Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature selection in large dataset processing, especially in the video domain

The rapid growth and wide applications of digital video data have led to a significant need for video classification. Unfortunately, the gap between high-level concepts and low-level features and the high time cost of video analysis are two important obstacles of efficient video data management. Feature selection, which can improve the prediction performance of the predictors, provide faster and more cost-effective predictors, and provide better understanding of the data, is introduced to address these two problems. But applying existing automatic feature selection algorithms to video data is impractical because of the unrealistic amount of computer time. So far, most feature selection technologies in video applications are based on researchers' intuition although human interaction can't satisfy the dramatic increase of video data and the multiple requirements of different users. The first automatic feature selection algorithm we proposed is the Basic Sort-Merge Tree (BSMT), which is well-adapted to the characteristics of video data. The linear time cost of BSMT allows us the practical implementation of video frame categorization. To address the problem of sparse and noisy training data in video retrieval, we proposed the Complement Sort-Merge Tree (CSMT). CSMT detects complementary relationships shown in the outer wrapper model's results, in order to reduce the influence of more coarsely quantized prediction error. We provide empirical validation of this method by instructional video retrieval. Fast-converging Sort-Merge Tree (FSMT) speeds up BSMT further by setting up only a selected portion of the feature selection tree with two evaluation metrics, in order to satisfy the higher time cost requirement of on-line video retrieval. We demonstrate it with sports video shot classification. Multi-Level Feature selection (MLFS), based on the hierarchical structure of BSMT, permits a coarse-fine scene segmentation. The basic idea is to apportion different classification costs based on the classification difficulty of the different data. We demonstrate its improvement of the efficiency of video segment boundary detection compared with BSMT with instructional video scene segmentation. Based on the feature selection algorithms mentioned, we proposed a fast video retrieval system using different feature selection algorithms and lazy evaluation. To show universality of our feature selection algorithms, we also provide some theoretical analysis. We simulate different feature selection algorithms on common synthetic datasets. The performance is compared from accuracy, efficiency and robustness. Finally, we propose the further work from two aspects: how to improve the feature selection algorithms and how to apply feature selection algorithms to different applications better.
J 1 . 2 SOURCE TERM ESTIMATION OF A VOLCANIC ASH CLOUD USING A GENETIC ALGORITHM

Volcanic ash clouds pose a serious threat to aircraft. Complete avoidance of these clouds remains the safest mitigation strategy for aircraft (Casadevall 1994, OFCM 2004, Fox 2009, Chivers 2010, Langston 2010). Thus, there is a need to accurately forecast the movement of such ash clouds. The atmospheric transport and dispersion models used for predicting ash cloud movement require accurate source term and meteorology variable input, particularly the emission rate and the transporting wind, in order to forecast the future state of the cloud. A method is presented that applies the Genetic Algorithm Variational (GAVar) approach to retrieve the emission rate and representative wind speed and direction. With that information we can refine the forecast of the ash cloud movement and provide a more accurate forecast to warn and reroute aircraft in the region. A case study is made of one event of the eruption of Mount Redoubt in 2009 (section 2). The GA-Var approach (section 3) is applied to estimate wind data and an emission rate by comparing the observed satellite data (section 4) to forecasts generated by a dispersion model (section 5) via a cost function (Fig. 1). The GAVar approach is tested first with an identical twin experiment (section 6.1) and then with the observed satellite data (section 6.2). Section 7 discusses the results and prospects for the future.
The Degree of Using Electronic Alternative Evaluation Strategies among Faculty Members and Obstacles of Using Them According to Some Demographic Variables

This study aims at identifying the degree of using Electronic Alternative Evaluation (E-AE) strategies among Faculty Members and obstacles of using them, as well as identifying significant differences in their using according to their Faculty type, years of experience, and training courses. The study used judgmental/purposive sampling method which enabled the research to choose 162 Faculty Members from two faculties (Education & Arts and Social Sciences) at Sultan Qaboos University in MuscatOman, during the academic year 2017/2018; that fully filled in a 19-items survey instrument that was used to collect primary data. Secondary semistructured interviews were conducted with 14 faculty members from the two faculties; to detect the obstacles of using E-AE strategies. The study used both qualitative and quantitative techniques such as (SPSS) and ANOVA for data analysis. The findings revealed that the degree of using E-AE strategies was intermediate. The results also revealed no statistically significant differences in Faculty Members' using for E-AE strategies due to their years of experience, while these differences were observed due to their Faculty type, and training courses.
Study on Mobile Sink based Meteorological Data Transmission Method for Wireless Sensor Networks

Sink mobility has been viewed as an important technique to improve meteorological data transmission performance for wireless sensor networks (WSNs) such as energy consumption, network lifetime, throughput, end-to-end delay etc. Also, it can largely mitigate the hot spots near sink node as sink node moves randomly or autonomously. In many applications of WSNs like meteorological observation, sensors are deployed in areas accessed by laid roads and sinks can be assembled on mobile devices like bus or handcart. In this paper, we propose a Global Best Path (GBP) meteorological data gathering algorithm based on wireless Sensor Networks with single Mobile Sink (GBPMSSN). It aims at determining the best position for the single mobile sink and further using global sensors information to generate the best scheme to gather data from specified node. Generating of best scheme is conducted by GBP algorithm which can balance energy consumption among whole sensor networks and further prolong the network lifetime. Simulation results show that our GBP-MSSN algorithm outperforms conventional algorithms like LEACH, GAF, etc.
IceT users' guide and reference.

The Image Composition Engine for Tiles (IceT) is a high-performance sort-last parallel rendering library. In addition to providing accelerated rendering for a standard display, IceT provides the unique ability to generate images for tiled displays. The overall resolution of the display may be several times larger than any viewport that may be rendered by a single machine. This document is an overview of the user interface to IceT.
AUGMENTED REALITY BASED EXPERT LEARNING ASSISTANT MODEL 1

Nowadays, increasing data should be converted into knowledge and competence and individuals should be grown with the ability to adapt this change. Thus, augmented reality techniques can be used for education. Augmented reality is a content enrichment system that aims to empower the perception of the user through placing computer-made voice, video, 2D or 3D objects on the real scene. Augmented reality applications have the structure to realize 3D interactive physical experience for the users by placing virtual objects on the real scene in line with the planned scenario. Thus, it helps the education processes to be more efficient while making it enjoyable. In this work, along with the current technologies, a new education model, which would carry the printed learning routines further to e-learning practices with the use of augmented reality, will be proposed. In this education model, the infrastructure realizes the use of the augmented reality objects through tablet, mobile phone or smart glasses as the education materials, a layered content management platform that would help both the creation of educational materials and users to receive them, analysis of the big data through data mining techniques and using the output for the user by the expert learning assistant will be proposed. Keywords— E-learning, Expert System, Big Data, Augmented Reality, Data Mining
Value-based Q ( s , S ) policy for joint replenishments

We improve the recently proposed Q(s, S) policy for the stochastic joint replenishment problem. In our value-based Q(s, S) policy, item inventories are reviewed continuously when the total demand since the last replenishment is close to Q. At each such review instant a new replenishment order is issued, if the expected cost of ordering immediately according to the (s, S) policy is less than the expected cost of deferring the order until the next demand or until the level Q is reached. We use simulation to evaluate our policy. Applying the value-based Q(s, S) policy to a standard set of 12-item numerical examples from the literature, the long-run average cost of the best known solution is reduced by approximately 1%. Further examples are also investigated and in some cases for which the cost structure implies a high service level, the cost reduction exceeds 10% of the cost of the pure Q(s, S) policy.
SoK of Used Cryptography in Blockchain Mayank Raikwar

The underlying fundaments of blockchain are cryptography and cryptographic concepts that provide reliable and secure decentralized solutions. Although many recent papers study the use-cases of blockchain in different industrial areas, such as finance, health care, legal relations, IoT, information security, and consensus building systems, only few studies scrutinize the cryptographic concepts used in blockchain. To the best of our knowledge, there is no Systematization of Knowledge (SoK) that gives a complete picture of the existing cryptographic concepts which have been deployed or have the potential to be deployed in blockchain. In this paper, we thoroughly review and systematize all cryptographic concepts which are already used in blockchain. Additionally, we give a list of cryptographic concepts which have not yet been applied but have big potentials to improve the current blockchain solutions. We also include possible instantiations (references) of these cryptographic concepts in the blockchain domain. Last but not least, we explicitly postulate 18 challenging problems that cryptographers interested in blockchain can work on. Department of Information Security and Communication Technologies, Norwegian University of Science and Technology NTNU
Sensitivity Analysis of Oscillating Hybrid Systems

Many models of physical systems oscillate periodically and exhibit both discrete-state and continuous-state dynamics. These systems are called oscillating hybrid systems and find applications in diverse areas of science and engineering, including robotics, power systems, systems biology, and so on. A useful tool that can provide valuable insights into the influence of parameters on the dynamic behavior of such systems is sensitivity analysis. A theory for sensitivity analysis with respect to the initial conditions and/or parameters of oscillating hybrid systems is developed and discussed. Boundary-value formulations are presented for initial conditions, period, period sensitivity and initial conditions for the sensitivities. A difference equation analysis of general homogeneous equations and parametric sensitivity equations with linear periodic piecewise continuous coefficients is presented. It is noted that the monodromy matrix for these systems is not a fundamental matrix evaluated after one period, but depends on one. A three part decomposition of the sensitivities is presented based on the analysis. These three parts classify the influence of the parameters on the period, amplitude and relative phase of the limit-cycles of hybrid systems, respectively. The theory developed is then applied to the computation of sensitivity information for some examples of oscillating hybrid systems using existing numerical techniques and methods. The relevant information given by the sensitivity trajectory and its parts can be used in algorithms for different applications such as parameter estimation, control system design, stability analysis and dynamic optimization.
An EMG-driven musculoskeletal model of the human lower limb for the estimation of muscle forces and moments at the hip, knee and ankle joints in vivo

This work presents the design of a novel neuromusculoskele- tal model (NMS) of the human lower limb to estimate muscle forces and moments about the hip, knee and ankle joints. This research shows it is possible to use electromyographic (EMG) signals recorded from 16 mus- cles to drive 34 musculotendon actuators and constrain their operation to simultaneously satisfy the production of moments across several de- grees of freedom (DOF) including: hip adduction-abduction, hip exion- extension, knee exion-extension, ankle dorsi-plantar exion. Past re- search proposed the use single-DOF NMS model to estimate muscle forces and joint moments. However, these models do not properly al- low muscles to operate with respect to all the DOFs associated to the joints they span. This leads to unrealistic estimations of muscle activa- tion patterns and force production dynamics. Our proposed model was able to generate muscle forces that properly satised the moments gen- erated at hip, knee and ankle joints during a variety of dynamic motor tasks.
HESML V 1 R 4 Release notes

These release notes introduce the main novelties provided by the new HESML V1R4 software library detailed as follows: (1) a software implementation for the evaluation of three pre-trained word embedding file formats which support most of state-of–the-art models reported in the literature; (2) a software implementation of an intrinsic IC model and two new IC-based semantic similarity measures introduced by Cai et al. [10]; (3) a software implementation of a fast approximation of the Wu and Palmer [69] measure commonly used in the literature; (4) the integration of a very large set of word similarity benchmarks; and finally (5), the correction of an error in our software implementation of the Leacock and Chodorow [34] measure in previous HESML versions. HESML V1R4 was developed with the aim of carrying-out a very large experimental survey of word embedding models and ontology-based semantic similarity measures based on WordNet, whose aim was to evaluate the state of the problem on the estimation of the degree of similarity between word pairs. 1 Main novelties in HESML V1R4 This new release of HESML [32] introduces some useful contributions to improve significantly its capabilities to carry-out large experimental campaigns on word similarity based on WordNet in an easy, automatic and reproducible manner. However, most significant contribution of HESML V1R4 is its capability to evaluate three pre-trained word embedding model file formats which allows to use HESML for carrying-out large experimental surveys of ontology-based semantic similarity measures and word embedding models on a same software platform for the first time. The family of ontology-based semantic similarity measures based on WordNet and the most recent family of word embedding models whose pioneering works are ∗jlastra@invi.uned. es (corresponding author) †agarcia@lsi.uned. es
Effects of Elevated Atmospheric CO 2 on Biomass Production and C Sequestration : Conventional and Conservation Cropping Systems

Increasing atmospheric CO2 concentration may impact production agriculture‘s role in sequestering carbon (C). This study was initiated (fall 1997) to compare the effects of elevated CO2 on two cropping systems (conventional and conservation). The experiment was a split-plot design replicated three times with two cropping systems as main plots and two CO2 levels (ambient and twice ambient) as subplots using open top field chambers on a Decatur silt loam (clayey, kaolinitic, thermic Rhodic Paleudults). The conventional system consisted of a grain sorghum [Sorghum bicolor (L.) Moench. ] and soybean [Glycine max (L.) Merr. ] rotation using conventional tillage practices and winter fallow. In the conservation system, sorghum and soybean were rotated and three cover crops (also rotated) were used [crimson clover (Trifolium incarnatum L.), sunn hemp (Crotalaria juncea L.), and wheat (Triticum aestivum L.)] using no-tillage practices. The conservation system had either cash or cover crops grown throughout the year with no fallow periods (in order of: clover, sorghum, sunn hemp, wheat, and soybean). Biomass responses over two complete 2-year cropping cycles (total of 4 years) and the effect of these two contrasting management systems on C sequestration were evaluated. In the conservation system, cover crop residue production (clover, sunn hemp, and wheat) was increased by high CO2, but CO2 effects on weed residue were variable in the conventional system. Elevated CO2 had a greater effect on increasing soybean residue compared to sorghum. Grain yield increases due to added CO2 were greater for soybean followed by wheat and sorghum. Differences in sorghum and soybean residue production within the different management systems were small and variable. Cumulative non-yield residue inputs (both 2 yr cycles) were increased by elevated CO2 and conservation management. Greater inputs resulted in a substantial increase in soil C concentration at the 0-5 cm depth increment in the conservation system under CO2-enriched conditions. Conservation management increased soil C concentration at lower depths (5-10 and 15-30 cm) and CO2-induced increases also occurred at the 5-10 cm depth (similar trend at15-30 cm) in both management systems. Results suggest that with conservation management in a high CO2 environment, greater amounts of crop residue could increase soil C storage as well as increase ground cover, improve water infiltration and soil water retention, and reduce erosion.
Model Checking of Software Development in Distributed Animation Rendering System

Distributed animation rendering systems are used to speed up of rendering i.e the computational-intensive phase of producing animation. How to develop softwares with high availability and efficiency is a hot topic in distributed animation rendering systems. In this paper, based on model checking method for system modeling, three modules (i.e. rendering management server, rendering nodes and storage system) are modeled and analyzed. Firstly, by analyzing the system’s typical infrastructure, messages-driven finite state machines of such three modules are studied. Then, the properties of such three modules are described using CTL(Computational Tree Logic). Furthermore, safety and livenesses are verified with SMV (Symbolic Model Verification). The methodology of this paper offers important theoretical guide to software development.
The Effects of Superimposed Whole-Body Electromyostimulation During Short-Term Strength Training on Physical Fitness in Physically Active Females: A Randomized Controlled Trial

The aim of this study was to compare the effects of short-term strength training with and without superimposed whole-body electromyostimulation (WB-EMS) on straight sprinting speed (SSS), change of direction speed (CODS), vertical and horizontal jumping, as well as on strength and power in physically active females. Twenty-two active female participants (n = 22; mean ± SD: age: 20.5 ± 2.3 years; height: 171.9 ± 5.5 cm; body mass: 64.0 ± 8.2 kg; strength training experience 5.1 ± 3.6 years) were randomly assigned to two groups: strength training (S) or strength training with superimposed WB-EMS (S+E). Both groups trained twice a week over a period of 4 weeks and differed in the application of free weights or WB-EMS during four strength (e.g., split squats, glute-ham raises) and five sprinting and jumping exercises (e.g., side and box jumps, skippings). The WB-EMS impulse intensity was adjusted to 70% of individual maximal sustainable pain. SSS was tested via 30-m sprinting, CODS by a T-run, vertical and horizontal jumping using four different jump tests at pre-, post-, and retests. Maximal strength (Fmax) and power (Pmax) testing procedures were conducted on the Leg Press (LP), Leg Extension (LE), and Leg Curl (LC) machine. Significant time × group interaction effects revealed significant decreases of contact time of the Drop Jump and split time of CODS (p ≤ 0.043; ηp2 = 0.15–0.25) for S (≤ 11.6%) compared to S+E (≤ 5.7%). Significant time effects (p < 0.024; ηp2 = 0.17–0.57) were observed in both groups for SSS (S+E: ≤6.3%; S: ≤8.0%) and CODS (S+E: ≤1.8%; S: ≤2.0%) at retest, for jump test performances (S+E: ≤13.2%; S: ≤9.2%) as well as Fmax and Pmax for LE (S+E: ≤13.5%; S: ≤13.3%) and LC (S+E: ≤18.2%; S: ≤26.7%) at post- and retests. The findings of this study indicate comparable effects of short-term strength training with and without superimposed WB-EMS on physical fitness in physically active females. Therefore, WB-EMS training could serve as a reasonable but not superior alternative to classic training regimes in female exercisers.
The South American Fruit Fly: An Important Pest Insect With RNAi-Sensitive Larval Stages

RNA interference (RNAi) technology has been used in the development of approaches for pest control. The presence of some essential genes, the so-called “core genes,” in the RNAi machinery is crucial for its efficiency and robust response in gene silencing. Thus, our study was designed to examine whether the RNAi machinery is functional in the South American (SA) fruit fly Anastrepha fraterculus (Diptera: Tephritidae) and whether the sensitivity to the uptake of double-stranded RNA (dsRNA) could generate an RNAi response in this fruit fly species. To prepare a transcriptome database of the SA fruit fly, total RNA was extracted from all the life stages for later cDNA synthesis and Illumina sequencing. After the de novo transcriptome assembly and gene annotation, the transcriptome was screened for RNAi pathway genes, as well as the duplication or loss of genes and novel target genes to dsRNA delivery bioassays. The dsRNA delivery assay by soaking was performed in larvae to evaluate the gene-silencing of V-ATPase, and the upregulation of Dicer-2 and Argonaute-2 after dsRNA delivery was analyzed to verify the activation of siRNAi machinery. We tested the stability of dsRNA using dsGFP with an in vitro incubation of larvae body fluid (hemolymph). We identified 55 genes related to the RNAi machinery with duplication and loss for some genes and selected 143 different target genes related to biological processes involved in post-embryonic growth/development and reproduction of A. fraterculus. Larvae soaked in dsRNA (dsV-ATPase) solution showed a strong knockdown of V-ATPase after 48 h, and the expression of Dicer-2 and Argonaute-2 responded with an increase upon the exposure to dsRNA. Our data demonstrated the existence of a functional RNAi machinery in the SA fruit fly, and we present an easy and robust physiological bioassay with the larval stages that can further be used for screening of target genes at in vivo organisms’ level for RNAi-based control of fruit fly pests. This is the first study that provides evidence of a functional siRNA machinery in the SA fruit fly.