Miracle-dz/UniFAR-FTUs huggingface dataset:
A Java-based, 3-tier framework for stateful database applications on the WWW

The WWW is currently the dominant software techology for the deployment of telematic applications as well as corporate intranet applications. It is based on a stateless model of operation which causes substantial problems in the adaptation of legacy, stateful information systems (and related applications). A number of issues pertaining to the porting of systems like DBMSs to the WWW are investigated. The practices that have been followed are reviewed. We propose a CORBA based architecture that operated behind a Servlet-enabled WWW server. Database related operations are performed statefully while the relevant objects are persistently handled across hits belonging to the same session. We show how the suggested sheme deals with statefull applications through a series of examples.
A Unified View of Multi-step Temporal Difference Learning

Temporal-difference (TD) learning is an important approach for predictive knowledge representation and sequential decision making. Within TD learning exists multi-step methods which unify one-step TD learning and Monte Carlo methods in a way where intermediate algorithms can outperform either extreme. Multi-step TD methods allows a practitioner to address a biasvariance trade-off between reliance on current estimates, which could be poor, and incorporating longer sequences of sampled information, which could have large variance. In this dissertation, we investigate an extension of multi-step TD learning aimed at reducing the variance in the estimates, and provide a unified view of the space of multi-step TD algorithms. In Monte Carlo methods, information about the error of a known quantity is sometimes incorporated in an attempt to reduce the error in the estimation of an unknown quantity. This is known as the method of control variates, and has not been extensively explored in TD learning. We show that control variates can be formulated in multi-step TD learning, and demonstrate their improvement in terms of learning speed and accuracy. We then show how the inclusion of control variates provides a deeper understanding of how n-step TD methods relate to TD(λ) algorithms. We then look at a previously proposed method to unify the space of n-step TD algorithms, the n-step Q(σ) algorithm. We provide empirical results and analyze properties of this algorithm, suggest an improvement based on insight from the control variates, and derive the TD(λ) version of the algorithm. This ii generalization can recover existing multi-step TD algorithms as special cases, providing an alternative, unified view of them. Lastly, we bring attention to the discount rate in TD learning. The discount rate is typically used to specify the horizon of interest in sequential decision making problems, but we introduce an alternate view of the parameter with insight from digital signal processing. By allowing the discount rate to take on complex numbers within the complex unit circle, we can extend the types of knowledge learnable by a TD agent into the frequency domain. This allows for online and incremental estimation of the extent at which particular frequencies exist in a signal, with the standard discounting framework corresponding to the zero frequency case.
Evaluation of Ration Based on Soy Sauce By-Product on Addition of Acacia and Chestnut Tannin: An In Vitro Study

Feed is a main challenge for a livestock farm, especially during dry season when feed production is low. Such condition may cause low livestock productivity due to insufficient nutrient intake. Provision of high quality forages from grasses and legumes are limited by land competition for various developmental reasons. Therefore, an opportunity to provide feed for livestock is through utilization of agroindustrial by-products such as soy sauce by-product. The objective of the present study was to evaluate soy sauce by-product on addition of tannins, i.e., Acacia mangium Wild. (AT) and chestnut tannin (CT) using an in vitro rumen fermentation technique. The following treatments were tested: soy sauce by-product as control (R1), soy sauce by-product + 1% AT (R2), soy sauce by-product + 2% AT (R3), soy sauce by-product + 1% CT (R4) and soy sauce by-product + 2% CT (R5). The treatments were incubated in vitro with buffered-rumen fluid in four replicates, represented by three incubation units per replicate, and conducted for 24 h at 39 °C. Parameters measured in this research were in vitro gas production at regular time point intervals. Other parameters measured after the incubation were dry matter degradation, organic matter degradation, total volatile fatty acids (VFA), ammonia (NH3) and pH. Data were tested using analysis of variance (ANOVA), if there was a significant different at P<0.05 then continued with Duncan’s multiple range test. The results of this study showed that the addition of tannins, both acacia and chestnuts, had no significant effect (p>0.05) to dry matter degradation, organic matter degradation, total volatile fatty acids (tVFA), NH3 and pH. Acacia tannin was able to influence (p<0.05) the accumulation of gas production at 8, 12 and 24 h. It can be concluded that the addition of tannin, both acacia and chestnuts up to level 2% does not interfere with the fermentation process of soy sauce by-product in the rumen an in vitro.
Accurate, Detailed and Automatic Tree Modelling from Point Clouds

Trees are of great significance throughout the world, both in urban scenes and in natural environments. Models of trees can be widely applied in various fields, for instance, landscape design, geo-simulation, environment modelling, and forestry inventories. Recently, laser scanning technology has been rapidly developed, making it possible to effectively acquire geometric attributes of trees and achieve accurate 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. Nevertheless, some works don’t ensure sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user interactions. In this thesis, we propose a novel method to accurately and automatically reconstruct tree branches from laser scanned points. We first employ the Minimum Spanning Tree (MST) algorithm to extract an initial tree skeleton over the single tree point cloud, then simplify the skeleton through iterative removal of redundant components. A global-optimization approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. The results show that our approach is adaptable to various trees with different data qualities. We also demonstrate both the topological fidelity and geometrical accuracy of our approach without significant user interactions. The resulted tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc.
MEDULLARY CONGESTION IN OVARY OF TOPICALLY APPLIED PARAPHENYLENE DIAMINE EFFECTED RAT

Objective: To evaluate the effect of topically applied paraphenylene diamine (PPD) on medulla of ovary in Sprague dawley rat. Study Design: Laboratory based randomized control trial. Place and Duration of Study: The study was carried out in the Anatomy Department, Army Medical College, Rawalpindi; in collaboration with animal house, National Institute of Health, Islamabad. The study duration was one year, from Jan 2016 to Dec 2016. Material and Methods: Forty adult Sprague Dawley female rats, weighing 200-300 grams, were used and divided into 4 groups with 10 rats in each group. Group A served as control group and animals were applied with distilled water on dorsal surface clipped free of hair. Group B, C and D were painted with 1mg, 2mg and 3mg per kg body weight of paraphenylene diamine in addition to distilled water on dorsal surface clipped free of hair. The solution was applied for 30 minutes daily for a continued duration of 60 days. All animals were sacrificed on day 60 and right ovary of each rat was removed, fixed in 10% formalin, processed and sectioned. For histological study haematoxylin and eosin (H&E) stains were used. Results: On microscopic examination it was observed that topically applied paraphenylene diamine solution caused vascular congestion in the medulla of ovary. Conclusion: It was concluded from results that topically applied paraphenylene diamine solution induces medullary congestion in ovary of rat.
Removal of cationic dyes by adsorption in a single and binary system using activated carbon prepared from the binary mixture

ABSTRACT An activated carbon (AC) was developed from a mixture of almond and apricot stone shell with a sufficient source of raw material to investigate its ability to remove BY51 and BB3 dyes from aqueous solutions. It was found that the adsorption of BY51 (657.89 mg/g) and BB3 (588.24 mg/g) dyes in a single system can be better described with Langmuir isotherm according to the R2 values. Equilibrium uptake of BY51 and BB3 in binary mixture onto AC declined with a rise in concentrations of the other dye due to their antagonistic behavior. ELE and JSME models were employed to determine the isotherm data for the binary systems. BY51 and BB3 adsorption kinetics were also analyzed in single and binary systems.
A modelling framework for collaborative network emotions

ABSTRACT Recent research in collaborative networks have shown that the development of “human-inspired” friendly systems with cognitive models of factors such as stress, trust, decision-making ability, and emotion can be seen as promising approach to prevent technological failures and, consequently contribute to their sustainability. Accordingly, an human-emotions-based approach is proposed aiming at conceptualizing the idea of collaborative network players “having” and “feeling” emotions, helping in the management of potential collaboration conflicts, decision-making, and collaborative network governance. This paper presents the underlying theoretical background to support emotional processes within collaborative networks and the description of the proposed modelling framework – the Collaborative EMOtion modelling framework (C-EMO).
Characteristics of Single Aerosol Particles during Pollution in Winter in an Urban Area of Ningbo, China

The size and composition of individual atmospheric particles were assessed using a single particle aerosol mass spectrometer in Ningbo, China, from 30 December 2016 to 12 January 2017. The particles were primarily carbonaceous and inorganic, with the majority (60%) being carbonaceous. All of the major particle types contained internally mixed secondary species, such as nitrate and sulfate. The temporal trends of the particle number concentrations and PM2.5 (particulate matter with an aerodynamic diameter < 2.5 µm) mass concentrations indicated that secondary formation in Ningbo severely affected the air quality. The sampling period was divided into three subperiods according to the PM2.5 mass concentration. During Period I, local emission and secondary formation were the primary contributors of the pollution, and during Periods II and III, the abundance of particles occurring with high-speed winds indicated that regional transport was a primary factor in these two pollution processes. Additionally, high PM2.5 mass concentrations were often observed at night during high relative humidity and low temperatures across the entire sampling period. These results demonstrate that stagnant meteorological conditions increase pollution during winter. Overall, this study enhances our understanding of particulate pollution in the southern Yangtze River Delta region and provides useful information on the formation and growth of atmospheric aerosols.
A novel thermal acoustic device based on vertical graphene film

Graphene is an excellent thermoacoustic material due to its small heat capacity per unit area. However, there is obvious thermal leakage from the substrate which lower the sound emission efficiency. Here, in order to improve the efficiency, vertical graphene has been used as thermoacoustic device. In this paper, the high-quality vertical graphene was prepared by the MPCVD method, and the height of the vertical graphene was about 3.4 μm. Then a thermoacoustic device was fabricated based on vertical graphene. Compared to graphene, the unique structure of vertical graphene makes the device less area to substrate with less thermal leakage and can produce a higher sound pressure level (SPL) per unit area under the same height, the SPL value can reach as high as 60 dB at a measure distance of 0.3 cm with input power of 1.2W. This sound device based on vertical graphene is potential to be used in a wide range of applications.
Learning Prosody in a Video Game-Based Learning Approach

With the growth in popularity of video games in our society many teachers have worked to incorporate gaming into their classroom. It is generally agreed that by adding something fun to the learning process students become more engaged and, consequently, retain more knowledge. However, although the characteristics of video games facilitate the dynamics of the educational process it is necessary to plan a pedagogical project that includes delimitation of learning goals and profile of the addressees, the conditions of application of the educational project, and the methodologies of evaluation of the learning progress. This is how we can make a real difference between gamification and video game based learning. The paper addresses the design of an educational resource for special education needs (SEN) students that aims to help teach communicative skills related to prosody. The technological choices made to support the pedagogic issues that underlie the educational product, the strategies to convert learning content into playful material, and the methodology to obtain measures of its playability and effectiveness are described. The results of the motivation test certified that the video game is useful in encouraging the users to exercise their voice and the indicators of the degree of achievement of the learning goals serve to identify the most affected prosodic skills.
Research on Road Traffic Flow Status Based on Multi-source Monitoring Data

This paper mainly solves the traffic flow problem based on multi-source monitoring data. By using the methods of time-space fusion, traffic flow dynamics, in-depth learning and queuing theory model, the multi-monitoring data of Shenzhen's main roads are analyzed. The traffic flow description model and congestion prediction model are constructed, and the optimal plan for traffic police departments to cope with the traffic situation is explored, which provides a new way to solve the urban traffic pressure.
Identifying the Ranking of Focal Points in Coordination Games on the Individual Level

We propose a method to identify the ranking of focal points (Schelling, 1960) on the individual level. By contrast to conventional coordination, where subjects bet on only one alternative, subjects coordinate by the distribution of points. This allows them to invest in multiple alternatives and to weigh their choices. As a result, subjects not only reveal which alternative appears most focal to them, but the ranking of the available alternatives with regard to the degree of focality. In an experiment on the elicitation of social norms (Krupka and Weber, 2013), we compare the proposed mechanism with conventional coordination. The data confirms the theoretical predictions regarding coordination behavior and demonstrates that the proposed technique is suited to identify the heterogeneity of focal points on the individual level. Moreover, using Monte Carlo simulations, we find that the proposed mechanism identifies focal points on the group level significantly more efficiently than ordinary coordination. Finally, we point to the possibility to use the mechanism as a simple and direct tool to measure the degree of strategic uncertainty on the individual level.
Spike Response Function weight and delay updating strategy using delay rules

Spike Response Function (SRF) plays an important role in the temporal coding Spiking Neural Network (SNN) as it has a significant role to determine when the neuron should fire. This paper studies the important role of the SRF in the SNN learning stability. It proposes a novel method to find out the rules to update delay for each class to make SRF stable, and then using these rules to update delay and weight simultaneously at the SNN learning rule. This method updates the delay depending on the local result to make SRF stable. The main issue of this paper is to put forward the idea that weight and delay parameters could and need to be updated simultaneously to make both SRF and SNN stable during the learning process. The delay rules strategy which have been found could be used for pattern recognition application which use SNN. The limitation of this work is that; getting the updating delay rules depends on a sample data from each class and the way of selecting the rules.
Analyse von Trackingsystemen für Kopf-Tracking in virtuellen Umgebungen

There are multiple possibilities for navigation in a virtual environment. One of them is “real walking“ in the real world. A common solution to show the virtual world to the user is HMD (head mounted display). Hence, for continuously updating the virtual scene, the user motion and head orientation must be tracked. This can be done with a tracking-system. Apart from introducing to the basic concepts of tracking, this thesis compares an inside-out and an outside-in system. Hereby the requirements of the virtual reality application and the setup of the trackingsystem play an important role. Performance parameters for tracking-systems are introduced and measurement concepts will be presented. With the proposed measurement methods differences between the two tracking-systems were found. The outside-in system has the better stability and precision. In contrast, the inside-out system is very flexible in mounting and can easily be scaled to any room size. For comparing the measurement results, the working principle of the tracking-system has to be considered. The application has a high impact on the choice for a specific tracking-system as well.
An intelligent malware classification framework

Malicious software or malware has risen to become a primary source of most of the attacks taking place across the Internet over the last decades. This prevalence of new malware, for which signatures are not available, along with the challenge of anti-malware software to keep up with the continuous stream of new malware, has made the adoption of classification/clustering approaches necessary. Machine-learning methods have been excessively applied to classify or cluster malware into families, based on different features derived from static or dynamic review of the malware. While these approaches demonstrate promise, they are themselves subject to a growing array of countermeasures. In this work, we propose a framework to enhance the traditional machine learning-based classification by utilizing high-level domain knowledge. We outline major behaviours of Windows malware from an analyst’s point of view and provide possible methods (rules) to extract them from the output of static and dynamic analysis tools. We also take advantage of memory forensics to extract other stealthy aspects of an executable, which otherwise remain undetected. Our comparative experimental results with the state-of-the-art malware classification approaches, confirm the effectiveness of our framework by an average classification accuracy of 81%, while leaving only 0.5% of samples unlabeled.
iAgent: When AI Meets Mobile Agent

The past few decades have seen the rapid development of artificial intelligence (AI) technology in diverse industries. Inspired by the fact that an agent is the central part of AI, we combine AI with the technology of the mobile agent. A mobile agent used in wireless sensor networks (WSNs) is popular for its mobility, executability, and autonomy. To improve the intelligence of the mobile agent, we proposed a conceptual theoretical framework named iAgent, where i means intelligent, and the agent refers to the mobile agent. Four designs of iAgent are detailed. Compared with the old mobile agent, the iAgent has a learning ability, which means that it can dynamically plan the path according to the external environment in order to reduce energy consumption. Based on iAgent, we also proposed a method to determine the number of iAgents and their visiting areas in a multi-iAgent WSN environment. The extensive simulation indicates that the multi-iAgent algorithm can significantly improve the performance of the WSNs, especially in saving energy and balancing network load.
High-Performance PEDOT:PSS Flexible Thermoelectric Materials and Their Devices by Triple Post-Treatments

Searching an effective method to enhance the thermo-electric properties of flexible organic films can significantly widen the application of flexible thermoelectric devices. Tuning the microstacking structure and oxidation level can effectively optimize the thermoelectric properties of poly(3,4-ethylenedioxithiophene):poly(styrenesulfonate) (PEDOT:PSS) organic films. Here, we adopt triple post-treatments with formamide (CH3NO), concentrated sulfuric acid (H2SO4), and sodium borohydride in sequence to engineer flexible PEDOT:PSS thermoelectric films. A high power factor of 141 mu Wm(-1) K-2 at 25 degrees C has been obtained for the PEDOT:PSS film. Such a high power factor stems from the high sigma (1786 Scm(-1)) and S (28.1 mu VK-1) after post-treatment with CH3NO, H2SO4, and NaBH4 in order. The increased carrier mobility resulting from both the selective removal of excess insulating PSS within the films and the conformation transition after CH3NO and H2SO4 treatments is responsible for the enhancement of sigma, while the subsequent NaBH4 treatment optimize the electrical properties (sigma and S) by modulating the oxidation level. A homemade thermoelectric device has also been fabricated using the as-prepared flexible PEDOT:PSS films and had a high output power density of similar to 1 mu Wcm(-2) with human arm as a heating source. This study indicates that flexible thermoelectric devices based on cheap conducting polymers have great potential in wearable electronics.
Survey of Multi Entity Bayesian Networks (MEBN) and its applications in probabilistic reasoning

: Bayesian networks have been at the core of the research pertaining to probabilistic reasoning. Several machine learning algorithms and techniques highly rely on Bayesian networks for their reasoning capabilities. Since several decades Bayesian networks proved to be the essential tool in the hands of researchers working in artificial intelligence domain. Yet Bayesian networks do have certain limitations [1], Bayesian networks need extensions to be more expressive and functional for probabilistic reasoning needs of various domains. Multi Entity Bayesian Networks (MEBN) is a theory combining expressivity of first-order logic principles and probabilistic reasoning of Bayesian networks. The paper thoroughly introduces the MEBN theory with an example modelling for a problem description and discusses various other works which included MEBN theory as a core of their research. The study in this paper concludes with the current highlights and challenges in MEBN and future developments.