Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of seed availability on hoarding behaviors of Siberian chipmunk (Tamias sibiricus) in semi-natural enclosures

Abstract Mast seeding is a common phenomenon in many plant species. It is widely believed to be an adaptive strategy for large-seeded plants to escape predation by small rodents. At population level, effects of mast seeding have been well investigated but studies at individual level are still rare. In this study, using semi-natural enclosures, we investigated the effects of seed availability (simulating mast seeding) on seed hoarding behaviors of the Siberian chipmunk Tamias sibiricus on Pinus koraiensis seeds in a temperate forest, northeastern China. We found that T. sibiricus per se tended to disperse and scatter-hoard more seeds of P. koraiensis when provided with high level of seeds. With increase of seed availability, the amount of seed consumption, cache size and seed dispersal distance, cache survival tended to be larger. Our results indicate that mast seeding can benefit seed dispersal and regeneration of P. koraiensis at both individual and population levels.
Computational Complexity Evaluation of ANN Algorithms for Image Steganalysis

- The major growth of information technology is based on the way how the security measures are implemented. Steganography is a technique that implements high level security by hiding a message in a multimedia object such as image. Steganalysis is the way of detecting such hidden messages. In order to detect the presence of hidden message, artificial neural network algorithms such as Back Propagation and Radial Basis Function are used. This paper performs the computational complexity evaluation of these two algorithms.
A Synthetic Green Fluorescent Protein Gene for Plant Biotechnology

Since Green Fluorescent Protein (GFP) from Aequorea jellyfish emits green fluorescence without any additional substrates or co-factors, it has emerged as a powerful new reporter for use in a variety of organisms. However, improvements such as the alteration of the excitation spectrum and the elimination of a cryptic intron site were necessary for its efficient use in plants. An engineered synthetic GFP with a S65T mutation (replacement of the serine in position 65 with a threonine) in the chromophore has provided up to 100-fold brighter fluorescent signals than the wild-type jellyfish GFP sequence without any toxic effects. The sGFP(S65T) has been widely used for studies such as localization analysis and promoter assays. This sGFP(S65T) is also an ideal candidate for the development of transformation methods for crops and trees in which it has previously proved difficult to obtain transformants via conventional methods. A non-invasive, quantitative detection technique for this GFP has been achieved in conjunction with a fluorescent imaging system.
The Secure Migration of Data to Cloud using Data Sanitization and MAC address based AES

: Cloud computing is the most emerging technology in current information technology era. It is internet based technology where user has to pay as per usage. However the major challenges for the wider acceptance of cloud are privacy and security of data in cloud. The mathematical model of data sanitization for giving false look to sensitive data before transferring data to cloud and mac address dependent AES technique for transferring non sensitive data and sanitized data is proposed in the paper.
On Length Independent Security Bounds for the PMAC Family

At FSE 2017, Gaži et al. demonstrated a pseudorandom function (PRF) distinguisher (Gaži et al., ToSC 2016(2)) on PMAC with Ω(lq2/2n) advantage, where q, l, and n, denote the number of queries, maximum permissible query length (in terms of n-bit blocks), and block size of the underlying block cipher. This, in combination with the upper bounds of Ο(lq2/2n) (Minematsu and Matsushima, FSE 2007) and Ο(qσ/2n) (Nandi and Mandal, J. Mathematical Cryptology 2008(2)), resolved the long-standing problem of exact security of PMAC. Gaži et al. also showed that the dependency on l can be dropped (i.e. O(q2/2n) bound up to l ≤ 2n/2) for a simplified version of PMAC, called sPMAC, by replacing the Gray code-based masking in PMAC with any 4-wise independent universal hash-based masking. Recently, Naito proposed another variant of PMAC with two powering-up maskings (Naito, ToSC 2019(2)) that achieves l-free bound of O(q2/2n), provided l ≤ 2n/2. In this work, we first identify a flaw in the analysis of Naito’s PMAC variant that invalidates the security proof. Apparently, the flaw is not easy to fix under the existing proof setup. We then formulate an equivalent problem which must be solved in order to achieve l-free security bounds for this variant. Second, we show that sPMAC achieves O(q2/2n) bound for a weaker notion of universality as compared to the earlier condition of 4-wise independence. Third, we analyze the security of PMAC1 (a popular variant of PMAC) with a simple modification in the linear combination of block cipher outputs. We show that this simple modification of PMAC1 has tight security O(q2/2n) provided l ≤ 2n/4. Even if l < 2n/4, we still achieve same tight bound as long as total number of blocks in all queries is less than 22n/3.
Being a part of it: People with Intellectual Disabillities as volunteers in the Youth Olympic Games

During the Youth Olympic Winter Games event in Lillehammer, Norway, a group of students with intellectual disabilities worked as volunteers. The teachers of the class functioned in a social entrepreneurial manner, using the event to create social value for this particular group. Qualitative interviews were conducted with the group of students (n=12), and observations were made during the event. The students’ teachers (n=3) and the head of volunteers (n=1) from the organizing committee were also interviewed for triangulation, thus verifying the interpretation of the data. This study demonstrated that social value was created through the practical tasks the students with intellectual disabilities were given, especially in relation to the Olympic context of the event, and the job itself was more important than those for whom they were doing it or why. Other important sources of social value were for the students to be outside of the classroom and to be cooperating and learning from each other within the group. Last, the students had the opportunity to aid and assist, instead of being aided and assisted, and to give something back to the local community.
The Role of Sketch Engine in Multiple Types of Corpora

A large number of dictionary projects use Sketch Engine as it is the most renowned corpus management and corpus query tool. This paper sheds light on the significant role Sketch Engine plays in relation to different types of corpora. The software’s features that support the creation of multilingual dictionaries and lexigraphy are also discussed. The software keeps on updating its old features to facilitate the users, and also adds entirely new features every now and then
Pair Wise Swapping Based Hypergraph Partitioning Algorithms for VLSI Design

A VLSI integrated circuit is the most significant part of electronic systems such as personal computer or workstation, digital camera, cell phone or a portable computing device, and automobile. So development within the field of electronic space depends on the design planning of VLSI integrated circuit. Circuit partitioning is most important step in VLSI physical design process. Many heuristic partitioning algorithms are proposed for this problem. The first heuristic algorithm for hypergraph partitioning in the domain of VLSI is FM algorithm. In this paper, I have proposed three variations of FM algorithm by utilizing pair insightful swapping strategies. I have played out a relative investigation of FM and my proposed algorithms utilizing two datasets for example ISPD98 and ISPD99. Test results demonstrate that my proposed calculations outflank the FM algorithm.
Bidirectional Residual LSTM-based Human Activity Recognition

The Residual Long Short Term Memory (LSTM) deep learning approach is attracting attension of many researchers due to its efficiency when trained on high dimensional datasets. Nowadays, Human Activity Recognition (HAR) has come with enormous challenges that have to be addressed. In addressing such a problem, one can think of developing an application that can help the elderly people as an assistant when it works in collaboration with other timely technologies such as wearable devices with the help of IoT. Many research works are using a standard dataset in evaluating their proposed method in this regard. The dataset comes with its own challenge such as imbalanced classes. In this work, we propose to apply different machine learning techniques to address the specified problems and the method is validated on a standard dataset. To validate the proposed method, we evaluated using different standard metrics such as classification accuracy, precision, recall, f1-score, and Receiver Operating Characteristic (ROC) curve. The proposed method achieves an Area Under Curve (AUC) of 100%, 97.66% of accuracy, 91.59% precision, 93.75% of recall and 92.66% of F1-score respectively.
Modified Classification Technique Encountering Parallelization of Genetic Algorithms

-Document categorization is one of the most important task in various fields like education, government sector, art, industry etc. Availability of a document and need of providing a category to a document motivated the concept of Classification / Document categorization. Manual document classification is an expensive and time consuming process. The proposed idea speeds up processing and reduces manual effort, by giving category to a document automatically. The basic idea involves designing and developing a software which will give a circumference over the present classification systems. In this implementation we parallelize GA to improve the processing speed. We used Hadoops MapReduce and HDFS (Hadoop Distributed File System) framework approach. The motivation for implementing GAs in Hadoop is that MapReduce fares well in terms of scalability, fault tolerance, and ease-of-use. Hadoop is an open-source implementation of MapReduce. Hadoop is written in Java.
Ice microphysical retrievals using polarimetric radar data

Accurate estimation of bulk microphysical characteristics of ice and snow such as ice water content (IWC), mean volume diameter (Dm), and total concentration (Nt) is a challenging task due to extreme variability of ice/snow habits, size distributions, densities, shapes, and orientations. The radar reflectivity Z was the primary radar measurement for estimating ice water content IWC for decades. There have been a number of studies suggesting various IWC(Z) relations in ice / snow clouds (e.g., Sassen 1987; Atlas et al. 1995; Liu and Illingworth 2000; Hogan et al. 2006; Delanoe et al. 2014; Heymsfield et al. 2005, 2016; Protat et al. 2016). The inherent uncertainty in the IWC(Z) estimate is caused by the variability of the size distribution (SD) of ice / snow particles and (to lesser extent) by the variations of density. The IWC(Z) relations are parameterized by either Dm or Nt (e.g., Hogan et al. 2006; Bukovcic et al. 2018) which have to be estimated independently. Any generic IWC(Z) relation tends to severely underestimate high ice water content (HIWC) of very small ice crystals in high concentrations commonly encountered by commercial aviation in tropical cyclones, thunderstorm anvils, and stratiform parts of mesoscale convective systems. These encounters pose a serious risk for commercial airplanes due to possible engine power loss and damage. The values of IWC in the HIWC regions can be as high as 3 4 g m with typical median volume diameters of ice particles less than 0.5 mm (Fridlind et al. 2015; Strapp et al. 2016; Leroy et al. 2017). Because the corresponding Z is much lower than the aviation safety threshold of 20 dBZ, commuter planes do not expect high amount of ice there. Existing cloud models experience serious problems with adequate representation of ice microphysical processes partially due to the lack of reliable information about IWC, Dm, and Nt from atmospheric remote sensors and multiparameter dualpolarization measurements offer a unique opportunity to fill the gap in the existing knowledge.
Wax ester fraction of edible oils: Analysis by on‐line LC‐GC‐MS and GC×GC‐FID

The wax ester fraction of various plant oils was isolated by normal-phase HPLC (NPLC) on-line coupled to GC via the on-column interface and applying concurrent eluent evaporation. The esters were analyzed by on-line NPLC-GC-MS and by comprehensive two-dimensional GC with flame ionization detection (GC x GC-FID) off-line combined with NPLC-GC. GC x GC-FID enables to group the various classes of wax esters, in particular the phytol esters, geranylgeraniol esters and the straight-chain esters of palmitic acids and the unsaturated C 18 acids. Optimization of the GC x GC columns and the conditions must take into account the limited thermostability of the diterpene esters. Chromatograms are shown for a range of oils, with particular focus on the various classes of wax esters in olive oil and the geranylgeraniol esters 22:0 and 24:0 in a variety of oils.
Hydroxypropyl‐β‐cyclodextrin inhibits spray‐drying‐induced inactivation of β‐galactosidase

The single‐step, fast spray‐drying process may represent a valuable alternative to the multistep, time‐consuming freeze‐drying process in the area of formulation and processing of biopharmaceu‐ticals. In this study, we tested the use of sucrose and hydroxypropyl‐β‐cyclodextrin (HP‐β‐CD) as stabilizing excipients in the spray‐drying of a model protein, β‐galactosidase. The solutions were processed using a Bu? chi 190 cocurrent Mini Spray Dryer at an outlet temperature of 61 ± 2 °C. The powders were redissolved and analyzed for catalytic activity, aggregation, chemical decomposition, and thermal susceptibility as observed by high‐resolution calorimetry. Spray‐drying significantly inactivated β‐galactosidase. Spray‐drying β‐galactosidase in the presence of sucrose did not prevent inactivation. However, after spray‐drying β‐galactosidase in the presence of HP‐β‐CD, or HP‐β‐CD and sucrose, full catalytic activity was exhibited on reconstitution. Furthermore, the reconstituted product was unchanged in terms of molecular weight, charge, and thermal stability. These findings are consistent with a hypothesis that the change responsible for inactivation of β‐galactosidase was mainly a monomolecular, noncovalent change, i.e., the formation of incorrect structures, that arose from surface denaturation. This study clearly demonstrates that cyclodextrins can be useful stabilizing excipients in the preparation of spray‐dried protein pharmaceuticals.
DEVELOPMENT OF A Na-ALGINATE-BASED BIOFORMULATION AND ITS USE IN THE MANAGEMENT OF CHARCOAL ROT OF SUNFLOWER (HELIANTHUS ANNUUS L.)

The treatment of sunflower seeds with Na-alginate in combination with Ca-carbonate and carboxymethyl cellulose (CMC) showed maximum increase in plant length and weight followed by seed coated with Na-alginate in combination with Ca-carbonate and gum arabic. Maximum control of Macrophomina phaseolina infection was recorded when seeds were coated with Na-alginate in combination with Ca-carbonate and gum arabic followed by Na-alginate in combination with Cacarbonate and CMC. In another experiment when sunflower seeds were coated with Na-alginate at 1, 2 and 4% w/w with or without Trichoderma viride, T. resei, significant increase in germination was recorded when seeds were coated with Trichoderma species using Na alginate at different concentrations as a sticker. Plant length and weight was significantly higher in treated seeds as compared to non treated control. Seeds coated with T. viride using 2% Na-alginate as a sticker showed maximum increase in plant length and weight followed by seed coated with T. viride in combination with 1% Naalginate. Maximum vigor index were observed in sunflower seeds treated with T. viridi using 2% Na-alginate.
Crowd-Based Profiling : A Framework To Detect Psychological Disorders In Social Media Users

-Psychological disorders are presently striking a large number of population from various civilization, society, occupation and different locations around the world. The main obstacle of psychological disorders is the difficulty to detect on people suffering from these disorders, hence resulting in introducing a worrying amount of undetectable cases and false detection issue. Our methodology aims at constructing detective models to identify psychological disorders among online social media users. These detective models are attainable by engaging a basic data collection process formulated as crowd based profiling, which assists us to collect accurate and more efficient data set of people from various categories. Our experiment proposes that obtaining specific English language patterns and socializing attributes from data sets paves the way to deal with advanced experiments on psychological disorders.
USING COMBINATION METHODS FOR AUTOMATIC RECOGNITION OF WORDS IN ARABIC

In this paper, we propose an analytical approach for an off-line Arabic handwritten recognition system. The system is based on combining methods of decision fusion approach. The proposed approach introduces a methodology using the HMM-Toolkit (HTK) for a rapid implementation of our designed recognition system. First, a pre-processing phase is done toprepare the image of the introduced text (eliminate or reduce noise, thresholding, screening, detection of the inclination angle and the segmentation of the image of the text line). The obtained images are then used for features extraction with Sliding window technique. These features are modeled separately using Hidden Markov Models classifiers. The combination of the multiple HMMs classifiers was applied by using the different methods of decision fusion approach. The proposed system is evaluated using the IFN/ENIT database.
Simple People Tracking System Using a Static Camera

-People tracking systems are used in surveillance applications. Nowadays surveillance systems are very common in offices, ATM centers, shopping malls etc. In this paper, a method to detect and track humans from a video input is explained. For the simplicity of the work the input video is taken from a stationary camera. In this method, there are 2 basic steps (i) Foreground Extraction using Gaussian statistical model and (ii) Tracking of human is done in RGB color Space.
Fast screening for children’s developmental language disorders via comprehensive speech ability evaluation—using a novel deep learning framework

Background Developmental language disorders (DLDs) are the most common developmental disorders in children. For screening DLDs, speech ability (SA) is one of the most important indicators. Methods In this paper, we propose a solution for the fast screening of children’s DLDs based on a comprehensive SA evaluation and a deep framework of machine learning. Fast screening is crucial for promoting the prevalence and practicality of DLD screening which in turn is important for the treatment of DLDs and related social and behavioral abnormalities (e.g., dyslexia and autism). Our solution is focused on addressing the drawbacks existing in the previous DLD screening methods which include test failure due to text-based inducing material design and illiteracy of most young children, incomplete language evaluation indicators, and professional-reliant evaluation procedures. First, to avoid test failure, a novel comprehensive inducing procedure (CIP) with non-text (i.e., audio-visual) stimulus materials was designed that could cover a large range of modalities to adequately explore the comprehensive SA of the subjects. Second, to address incomplete language evaluation, a set of comprehensive evaluation indicators with full consideration of the characteristics of the children’s language acquisition is proposed; furthermore, to break the professional-reliant limitation, we specifically designed a deep framework for fast and accurate screening. Results Experimental results showed that the proposed deep framework is effective and professional with a 92.6% accuracy on DLD screening. Additionally, to provide a benchmark for the novel problem, we provide a CIP dataset with about 2,200 responses from over 200 children, which may also be useful for further DLD studies and insightful for the fast screening design of other behavioral abnormalities. Conclusions Fast screening of children’s DLDs can be achieved at accuracy up to 92.6% by our proposed deep learning framework. For successful fast screening, an elaborated CIP with corresponding comprehensive evaluating indicators is necessary to be designed for children suspected to have DLDs.
Assessing Eye Aesthetics for Automatic Multi-Reference Eye In-Painting

With the wide use of artistic images, aesthetic quality assessment has been widely concerned. How to integrate aesthetics into image editing is still a problem worthy of discussion. In this paper, aesthetic assessment is introduced into eye in-painting task for the first time. We construct an eye aesthetic dataset, and train the eye aesthetic assessment network on this basis. Then we propose a novel eye aesthetic and face semantic guided multi-reference eye inpainting GAN approach (AesGAN), which automatically selects the best reference under the guidance of eye aesthetics. A new aesthetic loss has also been introduced into the network to learn the eye aesthetic features and generate highquality eyes. We prove the effectiveness of eye aesthetic assessment in our experiments, which may inspire more applications of aesthetics assessment. Both qualitative and quantitative experimental results show that the proposed AesGAN can produce more natural and visually attractive eyes compared with state-of-the-art methods.
AN Internet of Things based Software Defined Security Service Provisioning Framework

-Software Defined Networking (SDN) is a propelling system structure perspective that spotlights on the parcel of control and data planes. SDN gets creating care both from the academic network and adventure, all through an immense number of utility spaces. The proposal of extended progression in system purposes and diminished expense for framework directors has gotten over the frameworks organization world to the innovative and farsighted of Software Defined Networking (SDN). With the surge of widely inclusive detectable quality over the system and the likelihood to programming framework contraptions, architects have rushed to remunerate a variety generally SDN-pleasant hardware, programming and organizations. Regardless, amidst this free for all of movement, one key segment has least troublesome not exceptionally far in the past entered the discourse: organize protection. In this article, confirmation in SDN is considered offering both the investigation social event and industry advances with respect to this issue.