed method is highly effective and could achieve the state‐of‐the‐art on these datasets.
Characterization of the sources of degradation in remote sensing satellite images

Remote sensing images are formed by the acquisition of the electromagnetic energy reflected or emitted from an object under observation using an optical system. During this process the acquired data is lost due to several degradation effects including atmospheric turbulence, relative motion between observed object and sensor, and lenses-of-focus, hence causing severe blurring in satellite digital images. This blurring effect can be mathematically modeled using point spread function in the image’s spatial domain, or equivalently using the optical transfer function in the image’s frequency domain. The degree of spreading blurring of the point object is a measure of the quality of an imaging system. The key result of this paper is investigating the main sources of image degradation and modeling their effects on the quality of images acquired by remote sensing satellite systems.
3-D Optical Path Drawing Method

This paper mainly proposes a simple method to record scattered light rays via photography, presenting a clearer and brighter representation of the optical path. This technique is applied to the double-slit interference experiment and single-slit diffraction experiment typically conducted in general physics courses, and shows the intuitive results concerning the relationship between light intensity and interference or diffraction angle through photographic images. Moreover, a photograph of the optical path presented by a converging lens is provided to better understand visually the phenomena of optical aberration and spherical aberration. The optical path drawing method can be applied to various fields of physics and engineering.
An augmented sequential MCMC procedure for particle based learning in dynamical systems

Dynamical systems elicited via state space models are systems that consist of two components: a state and a measurement equation model that evolve over time. This paper addresses Bayesian inference of unknown parameters, or parameter learning, of such systems. Particle-based parameter learning methods form a well-known class of procedures for obtaining inference in state space models where a collection of particles are used to represent the posterior distributions of parameters. However, particle-based learning procedures require the availability of sufficient statistics and tractable posterior distributions of parameters based on these statistics for sampling, which is not always the case in many situations. We address the problem of particle-based learning when sufficient statistics and tractable distributions for sampling are not available. An augmented sequential Markov Chain Monte Carlo (ASMCMC) algorithm is developed for obtaining the posterior distribution of unknown parameters. We provide three guiding examples of nonlinear dynamical systems for which sufficient statistics and tractable distributions for sampling are not available, and illustrate the proposed ASMCMC methodology on these examples based on simulated data.
Efficient Construction for Full Black-Box Accountable Authority Identity-Based Encryption

Accountable authority identity-based encryption (A-IBE), as an attractive way to guarantee the user privacy security, enables a malicious private key generator (PKG) to be traced if it generates and re-distributes a user private key. Particularly, an A-IBE scheme achieves full black-box security if it can further trace a decoder box and is secure against a malicious PKG who can access the user decryption results. In PKC'11, Sahai and Seyalioglu presented a generic construction for full black-box A-IBE from a primitive called dummy identity-based encryption, which is a hybrid between IBE and attribute-based encryption (ABE). However, as the complexity of ABE, their construction is inefficient and the size of private keys and ciphertexts in their instantiation is linear in the length of user identity. In this paper, we present a new efficient generic construction for full black-box A-IBE from a new primitive called token-based identity-based encryption (TB-IBE), without using ABE. We first formalize the definition and security model for TB-IBE. Subsequently, we show that a TB-IBE scheme satisfying some properties can be converted to a full black-box A-IBE scheme, which is as efficient as the underlying TB-IBE scheme in terms of computational complexity and parameter sizes. Finally, we give an instantiation with the computational complexity as O(1) and the constant size master key pair, private keys, and ciphertexts.
FTConv

Transposed convolution, which is often used to scale up feature maps in various computer vision tasks, is a structural inverse process of convolution. Both convolution and transposed convolution, if any, account for the majority of computation in the inferences of deep neural networks. While convolution has been studied extensively, there are few investigations on accelerating transposed convolution. In this paper, we propose a fast algorithm, FTConv, to reduce the computation of transposed convolution using the Winograd algorithm, which has also been used for convolution with small kernels. Specifically, a transposed convolution can be converted into multiple convolutions after dividing the kernel into several congruence classes. Thus, we can accelerate the multiple convolutions using a modified Winograd algorithm. The transposed convolution can be obtained by interleaving output feature elements of each congruence class. We also design a Winograd ALU in four pipeline stages to further accelerate the computation on FPGA. By carefully designing a sliding window for on-chip buffer reuse according to the memory access pattern of transposed convolution, we save the memory bandwidth by 88.2% compared with a straightforward method. We evaluate FTConv using FSRCNN-s, a neural network for super-resolution. The number of multiplications in the transposed convolution layer can be reduced by 69% over the direct computation of FSRCNN-s.
Role of Fuzzy Cognitive Maps in Smart Education System

This paper studies the fuzzy cognitive maps and its role in the development of a smart education system. A basic Fuzzy cognitive map (FCM) model is proposed which can overcome some of difficulties in the static learning environment and digital learning environments. The Proposed FCM models a dynamic learning system, identify learning styles, evaluates critical factors of a learning management system, assess FCM teaching and learning mechanism and its developments.
Heavy Metal Concentration of Sediments from the Eastern Niger Delta Basin, South-East Nigeria

This study documents the baseline geochemistry of sediments in portions of the Eastern Niger Delta, focusing on the distribution of selected heavy metals (Pb, Zn, Cd, Cr, As and Ni). Concentration level of metals was determined through Atomic Absorption Spectrophotometry (AAS) and results interpreted with statistical techniques. Interrelations between elements reveal a control by three dominant geogenic factors. The result of this study shows that heavy metal geochemistry of sediments is related to regional and local geology with minor imprints of anthropogenic activities.