Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Control and Actuation System Development for Biomimetic Morphing

Increasing use of Unmanned Aerial Vehicles (UAVs) in civil and defense sectors, has led to increase in demands on UAV capabilities as well. Across several Department of Defense (DoD) agencies, such as Air Force, Navy, DARPA, and DARO, there has been a need for “multi-mission” capable UAVs that can achieve several objectives using a single platform. While conventional fixed-wing or rotary-wing platforms may be able to perform several missions, the ability to morph allows the vehicle to become truly efficient in meeting multiple objectives. Morphing aircraft have been studied for decades, however a truly biomimetic morphing capability with seamless aerodynamic transitions; remains elusive. Furthermore, the control of such vehicles that ensures stability during transitions, is still an active area of research. In this research, we have developed actuation mechanisms to achieve span extension, tip twist, sweep and anhedral/dihedral. These mechanisms were demonstrated in hardware, and initial free jet wind tunnel testing was also performed using a scaled prototype. The control of the aircraft undergoing large shape changes, also brings in the need for adaptive control, which was investigated in this study and a novel indirect adaptive control scheme, combining subspace system identification with optimal control was developed. We perform several studies using the control scheme and demonstrate applicability of the technique, as well as extensions to perform robust adaptive control; which becomes critical for the morphing aircraft application. These studies were performed in simulation in the current work, using models based on aerodynamic analysis of conventional and morphing wing mechanisms.
Online transfer learning with extreme learning machine

In this paper, we propose a new transfer learning algorithm for online training. The proposed algorithm, which is called Online Transfer Extreme Learning Machine (OTELM), is based on Online Sequential Extreme Learning Machine (OSELM) while it introduces Semi-Supervised Extreme Learning Machine (SSELM) to transfer knowledge from the source to the target domain. With the manifold regularization, SSELM picks out instances from the source domain that are less relevant to those in the target domain to initialize the online training, so as to improve the classification performance. Experimental results demonstrate that the proposed OTELM can effectively use instances in the source domain to enhance the learning performance.
Development of existing mesh fitting technique for newly designed CAD-data

We developed a mesh morphing technique, which can be used to change the shape of existing analytical mesh models, and generated a mesh model for a new shape by morphing the mesh model for the previous one. Therefore, our method is suitable to use for analysis during the early stage of design because it reduces the amount of time required to generate analytical mesh models. However, this technique is impossible to morph a free-form surface, and to change surface type. Accordingly, we developed the fitting morphing technique for solving the problem. This technique is possible to morph based on specification of the STL data as morphed shapes and indication of fitting nodes and movable nodes. We developed a series of techniques that include three distinct features. (1) Geometric feature recognition algorithm based on region growing method. (2) Geometric feature assignment algorithm on the basis of distance and angle of geometric features between original mesh and morphed shapes. (3) Node fitting and moving algorithm by displacement coefficient calculation using Ferguson curve characteristics. We confirmed that this technique can morphed to free-form surface from cylindrical surface by applying to the piston head model. Furthermore, the technique can morphed to maintain the boundary continuity of the shape by applying to the gear model.
A study of an implementation of the kinesthetic feedback on the game framework applying the haptic

In this paper, we proposed a game framework that applied the haptic device to the game for efficient conveyance of the tactile sensation and it is based on the input and output in the 3D. Also, we propose the kinesthetic feedback on the game framework and implement a realistic game in serious game using this proposed framework. Then we verify the efficiency of ours framework by exam the satisfaction of the user in this game.
Construction of multi-agent mobile robots control system in the problem of persecution with using a modified reinforcement learning method based on neural networks

A method for constructing a multi-agent control system for mobile robots based on training with reinforcement using deep neural networks is considered. Synthesis of the management system is proposed to be carried out with reinforcement training and the modified Actor-Critic method, in which the Actor module is divided into Action Actor and Communication Actor in order to simultaneously manage mobile robots and communicate with partners. Communication is carried out by sending partners at each step a vector of real numbers that are added to the observation vector and affect the behaviour. Functions of Actors and Critic are approximated by deep neural networks. The Critics value function is trained by using the TD-error method and the Actor’s function by using DDPG. The Communication Actor’s neural network is trained through gradients received from partner agents. An environment in which a cooperative multi-agent interaction is present was developed, computer simulation of the application of this method in the control problem of two robots pursuing two goals was carried out.
Anatomy of a versatile page reader

An experimental printed-page reader that is easy to adapt to various languages is described. Changing the target language may involve simultaneous changes in symbol sets, typefaces, sizes of text, page layouts, linguistic contexts, and imaging defects. The strategy has been to isolate the effects of these sources of variation within separate, independent engineering subsystems. In this way, it has been possible to construct, with a minimum of manual effort, classifiers for arbitrary combinations of symbols, typefaces, sizes, and imaging defects. An attempt has been made to rid the algorithms of all language-specific rules, relying instead on automatic learning from examples and generalized table-driven methods. For some tasks it has been feasible to avoid language dependency altogether. Linguistic context can be exploited through data-directed filtering algorithms in a uniform and modular manner, so that preexisting tools developed by computational linguistics can readily be applied. These principles are illustrated by trials on English, Swedish, Tibetan, and special technical texts. >
Commonsense Knowledge Acquisition Using Compositional Relational Semantics

A method for the acquisition of commonsense knowledge based on instantiations of metarules is presented. The metarules refer to some properties and objects that have those properties. Metarules are instantiated by automatically identifying objects that have those properties. In order to increase the applicability of a commonsense property to objects, composition of semantic relations is used. The method has been implemented and tested over WordNet. Results show that a commonsense metarule can produce many knowledge base axioms. Keywords-knowledge acquisition; commonsense knowledge; semantics.
Combination of AI components for biosignal processing application to sleep stage recognition

We present a novel approach to combining artificial intelligence components for biomedical signal processing. The modular algorithm mimics the step-by-step type procedure of a human expert and includes the two assessment steps most important for sleep stage scoring, pattern recognition in electrophysiological signal channels and rule evaluation for classifying the current sequence of patterns. The application of sleep stage scoring is a complex task in medical informatics. The AR-TISANA (artificial intelligence in sleep analysis) algorithm we have developed provides high rates of correspondence with the results produced by human experts. Additional features are the transparent decision-making process and information about the detailed structure of sleep. This has been achieved by utilizing neural networks for pattern recognition and neuro-fuzzy systems for rule evaluation. The AI components chosen to perform these two classification steps were particularly successful due to their individual strengths.
Extensions of Task-based Runtime for High Performance Dense Linear Algebra Applications

On the road to exascale computing, the gap between hardware peak performance and application performance is increasing as system scale, chip density and inherent complexity of modern supercomputers are expanding. Even if we put aside the difficulty to express algorithmic parallelism and to efficiently execute applications at large scale, other open questions remain. The ever-growing scale of modern supercomputers induces a fast decline of the Mean Time To Failure. A generic, low-overhead, resilient extension becomes a desired aptitude for any programming paradigm. This dissertation addresses these two critical issues, designing an efficient unified linear algebra development environment using a task-based runtime, and extending a task-based runtime with fault tolerant capabilities to build a generic framework providing both soft and hard error resilience to task-based programming paradigm. To bridge the gap between hardware peak performance and application performance, a unified programming model is designed to take advantage of a lightweight task-based runtime to manage the resource-specific workload, and to control the dataflow and parallel execution of tasks. Under this unified development, linear algebra tasks are abstracted across different underlying heterogeneous resources, including multicore CPUs, GPUs and Intel Xeon Phi coprocessors. Performance portability is guaranteed and this programming model is adapted to a wide range of accelerators, supporting both shared and distributed-memory environments.
Spectral noise-shaping in integrate-and-fire neural networks

A theory of coordinated neuronal firing events is proposed that allows the low-noise transmission of analog signals through a network of coupled neurons. The inherently high noise levels of a biological neuron can be reduced over a specified frequency range using a network of interconnected neurons. These interconnections cause certain statistical temporal patterns to occur in the summed output of a modest number of neurons, and these temporal patterns can dramatically improve the signal-to-noise ratio over a given frequency range.
Detecting and Combining Programming Patterns

This thesis explores detecting patterns in the most general interface to computers: source and assembly program code. Because writing computer programs correctly is so difficult, there is a large assortment of software engineering techniques devoted to making this process easier and more efficient. Therefore, despite the huge space of possible programs, most programs written by humans will exhibit some of the same patterns. Project Laika detects the data structures used by guest programs using unsupervised Bayesian learning. Using these data structures, it can detect viruses with considerably better accuracy than ClamAV, a leading industry solution. Using high-level features makes Laika considerably more resistant to polymorphic worms, because it requires them to preserve their high-level structure through their polymorphic transformations. Project Macho generates Java programs from a combination of natural language, example inputs, and a database of Java code. Unlike past natural language programming systems, which were basically verbose versions of Visual Basic, Macho allows users to specify (small) programs in high-level language and use examples to fill in the details. We were able to generate satisfactory solutions for basic versions of several core utilities, including ls and grep, even when the natural language inputs were varied substantially.
The Influence of Context-based Complexity in CBC Choice Tasks

The Influence of context-based complexity (measured by similarity and conflict across alternatives) in choice tasks of Choice-based Conjoint (CBC) analysis is an open research issue. This study investigates the influence of context-based complexity by tracing people’s information search via eye tracking. To determine context-based complexity, we measure preferences at the individual level. Results show that an increased context-based complexity leads to an increase in information acquisition and more search by attributes. We also find that people switch earlier to alternative-wise processing in situations of low context-based complexity as compared to situations of high complexity.
Game Design for Project Management Game – A Tool to Educate on Software Project Management

Software project management is a fundamental skill for software engineering practitioners. Due to the practical nature of the subject, it is essential to have hands-on experience with software project management. Risk management board game [1] was defined as an effective way for students to learn project management in a classroom setting. This paper outlines a detailed design reference for an online project management game to serve as an implementation guide and efficiently teach and learn software project management.
A next-generation optical regional access network

We describe an optical regional access network which combines electronic IP routing with intelligent networking functionality of the optical WDM layer. The optical WDM layer provides such networking functions as network logical topology reconfiguration, optical flow switching to offload traffic and bypass IP routers, wavelength routing of signals, protection switching and restoration in the optical domain, and flexible network service provisioning by reconfigurable wavelength connectivity. We discuss key enabling technologies for the WDM layer and describe their limitations. The symbiosis of electronic and optical WDM networking functions also allows support for heterogeneous format traffic and will enable efficient gigabit-per-second user access in next-generation Internet networks.
Systematic Study of Data Science and Analytics Programs

Rapid advances in information technologies have led to the generation of massive data sets, especially in life science and biomedical informatics. These data sets are valuable assets and in great needs to be analyzed. However, there is a shortage of workforce for big data analysis. Education innovations are required to empower students with the skills and technologies for large dataset analysis. Over the last few years, there is a high demand for new programs in data science and analytics (DSA). We has performed a systematic study of the existing DSA programs in the US by checking the detailed information about the degree programs, the program competencies, the curriculum designs, the expected learning outcomes, program sizes, professional careers, and other related information. There are more than 70 DSA programs offered in the US. This study provides guidance on DSA related program development and curriculum design. It also provides the potential trainees in DSA with the current market needs and the required knowledge for their future career.
Increasing uplink capacity via user cooperation diversity

Mobile users' capacity is limited by the fact that within the duration of any given call, they experience severe variations in signal attenuation, thereby necessitating the use of some type of diversity. We are proposing a new form of diversity, whereby diversity gains are achieved via the cooperation of in-cell users. Results show that, even though the inter-user channel is noisy, cooperation leads not only to an increase in capacity but also to a more robust system, where users' achievable rates are less susceptible to channel variations.
Multimodal Optical Coherence Tomography in Visualization of Brain Tissue Structure at Glioblastoma (Experimental Study)

К. S. Yashin, PhD Student, Department of Neurosurgery1; М. М. Karabut, Junior Researcher, Laboratory of the Study of Optical Structure of Biotissue, Institute of Biomedical Technologies2; V.V. Fedoseeva, Junior Researcher, Laboratory Neuromorphology3; A.S. Khalansky, MD, PhD, Leading Researcher, Laboratory Neuromorphology3; L.A. Matveev, PhD, Researcher, Laboratory of Optical Coherence Tomography, Institute of Biomedical Technologies2; Researcher, Department of Geophysical Studies4; V.V. Elagin, Researcher, Laboratory of High Resolution Microscopy and Genetic Technologies, Institute of Biomedical Technologies2; S.S. Kuznetsov, MD, DSc, Professor, Department of Pathological Anatomy2; Е. B. Kiseleva, PhD, Junior Researcher, Laboratory for the Study of Optical Structure of Biotussie, Institute of Biomedical Technologies2; L.Ya. Kravets, MD, DSc, Professor, Chief Researcher, Group Microneurosurgery1; I. А. Medyanik, MD, PhD, Senior Researcher, Group Microneurosurgery1; N.D. Gladkova, MD, DSc, Professor, Vice Director for Science, Institute of Biomedical Technologies2
First Steps in Computer Science: Sequential or Parallel?

In educating computer scientists, typically the first steps deal with sequential algorithms and programs. This has unfortunate consequences in that the students mind becomes geared to a sequential way of thinking. Parallel programming on the other hand is very hard so it is not practical to introduce these concepts at an early stage. Our approach is to define a simple flexible language (SFL) which does not obligate sequential or parallel execution but enables these execution methods. This language has several faces. It can be viewed as a programming language, capable of being executed in a variety of ways, sequential or parallel, with identical end results. It can also be viewed as a command language for the user. Also, hardware block diagrams can be derived from "programs" in this language. Mathematical proofs using specifications and computational induction are clear and straightforward. It is a good choice for teaching computer science, and computer systems engineering concepts in view of its ability to describe both software and hardware aspects. The "core" language is based on programs (procedures or functions) with "IN", "OUT" but no "INOUT" parameters, and conditional statements. As in mathematics, variables, parameters, etc., receive a value once only. Blocks, loops, case statements etc. (including nested forms) are not part of the "core" language but viewed as abbreviations for certain compound forms in the "core" language.
Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method

Abstract. Traditional trial-and-error tuning of uncertain parameters in global atmospheric general circulation models (GCMs) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well-calibrated model, based on a predefined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.
Dynamics of online discussions about politics: a function of structural network properties, mass media attention or emotional utterances?

Easily accessible online discussion forums have greatly lowered the hurdles for citizens to join political debates. In this paper we employ automated content analysis of online discussions which oers as a new opportunity to assess the agenda of online discussions unobtrusively. We focus on the twofold Research Question how previous communication content and previous network structure aect (1) the current communication content and (2) the current network structure. By analysing the dynamics of the online discussions together with the related political content and emotional utterances, we obtain a new perspective towards the connection of online behaviour in the web and the traditional political science research.
Outline to the advantages and disadvantages of social media

Social media is the new technology of computer that enhances the sharing of thoughts and information in order to building virtual networks and communities. Social media is based on internet and facilitate easy electronic communication about personal information and contents, such as photos and videos. Users can engage with social media by using computer, smart phone or tablet via internet based software or web application. Social media is a tool that initially used by people to interact with family or friends but later it was adopted by businesses that take advantage of a new popular communication method to reach out to customers. The strength of social media is related to share and connect information with everybody all over the world who is using social media. This article argues on social media”s advantages and disadvantages in present era
Asynchronous distributed algorithms for static and dynamic directed rooted graphs

The paper provides a review of distributed graph algorithms research conducted by authors. We consider an asynchronous distributed system model represented by a strongly connected directed rooted graph with bounded edge capacity (in a sense that only a bounded number of messages can be sent through an edge in a given time interval). A graph can be static or dynamic, i.e. changing. For a static graph we propose a spanning (in- and out-) tree construction algorithm of time complexity O ( n / k + d ), requiring O ( n d log  + ) message size and the same size of memory of each computing agent located in graph vertex, where n is the number of vertices of the graph, k is the capacity of an edge, d is the maximum length of simple path in the graph,  + is the maximum outdegree of the vertices. The spanning trees constructed can be used in distributed computation of a function of the multiset of values assigned to graph vertices in a time not greater than 3 d . In a dynamic graph we suppose that k = 1 and an edge can appear, disappear, or change its end. We propose a dynamic graph monitoring algorithm than delivers information on any change to the root of the graph in O (n) or O ( d ) after the changes are stopped. We also propose graph exploration and marking algorithm with time complexity O ( n ). The marking provided by it is used in distributed computation of a function of the multiset of values assigned to dynamic graph vertices, which can be performed in time O ( n 2 ) with messages of size O ( log n ) or in time O ( n ) with messages of size O ( n log n ).