Reviewerly/exHarmony huggingface dataset:
Target tracking algorithm with adaptive learning rate complementary filtering

The Correlation filtering algorithm is not effective for fast deformation and fast movement. It is easy to lose when encountering problems such as occlusion. However, it has a good advantage of dealing with situations such as motion blur and lighting changes. A tracking algorithm based on color statistical features has a good effect on the rotation and translation of objects. The Staple algorithm combines the two algorithms to track using complementary fusion, but it also does not handle the occlusion and other issues well. In this paper, based on the Staple algorithm, the average peak correlation energy (APCE) and the maximum response are introduced. The value is used as the tracking confidence, and a detector using a support vector machine (SVM) is added. When the tracking confidence is low, the target is blocked or moved violently. At this time, the detector works, and the search area is expanded around the original area for the target. At the same time, because the traditional tracking algorithm uses a fixed learning rate to update the template, this paper uses an adaptive tracking learning rate. When the tracking confidence is low, the update speed of the target model is reduced, which can effectively deal with the occlusion deformation in the tracking process. OTB100 benchmark experiments show that this method can solve the occlusion problem during target tracking. The degree of change is robust and stability.
An End-to-End Quantization Framework for Fixed Point Fast Fourier Transform Hardware Implementation via Deep Neural Network

Fast Fourier Transform (FFT) plays an important role in signal processing nowadays. The quantization problem of sensed/sampled information has been revealed recently. In this paper, we present an end-to-end quantization framework via deep neural networks (DNNs). We have jointly optimized the system of signal quantization and de-quantization, and applied this system before fixed point FFT operation. The system can extract the features of the signal and reduce the hardware consumption of signal processing. Through numerous experiment, we find that the FFT calculation error of our quantization system is not smaller than results of uniform quantization and we analyzed underlying reasons. The designs of system brings a lot of new ideas to the future work. Our quantization system based on DNN can be used not only in FFT calculation, but also in other linear/non-linear symmetric operations.
Accurate trajectory prediction in a smart building using recurrent neural networks

Occupant behavioral patterns, once extracted, could reveal cues about activities and space usage that could effectively get used for building systems to achieve energy savings. The ability to accurately predict the trajectories of occupants inside a room branched into different zones has many notable and compelling applications. For example - efficient space utilization and floor plans, intelligent building operations, crowd management, comfortable indoor environment, security, and evacuation or managing personnel. This paper proposes future occupant trajectory prediction using state-of-the-art time series prediction methods, i.e., Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. These models are being implemented and compared to forecast occupant trajectories at a given time and location in a non-intrusive and reliable manner. The considered test-space for the collection of the dataset is a multi-utility area in an instrumented public building. The deployed 3D Stereo Vision Cameras capture the spatial location coordinates (x- and y- coordinates) from a bird's view angle without eliciting any other information that could reveal confidential data or uniquely identify a person. Our results showed that the GRU model forecasts were considerably more accurate than the LSTM model for the trajectory prediction. GRU prediction model achieved a Mean Squared Error (MSE) of 30.72 cm between actual and predicted location coordinates, and LSTM achieved an MSE of 47.13 cm, respectively, for multiple occupant trajectories within the monitored area. Another evaluation metric Mean Absolute Error (MAE) is used, and the GRU prediction model achieved an MAE of 3.14 cm, and the LSTM model achieved an MAE of 4.07 cm. The GRU model guarantees a high-fidelity occupant trajectory prediction for any given case with higher accuracy when compared to the baseline LSTM model.
Emotion and brain state based freezing of gait occurrence analysis with electroencephalography data

The lighting service state of road tunnels has an important impact on traffic safety. To assess and predict traffic safety in road tunnels under different lighting service states, a traffic safety assessment system of road tunnels was innovatively established by using visual recognition clarity data from 300 field experiments, which uses the proposed traffic safety factor to assess the traffic safety of road tunnels under different lighting service states. Then, the Particle Swarm Optimization (PSO) algorithm is used to optimize the Back Propagation (BP) neural network, and a new traffic safety intelligent prediction method in road tunnels was constructed to predict the traffic safety factor under different lighting service states. The results of the study show that there is a negative correlation between simulated vehicle speed and visual clarity and a positive correlation between lighting attenuation and visual clarity. There is a negative correlation between the number of luminaire failures and the visual recognition clarity. When the lighting attenuation is below 70% or four and above luminaires fail, the lighting service states of road tunnels can seriously threaten traffic safety. The PSO-BP neural network model can accurately predict the traffic safety factor. The critical value of the traffic safety factor is 0.5965. This means that the lighting service state poses a threat to traffic safety in road tunnels when the traffic safety factor is lower than this value. The results of the study can provide the basis for developing a safe and low-carbon tunnel lighting maintenance program.
A Lightweight Target Detection Network for Embedded Platforms

This paper studies a target detection algorithm based on deep learning which has to be implemented on embedded platforms with limited processing capability. First, the detection requirements are given. Then, a lightweight detection network is designed based on YOLOv3 in which a streamlined MobileNetV2 is used as the feature extraction network. To shorten the training time, a pre-trained model is adopted to initialize parameters. Finally, the test experiments are performed by operating the proposed algorithm on a smartphone. The results show that the algorithm can achieve a good performance both in accuracy and speed in a given environment under limited computing resources.
Efficient Instance Segmentation Network

We present an efficient, flexible, fast and accurate framework for real-time instance segmentation. We call it Efficient Instance Segmentation Network, denoted as EISNET. Our method is motivated by the Mask R-CNN and YOLACT. Mask R-CNN enables instance segmentation by adding an extra branch at the Faster R-CNN framework to produce mask for each object. Due to limitation of the inefficiency of two stage detector, Mask R-CNN is not suitable for real-time scene. We therefore propose EISNET which enables instance segmentation by adding two branches to the one-stage detector-RetinaNet. We call it Efficient since we use modified EfficientNet as the backbone of our framework, which results in high accuracy with few parameters and FLOPS. In addition, we provide a modified bi-directional FPN (Feature Pyramid Network) module, which thus allows efficient multi-scale feature fusion. Given the credit to these design techniques, our EISNET achieves 31.2 mAP with only 17.2M parameters and 3.5B FLOPS on the COCO dataset. More significantly, our model can achieve more than 35 FPS on single 1080Ti GPU, which fits the most real-time requirements. With a better GPU, we could even achieve higher mAP while keeping the real-time property of more than 30 FPS.
The Application of Thai Natural Language Processing

The purpose of this chapter is to present an example of creating a complete application on Thai Natural Language Processing to help the readers get some ideas for NLP project development. To achieve this purpose, we create a Natural Language Interface to Database (NLIDB) model that helps users easily retrieve information from a database by natural language.
Predictive Modeling Concerning Mobile Learning Advance

The paper treats an application of predictive modeling in the field of mobile learning. A methodology to facilitate the realization of a model predicting the most utilized research topics that are close to the term mobile learning is developed. The constructed model is based on machine learning technique and fuzzy logic method and it predicts the implementation of mobile learning in different educational context. The results point out the found dependency and tendency for future advance of mobile learning.
DNN Application Oriented Migration Scheduling Strategy based on Genetic Algorithm

Migration Scheduling Strategy for Deep Neural Network (DNN) is a considerably popular method to migrate some complex neural network layers to the edge nodes having the rich resources for computation. In spite of this, this migration process causes additional time overhead and energy overhead and thus impacts the quality of experience for users. To this end, with the consideration of the DNN applications, this paper proposes a Genetic Algorithm (GA) based migration strategy to minimize response time and energy consumption for the multiple task scheduling condition. To be specific, the migration scheduling problem for DNN applications is formulated firstly. Then, the concrete strategy based on GA is devised. In addition, the simulation is implemented over the designated edge environment, and the experimental results demonstrate that the proposed strategy outperforms two baselines in terms of response time and energy consumption.
Sharing Mechanism of Intelligent Vehicles Trust Points based on Blockchain for Vehicular Networks

Nowadays, there exists strong need to enable the Intelligent Vehicle (IV) communication for applications such as safety messaging, traffic monitoring and many other internet access purposes. In this work, we have introduced an Intelligent Vehicle Trust Points (IVTPs) sharing mechanism between vehicle to vehicle, vehicle to infrastructure and vehicle to roadside units. Existing models have already embeded Blockchain (BC), which is valuable for many purposes like security in different data transmission circumstances. However, our proposed scheme uses this BC feature along with IVTPs to ensure the trustworthiness in the communication environment. Performance of our proposed system is evaluated on the basis of IVs' processing time, which are totally based on IVTPs. Our proposed system is efficient as compared to existing one which handles less number of vehicles at intersection point where IVTPs are shared between moving vehicles in a scalable architecture.
Speech Controlled IoT System Based on Context-Driven Rule Engine

Smart systems are designed to improve various aspects of human activities, both in business and private environments. In most cases, such systems require a real-time insight into state and events in the given environment. We present a concept of a framework which can be used as a foundation for more advanced, experimental applications. The core framework provides generic, configurable monitoring and control mechanisms, and enables task automation based on events in the environment. Additionally, it includes a set of features allowing context sharing according to user-defined rules. Regarding possible applications, our emphasis was on speech interaction between human users and the system. Therefore, an experimental application is presented, providing recognition of simple spoken commands, with the ability to generate spoken notifications as well. The application is designed to support simple spoken interaction between a human user and the system in Croatian language, which makes a rare example.
Neural Collaborative Recommendation with Knowledge Graph

Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities' long-term relational dependencies for the recommendation. In this paper, a two-channel neural interaction method named Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose a Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark datasets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation.
2020 Market Analysis of Nanomaterials and Nanotechnology Conference

Nanotechnology is the improvement and engineering of devices so small that they are measured on a molecular scale. This developing field involves scientists from many different disciplines, including physicists, chemists, engineers, information technologists, and material Researchers, as well as biologists. Nanotechnology is being applied to almost every field imaginable, including electronics, magnetics, information technology, materials development and biomedicine. There are numerous areas of concern in the Nano science and nanotechnology markets which are expectable to be explored in the approaching years, for instance, nanoparticles of metals and semiconductors, nanowires, Nano biological systems and nanotubes.
A Trajectory Tracking Method Using Convex Optimization

To improve the efficiency of the existing convex optimization-based trajectory tracking method, and address the issue that the existing trajectory tracking methods are very sensitive to the deviations of axial states, a new trajectory tracking method using convex optimization in conjunction with a modified receding horizon control strategy is developed in this paper. The trajectory tracking is implemented in each segment of trajectory in successive using the convex optimization method. To ensure online guidance, the optimal control problem of trajectory tracking in each segment is transformed into an exact convex optimization, which can be solved in only one iteration. Meanwhile, to address the deviation of axial states, a time re-planning method is proposed to ensure the accuracy and robustness of trajectory tracking. Simulation results show that the proposed methods can track the reference trajectory with high accuracy and robustness with respect to various disturbances.
LIFETIME EDITIONS OF M. V. LOMONOSOV’S WORKS IN THE DEPARTMENT OF VALUABLE AND RARE BOOKS DEPARTMENT OF NOVOSIBIRSK STATE REGIONAL SCIENTIFIC LIBRARY

Novosibirsk state regional scientific library holds the collection of books from Kolyvan- Voskresensk mining plants. This collection includes lifetime (1711–1765) editions of Mikhail Lomonosov’s works. The article provides the thematic characteristic of copies preserved. It examines different signs (such as holder’s recordings, library stamps and notes), representing various periods in the history of public existence of these books. Special attention is paid at the publications that had a great impact on the development of mining in Siberia.
Breaking the Walls of Jericho

Access to information and knowledge is a fundamental element in an informed and democratic society. Open access (OA) allows users to freely access information and knowledge if they have access to internet. This chapter presents barriers to enhancing OA to information and knowledge with a reference to Africa. The said barriers are akin to the Biblical Walls of Jericho which prevented Israelites from capturing their Promised Land of Canaan. It starts by defining what OA entails, highlights the characteristics of OA and its types. Furthermore, the chapter highlights opportunities brought about by OA to information and knowledge which supports the attainment of sustainable development goals (SDGs). Lastly, the chapter suggest strategies that can be adopted to enhance OA in Africa and thus make it a precursor for the achievement of SDGs. This study is qualitative and used content analysis from available literature to address the research problem.
Az információbiztonság fejlődéstörténeti vizsgálata – az ERP rendszerek fejlődése

Today, the most common corporate decision support systems are Enterprise Resource Planning (ERP) systems. In this, IT systems are based on corporate processes and incorporate the principles of information security.The present study takes stock of the history of corporate governance systems, presents some aspects related to corporate information security, and briefly summarizes the ERP system vendors that “dominate” the data of larger than average companies today. The study also has interdisciplinary aspects: starting from the foundations of security science, it also touches the fields of informatics and history.
Jordanian Digital Learning Management System Model (JDLMS)

Universities are providing higher education and research and are one of the biggest creators of innovation, but very little is understood in terms of digital branding of such innovations that stem from rigorous research activities at universities. Often the university's brand name in terms of its reputation is associated with producing and delivering such innovation but what contributes towards digitally branding that innovation to prospective customers, clients, and marketers for appeal needs to be explored. This chapter presents the main hypotheses effecting Jordanian Digital Learning Management System (JDLMS) model and highlights the benefits and weakness area for each one of them. It also discusses the main factors influencing the acceptance of using DLMS at Jordanian universities, which are IT infrastructure and culture, and the hypotheses between both main factors with perceived usefulness and perceived ease of use to generalise this model in higher educational sectors in Jordanian universities.
Effects of a high nonlinear interaction between an open parametric amplifier cavity and a qubit on dynamics of the correlation function and quantum Fisher information

In this paper, we provide an analytical description of the intrinsic noise model of the two-mode cavity, containing a single two-level atom through su(1,1)-algebraic treatment. Each field interacts with the qubit through a four-photon process and is assumed initially in Barut-Girardello coherent state. The Atomic Quantum Fisher information (AQFI), atomic entropy and the correlation function are analyzed under the effects of the intrinsic damping and the superposition of the initial generalized Barut-Girardello Coherent States (B-GCS). Under the nonlinear interactions, the AQFI has irregular oscillatory behavior that depends on the superposition of the B-GCS. Due to the high nonlinear interactions, the damping work with a very small value and leads to the degradation of the AQFI. This degradation can be enhanced in the case of the even B-GCS. The generated entanglement and mixedness of the atomic entropy are explored, they are very sensitive to the physical parameters of the intrinsic damping, the detuning and the initial cavity state. It is found that the second-order correlation depends on the mean photon number, the superposition of the B-GCS, and the damping. For the small value of the mean photon number and with the damping, the sub-Poissonian effects increase with the B-GCS, while the super-Poissonian behavior increases with its even states. For the off-resonant case, the frequency, the regularity and stability of the NSOC function, the atomic entropy and the AQFI are enhanced.
Do Data Breaches Damage Reputation? Evidence from 43 Companies Between 2002 and 2018

While data breaches have become more common, there is little evidence that companies that incur them experience a persistent decline in financial performance or security prices. Using new firm-level data between 2002 and 2018, this paper finds that firms experience a 26-29% increase in intangible capital following an average data breach. However, the largest and most salient breaches are associated with a 5-9% decline in intangible capital following a data breach. These effects are concentrated among firms in consumer-facing industries: smaller (larger) data breaches are associated with more positive (negative) effects on intangible capital. These results suggest that current regulatory guidance may not provide complete incentives for firms to invest in cybersecurity capabilities, particularly for small to medium size breaches.
INFORMATION MODELS OF PROFESSIONAL ARTICLES ON LIBRARY AND INFORMATION ACTIVITIES

The essay designates information models of texts. The formal features of basic information models – functional- stylistic and functional- semantic – are specified. Particular attention is paid at special models of professional texts of articles on library and information topics, reflecting genre, composition and content features. Stressed is an important role of information modeling for teaching professionals-non-philologists copyright skills.
Towards a quantum theory of musical creativity

In putting forward a case for a quantum theory of musical creativity, this chapter is designed to recognise that such a theory is not only simply a contention, rather than an explanatory formulation that can stand for all time, but also, that it is only 'a snapshot of a snapshot'. Karen Barad's project presents a set of technical terms and concepts that are drawn from quantum physics and uses them as a basis for building her own epistemological and ontological model of 'agential realism'. Barad makes a sustained engagement with the concept of diffraction, which for her, denotes the way in which waves overlap and disturb each other: Diffraction is a phenomenon that is unique to wave behaviour. The nature of entanglement is central to the model of quantum physics that Barad builds, and another foundation for her approach is the idea that 'space, time and matter do not exist prior to the intra-actions that reconstitute entanglements'.