Miracle-dz/UniFAR-FTUs huggingface dataset:
Acceleration by programmable GPU on realizing 3D movie playback

Detailed research is conducted on video texture for 3D movie play application and current research situation, problem and challenge in the world are analyzed in this paper. Then an effective solution is presented. Direct Show is used to manage the video data while Direct 3D serves as graphic rendering API. This paper extracts the parallel computing module from the fixed rendering pipeline and takes full use of programmable GPU to accelerate the rendering process. In plus, GPU texture compression is used to solve the problem of limited graphic memory. The experiment and simulation prove that the designed method can effectively uplift the frame rate, alleviate the CPU burden and solve the bottleneck in real-time rendering. Finally, it is rather practical in certain applications.
Structural analysis of behavioral networks from the Internet

In spite of the Internet's phenomenal growth and social impact, many aspects of the collective communication behavior of its users are largely unknown. Understanding the structure and dynamics of the behavioral networks that connect users with each other and with services across the Internet is key to modeling the network and designing future applications. We present a characterization of the properties of the behavioral networks generated by several million users of the Abilene (Internet2) network. Structural features of these networks offer new insights into scaling properties of network activity and ways of distinguishing particular patterns of traffic. For example, we find that the structure of the behavioral network associated with Web activity is characterized by such extreme heterogeneity as to challenge any simple attempt to model Web server traffic.
Geographic analysis of traffic injury in Wisconsin: impact on case fatality of distance to level I/II trauma care.

PURPOSE Traffic injuries are an important public health problem in Wisconsin. This paper examines geographic variations in the incidence and case fatality of traffic injuries, and evaluates proximity to Level I/II trauma care as a predictor of case fatality. METHODS Data on all persons injured in motor vehicle crashes on Wisconsin roadways during a 10-year period (1992-2001) were obtained from the Wisconsin Crash Outcome Data Evaluation System. Injury rates per 100 million vehicle miles traveled (VMT) and case fatality were visually displayed by county. Multiple logistic regression models were used to evaluate distance between crash site and Level I/II trauma care as a risk factor for fatality, controlling for other factors. RESULTS During the 10-year period, 50,300 people were severely injured in motor vehicle crashes and 7450 (15%) died. Injury rates per VMT were highest in the southeast counties. In contrast, case-fatality for all injuries and for severe injuries was highest in northern and western counties and significantly associated with distance from Level I/II trauma care after controlling for other predictors of case fatality. CONCLUSIONS Strategies are needed to reduce the disparity in traffic injury case fatality across the state and to improve outcomes for persons injured in counties distant from Level I/II trauma care.
Culture and getting to yes : The linguistic signature of creative agreements in the United States and Egypt

Summary We complement the dominant rational model of negotiation found in the West with a new honor model of negotiation found in many Arabic-speaking populations and illustrate the linguistic processes that facilitate creativity in negotiation agreements in the United States and Egypt. Community samples (N = 136) were recruited in the United States and Egypt and negotiated an integrative bargaining task, Discount Marketplace. Analyses of categories of the Linguistic Inquiry and Word Count (LIWC) and our own newly developed honor dictionary illustrate that the same language that predicts integrative agreements in the United States, namely, that which is rational and logical (cognitive mechanisms, LIWC), actually backfires and hinders agreements in Egypt. Creativity in Egypt, by contrast, reflects an honor model of negotiating with language that promotes honor gain (i.e., moral integrity) and honor protection (i.e., image and strength). Theoretical and practical implications are discussed. Copyright © 2015 John Wiley & Sons, Ltd.
Voice conversion for various types of body transmitted speech

In this paper, we review our proposed statistical voice conversion approaches to enhancing various types of body transmitted speech captured with non-audible murmur (NAM) microphone. Body transmitted speech conversion is a potential technique to bring a new paradigm to human-to-human speech communication. In addition to our previously proposed methods of enhancing body transmitted unvoiced speech for silent speech communication and of enhancing body transmitted artificial speech for speaking aid, we further propose conversion methods of enhancing body transmitted voiced speech for noise robust speech communication. An experimental result demonstrates that the proposed methods yield significant improvements in quality of body transmitted voiced speech.
Enhanced production of prodigiosin by Serratia marcescens MO-1 using ram horn peptone

This work addresses the production of prodigiosin from ram horn peptone (RHP) using MO-1, a local isolate in submerged culture. First, a novel gram-negative and rod-shaped bacterial strain, MO-1, was isolated from the body of the grasshopper (Poecilemon tauricola Ramme 1951), which was collected from pesticide-contaminated fields. Sequence analysis of 16S rDNA classified the microbe as Serratia marcescens. The substrate utilization potential (BIOLOG) and fatty acid methyl ester profile (FAME) of S. marcescens were also determined. The effect of RHP on the production of prodigiosin by S. marcescens MO-1 was investigated, and the results showed that RHP supplementation promoted the growth of MO-1 and increased the production of prodigiosin. A concentration of 0.4% (w/v) RHP resulted in the greatest yield of prodigiosin (277.74 mg/L) after 48 h when mannitol was used as the sole source of carbon. The pigment yield was also influenced by the types of carbon sources and peptones. As a result, RHP was demonstrated to be a suitable substrate for prodigiosin production. These results revealed that prodigiosin could be produced efficiently by S. marcescens using RHP.
Evolutionary and Comparative Genomics to Drive Rational Drug Design, with Particular Focus on Neuropeptide Seven-Transmembrane Receptors

Seven transmembrane receptors (7TMRs), also known as G protein-coupled receptors, are popular targets of drug development, particularly 7TMR systems that are activated by peptide ligands. Although many pharmaceutical drugs have been discovered via conventional bulk analysis techniques the increasing availability of structural and evolutionary data are facilitating change to rational, targeted drug design. This article discusses the appeal of neuropeptide-7TMR systems as drug targets and provides an overview of concepts in the evolution of vertebrate genomes and gene families. Subsequently, methods that use evolutionary concepts and comparative analysis techniques to aid in gene discovery, gene function identification, and novel drug design are provided along with case study examples.
Telling the Difference Between Asking and Stealing: Moral Emotions in Value-based Narrative Characters

In this paper, we translate a model of value-based emo- tional agents into an architecture for narrative characters and we validate it in a narrative scenario. The advantage of using such model is that different moral behaviors can be obtained as a consequence of the emotional ap- praisal of moral values, a desirable feature for digital storytelling techniques.
Query-by-Example: A Data Base Language

Discussed is a high-level data base management language that provides the user with a convenient and unified interface to query, update, define, and control a data base. When the user performs an operation against the data base, he fills in an example of a solution to that operation in skeleton tables that can be associated with actual tables in the data base. The system is currently being used experimentally for various applications.
The Informational, Psychological and Social Needs of Patients Undergoing Chemotherapy, Visiting Imam Reza Teaching Hospital, Tabriz, in 2016

Summary Understanding physical, psychological and social needs is the basis for disease management. The purpose of this study was to investigate the informational, psychological, and social needs of patients undergoing chemotherapy. The present study is a descriptive-correlational one conducted on 400 patients undergoing chemotherapy visiting a teaching hospital in Northwest Iran. The data collection instrument consisted of three sections, including the demographic information of patients, a researcher-made questionnaire to determine the informational needs of patients, and a Psychosocial Needs Index (PNI) provided to patients while receiving chemotherapy. Data were analyzed by descriptive and inferential statistics in SPSS 21 version. The results showed that the mean score of patients’ informational needs was high and the mean score of their psychosocial needs was moderate. Regarding the informational dimension, the highest level of need was reported for the item “I need to be aware of things to help me recover from my disease”; in the psychosocial dimension, the highest level of need belonged to the psychological dimension with the item “I need help to reduce my family’s concerns about my illness”; in the physical and daily activity dimension, the highest level of need was related to item “I need help to reduce fatigue and gain energy”; in the care and support dimension, the highest level of need belonged to the item “I need the timely attendance of medical staff in case of physical needs and complications”; and finally, in the sexual dimension, the highest level of need was reported for the item “ I need help to control changes in my sexual desire”. The findings showed that patients with cancer have extensive needs after the diagnosis of the disease and during chemotherapy, and understanding these needs is important in planning care for these patients.
Mapping Real Time Applications on NoC Architecture with Hybrid Multi-objective Algorithm

The work presented in this paper is a contribution to solving a widespread problem in the field of system design, embedded the placement of a large application on an architecture (NOC). Application is represented by a set of tasks that communicate with each other by sending message via bus on a heterogeneous architecture. Our role is to place the tiles (task) on different elements (core) of architecture with the objectives of minimizing time execution and the energy consumption under the constraints of load balancing, bandwidth, available memory and size of the queue waiting processors. To solve this problem, we used in the context of our present work, a new meta-heuristic algorithm Particle Swarm. it has proved its effectiveness in many fields such as optimization of networks, image processing and even control of industrial systems but it was never applied in our domain.
Real-time dense communication among agents for active tracking

For multi-target tracking, we realize the increase in number of simultaneously trackable objects and tracking stability by improving the way of communication proposed in [1] so that agents exchange all detected information with each other without increasing network traffic as follows: (1) an agent that provides information of observed objects for multiple agencies (an agency is a group of agents that track the same target) and (2) a vacant-agency that receives information of its target from agents tracking other objects.
Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

Particle filters are key algorithms for object tracking under non-linear, non-Gaussian dynamics. The high computational cost of particle filters, however, hampers their applicability in cases where the likelihood model is costly to evaluate, or where large numbers of particles are required to represent the posterior. We introduce the piecewise constant sequential importance sampling/resampling (pcSIR) algorithm, which aims at reducing the cost of traditional particle filters by approximating the likelihood with a mixture of uniform distributions over pre-defined cells or bins. The particles in each bin are represented by a dummy particle at the center of mass of the original particle distribution and with a state vector that is the average of the states of all particles in the same bin. The likelihood is only evaluated for the dummy particles, and the resulting weight is identically assigned to all particles in the bin. We derive upper bounds on the approximation error of the so-obtained piecewise constant function representation, and analyze how bin size affects tracking accuracy and runtime. Further, we show numerically that the pcSIR approximation error converges to that of sequential importance sampling/resampling (SIR) as the bin size is decreased. We present a set of numerical experiments from the field of biological image processing and tracking that demonstrate pcSIR's capabilities. Overall, we consider pcSIR a promising candidate for simple, fast particle filtering in generic applications, especially in those with a costly likelihood update step.
Caring Process in Hematopoietic Stem Cell Transplantation: A Grounded Theory Study

Background: Caring is one of the main concepts in nursing and its modes of delivery in different diseases have been widely studied. Hematopoietic Stem Cell Transplantation (HSCT) is a novel, complex, and time-consuming clinical intervention which is applied as a final medical choice in several life-threatening diseases. The aim of the current study was to explore the process of caring for patients undergoing HSCT. Materials and Methods: In this article, we present a qualitative research study conducted between 2011 and 2013 in accordance with the procedures of grounded theory methodology. Data were gathered by interviewing and observing health professionals involved in HSCT process, as well as patients and their families. The study participants consisted of 18 HSCT nurses, 2 physicians, 12 patients, and 7 members of patients’ families. The initial sampling in the study was purposeful, followed by theoretical sampling. Data were analyzed using the Corbin & Strauss (2008) method. Results: Four main categories, reflecting 13 sub-categories, were emerged by analyzing the data: struggling of patients between life and death, trying to reduce the chance of patient’s death, enforcing patients’ spirit and caring achievements. The core variable of study, defined as “supporting patients to go through the HSCT process successfully”, represented the nature and efficiency of care delivered to HSCT patients in the study setting. Conclusion: HSCT patients enter the caring process in the context of life-and-death limbo. The caring strategy in HSCT patients is aimed at trying to reduce the chance of the patient’s death, as well as enforcing patients’ spirit. The HSCT process affects all areas involved in various ways and has some outcomes. The findings and the theoretical conclusions of this study are potentially valuable in improving nursing practice, designing of educational programs and setting of caring policies.
Value creation drivers in SOA for research & innovation: A proposed framework for HEI context

Currently, higher education institutions (HEIs) are looking for new ways of collaborating to share specialized competences (knowledge & skills) both internally and externally. This is aimed at improving their business processes as well as increasing their productivity. Value co-creation (VCC) is an effective model to improve the way organizations collaborate and share resources. VCC is a concept that defines how products or services can be collaboratively created between the firm, its customers, partners and other stakeholders. However, there is insufficient research on how value can be co-created within the HEIs context. Hence the aim of this study is to examine the concept of value co-creation in the context of research collaboration in HEIs. The main objective of this study will be addressed through answering the following research question: How can ICT be an enabler of value co-creation within the context of HEIs. Drawing from the theory of service dominant logic (SDL) and the work systems theory (WST), we posit that; using a service-oriented enterprise architecture (SOEA) as an enabler, HEIs can co-create value between themselves through six key value drivers i.e.; connectivity, automation, agility, service interactions, intelligence, and resource integration. This extends Ching et al, [1] model on the co-creation dynamics in B2B environments.
Active online learning of the bipedal walking

For legged robot walking pattern learning, the current mainstream and state-of-the-art researches are most under a so-called computer simulation based framework, where the walking pattern is learned via a pre-established simulation platform. However, when the learned walking pattern is applied to a real robot, an additional adapting procedure is always required, due to the big difference between simulation and real walking circumstances. This turns out to be more critical for a bipedal walking, because its controlling is more difficult than others, such as quadruped robot. In this paper, a novel framework for active online learning bipedal walking directly on a physical robot is proposed. To let the learning procedure to be of both fast convergence and high efficiency, a polynomial response surrogate model, an orthogonal experimental design based active learning strategy as well as a gradient ascent algorithm are used. The experimental results on a real humanoid robot PKU-HR3 show its effectiveness, indicating that the proposed learning framework is a promising alternative for bipedal walking pattern learning.
Achievable environment rigidity of a haptic display to guarantee stability

To study the effect of transmission stiffness on the stability of a haptic display in virtual reality, a one-DOF(degree of freedom) haptic display system is modeled as a two-port electric network, and its absolute stable condition is provided according to two-port stable theory. Stability of a haptic display system can be evaluated by the achievable spring rigidity when displaying a parallel spring and damping virtual environment. The suggested absolute stable condition meets well with what is already generally accepted when the transmission stiffness trends to be infinite. Lastly the PHANToMDesktop is equivalent to be one-DOF on the manipulating space, and method for analyzing the stability and dynamic isotropy of analogous multi-DOF device is provided. Experimental results based on PHANToMDesktop show that the transmission stiffness has large effect on system stability.
PubChem's BioAssay Database

PubChem (http://pubchem.ncbi.nlm.nih.gov) is a public repository for biological activity data of small molecules and RNAi reagents. The mission of PubChem is to deliver free and easy access to all deposited data, and to provide intuitive data analysis tools. The PubChem BioAssay database currently contains 500 000 descriptions of assay protocols, covering 5000 protein targets, 30 000 gene targets and providing over 130 million bioactivity outcomes. PubChem's bioassay data are integrated into the NCBI Entrez information retrieval system, thus making PubChem data searchable and accessible by Entrez queries. Also, as a repository, PubChem constantly optimizes and develops its deposition system answering many demands of both high- and low-volume depositors. The PubChem information platform allows users to search, review and download bioassay description and data. The PubChem platform also enables researchers to collect, compare and analyze biological test results through web-based and programmatic tools. In this work, we provide an update for the PubChem BioAssay resource, including information content growth, data model extension and new developments of data submission, retrieval, analysis and download tools.
Resolving the paradox of evolvability with learning theory: How evolution learns to improve evolvability on rugged fitness landscapes

It has been hypothesized that one of the main reasons evolution has been able to produce such impressive adaptations is because it has improved its own ability to evolve -- "the evolution of evolvability". Rupert Riedl, for example, an early pioneer of evolutionary developmental biology, suggested that the evolution of complex adaptations is facilitated by a developmental organization that is itself shaped by past selection to facilitate evolutionary innovation. However, selection for characteristics that enable future innovation seems paradoxical: natural selection cannot favor structures for benefits they have not yet produced, and favoring characteristics for benefits that have already been produced does not constitute future innovation. Here we resolve this paradox by exploiting a formal equivalence between the evolution of evolvability and learning systems. We use the conditions that enable simple learning systems to generalize, i.e., to use past experience to improve performance on previously unseen, future test cases, to demonstrate conditions where natural selection can systematically favor developmental organizations that benefit future evolvability. Using numerical simulations of evolution on highly epistatic fitness landscapes, we illustrate how the structure of evolved gene regulation networks can result in increased evolvability capable of avoiding local fitness peaks and discovering higher fitness phenotypes. Our findings support Riedl's intuition: Developmental organizations that "mimic" the organization of constraints on phenotypes can be favored by short-term selection and also facilitate future innovation. Importantly, the conditions that enable the evolution of such surprising evolvability follow from the same non-mysterious conditions that permit generalization in learning systems.
Graph based segmentation with minimal user interaction

In this paper, we present a graph based segmentation method that only requires a single point from user initialization. We incorporate a new image feature into the segmentation scheme. It is derived from a vector field that takes into account gradient vector interactions across the image domain, and has the simplicity of edge based features but also proves to be a useful region indication in two-level segmentation. Effective vector field diffusion is proposed to deal with excessive image noise. Based on a single user point we unravel the image and transfer the object segmentation into a height field segmentation in polar coordinates, which in effect imposes a star shape prior. The search of a minimum closed set on a node weighted, directed graph produces the segmentation result. Comparative analysis on real world images demonstrates promising performances of the proposed method in segmentation accuracy and its simplicity in user interaction.
Design of TCP SYN Flood DDoS attack detection using artificial immune systems

TCP Flood DDoS attack is one of the most commonly-used attacks. DDoS has a huge impact on the victim because DDoS attack can multiply the power of attack and makes the victim server unavailable for the normal user. Recently, there are many types of research about DDoS attacks and the detection or mitigation techniques. One of the novel methods to detect DDoS is using dendritic cell algorithm (DCA). DCA is a kind of artificial immune system in the evolutionary algorithm that can be used as an anomaly detection. The DCA is also designed to solve the problem in network intrusion detection. In this paper tries to make a design of TCP Flood DDoS attack detection using artificial immune systems, especially dendritic cell algorithm.