Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonretinotopic Particle Filter for Visual Tracking

Visual tracking is the problem of using visual sensor measurements to determine location and path of target object. One of big challenges for visual tracking is full occlusion. When full occlusions are present, image data alone can be unreliable, and is not sufficient to detect the target object. The developed tracking algorithm is based on bootstrap particle filter and using color feature target. Furthermore the algorithm is modified using nonretinotopic concept, inspired from the way of human visual cortex handles occlusion by constructing nonretinotopic layers. We interpreted the concept by using past tracking memory about motion dynamics rather than current measurement when quality level of tracking reliability below a threshold. Using experiments, we found (i) the performance of the object tracking algorithm in handling occlusion can be improved using nonretinotopic concept, (ii) dynamic model is crucial for object tracking, especially when the target object experienced occlusion and maneuver motions, (iii) the dependency of the tracker performance on the accuracy of tracking quality threshold when facing illumination challenge. Preliminary experimental results are provided.
Energy efficient strategy for cloud based big data

Many sectors are taking interest in big data due to the huge potential associated with big data. Big data demands large computing power and distributed storage to handle the data problems, to which cloud can provides the elastic on-demand compute power and storage to big data. Cloud data center can acts as infrastructure layer to big data. As the volume of big data is increasing with high speed, the cloud data centers are also scaling up. And they consume huge amount of energy to provide infrastructure layer to big data continuously. The aim of this paper is not to extensively cover big data, but to efficiently utilize cloud data centers for big data using virtualization, so that energy can be saved.
Quantitative analysis of maintenance processes at the micro level

The current state of mining software repository tools and technologies has provided opportunities for quantitative studies in software engineering. In this dissertation, these mined data are used to reconstruct the micro processes performed daily by developers (referred to as Micro Process Analysis (MPA)). We investigated how MPA complements the current software process improvement (SPI) initiatives. Unlike typical macro level SPI models, we demonstrated the application of MPA at the maintenance phase. Specifically, we targeted micro processes associated with bug & patch resolution and peer review. For bug and patch processes, we quantitatively re-established Lehman’s law between maintenance effort and code complexity. With three open source software (OSS) projects and a closed experiment, our proposed metrics proved this relationship to be statistically significant. For peer review processes, we developed two models to assist OSS members identifying their social standing and career trajectory. SPI is achieved by more efficient and higher quality reviews, through the identification of expertise. Providing a career trajectory model encourages member participation, thus it promotes the sustainability of peer reviews within a project. Our techniques and approaches validated the application of MPA for software maintenance. We concluded that micro processes could serve as supplements to macro processes, therefore providing an ‘added dimension’ to SPI.
What is Special about Bethlehem, Pennsylvania? Identifying Unusual Facts about DBpedia Entities

Most Linked Data browsers list all facts about an entity in an equal manner. In this paper, we present a prototype for identifying unexpected facts about entities, i.e., those facts that deviate from the expectations. To that end, we use an attribute-wise method for anomaly detection, which is also capable of providing qualitative explanations for the anomalies found. By comparing an entity at hand to a reference set of similar entities, we can provide information on how the entity at hand differs from the typical patterns found for similar entities, and display those unexpected facts together with a short explanation.
Towards a hybrid software architecture and multi-agent approach for autonomous robot software

To support robust plan execution of autonomous robots in dynamic environments, autonomous robot software should include adaptive and reactive capabilities to cope with the dynamics and uncertainties of the evolving states of real-world environments. However, conventional software architectures such as sense-model-plan-act and behaviour-based paradigms are inadequate for meeting the requirements. A lack of sensing during acting in the sense-model-plan-act paradigm makes the software slow to react to run-time contingencies, whereas the behaviour-based architectures typically fall short in planning of long-range steps and making optimized plan adaptations. This article proposes a hybrid software architecture that maintains both adaptivity and reactivity of robot behaviours in dynamic environments. To implement this architecture, we further present the multi-agent development framework known as AutoRobot, which views the robot software as a multi-agent system in which diverse agent roles collaborate to achieve software functionalities. To demonstrate the applicability and validity of our concrete framework and software architecture, we conduct an experiment to implement a typical case, for example, a robot that autonomously picks up and drops off dishes for remote guests, which requires the robot to plan and navigate in a highly dynamic environment and can adapt its behaviours to unexpected situations.
Multi-domain VNF Mapping

5G technologies are taking benefit of the Network Function Virtualization to achieve more flexible deployments. This new paradigm allows the resource sharing between operators in federated environments thanks to the decomposition of services into virtual network functions connected together composing a “service function chain”. This work proposes algorithms to solve the placement of such chains in federated multi-domain scenarios satisfying imposed restrictions in terms of resource sharing. Algorithms run on top of an implemented simulator for federated scenarios where multiple operators are involved. Two of our proposed solutions reach O(N ) running times in certain scenarios. Our results also show that we achieve acceptance ratios very similar to those obtained using a tabu meta-heuristic implementation.
Constructing automatic domain-specific sentiment lexicon using KNN search via terms discrimination vectors

ABSTRACT Web textual data content is a viable source for decision-makers’ knowledge, so are text analytic applications. Sentiment analysis (SA) is one of text mining fields, in which text is analyzed to recognize text writer implied opinion. In this paper, a new approach had been presented for automatic Arabic language sentiment lexicon constructing. Popular KNN search algorithm is utilized for this objective. Cosine distance between seeds terms and corpus terms is employed in KNN search query. Generated lexicon terms are launched from sentiment seeds and seeds terms are augmented via Arabic-specific NLP-based algorithm, which is helped to enhance seeds terms selection process. Term discrimination vector (TDV) is the main part of KNN query inputs TDV components are computed for each corpus term and it is constituted by four term weight techniques. According to the experimental results, TDV accomplished better results than TF-IDF traditional method with lower computation cost. Also, constructed lexicons outperformed premade lexicons accuracy results.
Making their own landscape: smartphones and student designed language learning environments

ABSTRACT This study examined how Korean university students, majoring in English interpretation and translation, used their smartphones to meet their language learning needs. We surveyed 241 students on their smartphone use in four conditions: overall use, use related to coursework, use in English related to coursework, and use in English not related to coursework; as well as while engaging in four functional categories: communicating with others, accessing information, Creating and collecting content, and Listening, viewing, and reading. The survey results showed that a majority of participants used English for a variety of activities at least once a day: over 50% used English to communicate with others and to access information daily, over 40% listened to, viewed or read material in English daily, and over 30% created or collected content in English daily. These activities were nearly evenly distributed between activities related to the participants’ coursework and more informal activities. We interviewed 28 of the 241 participants to investigate how they used their smartphones to meet their language learning goals. We found that the interviewed participants used their smartphones to create language learning environments, and that these participants took three general approaches to creating their individual language learning environments.
Antioxidant Mediated Defense Role of the Dietary Phytochemical Ferulic Acid, against Carbon Tetrachloride (CCl4) Induced Toxic Hepatitis

Ferulic acid (FA) is a dietary phytochemical formed during the metabolism of phenylalanine and tyrosine.Disruption of mitochondria and free radical mediated tissue injury has been reported during liver toxicity induced by carbon tetrachloride, potent hepatotoxic agent. The present study was designed to investigate the antioxidant protective effect of ferulic acid on hepatocytes, liver function and mitochondrial electron respiratory chain enzymes in hepatotoxic rats. Rats were classified into four groups : normal control group, hepatotoxic group subcutaneously injected with CCl4 once weekly for 4 weeks; thirdgroup: hepatotoxic group daily administered orally FA with a dose of 20 mg/Kg b.wt. , and forth group: hepatotoxic group daily administered orally FA with a dose of 80 mg/Kg b.wt. The model of CCl 4-injected hepatocellular rats elicited declines in liver antioxidant enzyme activities; glutathione peroxidase, superoxide dismutase , Catalase in association of a reduction in reduced glutathione, serum total protein with concomitant
Design methods and considerations of supply modulated switched RF power amplifiers

This thesis studies the design methods and properties of supply-modulated switch-mode radio frequency power amplifiers. Besides simulation based studies and theory review, two amplifiers were designed: a discrete MESFET class E amplifier (0.5 W at 1 GHz), and an integrated pHEMT class E-1 amplifier (2.0 W at 1.6 GHz) with an on-chip resonator. The existing design methods of the resonant output network of switching amplifiers were reviewed and some extensions on the handling of nonlinear capacitances were proposed. The effects of varying supply voltage were studied and suggestions were given to minimize Vdd / AM and Vdd / PM distortion in supply modulated amplifiers. The implementation of the bias feed was also discussed resulting in proposing a combination of a short transmission line and a small inductor, which provides both fast supply modulation and little effect on harmonic impedances. The main contributions are related to the study of the input impedance of a class E power amplifier, where the effects of supply dependent input impedance and timing skew generated by injected harmonic distortion were analyzed. The stabilization of the amplifier was also discussed. Based on the findings, a push-pull class E amplifier with extra cross-coupled feedback capacitors and second harmonic traps at the gates appears to be a very good starting point for a further study.
Gaussian Profile based Vehicular Mobility Modeling

The driving behavior of a person is influenced by a multitude of local and global factors. These include nature of the driver, distance between the source and destination, condition of the road selected by the driver, traffic conditions, time constraints, lane changing, overtaking actions, safe distance, reaction time etc. are vital for modeling the mobility of a vehicle and are largely determined by the driving behavior. In this paper, a profile based model has been proposed to define the mobility behavior of the vehicles. Different types of influence on the mobility have been lumped and the profile of the motion of vehicles has been modeled as a Gaussian profile. In this model, a driver, first, selects the source, destination and his path and then, selects the parameters of the profile according to his/her presumption. Simulation and results show that profile based model is able to generate realistic and smooth mobility patterns.
Resident space object tracking using an interacting multiple model mixing scheme

A multiple model estimation scheme is proposed to enhance the robustness of a resident space object (RSO) tracker subject to its maneuverability uncertainties (unplanned or unknown jet firing activities) and other system variations. The concept is based on the Interacting Multiple Model (IMM) estimation scheme. Within the IMM framework, two Extended Kalman Filter (EKF) models: (i) a 6 State (Position and Velocity of a constant orbiting RSO) EKF and (ii) a 9 state (Position, Velocity, and Acceleration of a maneuvering RSO) EKF are designed and implemented to achieve RSO maneuvering detection and enhanced tracking accuracy. The IMM estimation scheme is capable of providing enhanced state vector estimation accuracy and consistent prediction of the RSO maneuvering status, thus offering an attractive design feature for future Space Situational Awareness (SSA) missions. The design concept is illustrated using the Matlab/Based Simulation testing environment.
The Modeling of Institutional Modernization by Means of Fractal Geometry

The article presents the General Fractal Model of Transit (GFMT) designed to apply modeling in the sociopolitical disciplines. For the purposes of conducting computer experiments with the model, a “Modernofractal 5.1” program was created. Constructing procedures for algebraic fractals enables to simulate social evolutions in the phase space, mainly their starting and end points. The GFMT formula iteration simulated interaction of a series of abstract governing factors, which may be treated as the most significant for different social systems under the stage of transformation. The GFMT has the potential “tuned,” made more specific, for studying various social phenomena. The authors examined the heuristic opportunities of this analysis tool as applied to the fields of historical and political science research (the object of study was rapid modernization of institutions with informal “game rules”). The article contains descriptions of several GFMT computer experiments demonstrating the analysis tools and interpretability of outcome data.
Birefringence Analysis of Transparent Plastic Plate Based on Moldflow

For transparent amorphous Polymers, birefringence can be treated as a method to characteristic residual stress and frozen orientation and also a character of microstructure of Products. Birefringence represents the anisotropy of Products, so it is one of hot pot in the field of polymer processing to study the ingredient and variety distribution of birefringence in injection molded products. In this paper a transparent plastic plate is studied and simulated in different mold temperatures. The results show that the mold temperature can be increased to improve the liquidity of the fluid. When the mold temperature is between 70°C and 120°C, the value of refractive index decrease before increase, the minimum appeared at 90°C. The maximum value of retardation and phase shift for light occurs near the gate.
Ten Years of the MIA-QSAR Strategy: Historical Development and Applications

It has been a decade since the introduction of the MIA-QSAR (acronym of Multivariate Image Analysis applied to Quantitative Structure Activity Relationship) method. While many successes have been achieved over the years, the path for the progressive development of this method has been far from straight; several hurdles have been encountered and corresponding solutions devised, some immediately and others gradually. This report offers a comprehensive treatise of the most relevant stages in the historical development of the MIA-QSAR strategy. New challenges and future prospects are also discussed. KeYwoRDS 2D-Discrete Fourier Transform, Ball & Stick, Chemoface, MIA-QSAR, Molecular Modeling, Multivariate Image, Van der Waals radii, Wireframes
Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI

Degree centrality (DC) and local functional connectivity density (lFCD) are statistics calculated from brain connectivity graphs that measure how important a brain region is to the graph. DC (a.k.a. global functional connectivity density) is calculated as the number of connections a region has with the rest of the brain (binary DC), or the sum of weights for those connections (weighted DC). lFCD was developed to be a surrogate measure of DC that is faster to calculate by restricting its computation to regions that are spatially adjacent. Although both of these measures are popular for investigating inter-individual variation in brain connectivity, efficient neuroimaging tools for computing them are scarce. The goal of this Brainhack project was to contribute optimized implementations of these algorithms to the widely used, open source, AFNI software package.
Tree Physiology Optimization in Constrained Optimization Problem

Metaheuristic algorithms are proven to be more effective on finding global optimum in numerous problems including the constrained optimization area. The algorithms have the capacity to prevail over many deficiencies in conventional algorithms. Besides of good quality of performance, some metaheuristic algorithms have limitations that may deteriorate by certain degree of difficulties especially in real-world application. Most of the real-world problems consist of constrained problem that is significantly important in modern engineering design and must be considered in order to perform any optimization task. Therefore, it is essential to compare the performance of the algorithm in diverse level of difficulties in constrained region. This paper introduces Tree Physiology Optimization (TPO) algorithm for solving constrained optimization problem and compares the performance with other existing metaheuristic algorithms. The constrained problems that are included in the comparison are three engineering design and nonlinear mathematic problems. The difficulties of each proposed problem are the function complexity, number of constraints, and dimension of variables. The performance measure of each algorithm is the statistical results of finding the global optimum and the convergence towards global optimum.
Survey of Network Security Situation Awareness

: With the increasing significance of network space security continues, the research and application of network security situation awareness has gained more and more attention. In this paper, it described the development as well as the concept of network security situation awareness, then analyzed the structure model of network security situation awareness, lastly, according to the structure model from the situation factors based on multi-classifier fusion extraction, network security situation assessment as well as the prediction of network security situation, it analyzed three levels of the key technology of network security situation awareness.
Exploring timeliness for accurate recommendation in location-based social networks

An individual's location history in the real world implies his or her interests and behaviors. This paper analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.
Architecture-Based Multivariate Anomaly Detection for Software Systems

With growing software systems appropriate monitoring concepts become more and more important. For large-scale software systems this becomes a problem, simply because of the overwhelming size and complexity these systems can reach. Performance data is of special interest in this context, since it is related to factors such as the confidence and satisfaction customers have in the offered services. In many cases, performance issues manifest themselves in so-called anomalies, that do not fit to the normal behavior of the software system. OPADx, an approach and a corresponding implementation, that is developed in the process of this thesis, is able to recognize such anomalies. In contrast to often used offline analysis approaches, the OPADx approach is able to process performance data online, which makes fast reactions to the anomalies and the correlated issues possible. OPADx is based on an approach called OPAD, that was presented by Bielefeld in 2012. An analysis of OPAD reveals weaknesses of the approach, whenever anomalies are occurring over a long time period. Based on this analysis, extensions are developed and implemented to improve the approach. With the help of the implementation, the extended approach, then named OPADx, is evaluated on past and actual data of the social network Xing. Xing serves as case study environment for the actual thesis and also for the work of Bielefeld, which makes the results comparable. It turns out, that the extensions lead to improved anomaly detection results and an enhanced usability of the approach.