Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of Basil in Aspects of Digital Information Retrieval and Data Mining

This research aims to propose a novel process for classifying two types of basil, which have different species but share common genus, based on main characteristics of their leaves. One type of the basil proposed in this research is called holy basil and another type is called sweet basil. Considering their leaves, both types have similar features in various points of view, for example, their color shade, size or dimensions, scribbled patterns of the leaf edge, etc. They have a lot of similar features because they share the same genus. However, their similar features are the challenged characteristics this research aims to explore and also to demonstrate how well our proposed method work.
CONCERNING THE EMERGENCE OF TAG-MEDIATED LOOKAHEAD

This paper, after a general introduction to the area, discusses the architecture and learning algorithms that permit automatic parallel, distributed lookahead to emerge in classifier systems. Simple additions to a "standard" classifier system suffice, principally a new register called the virtual strength register, and a provision to use the bucket brigade credit assignment algorithm in "virtual" mode to modify values in this register. With these additions, current actions are decided on the basis of the expected values associated with the "lookahead cones" of possible alternatives.
On treadmill automation and physiological control systems

This thesis deals with a new approach to Treadmill Automation and Physiological Control Systems that will serve as a platform for enhanced rehabilitation therapy, and will open up and facilitate a major new area of research in physiological control systems. On treadmill automation, the investigation focussed on the feasibility of a low-cost non- contact position control system with the aim of maintaining a subject at a prescribed position during a treadmill training exercise in order to ensure safety at all times. The development of an automatic speed control for the treadmill was first carried out using an identified model for the treadmill motor dynamics (response from speed command to actual belt speed). Subsequently, the positioning control system was designed and tested. The fundamental limitations to treadmill automation performance include low bandwidth and long time delays of the hardware (treadmill and ultrasonic sensor). Interactions between natural human control and the position controller, and spontaneous variability of human movement due to body oscillations and swaying, are discussed. On physiological control systems, we considered two key variables - the heart rate and oxygen uptake. The purpose of this is to develop a means of controlling exercise intensity during treadmill exercise. On the control of heart rate, a model of heart rate response to changes in speed was obtained via the system identification method. Thereafter, a heart rate controller was developed, tested, and evaluated on three healthy subjects during treadmill exercise. The results of the experiments demonstrated that the developed heart rate controller is superior to the in-built treadmill heart rate controller. A novel system is developed for the control of oxygen uptake, and is thus presented. The system proved that it is possible to control exercise intensity using the level of oxygen uptake during moderate exercise. The results of this work demonstrate that a linear first order model is able to sufficiently capture the complex dynamics of oxygen uptake during treadmill exercise. A controller was developed using this model and tested on healthy subjects. Six healthy active subjects participated completely in the tests. The results of the tests with these subjects establish the robustness of the controllers to inter-subject variability. Furthermore, the controller was refined and tuned to improve the performance of the control signal (treadmill speed). This was achieved by designing a controller that incorporated a pre-filter in the system. The results of the experiments using this set-up with the same set of healthy subjects show a significant improvement on the control signal (smoother treadmill speed).
Multilevel Interference-aware Scheduling On Modern Gpus

Multilevel Interference-aware Scheduling on Modern GPUs by Leiming Yu Doctor of Philosophy in Electrical and Computer Engineering Northeastern University, April 2019 Dr. David Kaeli, Advisor Driven by their impressive parallel processing capabilities, Graphics Processing Units (GPUs) have become the accelerator of choice for high-performance computing. Many data-parallel applications have enjoyed significant speedups after being re-engineered to leverage the thousands of cores on the GPU. For instance, training a complex deep neural network model on a GPU can be done within hours, versus the weeks of time it might take on more traditional CPUs. While most deep neural networks are hungry for more and more computing resources, a number of application kernels only use a fraction of the available resources. To better utilize the massive resources on the GPU, device vendors have started to support Concurrent Kernel Execution (CKE). The Hyper-Q technology from NVIDIA allows up to 32 data-independent kernels to run concurrently, leveraging parallel hardware work queues. These hardware work queues can execute concurrent kernels from either a single GPU context or multiple GPU contexts. With support for concurrent kernel execution, multiple applications can be co-located and co-scheduled on the same GPU, significantly improving resource utilization. The application throughput provided by CKE is subject to a number of factors, including the kernel configuration attributes, the dynamic behavior of each kernel (e.g., compute-intensive vs. memory-intensive), the kernel launch order and inter-kernel dependencies, etc. Launching more concurrent kernels does not always achieve better performance. It is challenging to predict the potential performance benefits of using CKE. Typically, a developer will have to compile and run their program many times to obtain the best performance. In addition, as multiple GPU applications co-scheduled on the device, the contentions for shared resources, such as memory bandwidth and computational pipelines, result in interference which can often impact the CKE performance. In this thesis, we seek to optimize the execution efficiency for GPU workloads at a kernel granularity, as well as at an application granularity. We focus on providing a performance tuning
VRSTC: Occlusion-Free Video Person Re-Identification

Video person re-identification (re-ID) plays an important role in surveillance video analysis. However, the performance of video re-ID degenerates severely under partial occlusion. In this paper, we propose a novel network, called Spatio-Temporal Completion network (STCnet), to explicitly handle partial occlusion problem. Different from most previous works that discard the occluded frames, STCnet can recover the appearance of the occluded parts. For one thing, the spatial structure of a pedestrian frame can be used to predict the occluded body parts from the unoccluded body parts of this frame. For another, the temporal patterns of pedestrian sequence provide important clues to generate the contents of occluded parts. With the spatio-temporal information, STCnet can recover the appearance for the occluded parts, which could be leveraged with those unoccluded parts for more accurate video re-ID. By combining a re-ID network with STCnet, a video re-ID framework robust to partial occlusion (VRSTC) is proposed. Experiments on three challenging video re-ID databases demonstrate that the proposed approach outperforms the state-of-the-arts.
Hierarchical transmission of video signal using multi-code DS/SS modulation based on DCT coefficients

We propose a hierarchical transmission of video signal using multi-code DS/SS modulation. Assigning an appropriate distribution of transmission energy to DCT coefficients, it is possible to control the picture quality depending on the channel quality. We consider a hierarchical structure based on the variance of DCT coefficient and transmit each coefficient using multi-code DS/SS modulation. The analysis is carried out to show the effectiveness of our scheme using 2-D Gauss-Markov random field, which represents image data. It is shown that by an appropriate energy assignment to each stratum (a branch of multi-code DS/SS), it is possible to maintain picture quality even in bad channel quality.
Subscription Aggregation for Scalability and Efficiency in XPath/XML-Based Publish/Subscribe Systems

Introduction Content-based publish/subscribe systems provide a new communication paradigm to deliver relevant messages to various participants according to their expressed interests. In these systems, messages from senders (sources) are routed to receivers (ultimate destinations) based on their content, rather than a fixed destination address. Receivers describe their interests in receiving a particular category of messages by registering subscriptions, which are predicates on message content, in the system. When senders generate and inject messages in the network, the network routers (brokers) evaluate these messages against the registered subscriptions and route matched messages to their receivers. Content-based subscriptions provide receivers with a high degree of control over the type of information they wish to receive and hence limit the amount of network traffic. The challenging task of matching messages with many subscriptions is left to the network infrastructure, which is typically an overly network of distributed applicationlevel routers.
Text Realization for Dialog

We present a natural language realization component, called YAG, that is suitable for intelligent tutoring systems that use dialog. Dialog imposes unique requirements on a generation component, namely: dialog systems must interact in real-time; they must be capable of producing fragmentary output; and they may be re-deployed in a number of different domains. Our approach to real-time natural language realization combines a declarative, template-based approach for the representation of text structure with knowledge-based methods for representing semantic content. Possible text structures are defined in a declarative language that is easy to understand, maintain, and reuse. A dialog system can use YAG to realize text structures by specifying a template and content from its knowledge base. Content can be specified in one of two ways: (1) as a sequence of propositions along with some control features; or (2) a set of feature-value pairs. YAG’s template realization algorithm realizes text without any search (in contrast to systems that must find rules that unify with a feature structure).
Estimation probabiliste de la complexité de circuits VLSI pour le traitement du signal

Afin de raccourcir le cycle de conception d’un système VLSI, il est nécessaire de pouvoir estimer les performances (temps, coût, consommation) de différentes solutions architecturales et algorithmiques au niveau d’abstraction le plus élevé. Nous présentons une nouvelle approche d’estimation dynamique de la complexité matérielle, appliquée aux architectures pipelines sous contrainte de temps. Elle se situe au niveau algorithmique, tout en restant indépendante de la méthode de synthèse qui sera choisie ultérieurement. Nous employons une méthode probabiliste prenant en compte réellement les contraintes entre opérations, dans le but de guider le choix des transformations et des algorithmes impliqués dans la spécification. Enfin, nous présentons des résultats d’estimation, et les comparons avec des résultats de synthèse architecturale. ABSTRACT
Analog Circuit Fault Prognostic Approach using Optimized RVM

In this paper, a novel analog circuit fault prognostic approach is presented. The Pearson product-moment correlation coefficient (PPMCC) is used to calculate the circuit's health degree on the basis of the extracted output voltages. The relevance vector machine (RVM) algorithm with kernel function optimized by the quantum-behaved particle swarm optimization (QPSO) algorithm is utilized to estimate the remaining useful performance (RUP). A leapfrog filter is used in a fault prognostic experiment to verify the prognostic approach, and the experimental results reveal that the presented approach can forecast the analog circuit's RUP precisely.
Path-following control for power generating kites using economic model predictive control approach

Exploiting high altitude wind energy using power kites is an emerging topic in the field of renewable energy. The claimed advantages of power kites over traditional wind power technologies are the lower construction costs, less land occupation and more importantly, the possibility of efficiently harvesting wind energy at high altitudes, where more dense and steady wind power exists. One of the most challenging issues to bring the power kite concept to real industrialization is the controller design. While traditional wind turbines can be inherently stabilized, the airborne nature of kites causes a strong instability of the systems. This thesis aims to develop a novel economic model predictive path-following control (EMPFC) framework to tackle the path-following control of power kites, as well as provide insightful stability analysis of the proposed control scheme. Chapter 3 is focused on the stability analysis of EMPFC. We proceed with a sampled-data EMPC scheme for set-point stabilization problems. An extended definition of dissipativity is introduced for continuous-time systems, followed by giving
Ischaemic stroke etiological classification system: the agreement analysis of CISS, SPARKLE and TOAST

Background and purpose The ideal stroke classification system needs to have validity, high reliability and applicability among different stroke research settings. The Chinese Ischemic Stroke Subclassification (CISS) and the Subtypes of Ischemic Stroke Classification System (SPARKLE) have emerged recently but have not been tested using agreement analysis. As a result, the objective of this study is to investigate the level of agreement among stroke subtype classifications using CISS, SPARKLE and Trial of Org 10172 in Acute Stroke Treatment (TOAST). We also analyse the inter-rater reliability of CISS. Methods The data include 623 inpatients who have had an ischaemic stroke, accrued from Beijing Tiantan Hospital between 1 October 2015 and 19 April 2016. According to the diagnostic standards of the three subtype classification systems, 299 inpatients who satisfied the requirements of our study were independently classified with etiological subtypes, and we compared the three subclassifications. Results There was substantial overall agreement among the three classification systems: CISS versus SPARKLE (kappa value=0.684, p<0.001), CISS versus TOAST (kappa value=0.615, p<0.001) and SPARKLE versus TOAST (kappa value=0.675, p<0.001). The inter-rater reliability of CISS was excellent (kappa value=0.857, p<0.001). Furthermore, among the three subtype classification systems, the variance analysis results of the etiological subtypes were not uniform. Conclusion There were generally substantial agreements among three ischaemic stroke etiological classification systems. CISS is a valid and reliable classification system, with which different stroke research centres can apply and compare data.
WORKABLE MONITORING SYSTEM BASED ON SPACEBORNE SAR IMAGES FOR MINING AREAS - STINGS DEVELOPMENT PROJECT

Abstract. Nowadays monitoring of mining areas, e.g., regarding dam stability, has become increasingly important with rising awareness of safety and environmental protection. An appropriate monitoring scheme is necessitated to legally activate, reactivate, or terminate mining operations. Usually such monitoring relies on in situ surveys, which are unrealistic to cover an extensive mining area. Alternatively, remote sensing based on spaceborne data offers efficient and cost-effective solutions for regular surveillance of large areas. Spaceborne SAR sensors provide images captured rapidly over vast areas at fine spatiotemporal resolution. These sensors are characterized by weather independent and day-and-night vision, which guarantees intensive image series without cloud occlusion. Using multi-temporal SAR images, advanced DInSAR such as PSI and SBAS is a mature technique to evaluate surface deformation at best millimetre level. This technique has been commercialized as a standard service in many Geoinformation companies. Nevertheless, experts from other fields like mining engineers often doubt the information about movement derived from DInSAR. Our duty in industry is to solve these doubts and tailor our techniques for various applications. With the support of STINGS project, we have developed an initial prototype of our monitoring system. The final goal is to launch an interactive GIS-based platform as an early warning system to the public. In this paper, we demonstrate our initial test result using Sentinel-1 images at a mining site in Chile. We also propose the strategies to solve the problems in real applications and discuss how to improve the overall quality.
An Agent-Like Architecture for Autonomous Robot Motion in Unknown Environment

This paper proposes a general methodology for solving specific categories of robotic problems. This methodology decomposes the problem to simpler ones, every one of which is faced by an appropriate agent-like subsystem. The overall output is produced by merging the outputs of these agents taking into account its "degree of responsibility". This way, a complex function's universe of discourse is broken down to "domain of responsibility" of the respective agents. This general methodology is then applied to solve the problem of path planning in unknown environment. To this end, every part of the system is specified and analyzed, and appropriate agents are defined. Some experimental results obtained through simulation are finally given.
File Service Architecture for Heterogeneous Clients

Abstract- Internet usage has gone far beyond expectation and continues to grow. Services on the Internet are also more innovative than ever before. Many more different system architectures are currently used to obtain the same pool of services. Now, the trend is to make uniform architecture to provide services. SOA is a service that aims to integrate and be interoperable with various implementation languages. SOA is a concept of distributed computing, the best example of which is file sharing. Remote sharing remains an issue. Although many services are available to provide remote file sharing, they have problems in accessing file with ease, user friendliness, a generic communication protocol, a secured method to communicate during transferring and traversing the secured firewalls. This paper focuses on how easily the file can be shared with the generic communication protocol and a secured method by bypassing the issues of NAT. To provide such service, we do not have any option other than XML, which enables the system to service the end users, who can choose to download or upload files using a browser or an application client. Thus, we have built a middleware for file sharing. The middleware and the application client are equipped with the NAT facilities to traverse the firewalls. Our proposed middleware can service the users of both systems (Browser and Application client). The performance of the proposed system for remote sharing suggests using XML as an interface to implement the SOA-based applications.
DOMUS TUTOR: A CBR TUTORING AGENT FOR STUDENT SUPPORT

The changes introduced by the Bologna process in the educational paradigm, moving from a lecturer centered paradigm to a learner centered paradigm, involves a more supported learning process based on learning outcomes and the adoption of new pedagogical methodologies. In this paper we present our strategy of integration of tutoring agents in learning environments, using the features of intelligent tutoring systems adapted to collaborative environments. The Domus Tutor agent is the face of the adaptive learning environment that integrates Learning Design, groupware and collaborative work technologies. The adaptation of the system to the learner profile is based on case-based reasoning methodology; witch is one of the major reasoning paradigms in artificial intelligence.
The Pragmatic Dimension of Text

This article looks at how the pragmatic aspects of language have led to the birth of a separate discipline, pragmatics, and also, to the critical of some branches of linguistics, as discourse analysis and text linguistics. Being outlined initially through opposition with analytic philosophy, the pragmatic aspects are studied within the philosophy of natural language. Notions as speech act, non-natural meaning or conversational implicature are rapidly integrated in linguistic research, that tries, through these concepts, to explain the non-linguistic elements of the transmission of messages within the linguistic framework. It is the case of integrated pragmatics in France. The interference of linguistics with the sciences of communication led to the emergence of some disciplines that integrate the pragmatic facts, as discourse analysis and text linguistics. The breaking with linguistics occurs with cognitive pragmatics, when the interpretation of transmission of messages as a process of coding and decoding becomes unfitted to explain the non-linguistic facts that do not belong to language. In this context it is developed a critique to the principles of the disciplines that postulate the existence of a some objects, beyond the sentence, as discourse or text.
Dynamic Asset Liability Management Under Model Uncertainty

We analyze a dynamic Asset Liability Management problem with model uncertainty in a complete market. The fund manager acts in the best interest of the pension holders by maximizing the expected utility derived from the terminal funding ratio. We solve the robust multi-period Asset Liability Management problem in closed form, and identify two constituents of the optimal portfolio: the myopic demand, and the liability hedge demand. We find that even though the investment opportunity set is stochastic, the investor does not have intertemporal hedging demand. We also find that model uncertainty induces a more conservative investment policy regardless of the risk attitude of the fund manager, i.e., a robust investment strategy corresponds to risk exposures which provide a much stronger liability hedge.
Virtualization, The Great Thing and Issues in Cloud Computing

Now within IT industry virtualization is used between today’s interfaces to abstract different layers from each other when one layer is updated, the others one needs update too. When one layer is defined, the upper and lower layer cannot be used for anything else. One of the Top must’s rapidly evolving and widely deployed technologies are server virtualization. Many organizations are already the cost savings from implementing virtualized servers and system administrators love the ease of deployment and management for virtualized systems. In this paper we discuss about some major threats of virtualization and cloud computing and discussed about how Google cloud works and a key management algorithm for security purposes in cloud computing.
Attention Network Test fMRI data for participants with Parkinson’s disease and healthy elderly

Here, we present unprocessed and preprocessed Attention Network Test data from 25 adults with Parkinson’s disease and 21 healthy adults, along with the associated defaced structural scans. The preprocessed data has been processed with a provided Analysis of Functional NeuroImages afni_proc. py script and includes structural scans that were skull-stripped before defacing. All acquired demographic and neuropsychological data are included.