Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavior Modeling and Recognition Based on Space-Time Image Features

A novel method based on space-time image features is proposed for automatic behavior modeling and recognition. The method is composed of the following three steps: (1) video sequences are converted into a space-time image, and the image is divided into equal length segments; (2) from these segments, an unsupervised technique based on Dynamic Time Warping is used to determine the groups of different behaviors; (3) the behavior templates are built according to these groups, and are used for the recognition of behaviors. The method does not need to track the body parts and can model the behaviors without any prior knowledge. Experiments also demonstrate the effectiveness of our new method
Different Audiences but Similar Engagement Goals: In-Progress Work on Two Course Transformations

This paper reports our experience in transforming two undergraduate Computer Science courses at the University of British Columbia (UBC). In particular, we are applying an assortment of best practices from educational research known to increase student engagement. The two courses are being transformed in different ways because their learning goals and audiences vary greatly. For example, the courses use different programming languages; they differ with respect to the number, type and frequency of labs, tutorials, and class time; and one is an elective for computer scientists, whereas the other is a required course for non-specialists. Despite these differences, both courses have a greater active learning component compared to a “traditional” lecture, with one of them adopting a flipped classroom approach. To judge the success of these efforts, we conducted numerous surveys to determine changes in student attitudes and to identify what works—and what doesn’t—for these courses.
Recalling Recollections according to Temporal Contexts Applying a Novel Cognitive Knowledge Representation Approach

In an experiment whose purpose is to model the interruptions phenomenon and its impact on tasks achievement, we show that in some cases, such as temporarily interrupted realisations of cooking recipes, the popular ACT-R knowledge representation approach cannot offer a model that is highly close to the natural human behaviour. We emphasize the incapacity of ACT-R to reproduce correctly the recall of information in a temporal context to achieve properly the action plan for a recipe realisation after a momentarily interruption. We propose an alternative knowledge representation theory, which uses additional knowledge structures that are inspired from the human memory, to faithfully reproduce a usual human behaviour in resuming correctly suspended activities
Angle, Distance, Shape, and their Relationship to Projective Relations

The semantics of spatial relations have been intensively studied in linguistics, psychology, and cognitive neuroscience. Angle, distance, and shape are widely considered to be the key factors when establishing spatial relations. In this work an empirical study shows that previous theories overemphasize variation and we clarify the interdependencies between angle, distance, and shape with respect to the acceptability of projective relations. It turned out that the angular deviation plays the key role for relations of this class. The degree of deviation was dependent upon the extension of the reference object perpendicular to the canonical direction of the relation. There was no major effect due to the distance. However, distance interacted with the angular deviation if the located object was very close to the reference object. The experimental results can now be used as a theoretical framework for validating existing computational models of projective relations for their cognitive plausibility.
A Multimodal Approach to Blind Source Separation of Moving Sources

A novel multimodal approach is proposed to solve the problem of blind source separation (BSS) of moving sources. The challenge of BSS for moving sources is that the mixing filters are time varying; thus, the unmixing filters should also be time varying, which are difficult to calculate in real time. In the proposed approach, the visual modality is utilized to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a 3-D tracker based on video cameras. Positions and velocities of the sources are obtained from the 3-D tracker based on a Markov Chain Monte Carlo particle filter (MCMC-PF), which results in high sampling efficiency. The full BSS solution is formed by integrating a frequency domain blind source separation algorithm and beamforming: if the sources are identified as stationary for a certain minimum period, a frequency domain BSS algorithm is implemented with an initialization derived from the positions of the source signals. Once the sources are moving, a beamforming algorithm which requires no prior statistical knowledge is used to perform real time speech enhancement and provide separation of the sources. Experimental results confirm that by utilizing the visual modality, the proposed algorithm not only improves the performance of the BSS algorithm and mitigates the permutation problem for stationary sources, but also provides a good BSS performance for moving sources in a low reverberant environment.
A new nonlinear observer using unscented Kalman filter to estimate sideslip angle, lateral tire road forces and tire road friction coefficient

The active safety systems, such as Electronic Stability Program (ESP) and Traction Control System (TCS), have been used widely in the modern production auto-mobile. These safety control systems function well only when the vehicle motion information is known. To achieve this purpose, various sensors are equipped in the production cars in order to detect vehicle motion information like yaw, wheel velocities and accelerations. However some key variables of the vehicle dynamic and road conditions, such as the sideslip angle, the tire road forces and the tire road friction coefficient are difficult to measure directly for technology or economic reasons. A new observer which uses the unscented Kalman filter to estimate simultaneously the sideslip angle, lateral tire road forces and the tire road friction coefficient has been proposed in this paper. The advantage of this observer is that no additional sensors is needed when the ESP has been installed in the auto-mobile. The performance of the proposed observer is demonstrated through the Chicane test.
Integration of Mesh Optimization with 3D All-Hex Mesh Generation

In an attempt to automatically produce high-quality all-hex meshes, we investigated a mesh improvement strategy: given an initial poor-quality all-hex mesh, we iteratively changed the element connectivity, adding and deleting elements and nodes, and optimized the node positions. We found a set of hex reconnection primitives. We improved the optimization algorithms so they can untangle a negative-Jacobian mesh, even considering Jacobians on the boundary, and subsequently optimize the condition number of elements in an untangled mesh. However, even after applying both the primitives and optimization we were unable to produce high-quality meshes in certain regions. Our experiences suggest that many boundary configurations of quadrilaterals admit no hexahedral mesh with positive Jacobians, although we have no proof of this.
Caffeic Acid, a Phenol Found in White Wine, Modulates Endothelial Nitric Oxide Production and Protects from Oxidative Stress-Associated Endothelial Cell Injury

Introduction Several studies demonstrated that endothelium dependent vasodilatation is impaired in cardiovascular and chronic kidney diseases because of oxidant stress-induced nitric oxide availability reduction. The Mediterranean diet, which is characterized by food containing phenols, was correlated with a reduced incidence of cardiovascular diseases and delayed progression toward end stage chronic renal failure. Previous studies demonstrated that both red and white wine exert cardioprotective effects. In particular, wine contains Caffeic acid (CAF), an active component with known antioxidant activities. Aim of the study The aim of the present study was to investigate the protective effect of low doses of CAF on oxidative stress-induced endothelial injury. Results CAF increased basal as well as acetylcholine—induced NO release by a mechanism independent from eNOS expression and phosphorylation. In addition, low doses of CAF (100 nM and 1 μM) increased proliferation and angiogenesis and inhibited leukocyte adhesion and endothelial cell apoptosis induced by hypoxia or by the uremic toxins ADMA, p-cresyl sulfate and indoxyl sulfate. The biological effects exerted by CAF on endothelial cells may be at least in part ascribed to modulation of NO release and by decreased ROS production. In an experimental model of kidney ischemia-reperfusion injury in mice, CAF significantly decreased tubular cell apoptosis, intraluminal cast deposition and leukocyte infiltration. Conclusion The results of the present study suggest that CAF, at very low dosages similar to those observed after moderate white wine consumption, may exert a protective effect on endothelial cell function by modulating NO release independently from eNOS expression and phosphorylation. CAF-induced NO modulation may limit cardiovascular and kidney disease progression associated with oxidative stress-mediated endothelial injury.
Characterization of sINR, a strict version of the Initiator core promoter element

The proximal promoter consists of binding sites for transcription regulators and a core promoter. We identified an overrepresented motif in the proximal promoter of human genes with an Initiator (INR) positional bias. The core of the motif fits the INR consensus but its sequence is more strict and flanked by additional conserved sequences. This strict INR (sINR) is enriched in TATA-less genes that belong to specific functional categories. Analysis of the sINR-containing DHX9 and ATP5F1 genes showed that the entire sINR sequence, including the strict core and the conserved flanking sequences, is important for transcription. A conventional INR sequence could not substitute for DHX9 sINR whereas, sINR could replace a conventional INR. The minimal region required to create the major TSS of the DHX9 promoter includes the sINR and an upstream Sp1 site. In a heterologous context, sINR substituted for the TATA box when positioned downstream to several Sp1 sites. Consistent with that the majority of sINR promoters contain at least one Sp1 site. Thus, sINR is a TATA-less-specific INR that functions in cooperation with Sp1. These findings support the idea that the INR is a family of related core promoter motifs.
Avian Influenza Risk Surveillance in North America with Online Media

The use of Internet-based sources of information for health surveillance applications has increased in recent years, as a greater share of social and media activity happens through online channels. The potential surveillance value in online sources of information about emergent health events include early warning, situational awareness, risk perception and evaluation of health messaging among others. The challenge in harnessing these sources of data is the vast number of potential sources to monitor and developing the tools to translate dynamic unstructured content into actionable information. In this paper we investigated the use of one social media outlet, Twitter, for surveillance of avian influenza risk in North America. We collected AI-related messages over a five-month period and compared these to official surveillance records of AI outbreaks. A fully automated data extraction and analysis pipeline was developed to acquire, structure, and analyze social media messages in an online context. Two methods of outbreak detection; a static threshold and a cumulative-sum dynamic threshold; based on a time series model of normal activity were evaluated for their ability to discern important time periods of AI-related messaging and media activity. Our findings show that peaks in activity were related to real-world events, with outbreaks in Nigeria, France and the USA receiving the most attention while those in China were less evident in the social media data. Topic models found themes related to specific AI events for the dynamic threshold method, while many for the static method were ambiguous. Further analyses of these data might focus on quantifying the bias in coverage and relation between outbreak characteristics and detectability in social media data. Finally, while the analyses here focused on broad themes and trends, there is likely additional value in developing methods for identifying low-frequency messages, operationalizing this methodology into a comprehensive system for visualizing patterns extracted from the Internet, and integrating these data with other sources of information such as wildlife, environment, and agricultural data.
Specifying the mode logic of a flight guidance system in CoRE and SCR

This paper describes the experiences of Rockwell Avionics & Communications in using the CORE and SCR methods to specify the requirements for the mode logic of a Flight Guidance System for a General Aviation class aircraft. The example was first specified by hand using the CORE method,inspected, then entered into a prototype tool supporting the SCR method provided by the Naval Research Lab. Despite careful review of the CORE model, use of the SCR tool revealed 27 errors, many of them sign& cant. Difficulties encountered, a brief analysis of the errors discovered, and issues for industrial use are discussed.
Generation of Neutralizing Monoclonal Antibodies against a Conformational Epitope of Human Adenovirus Type 7 (HAdv-7) Incorporated in Capsid Encoded in a HAdv-3-Based Vector

The generation of monoclonal antibodies (MAbs) by epitope-based immunization is difficult because the immunogenicity of simple peptides is poor and T cells must be potently stimulated and immunological memory elicited. A strategy in which antigen is incorporated into the adenoviral capsid protein has been used previously to develop antibody responses against several vaccine targets and may offer a solution to this problem. In this study, we used a similar strategy to develop HAdv-7-neutralizing MAbs using rAdMHE3 virions into which hexon hypervariable region 5 (HVR5) of adenovirus type 7 (HAdv-7) was incorporated. The epitope mutant rAdMHE3 was generated by replacing HVR5 of Ad3EGFP, a recombinant HAdv-3-based vector expressing enhanced green fluorescence protein, with HVR5 of HAdv-7. We immunized BALB/c mice with rAdMHE3 virions and produced 22 different MAbs against them, four of which showed neutralizing activity against HAdv-7 in vitro. Using an indirect enzyme-linked immunosorbent assay (ELISA) analysis and an antibody-binding-competition ELISA with Ad3EGFP, HAdv-7, and a series of chimeric adenoviral particles containing epitope mutants, we demonstrated that the four MAbs recognize the neutralization site within HVR5 of the HAdv-7 virion. Using an immunoblotting analysis and ELISA with HAdv-7, recombinant peptides, and a synthetic peptide, we also showed that the neutralizing epitope within HVR5 of the HAdv-7 virion is a conformational epitope. These findings suggest that it is feasible to use a strategy in which antigen is incorporated into the adenoviral capsid protein to generate neutralizing MAbs. This strategy may also be useful for developing therapeutic neutralizing MAbs and designing recombinant vector vaccines against HAdv-7, and in structural analysis of adenoviruses.
Reducing Premature Convergence Problem in Genetic Algorithm: Application on Travel Salesman Problem

Genetic algorithm (GA) is based on Darwin’s natural selection theory and is used extensively in combinatorial problems as these problems are demanding in terms of computational time. GA shows very good results in terms of both computational time and quality of solution for combinatorial problems as GAs have some traits that make them one of the best evolutionary algorithms (EAs). The use of both mutation and crossover operators make them, relative to other EAs, highly immune to be trapped in a local optima and thus less vulnerable to premature convergence problem. Traditionally, the solution for premature convergence problem is to maintain a certain degree of diversity of the GA’s population without affecting the convergence process itself. In this paper, this concept has been practiced where Frequency Crossover strategy (FC) along with nine different mutation strategies have been proposed and applied to travel salesman problem (TSP) to reduce the effect of premature convergence problem. Three sets of benchmark data have been used to test the effectiveness of this GA. The results showed that both the nine mutation types and the FC are essential for the proposed GA to perform well. While this GA has been applied on TSP in this paper, it is also believed that it is applicable on any problem that has an Order-Based chromosome representation.
An Improved Method to Reduce Over-Segmentation of Watershed Transformation and its Application in the Contour Extraction of Brain Image

Watershed transformation is a common technique for image segmentation. However, its use for medical image segmentation has been limited particularly due to over-segmentation. In response to the characteristics of medical image, especially the contour extraction from the MRI (Magnetic Resonance Imaging) brain image, this paper proposes an improved method in order to overcome the drawbacks. Firstly, multi-scale alternating sequential filtering by reconstruction is introduced to eliminate the noise and simplify the input images, and the loss of boundary information can be avoided. Secondly, two methods of h-minima and minima imposition are imposed on the gradient image to mark the minima regions, so all its local minima are suppressed. Finally, the watershed algorithm is applied to the marked gradient images to get the contour of brain. Experimental results show that the improved method can be applied to contour extraction of MRI brain image with good result, and the mean reduction of local minima in the over-segmented image of regions is 68.26% compared to the watershed transformation based on mark extraction.
SCALABLE MULTIDIMENSIONAL ANONYMIZATION ALGORITHM OVER BIG DATA USING MAP REDUCE ON PUBLIC CLOUD

It appears that everybody observes with special attention, the occurrence of big data and its practice. There is no disbelief that the big data uprising has instigated. Though the practices of big data propose favorable business paybacks, there are substantial privacy implications. Multidimensional generalization anonymization scheme is an actual method for data privacy preservation. Top-Down Specialization (TDS) and Bottom-Up generalization (BUG) are two methods to attain multidimensional anonymization. However, prevailing methodologies for multidimensional generalization anonymization scheme disconcerts parallelization proficiency, thereby missing scalability while managing big data on cloud. TDS and BUG suffer from poor performance for certain value of k-anonymity parameter if they are utilized individually. In this paper, we recommend a hybrid method that combines TDS and BUG together for competent multidimensional anonymization over big data. Additionally, Map reduce based algorithms for two components (TDS and BUG) to increase high scalability cloud are designed. Experiment estimations determine that the hybrid method expressively progresses the scalability and proficiency of multidimensional generalization anonymization system over prevailing methods.
STRING-MAN: a new wire robot for gait rehabilitation

This paper presents a novel robotic prototype for advanced gait rehabilitation. This system integrates sophisticated robotic technology with control algorithms. The robot opens up new possibilities for the field of rehabilitation for restoring posture balancing and gait motoric functions. The paper provides an overview of the system's fundamental components, such as mechanical structure, patient-machine interface, sensory systems, control algorithms, etc. The results of some initial experiments also are included.
High-flexible multi-technique acquisition system for human postural stability tests onboard ships

For validating theoretical models aiming to simulate the postural behaviour of working personnel one of the possible methods is the execution of trials onboard full-scale ships. This kind of tests requires measuring ship and human movement simultaneously. Up to now, international guidelines for standardizing human postural stability tests onboard have not been established. Scientific works focused on this topic do not make use of human motion capture techniques commonly employed for postural stability studies indoor. This paper presents an innovative motion acquisition system usable onboard ships while accomplishing the daily mission. The system integrates different techniques (photogrammetry, inertial measurements, global positioning system) for acquiring both ship and human motions. Its core is an owndeveloped low-cost motion capture system fundamental in analysing and understanding the measurements from the inertial sensors. Preliminary laboratory tests and results from measurement campaigns onboard are presented and discussed.
A Condition of Deadlock-Free Routing in Mesh Network

Mesh topology is one of the most popular architectures in interconnection networks. Although many deadlock-free schemes for mesh networks have been proposed, deadlock avoidance is still the most difficult issue to solve. In this paper, we propose a new concept of channel class dependency for deadlock-free routing algorithm in mesh networks. Based on the channel class dependency graph, a new condition of the presence of cycle in the channel dependency graph of routing algorithms is introduced. Since the number of channel classes and channel class dependencies is not dependent on the size of mesh networks, the channel class dependency graph is easy to be implemented. So the proposed deadlock-free routing condition is the key for the design of deadlock-free routing algorithms, which employs minimum routing restrictions and as few virtual channels as possible.
Advances in Functional Connectomics in Neuroscience : A Focus on Post-Traumatic Stress Disorder

Advances in Functional Connectomics in Neuroscience : A Focus on Post-Traumatic Stress Disorder Shinwon Park, BA,* Hyeonseok S. Jeong, PhD,* In Kyoon Lyoo, MD Department of Psychology, Ewha Womans University, Seoul, Korea Department of Radiology, Incheon St. Mary’s Hospital, The Catholic University of Korea, Incheon, Korea Department of Brain and Cognitive Sciences and Ewha Brain Institute, Ewha Womans University, Seoul, Korea
PORTFOLIO CHOICE VIA QUANTILES

A portfolio choice model in continuous time is formulated for both complete and incomplete markets, where the quantile function of the terminal cash flow, instead of the cash flow itself, is taken as the decision variable. This formulation covers a wide body of existing and new models with law‐invariant preference measures, including expected utility maximization, mean–variance, goal reaching, Yaari's dual model, Lopes' SP/A model, behavioral model under prospect theory, as well as those explicitly involving VaR and CVaR in objectives and/or constraints. A solution scheme to this quantile model is proposed, and then demonstrated by solving analytically the goal‐reaching model and Yaari's dual model. A general property derived for the quantile model is that the optimal terminal payment is anticomonotonic with the pricing kernel (or with the minimal pricing kernel in the case of an incomplete market if the investment opportunity set is deterministic). As a consequence, the mutual fund theorem still holds in a market where rational and irrational agents co‐exist.