Miracle-dz/UniFAR-FTUs huggingface dataset:
A Genetic Algorithm Optimized Decision Tree- SVM based Stock Market Trend Prediction System

Prediction of stock market trends has been an area of great interest both to researchers attempting to uncover the information hidden in the stock market data and for those who wish to profit by trading stocks. The extremely nonlinear nature of the stock market data makes it very difficult to design a system that can predict the future direction of the stock market with sufficient accuracy. This work presents a data mining based stock market trend prediction system, which produces highly accurate stock market forecasts. The proposed system is a genetic algorithm optimized decision tree-support vector machine (SVM) hybrid, which can predict one-day-ahead trends in stock markets. The uniqueness of the proposed system lies in the use of the hybrid system which can adapt itself to the changing market conditions and in the fact that while most of the attempts at stock market trend prediction have approached it as a regression problem, present study converts the trend prediction task into a classification problem, thus improving the prediction accuracy significantly. Performance of the proposed hybrid system is validated on the historical time series data from the Bombay stock exchange sensitive index (BSE-Sensex). The system performance is then compared to that of an artificial neural network (ANN) based system and a naive Bayes based system. It is found that the trend prediction accuracy is highest for the hybrid system and the genetic algorithm optimized decision tree- SVM hybrid system outperforms both the artificial neural network and the naive bayes based trend prediction systems.
THE RELATIONSHIP BETWEEN ONLINE READING RATES AND PERFORMANCE ON PROFICIENCY TESTS

This article examines the relationship between online reading speed rates and performance on proficiency tests, given the proliferating use of the Internet. The study involved twenty-five post-graduate students enrolled in an ESL Course at the Private Center for Teaching English as a Foreign Language in Kuwait and who are also involved in postgraduate studies. Twelve were familiar with the computer while others were not. Tools used included a background questionnaire to tap into readers’ online accessing of reading materials and their computer familiarity, the Online Speed Reading Test, and a simulated TOEFL Reading Subtest . The main finding of this study is that high rates of reading speeds are positively correlated with good performance on the CBT TOEFL subtest of Reading Comprehension. A correlation co-efficient assessed between the statistics of high -speed readers and high performance rates of subjects of the study reached 0.92. The results of this study suggest that designers of onlin e information should consider that reading online is slower than on paper, which may affect performance on proficiency assessments.
Imputed Meaning: An Exploration of How Teachers Interpret Grades

ABSTRACT Grading and the interpretation of grades are inextricable facets of teachers’ work. Nonetheless, teachers find it challenging to explain the inferences they draw from grades and the decisions they make based upon grades. This article explores teachers’ thinking about the grades assigned by their peers. This study used a think-aloud task in which 21 teachers placed 24 fictional student record cards in remedial, average, or advanced programs, based upon student grades earned from Grades 4 through 7. The teachers’ responses to the task exposed the many meanings they impute to grades and the biases that their interpretations revealed, providing concrete evidence of how biases influence the decisions teachers make about students.
Ubiquitin ligase UBR3 regulates cellular levels of the essential DNA repair protein APE1 and is required for genome stability

APE1 (Ref-1) is an essential human protein involved in DNA damage repair and regulation of transcription. Although the cellular functions and biochemical properties of APE1 are well characterized, the mechanism involved in regulation of the cellular levels of this important DNA repair/transcriptional regulation enzyme, remains poorly understood. Using an in vitro ubiquitylation assay, we have now purified the human E3 ubiquitin ligase UBR3 as a major activity that polyubiquitylates APE1 at multiple lysine residues clustered on the N-terminal tail. We further show that a knockout of the Ubr3 gene in mouse embryonic fibroblasts leads to an up-regulation of the cellular levels of APE1 protein and subsequent genomic instability. These data propose an important role for UBR3 in the control of the steady state levels of APE1 and consequently error free DNA repair.
Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions

MOTIVATION Gene fusions resulting from chromosomal aberrations are an important cause of cancer. The complexity of genomic changes in certain cancer types has hampered the identification of gene fusions by molecular cytogenetic methods, especially in carcinomas. This is changing with the advent of next-generation sequencing, which is detecting a substantial number of new fusion transcripts in individual cancer genomes. However, this poses the challenge of identifying those fusions with greater oncogenic potential amid a background of 'passenger' fusion sequences. RESULTS In the present work, we have used some recently identified genomic hallmarks of oncogenic fusion genes to develop a pipeline for the classification of fusion sequences, namely, Oncofuse. The pipeline predicts the oncogenic potential of novel fusion genes, calculating the probability that a fusion sequence behaves as 'driver' of the oncogenic process based on features present in known oncogenic fusions. Cross-validation and extensive validation tests on independent datasets suggest a robust behavior with good precision and recall rates. We believe that Oncofuse could become a useful tool to guide experimental validation studies of novel fusion sequences found during next-generation sequencing analysis of cancer transcriptomes. AVAILABILITY AND IMPLEMENTATION Oncofuse is a naive Bayes Network Classifier trained and tested using Weka machine learning package. The pipeline is executed by running a Java/Groovy script, available for download at www.unav. es/genetica/oncofuse.html.
Effects of a high rate of gain for increasing lengths of time on body and mammary growth in prepubertal dairy heifers

A high rate of gain in dairy heifers for #8805 3 mo is detrimental to prepubertal mammary growth and subsequent milk yield. However, the effects of a high rate of gain for shorter periods have not been reported. Our objective was to compare effects of high (1200 g/d) and low (600 g/d) rates of gain over different lengths of time (0, 3, 6, 12 wk) on body and mammary growth. Heifers (age = 11 wk, n = 64, BW = 107 kg, SE = 1.0) were randomly assigned to 1 of 4 treatments: H0 (low diet fed for 12 wk); H3 (low diet fed for 9 wk followed by high diet for 3 wk); H6 (low diet fed for 6 wk followed by high diet for 6 wk); and H12 (high diet fed for 12 wk). Animals were slaughtered at 23 wk of age. Statistical analysis used the GLM procedure of SAS and tested multiple comparisons using the Bonferroni test. Statistical significance was declared at P<0.05. Average daily gain and final live weights were different for all comparisons except H0 versus H3 (H0 = 662, H3 = 660, H6 = 848, H12 = 1124 g/d, SE = 12; H0 = 165, H3 = 166, H6 = 181, H12 = 203 kg, SE = 1). Final withers height was greater for H6 and H12 (H0 = 100, H3 = 100, H6 = 102, H12 = 104 cm; SE = 0.3). Carcass wt were different for all comparisons (H0 = 77, H3 = 82, H6 = 92, H12 = 107 kg; SE = 1). Mammary hemigland mass increased with time on high diet (H0 = 529, H3 = 591, H6 = 768, H12 = 864 g/100 kg carcass wt; SE = 43). Mass of perirenal fat also increased with time on the high diet (H0 = 900, H3 = 1181, H6 = 1608, H12 = 1794 g/100 kg carcass wt; SE = 105). Short-term changes in diet altered growth of body and mammary tissues. Composition analysis of carcass and mammary tissues are ongoing.
Hochschild Cohomology versus De Rham Cohomology without Formality Theorems

We exploit the Fedosov-Weinstein-Xu (FWX) resolution proposed in q-alg/9709043 to establish an isomorphism between the ring of Hochschild cohomology of the quantum algebra of functions on a symplectic manifold M and the ring H(M, C((h))) of De Rham cohomology of M with the coefficient field C((h)) without making use of any version of the formality theorem. We also show that the Gerstenhaber bracket induced on H(M,C((h))) via the isomorphism is vanishing. We discuss equivariant properties of the isomorphism and propose an analogue of this statement in an algebraic geometric setting.
New power aware routing algorithm for MANETs using gateway node

Mobile Adhoc Network (MANET) is a collection of wireless mobile nodes. Each node works as a router and forms a dynamic topology. Energy is an important resource in the MANET, since every node has equipped with limited battery-power. In this paper, we propose a model called a power-aware routing algorithm for MANET using gateway node. Focus of this work is to minimize number of control message packets, energy consumption and increases the throughput.
Understanding Exercise Behaviour: Examining the Interaction of Exercise Motivation and Personality in Predicting Exercise Frequency

This study tested the self-determination theory in relation to exercise participation with the aim of identifying whether degree of autonomy mediates the relationship between personality traits and exercise participation. One hundred gym attendees completed a questionnaire assessing Big Five personality traits. Behavioural Regulation scales, and frequency of exercise participation. As predicted by Self-Determination Theory, more autonomous forms of exercise motivation were more strongly correlated with increased exercise participation while External behavioural regulation and Amotivation were negatively correlated with exercise participation. In addition. Extraversion, Conscientiousness andAgreeableness predicted exercise frequency, with further analysis indicating that the Extraversion and Conscientiousness effects were mediated by Intrinsic and External motivation. Agreeableness was not mediated however, and indicates that individuals who are more self-focused are likely to engage in a more frequent exercise regime. These results help to elucidate previous contradictory findings on the effects of personality on exercise participation by emphasising the interaction of individual personality with the intrinsic motivation of a specific activity. Programs encouraging increased exercise should focus on helping participants to find intrinsically enjoyable activities and stress the importance of a certain level of self-focus in committing to a more active lifestyle if they are to succeed.
Supporting Everyday Thinking Practices in Information Visualization Interfaces

People commonly sketch externalizations on paper and whiteboards as part of their everyday thinking processes. While common, this practice is little understood, particularly as it may relate to digital visual representation (such as information visualization) and sketch-based interaction. My research aims to better understand these thinking sketches from the perspective of information visualization and pen-and-touch interaction and to apply this understanding to the design of interfaces that can better support complex and freeform everyday thinking practices.
An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific Phenotypic Traits

Recent advances in genotyping methodologies have allowed genome-wide association studies (GWAS) to accurately identify genetic variants that associate with common or pathological complex traits. Although most GWAS have focused on associations with single genetic variants, joint identification of multiple genetic variants, and how they interact, is essential for understanding the genetic architecture of complex phenotypic traits. Here, we propose an efficient stepwise method based on the Cochran-Mantel-Haenszel test (for stratified categorical data) to identify causal joint multiple genetic variants in GWAS. This method combines the CMH statistic with a stepwise procedure to detect multiple genetic variants associated with specific categorical traits, using a series of associated I × J contingency tables and a null hypothesis of no phenotype association. Through a new stratification scheme based on the sum of minor allele count criteria, we make the method more feasible for GWAS data having sample sizes of several thousands. We also examine the properties of the proposed stepwise method via simulation studies, and show that the stepwise CMH test performs better than other existing methods (e.g., logistic regression and detection of associations by Markov blanket) for identifying multiple genetic variants. Finally, we apply the proposed approach to two genomic sequencing datasets to detect linked genetic variants associated with bipolar disorder and obesity, respectively.
A Perspective Analysis of Traffic Accident using Data Mining Techniques

database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries in this world. Roadway patterns are useful in the development of traffic safety control policy. This paper deals with the some of classification models to predict the severity of injury that occurred during traffic accidents. I have compared Naive Bayes Bayesian classifier, AdaBoostM1 Meta classifier, PART Rule classifier, J48 Decision Tree classifier and Random Forest Tree classifier for classifying the type of injury severity of various traffic accidents. The final result shows that the Random Forest outperforms than other four algorithms.
Faculty Change Agents as Adult Learners: The Power of Situated Learning

ABSTRACT Faculty development is often under-emphasized in community colleges (or 2-year colleges; 2YCs). This research reports on data collected from a National Science Foundation (NSF) project involving 2YC geoscience faculty as “change agents. ” We studied 22 geoscience faculty involved in a NSF funded longitudinal project that focused on three project strands: supporting academic success of all students; facilitating professional pathways of students in geoscience and STEM; and broadening participation in the geosciences. The participants were involved in intensive professional development (PD) workshops that were supplemented by virtual activities. Using situated learning and adult learning theory as theoretical frameworks, we discuss connections between faculty as learners and effective practices for faculty development toward evidence-based teaching and learning practices. Based on baseline interview data with individual faculty prior to their involvement as change agents, we found that faculty members’ disciplinary background influenced how the participants approached their own learning. Using participants’ reflections and workshop feedback, we found that the situated learning that occurred during the PD sessions and change agent team meetings was supported by the opportunity to apply new teaching strategies into current classes and other aspects of their faculty work. This study concluded that leveraging adult learning theories provides enhanced opportunities for the occurrence of situated learning for faculty members.
Fusion and combination in audio-visual integration

Language is essentially multi-modal in its sensory origin, the daily conversation depending heavily on the audio-visual (AV) information. Although the perception of spoken language is primarily dominated by audition, the perception of facial expression, particularly that of the mouth, helps us comprehend speech. The McGurk effect is a striking phenomenon where the perceived phoneme is affected by the simultaneous observation of lip movement, and probably reflects the underlying AV integration process. The elucidation of the principles involved in this unique perceptual anomaly poses an interesting problem. Here we study the nature of the McGurk effect by means of neural networks (self-organizing maps, SOM) designed to extract patterns inherent in audio and visual stimuli. It is shown that a McGurk effect-like classification of incoming information occurs without any additional constraint or procedure added to the network, suggesting that the anomaly is a consequence of the AV integration process. Within this framework, an explanation is given for the asymmetric effect of AV pairs in causing the McGurk effect (fusion or combination) based on the ‘distance’ relationship between audio or visual information within the SOM. Our result reveals some generic features of the cognitive process of phoneme perception, and AV sensory integration in general.
Stability and Convergence of an Effective Numerical Method for the Time-Space Fractional Fokker-Planck Equation with a Nonlinear Source Term

Fractional Fokker-Planck equations (FFPEs) have gained much interest recently for describing transport dynamics in complex systems that are governed by anomalous diffusion and nonexponential relaxation patterns. However, effective numerical methods and analytic techniques for the FFPE are still in their embryonic state. In this paper, we consider a class of time-space fractional Fokker-Planck equations with a nonlinear source term (TSFFPENST), which involve the Caputo time fractional derivative (CTFD) of order 𝛼 ∈ (0, 1) and the symmetric Riesz space fractional derivative (RSFD) of order 𝜇 ∈ (1, 2]. Approximating the CTFD and RSFD using the L1-algorithm and shifted Grunwald method, respectively, a computationally effective numerical method is presented to solve the TSFFPE-NST. The stability and convergence of the proposed numerical method are investigated. Finally, numerical experiments are carried out to support the theoretical claims.
Dual-Band Matching Properties of the C-Section All-Pass Network

The all-pass C-section is introduced as a replacement for the classic quarter-wave section in the design of wideband dual-band impedance-matching networks. First, the impedance-matching properties of the all-pass C-section are presented, showing its dual-band behavior. Second, a simple adaptation to dual-band designs of the well-known wideband designs with binomial and Chebyshev responses is introduced. Finally, a dual-band power divider is presented, as a test of the introduced design technique.
SysML fault modelling in a traffic management system of systems

Systems of systems (SoSs) are vulnerable to faults, for example arising as a result of the distribution and independence of their constituent systems. Our previous work has presented an initial framework for reasoning about faults and fault-tolerant design within an SoS at the architectural level, using a simple example of a single failure. In this paper we present a motivating example of an SoS within which failures of constituent systems (CSs) may lead to a degraded or partial service for the SoS. We discuss a possible extension to our earlier framework to allow an architectural engineer to reason about the fault tolerance of an SoS where CSs contribute negatively or positively towards a goal or an SoS-level failure, and where multiple SoS faults may interact to affect SoS service quality.
Secure consensus averaging for secure information fusion in sensor networks

In this work, we have examined the problem of distributed consensus averaging over senor networks from a novel point of view considering the need for security. We have proposed a method for incorporating privacy into the scalable average consensus mechanisms. Our proposed method, Random Projections Method (RPM), is lightweight and transparent since it is not based on cryptography and does not require any change in the fusion system. RPM is based on introducing a simple, yet effective pre-fusion algorithm. We mathematically derived the correctness of RPM and analyzed its effect on convergence of the system through simulation. Robustness of RPM against honest-but-curious adversaries is analyzed and it is shown that the proposed method has maximum robustness saving that the victim has at least one non-colluding neighbor.
Experiment in Non Word Repetition by Monolingual Arabic Preschoolers

The paper attempted to check childrenʼs phonological memory and phonological awareness by asking them to repeat various non-words. A non word repetition experiment was conducted, using 30 non words which ranged from 2 to 4 syllables. Half of the non words were target-like and half were non target-like. Subjects were asked to repeat the non words as accurately as they could. Results showed that long non words were repeated more erroneously than short non words. In addition, word-likeness influenced the accuracy of repetition; where non words with high word likeness taxed more errors by children. A note was provided on specific items and individuals which triggered more errors than others.
An Accurate and Stochastic-Based Approach to Timing-Driven Partitioning and Placement

We present an accurate new approach to timing-driven partitioning and placement that is particularly suited to deep submicron technology, and to the large circuits that are implemented on such chips. We present rationale for why iterative-improvement based partition-driven placement should be the approach of choice for optimizing any objective like timing, power and area. We establish desirable characteristics for the nodegain function of such a placement approach for timing minimization, and then show how previous techniques have been deficient in them. These drawbacks are remedied in our proposed approach, which is expected to be faster and more effective than current techniques. The speed increase will derive partly from the fact that our method is partition-driven, which, being a divide-and-conquer technique, is much faster than numerical or statistical (e.g., simulated annealing) methods. Our technique’s increased effectiveness will result from our more accurate modeling of net connectivity and delay, and a node gain formulation that is directly related to minimizing the maximum path delay and that incorporates futuristic stochastic information. Finally, while formulating our new timing-driven approach, we also establish some results using the Elmore-delay model on good placement and routing of nets for minimizing the interconnect delays to their most-critical sink nodes.
Increased conspiracy beliefs among ethnic and Muslim minorities

In the present study, we tested whether Muslim minority members are more susceptible to conspiracy theories than majority members in the Netherlands. We examined conspiracy theories that are relevant (portraying the Muslim community as victim or Jewish people as perpetrators) and irrelevant for participants' Muslim identity (about the 2007 financial crisis, and other theories such as that the moon landings were fake). Results revealed that Muslims believed both identity-relevant and irrelevant conspiracy theories more strongly than non-Muslims. These differences could not be attributed to the contents of Muslim faith: Ethnic minority status exerted similar effects independent of Muslim identity. Instead, evidence suggested that feelings of both personal and group-based deprivation independently contribute to belief in conspiracy theories. We conclude that feelings of deprivation lead marginalized minority members to perceive the social and political system as rigged, stimulating belief in both identity-relevant and irrelevant conspiracy theories.