Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Prediction Model of Student Performance Based on Deep Neural Network Using Massive LMS Log Data

Log data accumulated in the Learning Management System (LMS) provide high-quality information for the learning process of students. Until now, various studies have been conducted to predict students’ academic achievement using LMS log data. However, previous studies were based on relatively small sample sizes of students and courses, limiting the possibility of generalization. This study developed and validated a deep neural network model for the early prediction of academic achievement of college students using massive LMS log data. To this end, we used 78,466,385 cases of LMS log data and 165,846 cases of grade data. The proposed model predicted the excellent-grade students with a high level of accuracy from the beginning of the semester. Meanwhile, the prediction accuracy for the moderate and underachieving groups was relatively low, but the accuracy improved as the time points of the prediction were delayed. This study is meaningful in that we developed an early prediction model based on a deep neural network with sufficient accuracy for practical utilization by only using LMS log data
Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments

This paper studies the problem of expected loss minimization given a data distribution that is dependent on the decision-maker's action and evolves dynamically in time according to a geometric decay process. Novel algorithms for both the information setting in which the decision-maker has a first order gradient oracle and the setting in which they have simply a loss function oracle are introduced. The algorithms operate on the same underlying principle: the decision-maker deploys a fixed decision repeatedly over the length of an epoch, thereby allowing the dynamically changing environment to sufficiently mix before updating the decision. The iteration complexity in each of the settings is shown to match existing rates for first and zero order stochastic gradient methods up to logarithmic factors. The algorithms are evaluated on a ``semi-synthetic" example using real world data from the SFpark dynamic pricing pilot study; it is shown that the announced prices result in an improvement for the institution's objective (target occupancy), while achieving an overall reduction in parking rates.
On‐farm management of saline drainage water in arid and semi‐arid regions

The irrigated arid and semi‐arid zones in India face twin problems of overall water scarcity and waterlogging accompanied by salinity. Subsurface drainage has proved successful in the rehabilitation and conservation of waterlogged saline soils. Drainage water from subsurface drainage systems seems to be an important source of water in the next millennium to tide us over the anticipated water scarcity problem in agriculture. However, the drainage waters from subsurface drainage systems are often saline and in the absence of a natural outlet, their disposal creates a problem. Various possible options have been discussed to mitigate the environmental threat posed by low quality drainage waters. Issues relating to potential reuse of low quality drainage waters along with management practices are addressed. Effects of factors influencing the long‐term sustainable use of saline drainage waters are presented and it is emphasized that special management practices have to be followed/evolved for site‐specific water quality, soil, crop and climatic conditions. However, the extent of salt leaching and crop establishment will depend on the total amount of monsoon rains and adequate subsurface drainage. It has been concluded that the use of a part of the saline drainage water in the post‐reclamation phase will not only augment the water supply but also minimize effluent disposal requirements. Copyright © 2004 John Wiley & Sons, Ltd.
WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER

Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements of up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.
Unique Fingerprint Liveness Recognition Utilizing Convolutional Neural Systems

: With the developing utilization of biometric confirmation frameworks in the current years, parody ﬁnger print recognition has turned out to be progressively essential. In this paper, we utilize convolutional neural systems (CNNs) for ﬁngerprint liveness discovery. Our framework is assessed on the informational indexes utilized as a part of the liveness location rivalry of the years 2009, 2011
Scene Understanding and Distribution Modeling with Mixed-Integer Scene Parsing

We present a probabilistic procedural modeling strategy for capturing the distribution of scenes of varying numbers of rigid objects, motivated by the need for such models to enable verification of robot behavior in the complex open world. Our model takes the form of a parameterized scene grammar over spatial objects, which we show admits maximum a posterior scene parsing via mixed-integer convex optimization. This parsing strategy can be used to explain and annotate unlabeled scenes with the structures specified by the model, and can be used to detect and explain outliers at a part level. We demonstrate our model’s expressiveness on a cluttered 3D kitchen scene, and explore how different grammar design choices can impact the performance of this technique
Generalized Shuffled Linear Regression

We consider the shuffled linear regression problem where the correspondences between covariates and responses are unknown. While the existing formulation assumes an ideal underlying bijection in which all pieces of data should match, such an assumption barely holds in real-world applications due to either missing data or outliers. Therefore, in this work, we generalize the formulation of shuffled linear regression to a broader range of conditions where only part of the data should correspond. Moreover, we present a remarkably simple yet effective optimization algorithm with guaranteed global convergence. Distinct tasks validate the effectiveness of the proposed method.1
Approximate Dynamic Programming with Recursive Preferences

This paper builds on the theory of Ren and Stachurski (2020) to study a value function iteration algorithm that uses isotone and convex/concave function approximation operators. Conditions that ensure the stability of the algorithm are developed and applied to multiple popular schemes used in economics such as piecewise continuous linear interpolation. An experimental comparison of various techniques suggests that variation diminishing spline approximation is a powerful and versatile tool for solving models with recursive preferences using ﬁtted value function iteration. We also show that introducing momentum in the algorithm can signiﬁcantly speed-up convergence.
A Survey on Web Transactions: Application, Process and Techniques

: Due to the tremendous growth in the World Wide Web, there has been large amount of web data generated. This web data can be mined to provide results which can greatly improve the performance and help business to grow. In this paper, we have discussed about the type of web mining which uses the web server logs as data sources known as web usage mining. We have surveyed on web usage mining in terms of different applications, Process and techniques which includes clustering, classification, association rule mining etc. We also studied about the various applications of web usage mining
Effect of Virtual Reality on Self-Esteem and Presence of Adolescents : A Pre-Post Study

The definition of „virtual‟ is near and reality is what we experience as human beings. So the term „virtual reality‟ basically means „near reality‟. This study tried to understand the difference in Self Esteem of Adolescents who is completing a Successful Task in Real Life Environment and in Immersive Virtual Reality experimentally. The investigator may randomly select 60 adolescents within the age group of 13 to 19 years old. These subjects may be regard as the representatives of the population. In this experiment, the experimenter wishes to have two values or conditions (real life and virtual reality). Accordingly, 80 adolescents will be divided into two groups (40 as experimental group and 40 as controlled group). The samples are matched with respect to age and sex. The samples who fail successfully in completing the task will be excluded from the study. Two randomized group design in between-group design will be carried out to assess whether two different groups can differentiate self-esteem between real and virtual experiences. Rosenberg Self-Esteem Scale (1965) and Slater-Usoh-Steed Questionnaire (SUS) were used for the study. Descriptive statistics, paired t-test and ANOVA were employed for the data analysis. The result revealed a significant increase in selfesteem of adolescents after completing the task both in an Immersive virtual reality environment and a real environment than before doing the task. Thus it can be interpreted that tasks which can give positive affirmations can be used in interventions of adolescents with low self esteem. There is no significant gender differences in self-esteem of adolescents who are completing a successful task in immersive virtual reality but in the real environment, the females showed higher self esteem than the males who are completing a successful task. There is no significant difference in presence and self esteem of adolescents completing a successful task in the real life environment against those completing the task in virtual reality. However, there is slight increase of mean difference of Self Esteem among students successfully completing a task in virtual reality than those completing the task in real environment.
Identification of 16SrII group phytoplasma in fecting commonbean(Phaseolusvulgaris L) in Karnataka

Abstract: Common bean (Phaseolus vulgaris L) belongs to Fabaceae family and important herbaceous plant grown worldwide as fodder and human food crop. Recently symptoms of yellowing and size reduction of leave and witches broom symptoms have been observed in farmer’s field near Hessaraghatta. For conformation of phytoplasma infection symptomatic and asymptomatic common bean samples were collected from field and by using a modified CTAB methodtotal DNA was extracted. Detection of phytoplasma was done by using specific primers P1/P7 and secYF2/R2 for 16Sr DNA and secY genes. The productsof expected size 1.7 kb and 1.5 kb were obtained by PCR, cloned sequenced and compare with the 16S rRNA and sec gene phytoplasma sequences available in the NCBI database.16Sr RNA and secY gene sequences of common bean phytoplasma sharedhighest nucleotide identity with 'Candidatus Phytoplasma australasiae' (Ca.P. australasiae) 16Sr II group isolates from different parts of the world. Phylogenetic analysis also revealed the current study of phytoplasma closely clustered with the Ca. P. australasiae16Sr II group. The virtual RFLP pattern of common bean 16S rDF2nR2 fragment is showing similar reference patterns of all previously established 16Sr IIgroups. This is the identification of 16SrIIPeanut WB group phytoplasmainfecting commonbean in Karnataka.
Comparative Analysis of Spark and Ignite for Big Spatial Data Processing

Recently, spatial data became one of the most interesting fields related to big data studies, in which the spatial data have been generated and consumed from different resources. However, the increasing numbers of location-based services and applications such as Google Maps, vehicle navigation, recommendation systems are the main foundation of the idea of spatial data. On the other hand, several researchers started to discover and compared spatial frameworks to understand the requirements for spatial database processing, manipulating, and analysis systems. Apache Spark, Apache Ignite, and Hadoop are the most widely known frameworks for large data processing. However, Apache Spark, Apache Ignite have integrated different spatial data operations and analysis queries, but each system has its advantages and disadvantages when dealing with spatial data. Dealing with a new framework or system that needs to integrate new functionality sometimes becomes a risky decision if we did not examine it well The main aim of this research is to conduct a comprehensive evaluation of big spatial data computing on two well-known data management systems Apache Ignite and Apache Spark. The comparative has been done on four different domains, experimental environment setup, supported features, supported functions and queries, and performance and execution time. The results show that GeoSpark has recorded more flexibility to use than SpatialIgnite. We thoroughly investigated and discovered that multiple factors affect the performance of both frameworks, such as CPU, Main memory, data set size the complexity of data type, and programming environment. spark is more advanced and equipped with several functionalities that made it well suitable with spatial data queries and indexing. such as kNN queries; in which these functionalities are not supported in SpatialIgnite. Keywords—Big spatial data; GeoSpark; SpatialIgnite; Apache Ignite; Apache Spark
The Correlation between Rs1800795 Variant of IL-6 and Sports Performance among Turkish Elite Athletes

Background: It has been reported that plasma levels of interleukin-6 (IL-6) increase during physical exercise and are involved in mediating endurance capacity. We compared allelic and genotypic frequencies of the IL-6 gene G174C variant (rs1800795) among elite athletes and non-athletic control subject. Materials Methods: The study included 92 elite Turkish athletes and 100 non-athletes. Genomic DNA isolated and genotyped using polymerase chain reaction based-restriction fragment length polymorphism (PCR-RFLP) analysis for the IL-6 gene G174C variant. Results: In this study, the allele distribution of IL-6 gene G174C variant exhibited statistically significant difference between the elite athletes and the controls (p=0.005). The G allele was more prevalent among the elite athletes than the control subjects. The genotype frequencies in the studied population were as follows: GG, GC, and CC in the controls were 40%, 45% and 15%, and in the elite athletes were 50%, 43.5%, and 6.5%, respectively. However, there was not any statistically significant difference in the genotype frequencies of the IL-6 G174C variant between the elite athletes and the control groups (p>0.05). Conclusion: Our study suggests that a strong association exists between the IL-6 G174C variant and elite endurance performance in the Turkish population. Further studies in populations of different ethnic background are necessary to prove the association of IL-6 variant with sports performance.
Analysis of Avoid Entering Dilemma Zone by Acquiring Information on Signal Change Timing

Road intersections are accident-prone locations for traffic. To avoid accidents in the vicinity of intersections, vehicles should avoid entering dilemma zone. In this paper, a model formula was compiled that represents vehicle movement at a signalized intersection to gain an understanding how to ensure safety by making an earlier decision before the signal change to yellow. This formula clearly shows that an earlier decision can result in better safety at signalized intersections by preventing entry to the dilemma zone.
The Comparison and Evaluation of Forecasters.

In this paper we present methods for comparing and evaluating forecasters whose predictions are presented as their subjective probability distributions of various random variables that will be observed in the future, e.g. weather forecasters who each day must specify their own probabilities that it will rain in a particular location. We begin by reviewing the concepts of calibration and refinement, and describiing the relationship between this notion of refinement and the notion of sufficiency in the comparison of statistical experiments. We also consider the question of interrelationships among forecasters and discuss methods by which an observer should combine the predictions from two or more different forecasters. Then we turn our attention to the concept of a proper scoring rule for evaluating forecasters, relating it to the concepts of calibration and refinement. Finally, we discuss conditions under which one fore- caster can exploit the predictions of another forecaster to obtain a better score. In this paper we describe some concepts and methods appropriate for evaluating and com- paring forecasters who repeatedly present their predictions of whether or not various events will occur in terms of their subjective probabilities of those events. The ideas we describe here are relevant in almost any situation in which forecasters must repeatedly make such probabilistic predictions, regardless of the particular subject matter or substantive area of the events being forecast. The forecaster might be an economist who at the beginning of each quarterly period makes predictions about unemployment, the rate of inflation, or Gross National Product in that quarter based on the values of various economic indicators; the forecaster might even make predictions using a large-scale econometric model of the United States economy based on hundreds of variables and econometric relations. In a different field, the forecaster might be the weatherman for a television station who at the beginning of each day must announce his probability that it will rain during the day. For ease of exposition, we present our discussions here in the context of such a weather forecaster who day after day must specify his subjective probability x that there will be at least a certain amount of rain at some given location during a specified time interval of the day. We refer to the occurrence of this well-specified event simply as "rain". Thus, at the begin- ning of each day the forecaster must specify his probability of rain and at the end of each day he observes whether or not rain actually occurred. The probability x specified by the forecaster on any particular day is called his prediction for that day. We shall make the realistic, and simultaneously simplifying, assumption that the
IMPACT OF ATTITUDE AND SUBJECTIVE NORMS ON BEHAVIOURAL INTENTION OF CONSUMERS TO USE E-COMMERCE IN INDIA: A STUDY BASED ON TRA

: In this era of internet technology, e-commerce based businesses are seen rapidly growing in India. Therefore, actual behavioural intention to use e-commerce services is required to study due to scarcity of research in this direction. This research has used theory of reasoned action to measure intention to use by two variables, namely, “attitude” and “subjective norms”. Actual behavioural intention is studied by analysing the willingness for “recommendation to others”. In this research, quantitative based research method has used questionnaire consisting 16 items and have analysed 170 valid responses. Our findings have identified that both “attitude” and “subjective norms” have significant effects on “intention to use” which further has remarkable impact on “recommendation to others”. Our research will contribute in theoretical research as well as aid marketing practitioners improve their e-commerce based services.
Latent Matters: Learning Deep State-Space Models

Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by max-imising the evidence lower bound. However, as we show, this does not ensure the model actually learns the underlying dynamics. We therefore propose a constrained optimisation framework as a general approach for training DSSMs. Building upon this, we introduce the extended Kalman VAE (EKVAE), which combines amortised variational inference with classic Bayesian ﬁltering/smoothing to model dynamics more accurately than RNN-based DSSMs. Our results show that the constrained optimisation framework signiﬁcantly improves system identiﬁcation and prediction accuracy on the example of established state-of-the-art DSSMs. The EKVAE out-performs previous models w.r. t. prediction accuracy, achieves remarkable results in identifying dynamical systems, and can furthermore successfully learn state-space representations where static and dynamic features are disentangled.
Performance Assessment of Hearing Protection and Communication Enhancement Devices: Peltor Comtac III and IV

Abstract : Hearing Protection and communication enhancement devices were designed for those who want to maintain natural hearing while protecting their ears from impulse and continuous noises like gunfire, explosions, vehicles, and machinery. There were potential advantages for this technology in military applications, provided an accurate and complete assessment of the performance was conducted. Peltor Comtac III (modified and standard) and Comtac IV headsets were assessed for: continuous noise attenuation, impulsive peak insertion loss, auditory localization, and speech intelligibility. The Peltor Comtac headsets reduced the noise level in the ear when the user was exposed to continuous and/or impulsive noise. The devices also reduced critical aural cues required to localize sounds essential to maintaining situational awareness. The active headsets may amplify low level sounds, but the localization performance was degraded in comparison to the open ear performance. The results from the speech intelligibility measurements for the Peltor Comtac headsets were acceptable in low to moderate noise environments, however, at 105 dB, the average scores did not meet current military standards.
Microstructure development in Laser Metal Deposition of Ti-5553

Laser Metal Deposition (LMD) is promoting increased interest with regard to manufacturing parts of complex geometry. It is especially important with respect to manufacturing cost reductions for relatively expensive Titanium alloys. The rapid and directional cooling processes inherent with LMD produce nonhomogeneous microstructures and large residual stresses. Knowledge of the LMD process to optimise deposited microstructures is in high demand. The high-strength β-Titanium alloy, Ti-5Al-5Mo-5V-3Cr (Ti5553), was deposited using LMD on to a heat-treated substrate of the same alloy. Two blocks of 15 x 15 x 6.4 mm3 were made with different laser power to powder feed rate ratios followed by microstructural analyses. Both blocks have almost identical geometry and density. Low ratios of laser power to powder feed rate resulted in pure β phase in the deposited layers and the re-melted material in the substrate. High ratios resulted in larger columnar β grains, the precipitation of nano-scaled α, and a pronounced increase in microhardness ≈1 mm above and below the substrate interface. This could be detrimental to the mechanical properties of the substrate and highlights the importance of the optimisation of LMD parameters.