Miracle-dz/UniFAR-FTUs huggingface dataset:
Who Wants to Join Me?: Companion Recommendation in Location Based Social Networks

We consider the problem of identifying possible companions for a user who is planning to visit a given venue. Specifically, we study the task of predicting which of the user's current friends, in a location based social network (LBSN), are most likely to be interested in joining the visit. An important underlying assumption of our model is that friendship relations can be clustered based on the kinds of interests that are shared by the friends. To identify these friendship types, we use a latent topic model, which moreover takes into account the geographic proximity of the user to the location of the proposed venue. To the best of our knowledge, our model is the first that addresses the task of recommending companions for a proposed activity. While a number of existing topic models can be adapted to make such predictions, we experimentally show that such methods are significantly outperformed by our model.
Separating morphologically similar pollen types using basic shape features from digital images: A preliminary study1

Premise of the study: One of the many advantages offered by automated palynology systems is the ability to vastly increase the number of observations made on a particular sample or samples. This is of particular benefit when attempting to fully quantify the degree of variation within or between closely related pollen types. Methods: An automated palynology system (Classifynder) has been used to further investigate the variation in pollen morphology between two New Zealand species of Myrtaceae (Leptospermum scoparium and Kunzea ericoides) that are of significance in the New Zealand honey industry. Seven geometric features extracted from automatically gathered digital images were used to characterize the range of shape and size of the two taxa, and to examine the extent of previously reported overlap in these variables. Results: Our results indicate a degree of overlap in all cases. The narrowest overlap was in measurements of maximum Feret diameter (MFD) in grains oriented in polar view. Multivariate statistical analysis using all seven factors provided the most robust discrimination between the two types. Discussion: Further work is required before this approach could be routinely applied to separating the two pollen types used in this study, most notably the development of comprehensive reference distributions for the types in question.
The K-hop Cooperative Video Streaming Protocol Using H.264/SVC Over the Hybrid Vehicular Networks

When a number of persons, e.g., a family or a group of friends, drive their vehicles for a trip together, they can form a fleet of vehicles and share their network resources during their trip. Let one member want to watch a video from the Internet. He may not have high resolution or video quality due to his limited 3G/3.5G bandwidth to the Internet. The cooperative video streaming scenario allows the requested member to ask other members of the same fleet to download video cooperatively. In other words, other members can help to download parts of the video from the Internet and then forward video data to the requested member hop by hop through the ad-hoc network. This work proposes the k-hop cooperative video streaming protocol using H.264/SVC over the hybrid vehicular networks which consist of 3G/3.5G cellular network and Dedicated Short-Range Communications (DSRC) ad-hoc network. In order to smooth video playback over the DSRC-based ad-hoc network, this work proposes: (1) one streaming task assignment scheme that schedules the streaming task to each member over the dynamic vehicular networks, and (2) packet forwarding strategies that decide the forwarding sequence of the buffered video data to the requested member hop by hop. Finally, we utilize the network simulator version 2 (NS2) to simulate the proposed protocol. Based on the simulation results, the proposed scheme can estimate the assignment interval adaptively and the playback priority first (PPF) strategy has the best performance for the k-hop video forwarding over the hybrid vehicular networks.
Efficient siRNA Delivery by the Cationic Liposome DOTAP in Human Hematopoietic Stem Cells Differentiating into Dendritic Cells

RNA interference technology is an ideal strategy to elucidate the mechanisms associated with human CD34+ hematopoietic stem cell differentiation into dendritic cells. Simple manipulations in vitro can unequivocally yield alloreactive or tolerogenic populations, suggesting key implications of biochemical players that might emerge as therapeutic targets for cancer or graft-versus-host disease. To knockdown proteins typically involved in the biology of dendritic cells, we employed an siRNA delivery system based on the cationic liposome DOTAP as the carrier. Freshly-isolated CD34+ cells were transfected with siRNA for cathepsin S with negligible cytotoxicity and transfection rates (>60%) comparable to the efficiency shown by lentiviral vectors. Further, cathepsin S knockdown was performed during both cell commitment and through the entire 14-day differentiation process with repeated transfection rounds that had no effect per se on cell development. Tested in parallel, other commercially-available chemical reagents failed to meet acceptable standards. In addition to safe and practical handling, a direct advantage of DOTAP over viral-mediated techniques is that transient silencing effects can be dynamically appraised through the recovery of targeted proteins. Thus, our findings identify DOTAP as an excellent reagent for gene silencing in resting and differentiating CD34+ cells, suggesting a potential for applications in related preclinical models.
Comparison of Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation

Background and Purpose— Several accepted algorithms exist to characterize the risk of thromboembolism in atrial fibrillation. We performed a comparative analysis to assess the predictive value of 9 such schemes. Methods— In a longitudinal community-based cohort study from Olmsted County, Minnesota, 2720 residents with atrial fibrillation were followed up for 4.4±3.6 years±SD from 1990 to 2004. Risk factors were identified using a diagnostic index integrated with the electronic medical record. Thromboembolism and cardiovascular event data were collected and analyzed. Results— We identified 350 validated thromboembolic events in our cohort. Multivariable analysis identified age >75 years (odds ratio, 2.08; P<0.0001), female sex (odds ratio, 1.45; P=0.0015), history of hypertension (odds ratio, 3.07; P<0.0001), diabetes mellitus (odds ratio, 1.58; P=0.0003), and history of heart failure (odds ratio, 1.50; P=0.0102) as significant predictors of clinical thromboembolism. The Stroke Prevention in Atrial Fibrillation (SPAF; hazard ratio, 2.75; c=0.659), CHADS2-revised (hazard ratio, 3.48; c=0.654), and CHADS2-classical (hazard ratio, 2.90; c=0.653) risk schemes were most accurate in risk stratification. The low-risk cohort within the CHA2DS2-VASc scheme had the lowest event rate among all low-risk cohorts (0.11 per 100 person-years). Conclusions— A direct comparison of 9 risk schemes reveals no profound differences in risk stratification accuracy for high-risk patients. Accurate prediction of low-risk patients is perhaps more valuable in determining those unlikely to benefit from oral anticoagulation therapy. Among our cohort, CHA2DS2-VASc performed best in this purpose.
Hierarchical Power Game with Dual-Utility in Two-Tier OFDMA Femtocell Networks

This paper addresses the cross-tier interference mitigation problem using power allocation in two-tier OFDMA femtocell networks. We formulate the power allocation problem as a dual-utility hierarchical (Stackelberg) game with a leader-follower approach. The leaders (macrocells) foresight the response of followers (femtocells), and maximize their individual utility in a non-cooperative manner. After the leaders apply their strategies, the followers also maximize their own utility in a non-cooperative manner. Then the Backward Induction method is applied to achieve the Stackelberg equilibrium. Simulation results show that the proposed hierarchical game with dual-utility outperforms the non-cooperative approach and the hierarchical game with single-utility in terms of average outage probability and average throughput.
Factors affecting team evolution during software projects

Forming teams from a large groups of developers and testers pose an important problem for software project management. There are only a few empirical studies on the topic of team evolution and the factors affecting it in software projects. In this paper, we analyzed the evolution of globally distributed testing and coding teams developing large enterprise software product. Our findings indicate that, during the software development process, testing and coding teams are naturally formed through the informal interaction of people. This informal structure is not necessarily a reflection of the formal organizational structure. Therefore, we recommend software development organizations to take into consideration this informal and, most of the time, effective natural team building structure and perhaps to consider collaborative (non-hierarchical) task assignment during SDLC.
Facial features extraction by accelerated implementation of circular hough transform and appearance evaluation

Extraction of facial feature points is essential task for many kinds of applications of face images. The feasibility of facial features extraction is determined by not only its accuracy but processing time. Some applications require real-time detection of facial features. This research aims to propose a method of facial features extraction by an accelerated implementation of circular Hough transform with gradients and appearance evaluation by histogram of gradient features. The acceleration implementation employs General Purpose computing on Graphics Processing Unit. Experiment using FERET database shows that the proposed method successfully extracted eyes and nose for 98.44% and 99.50% of frontal face images in the dataset. And 96.5% of computational time was reduced by accelerated implementation employing GPGPU.
From DBpedia and WordNet hierarchies to LinkedIn and Twitter

Previous research has demonstrated the benefits of using linguistic resources to analyze a user’s social media profiles in order to learn information about that user. However, numerous linguistic resources exist, raising the question of choosing the appropriate resource. This paper compares Extended WordNet Domains with DBpedia. The comparison takes the form of an investigation of the relationship between users’ descriptions of their knowledge and background on LinkedIn with their description of the same characteristics on Twitter. The analysis applied in this study consists of four parts. First, information a user has shared on each service is mined for keywords. These keywords are then linked with terms in DBpedia/Extended WordNet Domains. These terms are ranked in order to generate separate representations of the user’s interests and knowledge for LinkedIn and Twitter. Finally, the relationship between these separate representations is examined. In a user study with eight participants, the performance of this analysis using DBpedia is compared with the performance of this analysis using Extended WordNet Domains. The best results were obtained when DBpedia was used.
Wnt3alinks left-right determination with segmentation and anteroposterior axis elongation

The alignment of the left-right (LR) body axis relative to the anteroposterior (AP) and dorsoventral (DV) axes is central to the organization of the vertebrate body plan and is controlled by the node/organizer. Somitogenesis plays a key role in embryo morphogenesis as a principal component of AP elongation. How morphogenesis is coupled to axis specification is not well understood. We demonstrate that Wnt3a is required for LR asymmetry. Wnt3a activates the Delta/Notch pathway to regulate perinodal expression of the left determinant Nodal, while simultaneously controlling the segmentation clock and the molecular oscillations of the Wnt/β-catenin and Notch pathways. We provide evidence that Wnt3a, expressed in the primitive streak and dorsal posterior node, acts as a long-range signaling molecule, directly regulating target gene expression throughout the node and presomitic mesoderm. Wnt3a may also modulate the symmetry-breaking activity of mechanosensory cilia in the node. Thus, Wnt3a links the segmentation clock and AP axis elongation with key left-determining events, suggesting that Wnt3a is an integral component of the trunk organizer.
Aerodynamic Shape Optimization Investigations of the Common Research Model Wing Benchmark

Despite considerable research on aerodynamic shape optimization, there is no standard benchmark problem allowing researchers to compare results. This work addresses this issue by solving a series of aerodynamic shape optimization problems based on the Common Research Model wing benchmark case defined by the Aerodynamic Design Optimization Discussion Group. The aerodynamic model solves the Reynolds-averaged Navier–Stokes equations with a Spalart–Allmaras turbulence model. A gradient-based optimization algorithm is used in conjunction with an adjoint method that computes the required derivatives. The drag coefficient is minimized subject to lift, pitching moment, and geometric constraints. A multilevel technique is used to reduce the computational cost of the optimization. A single-point optimization is solved with 720 shape variables using a 28.8-million-cell mesh, reducing the drag by 8.5%. A more realistic design is achieved through a multipoint optimization. Multiple local minima are found when starting.
Multihypothesis prediction for compressed sensing and super-resolution of images

– [18] Y. Yang, B. Zhang, J. Han, L. Shao, and C. Chen, “Action Recognition Using 3D Histograms of Texture and A Multi-class Boosting Classifier,” IEEE Transactions on Image Processing, under review. – [17] C. Chen, B. Zhang, Z. Hou, J. Jiang, M. Liu, and Y. Yang, “Action Recognition from Depth Sequences Using Weighted Fusion of 2D and 3D Auto-Correlation of Gradients Features,” Multimedia Tools and Applications, available online, print to appear later. – [16] C. Chen, R. Jafari, and N. Kehtarnavaz, “A Survey of Depth and Inertial Sensor Fusion for Human Action Recognition,” Multimedia Tools and Applications, available online, print to appear later. – [15] C. Chen, R. Jafari, and N. Kehtarnavaz, “A Real-Time Human Action Recognition System Using Depth and Inertial Sensor Fusion,” IEEE Sensors Journal, vol. 16, no. 3, pp. 773-781, February 2016. – [14] C. Chen, R. Jafari, and N. Kehtarnavaz, “Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors,” IEEE Transactions on Human-Machine Systems, vol. 45, no. 1, pp. 51-61, February 2015. – [13] K. Liu, C. Chen, R. Jafari, and N. Kehtarnavaz, “Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition,” IEEE Sensors Journal, vol. 14, no. 6, pp. 1898-1903, June 2014. – [12] C. Chen, K. Liu, and N. Kehtarnavaz, “Real-Time Human Action Recognition Based on Depth Motion Maps,” Journal of Real-Time Image Processing, August 2013.
Comparative study of pathological lesions induced by multiwalled carbon nanotubes in lungs of mice by intratracheal instillation and inhalation

The pathological lesions induced by multiwalled carbon nanotubes (MWCNTs) in bronchi and alveoli of mice were studied by intratracheal instillation and inhalation. In instillation groups, the dose was 0.05 mg MWCNTs/mouse. Similar size clumps of MWCNTs were distributed in bronchi and alveoli. The clumps led to inflammation to the lining wall of bronchi and severe destruction to alveolar netted structure around them. In the inhalation groups, the mice were exposed to aerosolized MWCNTs with mean concentration of 32.61 mg/m3, the intralung deposition dose were roughly 0.07, 0.14, and 0.21 mg in the 8‐day group, 16‐day group, and 24‐day group, respectively. Most of aggregations of MWCNTs in the alveoli were smaller than that in bronchi. The aggregations induced proliferation and thickening of alveolar walls. With the exception of these moderate pathological lesions, the general alveolar structure was still remained. The preliminary study demonstrated a difference in lung pathological lesions induced by instilled MWCNTs and inhaled ones, which may be due to the different size and distribution of aggregations of MWCNTs in lung. © 2007 Wiley Periodicals, Inc. Environ Toxicol 22: 415–421, 2007.
Incorporating Genetics into Your Studies: A Guide for Social Scientists

There has been a surge of interest in recent years in incorporating genetic components into on-going longitudinal, developmental studies and related psychological studies. While this represents an exciting new direction in developmental science, much of the research on genetic topics in developmental science does not reflect the most current practice in genetics. This is likely due, in part, to the rapidly changing landscape of the field of genetics, and the difficulty this presents for developmental scientists who are trying to learn this new area. In this review, we present an overview of the paradigm shifts that have occurred in genetics and we introduce the reader to basic genetic methodologies. We present our view of the current stage of research ongoing at the intersection of genetics and social science, and we provide recommendations for how we could do better. We also address a number of issues that social scientists face as they integrate genetics into their projects, including choice of a study design (candidate gene versus genome-wide association versus sequencing), different methods of DNA collection, and special considerations involved in the analysis of genotypic data. Through this review, we hope to equip social scientists with a deeper understanding of the many considerations that go into genetics research, in an effort to foster more meaningful cross-disciplinary initiatives.
Plankton classification with deep convolutional neural networks

Traditional methods for measuring and monitoring plankton populations are time consuming and can not scale to the granularity or scope necessary for large-scale studies. Improved approaches are needed. Manual analysis of the imagery captured by underwater camera system is infeasible. Automated image classification using machine learning tools is an alternative to the manual approach. In this paper, we present a deep neural network model for plankton classification which exploits translational and rotational symmetry. In this work, we propose two constrains in the design of deep convolutional neural network structure to guarantee the performance gain when going deep. Firstly, for each convolutional layer, its capacity of learning more complex patterns should be guaranteed; Secondly, the receptive field of the topmost layer should be no larger than the image region. We also developed a “inception layer” like structure to deal with multi-size imagery input with convolutional neural network. The experimental result on Plankton Set 1.0 imagery data set show the feasibility and effectiveness of the proposed method.
Hyperspectral Image Classification Using Spatial and Spectral Features

Classification of hyperspectral remote sensing data with support vector machines (SVMs) is investigated. Hyper spectral image classification techniques that use both spectral and spatial information are more suitable, effective, and robust than those that use only spectral information. The Features employed in this include Morphological profiles, Grey level cooccurence matrix, Discrete Wavelet Transform.To classify the image Support vector machine(SVM) is used. To reduce the noise in the classification map, a Post Regularization (PR) step is employed. Experimental results show that it achieves good classification performance on Hyperspectral images.
Fragment retention characteristics in slack space — Analysis and measurements

When files are deleted, their information is not removed from the storage media. This is a well known fact, and there exist numerous undelete utilities to recover newly deleted files. When deleted files have been partly overwritten, the data from the part of the file that remains in unallocated space can be readily extracted by file carving. Such carving is often performed in forensic investigations. Furthermore, as a consequence of file system implementation specifics, there additionally exist small remains of the previous files in the space at the end of new files. In this paper we focus on these small remains of previous files, or micro-fragments, that exist even after all the space allocated to the previous file has been reallocated to new files. We derive expressions for modeling the number of micro-fragments that can be expected to be found, and perform experiments to evaluate the analytical model. The obtained results indicate good correspondence between the analytical predictions and the measured results.
A projection-based making-human-feel-safe system for human-robot cooperation

When a person is working on a human robot cooperation system, he/she tends to worry about possible collisions with the robot, because of a lack of internal information about the robot. This internal information is, for example, an awareness of a worker's presence or future robot motion. Therefore, this paper proposes a projection-based making-human-feel-safe system that not only ensures the safety of a human worker but also ensures that the worker feels safe when working with a robot by displaying the internal information of the robot using projection technology. To develop this system, we utilized a Kalman filter for predicting a worker's hand position and compensating the delay in the projection. An elastic band framework was implemented to plan a safe path considering the position of the worker. Finally, an evaluation experiment was conducted to prove the effectiveness of the system.
Bifurcation of Periodic States in Coupled BVP Neurons with External Impulsive Forces

We investigate bifurcation and chaos observed in coupled BVP neurons with external impulsive forces. Although the single neuron without the external force has only one equilibrium point, combining these n (n ≥ 3) neurons unidirectionally in a ring, nphase periodic solutions are generated. Applying the impulsive forces, successive period-doubling bifurcations of the n-phase solution occur and chaotic states, namely n-phase chaos, appear. When n = 2 and 3, we find the parameter regions in which the switching phenomena of burst firing are observed. Moreover, the mechanisms of the switching phenomena are clarified by numerical bifurcation analysis.
Realtime-enabled workflow management in service oriented infrastructures

Many applications, and especially the ones implementing multi-user collaborative environments, fall within the context of soft real time systems in which only small deviations from timing constraints are allowed. Furthermore, there is a growing trend that such applications are deployed in Service Oriented Infrastructures (SOIs), so as to take advantage of the capabilities that SOIs provide. In such an environment, the aforementioned applications are divided into multiple services within workflows. The dynamic nature of SOIs and their inability to adjust to the ever-changing resource availability and user demand poses an extra burden as workflow systems try to carry out more complex and mission-critical applications. In that frame, we discuss the work that has been carried out in the area of workflow management - developed to serve the needs of applications deployed in distributed environments, along with their limitations and potentials. Based on that, we present the research effort of the IRMOS EU-funded Project which advances the field of research in workflow management in order to support interactive real time applications on SOIs.