Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloning and Expanding Graph Transformation Rules for Refactoring

Refactoring is a software engineering technique that aims at enhancing the structure of object-oriented software while preserving its behavior. Several authors have studied how graph transformation can be used to specify refactoring, because such specifications are more precise and can thus, in principle, easier be verified to preserve a program's behavior. It has turned out that " standard " ways of graph transformation do not suffice to define refactoring: their expressive power must be increased if they shall be useful in this application area. Two mechanisms have been proposed so far: one for cloning, and one for expanding nodes by graphs. However, the mechanisms and notations needed are rather complex. In this paper we provide, in the context of double pushout graph transformation, a more elegant and intuitive description. It is based on a notion of rule instantiation, where the instantiation transforms rule schemes into rule instances by cloning and expansion. The power of the technique is demonstrated by an application to two well-known refactoring operations.
PREPUTATION BASED TRUST MANAGEMENT SYSTEM FOR MALICIOUS FOG NODE DETECTION

Fog computing is a geo-distributed computing network; trust must be established between fog nodes for secured data communication. Decision-making can be aided by each nodes' ability to foresee the behaviours of other nodes according to trust value calculated, either directly or indirectly. Especially in indirect trust value calculation, decision to accept the recommending nodes value is critic due to malicious fog nodes which cause internal attacks like self-promotion attack, bad-mouthing attack, and on-off attack. This paper proposes a reputation based trust management system (RTMS) for malicious fog node detection among the recommending fog nodes using geometric probability distribution on multi-dimensional attributes. This RTMS keeps track of the past records of the fog node to calculate the reputation value of each recommender using weighted geometric mean in the fog environment. The proposed technique successfully detects the malicious fog nodes, that are possible among the recommending fog nodes, and allows only the trustworthy node’s recommendation for indirect trust calculation, thereby eliminating malicious nodes. The simulation shows that RTMS outperforms the previous work in terms of suitability and security.
of ice motion during the outburst of a glacier-dammed

During the outburst flood of a glacier-dammed lake on Gornergletscher, Switzerland, in July 2004, the drained lake water triggered anomalous glacier motion. At the onset of the outburst, the ice-flow direction in the vicinity of the lake became closer to the central flowline. When the lake discharge magnitude decreased, the flow direction altered such that the ice moved back to the azimuth of the initial motion. At one of the survey points, where the ice flows parallel to the central flowline, the ice accelerated along the pre-event flow direction followed by a 180 8 backward motion that lasted over 2 days. These observations indicate the impact of the lake outburst on the subglacial and englacial stress conditions; however, the reversal in the flow direction is difficult to explain by drawing on our current understanding of glacier mechanics. The timing and the timescale of the flow-direction changes suggest that the elastic glacier motion and its rebound played a role under the rapidly changing stress conditions, but the Young’s modulus of ice is too large to cause the observed ice motion. Other processes, including basal separation and subglacial sediment deformation, are discussed as possible mechanisms for the reversal of the ice motion.
A greedy dimension reduction method for classification problems

In numerous classiﬁcation problems, the number of available samples to be used in the classiﬁer training phase is small, and each sample is a vector whose dimension is large. This regime, called high-dimensional/low sample size is particularly challenging when classiﬁcation tasks have to be performed. To overcome this shortcoming, several dimension reduction methods were proposed. This work investigates a greedy optimisation method that builds a low dimensional classiﬁer input. Some numerical examples are proposed to illustrate the performances of the method and compare it to other dimension reduction strategies.
Increased release of tumour necrosis factor-(cid:225) in human tear ﬂuid after excimer laser induced corneal wound

Aims —To measure the pre-and postoperative tear ﬂuid tumour necrosis factor-(cid:225) (TNF-(cid:225) ) concentration and release in patients undergoing excimer laser pho-torefractive keratectomy (PRK). Methods —Tear ﬂuid samples from 18 PRK patients were collected with scaled microcapillary tubes preoperatively (day 0), on the second (day 2), and on the seventh (day 7) postoperative days. The TNF-(cid:225) concentration was measured using a double antibody radioimmunoassay, and the TNF-(cid:225) release was calculated by multiplying the concentration by the tear ﬂuid ﬂow in the collection capillary. Results —The mean tear ﬂuid ﬂow in the capillary was 22.5 µl/min (range 1.5–93.2) on day 0, 80.7 µl/min (3.0–219, p = 0.0002) on day 2, and 14.6 µl/min (1.8–41.7, NS) on day 7. The mean TNF-(cid:225) concentration and release values were: day 0, 358 ng/l (110–680) and 9.5 pg/min (0.2–37.5, NS); day 2, 417 ng/l (< 5–750, NS) and 28.6 pg/min (0.6–81.5; p = 0.003); and day 7, 320 ng/l (< 5–735, NS) and 4.8 pg/min (0–25.4, NS), respectively.
VIDEO REPRESENTATION BASED ON OPTICAL FLOW FOR DYNAMIC CONTENT ANALYSIS

The efficient organization of multimedia databases challenges content -based representation to retrieve the video of interest. This paper aims to represent given video by considering its dynamic content through the analysis of optical flow. It is tended to have video segmented into overlapped sequence of frames based on gray content similarity. This step can facilitate analysis of complex video into elementary scenes. The principle involved in representing the content of video is considering the spatial movement of video content across the frames. The algorithm is designed to find the dynamic content by observing all levels of motion in the video through pyramid generation. Then, an optical flow is derived in terms of spatial and temporal information of motion regions. The histogram representation is created with both the rank and orientation of the optical flow. This kind of methodology contributes to efficient representation which enhances effective content analysis to improve the efficiency of further stages. The videos of You Tube 8M and UCF Sports data sets have used to evaluate the algorithm.
SECURITY THREATS, COUNTERMEASURES AND DATA ENCRYPTION TECNHINQUIES ON THE CLOUD COMPUTING ENVIROMENT

In the world of computing, cloud computing has emerged as one of the fastest-emerging technologies. As more and more businesses store data in the cloud, it's possible to hack, alter, or delete that data since the servers are in remote locations. The main problem with cloud computing is security breaches, which must be solved flawlessly. Since the use of cloud computing is increasing, more security issues are emerging. Therefore, it is necessary to study this topic, analyze the new emerging security issues targeting cloud computing data, and search for solutions to mitigate these issues. It is important to know what types of attacks target the cloud computing data, what techniques are used for such attacks, and how to protect cloud computing users from these security issues. Cloud computing data security can be implemented in a variety of ways, one of which is through data encryption. This paper reviews the major data security issues present in cloud computing. A comparison study was made of several encryption techniques used in cloud computing. Moreover, this paper discusses the encryption platforms available for safeguarding cloud data and compares them to others based on factors such as security, complexity, usability, supported OS, advantages, and disadvantages.
University Drop-Out - The Case of Italy

We combine individual and aggregate-level level data on educational attainment to study the determinants of university drop-out in Italy, one of the worst performers among developed countries. Based on detailed information on a representative sample of secondary school graduates and on local university supply we first show that family and educational background are relevant determinants of continuation probability. In particular, our results show that accounting for enrollment-induced sample selection significantly enhances the estimated coefficients with respect to standard probit analysis. We then combine our estimates with data on family and educational backgrounds of secondary school graduates in comparable European countries and find that differences in endowments only explain a minor fraction of the observed cross-country gap in studentsi? ½ attainments.
Landscape Builder: Software for the creation of initial landscapes for LANDIS from FIA data

I developed Landscape Builder to create spatially explicit landscapes as starting conditions for LANDIS Pro 7.0 and LANDIS II landscape forest simulation models from classified satellite imagery and Forest Inventory and Analysis (FIA) data collected over multiple years. LANDIS Pro and LANDIS II models project future landscapes by simulating tree growth, tree species succession, disease, insects, fire, wind, and management disturbance. Landscape Builder uses inventory plot attributes from the FIA inventory database, FIA unit map, National Forest type map, National Forest size class map, land cover map, and landform map to assign FIA plot attributes to raster pixels representing a real forest landscape. In addition to creating a detailed map of current (initial) forest landscape conditions, the software produces specific files required for use in LANDIS Pro 7.0 or LANDIS II format. Other tools include the ability to create a dominant species and age-class map from previously created LANDIS maps, a tool to create a dominant species and age-class map from a stand map and field plot data, and a tool to convert between Esri ascii rasters and Erdas file format types.
The Effect of Spatial Resolution on the Correlation Structure of Gate III Rainfall Fields

The presen叫t study uses a fine scale data set from GATE ph祖 e III to i 扭 nves品 t i ga叫 . t s pa訕也 i al eπ 'ects on rainfall cha缸 ra缸 ct扭 er吋 is刮 tic臼 s and its lagged aut岫 oc叩 or叮 Tela訕 tion. Due to the 直 ner s truct机 ure of the present data set, the physical processes invloved for a shorter distance scale can be identified, which has crucial information in the estimation for the sampling eπors of a rain field. It is found that for a rainrate less than 4 mm/ hr, a 1 km by 10 and 4 km by 40 data set will not make any noticeable difference to accumulated rainfall statistics. This is directly implied by stratiform rainfall associated with the mesoscale circulation system. The rainfall within that physical region is continuous with a ramra 個 less th祖 6 mm/hr. A rainfall rate greater than 40 mm/hr contributes 20 % to the total rainfall in 1 km by 10 d叫 a, in contrast to an 8 % contribution from the 4 km by 40 data. The area-averaged processes suppress extreme rainfall considerably (the extreme would have come from the convective scale). The variance and the autocorrelation calculated in this study reveal that the slope of the variance and autocorrelation for a smaller scale are different from those estimated through a larg叮叮 ea data. This is an indication that the convective scale rainfall field caused this change, and the existence of a horizontal integral length scale for the shorter scale end is pο曲 s ula扭 d. The implications of a horizontal integral length scale and suggestions for further research are discussed.
Metal-Based Nanoparticle Interactions with the Nervous System: Metal-Based Nanoparticle Interactions with the Nervous System: The Challenge of Brain Entry and the Risk of Retention in the The Challenge of Brain Entry and the Risk of Retention in the Organism Organism

This review of metal-and metal-oxide-based nanoparticles focuses on factors that influence their distribution into the nervous system, evidence that they enter brain parenchyma, and nervous system responses. Emphasis is placed on gold as a model metal-based nanoparticle and for risk assessment in the companion review. The anatomy and physiology of the nervous system, basics of colloid chemistry, and environmental factors that influence what cells see are reviewed to provide background on the biological, physical-chemical, and internal milieu factors that influence nervous system nanoparticle uptake. The results of literature searches reveal little nanoparticle research has included the nervous system, which about equally involved in vitro and in vivo methods, and very few human studies. The routes of uptake into the nervous system and mechanisms of nanoparticle uptake by cells are presented with examples. Brain nanoparticle uptake inversely correlates with size. The influence of shape has not been reported. Surface charge has not been clearly shown to affect flux across the blood-brain barrier. There is very little evidence for distribution of metal-based nanoparticles into brain parenchyma. Metal-based nanoparticle disruption of the blood-brain barrier and adverse brain changes have been shown, and are more pronounced for spheres than rods. Study concentrations need to be put in exposure contexts. Work with dorsal root ganglion cells and brain cells in vitro show the potential for metal-based nanoparticles to produce toxicity. Interpretation of these results must consider the ability of nanoparticles to distribute across the barriers protecting the nervous system of the intact organism. Effects of the persistence of poorly soluble metal-based nanoparticles are of particular concern.
USING ASSOCIATION RULE LEARNING TO DETECT DDOS ATTACK IN SPV

Lightweight Bitcoin clients can authenticate that a transaction is included in the blockchain, without the need of downloading the complete blockchain through Simple Payment Verification (SPV). A vast majority of lightweight clients utilizes SPV. However, with the increase in the popularity of SPV, many attacks have been launched against it such as Spoof, Sniff, Distributed Denial of Service (DDoS). As SPV requires high processing speed, flexibility, and stability, association rule learning can be effectively used for attack detection in SPV. In this paper, we implement a DDoS attack detection system using Association rule learning in SPV. Then, we used a KDD dataset to analyze the detecting accuracy and processing time in comparison with a machine learning approach. In addition, we also used NSL-KDD and GureKDD datasets to validate the effectiveness of our method. Our results show that Association rule learning algorithm is capable of detecting the DDoS attack in SPV.
HYBRID APPROACH FOR FACIAL CUES BASED EMOTION PROFILE GENERATION

Emotion analysis is very significant from the perspective of many applications like E-learning, cognitive assessment, driver alert system, pain monitoring system, healthcare services, interactive TV, animation etc. Outcome of the work is depicted in the form of emotion profile which is defined as graphical representation of the degree of presence or absence of all the universally accepted seven emotions on a single scale. This simple but unique representation helps in determining the presence of complex/naturalistic emotions. Complex emotions represents the emotional state of a person when he/she is able to feel and express more than one emotions simultaneously at the time of observation. Emotion analysis through facial expressions is experimented on JAFFE and MIST Database - a locally created context specific database of images. Images of the subjects under study are captured and experimented for all seven universally accepted emotions and depicted in the form of emotion profiles. Emotions are categorized as neutral, positive and negative for MIST database and in seven categories of emotions like happy, surprise, anger, sad, disgust, fear and neutral for JAFFE database. Average accuracy obtained as 91.31% for MIST database and 93.84% for JAFFE Database. FPR - false positive rate and FNR - false negative rate, values for JAFFE database are 6.38% and 6.62% respectively. FPR and FNR values for MIST database are 8.18% and 8.60% respectively. Emotion recognition time for JAFFE database 1.108 sec and for MIST database 1.392 Sec. These performance parameters especially the accuracy more than 90% which is at par with the research results published in renowned journals in this domain makes this work qualify to be used for various applications. Analysis of complex emotions is typically useful for the professionals working in the field of Human resource, clinical psychology, cognitive analysis etc. More accurate emotion recognition is still challenging and open research problem
A Revised Interpretation of Marine Primary Productivity in the Indian Ocean: The Role of Mixoplankton

Traditional interpretations of marine plankton ecology, such as that in the Indian Ocean, mirror the plant-animal dichotomy of terrestrial ecology. Thus, single-celled phytoplankton produce food consumed by single-celled zooplankton, and these are in turn consumed by larger zooplankton through to higher trophic levels. Our routine monitoring surveys, research, models, and water management protocols all re ﬂ ect this interpretation. The last decade has witnessed the development of an important revision of that traditional vision. We now know that the phytoplankton-zooplankton dichotomy represents, at best, a gross simpli ﬁ cation. A signi ﬁ cant proportion of the protist plankton at the base of the oceanic food-web can photosynthesise (make food ‘ like plants ’ ) and ingest food (eat ‘ like animals ’ ), thus contributing to both primary and secondary production simultaneously in the same cell. These protists are termed ‘ mixoplankton ’ , and include many species traditionally labelled as ‘ phytoplankton ’ (a term now reserved for phototrophic microbes that are incapable of phagocytosis) or labelled as ‘ protist zooplankton ’ (now reserved for protist plankton incapable of phototrophy). Mixoplankton include various harmful algal species, most likely all the phototrophic dino ﬂ agellates, and even iconic exemplar ‘ phytoplankton ’ such as coccolithophorids (which can consume bacteria). Like all signi ﬁ cant revisions to ecology, the mixoplankton paradigm will take time to mature but to ignore it means that we fail to properly represent plankton ecology in teaching, science, management, and policy. This chapter introduces the mixoplankton functional groups and provides the ﬁ rst insight into the biogeography of these organisms in the Indian Ocean. A ﬁ rst attempt to consider the implications of the mixoplankton paradigm on marine primary productivity and ecology in the Indian
Digital Kenyon: Research, Digital Kenyon: Research, Scholarship, and Creative Exchange Scholarship, and Creative Exchange

During aerobic growth of Escherichia coli , expression of catabolic enzymes and envelope and periplasmic proteins is regulated by pH. Additional modes of pH regulation were revealed under anaerobiosis. E. coli K-12 strain W3110 was cultured anaerobically in broth medium buffered at pH 5.5 or 8.5 for protein identiﬁcation on proteomic two-dimensional gels. A total of 32 proteins from anaerobic cultures show pH-dependent expression, and only four of these proteins (DsbA, TnaA, GatY, and HdeA) showed pH regulation in aerated cultures. The levels of 19 proteins were elevated at the high pH; these proteins included metabolic enzymes (DhaKLM, GapA, TnaA, HisC, and HisD), periplasmic proteins (ProX, OppA, DegQ, MalB, and MglB), and stress proteins (DsbA, Tig, and UspA). High-pH induction of the glycolytic enzymes DhaKLM and GapA suggested that there was increased fermentation to acids, which helped neutralize alkalinity. Reporter lac fusion constructs showed base induction of sdaA encoding serine deaminase under anaerobiosis; in addition, the glutamate decarboxylase genes gadA and gadB were induced at the high pH anaerobically but not with aeration. This result is consistent with the hypothesis that there is a connection between the gad system and GabT metabolism of 4-aminobutanoate. On the other hand, 13 other proteins were induced by acid; these
Impact Of Anxiety On Performance Of Taekwondo Athlete Through Progressive Muscle Relaxation Training

: Anxiety is a reaction of body in certain situation that includes fatigue, restlessness, irritability, tense muscles, panic attack & irrational fears. Anxiety has overwhelming impact on athlete’s performance and it is very important to overcome anxiety without any adjournment. In this Perspective Muscle Relaxation techniques contribute vehemently to control anxiety and helps to enliven the potentials of the athlete. Progressive muscle relaxation is known as a beneficial technique which is employed to relax the muscles. The purpose of this study is to calculate the impact of anxiety on performance. As anxiety has inverted-U impact on athlete’s performance, however the impact of anxiety has been measured. The second purpose of this study has been to analyze the impact of anxiety through progressive muscle relation on athlete’s performance that how an athlete can overcome his/her anxiety during competition. To proceed this study, 20-taekwondo athletes participated in it, who were divided into two experimental group and control group. Anxiety questionnaire was used to measure the anxiety level of athletes. After that 6-week progressive muscle relaxation training was conducted for experimental group. Pre and Post anxiety level were measured. A competition was conducted between experimental and control group to measure the performance. Performance was measured by scoring points of both groups. The results of this study show that experimental group has a high level of performance rather than control group. Hence, it proved that progressive muscle relaxation has positive impact athlete’s performance.
Analyses Of Scale Free Models And Inter-Disciplinary Applications

: Scale Free Networks have emerged as a promising field of network science. These networks are topologically evolving networks where stochastic processes govern the addition of new nodes and links and removal of old ones from the network. Recently, the scale free topological features have been observed in diverse and broad range of networks across the world which has surged interest in this field of network science. Though scale free networks are so pervasive, yet very little is known about their structure and behaviour. This paper discusses the basic characteristics of scale free networks and critically analyses various scale free architectural models and their applications across different fields. Thereafter, few open research areas are discussed to offer opportunities for future research directions.