Miracle-dz/UniFAR-FTUs huggingface dataset:
EEG based Emotion Classification using Bayesian Belief Networks in Pleasant and Unpleasant Odorant Stimuli

In this paper, we proposes an emotion classification method based on Bayesian Belief Networks (BBN) to classify the EEG signals which are induced by the olfactory stimuli. In order to stimulate the olfactory organ, the citralva is used as the pleasant smell and 2-melcaptoethanol is used as the unpleasant smell. We placed the 4-channel EEG electrodes on F3, F4 at frontal lobe and T3, T4 at temporal lobe to acquire EEG signals in according to the standard electrode placement, which is called international 10-20 system. The participants are five high school students (4 male and 1 female) whose ages are from 17 to 18. To extract features from EEG signals, the time-frequency analysis is performed by using the Event-Related Spectral Perturbation (ERSP). The average values of relative power of the frequency in each time domain are used as the features for the BBN classifier. To evaluate the performance of the proposed method, we compared the performance of the BBN and Naive Bayesian Networks (BN). As a result of the comparison, we confirm that the classification rate of the BBN is increased by approximately 11%. Keywords: electroencephalography, emotion classification, bayesian belief networks
Design Principles (DPs) for Building Social Media Enabled Collaborative Learning Environments (SMECLEs)

Twenty years ago, it was proclaimed that collaborative technologies were able to impact the learning environments of educational institutions. Despite research indicating that these collaborative technologies could impact the learning environments in a positive way, no change occurred. Twenty years later, the same claims are being made, where a new collaborative technology, social media, is being proclaimed to be able to impact the learning environments of education institutions, by changing, and possibly improving, the learning approach. However, while these calls are being made, there is currently a lack of understanding on whether social media platforms are effective at enabling collaborative learning. To address this lack of understanding, it is first necessary to create prescriptive knowledge on how we can build collaborative learning environments with the platforms of social media, which this study does.
Dartmouth Digital Commons Dartmouth Digital Commons

Scienti(cid:28)c applications are increasingly being implementedon massively parallel supercomputers. Many of these applications have intense I/O demands, as well as massive computational requirements. This paper is essentially an annotated bibliography of papers and other sources of information about scienti(cid:28)c applications using parallel I/O. It will be updated periodically.
CONCURRENCY AND COMPUTATION : PRACTICE AND EXPERIENCE Concurrency Computat

This special issue focuses on ‘advanced scheduling strategies and Grid programming environments’, and is based on selected papers from the 6th International Workshop on Middleware for Grid Computing (MGC 2008) and 2nd Latin American Grid (LAGrid 2008) workshop. The authors were invited to provide extended versions of their original papers taking into account comments and suggestions raised during the peer review process and comments from the audience during the workshops. MGC 2008 relevant contributions have been provided by Kane and Dillaway, Abbes et al., Reis and Cerqueira, Finger et al. and Kuijl et al. From the LAGrid 2008 side also relevant contributions have been provided by Martins et al., Costa and Cardozo, Mury et al. and Cáceres et al. These contributions focus mainly on:
Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming (Supplementary)

Consider a set of random variables X = (X1, X2, ., Xn). A Bayesian network is a directed acyclic conditional probability model. It encodes the conditional probabilistic distributions among these random variables in a graphical model (Heckerman, 1995). Using the Bayes’ rule, the network also represents the joint probability p(X = x) of the variables, where x = (x1, x2, ., xn) is the assignment of states. A factorization of the joint probability via the Bayes’ rule gives rise to these conditional probabilistic distributions, i.e. p(X = x) = ∏n i=1 p(xi|pa(xi)). A Bayesian network has two components: structure and parameters. The structure is a directed acyclic graph. In the structure, nodes represent the variables and edges indicate the conditional dependencies. Each node Xi is associated with parameters: a conditional distribution p(xi|pa(xi)), where pa(xi) denotes the configuration of the parents of Xi in the network. Suppose all random variables are discrete. Conditional probability tables are designed to store the conditional probability distribution for each single variable with respect to the others. In supervised classification problem with Bayesian network, we want to assign a vector x to one of the classes using Bayesian inference given the vector x = (x1, x2, ., xn) and a set of classes C. The objective is to find the best assignment to the sample x. The best choice is the class with the highest posterior probability, i.e. argmaxc p(c|x). Take for example naı̈ve Bayes classifier (Figure 1a), the class node (i.e. node c) is the only parent of all other predictors (i.e. x1,x2,x3 and x4), which are conditionally independent given the class variable. Tree augmented naı̈ve Bayes (TAN) (Friedman et al., 1997) approach adds new conditional dependencies to weaken the
Quantization-based language model compression

This paper describes two techniques for reducing the size of statistical back-off gram language models in computer memory. Language model compression is achieved through a combination of quantizing language model probabilities and back-off weights and the pruning of parameters that are determined to be unnecessary after quantization. The recognition performance of the original and compressed language models is evaluated across three different language models and two different recognition tasks. The results show that the language models can be compressed by up to 60% of their original size with no significant loss in recognition performance. Moreover, the techniques that are described provide a principled method with which to compress language models further while minimising degradation in recognition performance.
European Journal of Education Studies-ISSN 2501-1111

With the rapid growth of information, the ability of students to be information literate has become critically important. However students information retrieval skills required for scholarly information management are still wanting as most papers written by postgraduate learners contain irrelevant information that are not related to their study topics. This study investigated the effects of students’ information retrieval skills on scholarly information management among post graduate students in Kiambu County Kenya. The study adopted information management theory as it showed that information literacy practices influence scholarly information management. A mixed methodology with concurrent triangulation design was used to conduct the research as it allowed the researcher to collect and analyse qualitative and quantitative data concurrently. Generalization was made based on findings generated from the collected data. The study had a target of 2451 which included 40 librarians, 40 supervisors, and 249 postgraduate students. The study participants were selected through purposive and simple random procedures. The sample population included 245 postgraduate students from 4 selected Universities and 4 library staff from each of the selected universities. Data was collected using self-administered questionnaires for students and interview schedule for librarians. Pilot testing ensured certainty of instruments, the retest method helped estimate reliability (r=0.70) to ensure that data gathered were accurate and reliable and the instruments were validated by seeking constant guidance of the supervisor. To achieve all the study objectives, both qualitative and quantitative data were gathered and analyzed to generate descriptive, inferential as well as qualitatively. Quantitative data collected was analyzed using SPSS version 21. Both descriptive and Leah Muthoni Njiru, Ruth W. Thinguri, Mary Mugwe Chui A STUDY ON THE EFFECTS OF STUDENTS INFORMATION RETRIEVAL SKILLS ON SCHOLARLY INFORMATION MANAGEMENT AMONG POST GRADUATE STUDENTS IN KIAMBU COUNTY, KENYA European Journal of Education Studies Volume 4 │ Issue 12 │ 2018 246 inferential statistics were used in analyzing quantitative data while thematic analysis was used to analyze qualitative data. The analyzed quantitative data were presented using Tables. The study findings indicated that postgraduate students’ information retrieval skills normally had positive effects on the students’ scholarly information management. The results further revealed that there exists a statistical significant relationship between the independent variables and the dependent variable investigated.
European Journal of Education Studies-ISSN 2501-1111

Of the sport sciences students, 400 students were voluntarily recruited to respond to goal commitment scale and personality trait inventory. Totally 387 data including 262 males and 125 females were taken in analyses process. The relationship between personality traits and goal commitment was examined for the study. Data were analyzed in SPSS (version 16.0). The differences between the two variables were analyzed with an independent t-test. The differences between departments were analyzed with one-way ANOVA. The significance level was accepted as 0.05. The age mean was found to be 20.93±2.58. Of the participants, 34.6% were reported to be living in the county while 32.4% reported being living in the metropole. There were significant differences between genders in terms of goal commitment and personality traits (t=3.112, p<0.05). A positive correlation was found between goal commitment and extraversion (r=0.156, p<0.01) while negative correlation was found with openness to experiences (r=-0.114, p<0.05). Consequently, goal commitment and personality traits differed according to gender.
@UMass Amherst

Although semi-supervised learning has been an active area of research, its use in deployed applications is still relatively rare because the methods are often diﬃcult to implement, fragile in tuning, or lacking in scalability. This paper presents expectation regularization , a semi-supervised learning method for exponential family parametric models that augments the traditional conditional label-likelihood objective function with an additional term that encourages model predictions on unlabeled data to match certain expectations—such as label priors. The method is extremely easy to implement, scales as well as logistic regression, and can handle non-independent features. We present experiments on ﬁve different data sets, showing accuracy improvements over other semi-supervised methods.
The Early Lake Ontario barrier beach: evidence for sea level about 12.8–12.5 cal. ka BP beneath western Lake Ontario in eastern North America

An overstepped, concave‐eastward, barrier beach beneath Holocene mud in western Lake Ontario has been delineated by acoustic and seismic reflection profiles and piston cores, and related to Early Lake Ontario (ELO). The average ELO barrier depth below present mean lake level is 77.4 to 80.6 m, or about −6 to −2.8 m above present sea level. Trend surface analysis of Champlain Sea (Atlantic Ocean) marine limits defined the contemporaneous marine water surface, and projections of this surface pass ~25 m above the outlet sill of the Lake Ontario basin and extend to the ELO palaeo‐barrier, a unique sand and gravel deposit beneath western Lake Ontario. ELO was connected to the Champlain Sea above the isostatically rising outlet sill for up to three centuries after about 12.8 cal. ka BP, while the glacio‐isostatically depressed St. Lawrence River Valley was inundated by the Atlantic Ocean. During the period of this connection, ELO level was confluent with slowly rising sea level, and the lake constructed a transgressive beach deposit with washover surfaces. ELO remained fresh due to a high flux of meltwater inflow. The marine water level connection stabilized water level in ELO relative to its shore and facilitated shore erosion, sediment supply and barrier construction. Glacio‐isostatic uplift of the outlet sill, faster than sea‐level rise, lifted ELO above the Champlain Sea about 12.5 cal. ka. Shortly after, a hydrological deficit due mainly to a combination of diverted meltwater inflow and dry climate, well known from regional pollen studies, forced the lake into a lowstand. The lowstand stranded the barrier, which remains as evidence of sea level, the farthest inland in eastern North America north of the Gulf of Mexico at the time. The highest palaeo‐washover surface provides a sea‐level index point.
A Framework for Generating Query Language Code from OCL Invariants

The semantical integrity of business data is of great importance for the implementation of business applications. Model-Driven Software Development (MDSD) allows for specifying the relevant domain concepts, their interrelations and their concise semantics using a plethora of modelling languages. Since model transformations enable an automatic mapping of platform independent models (PIMs) to platform specific models (PSMs) and code, it is reasonable to utilise them to derive data schemas and integrity rules for business applications. Most current approaches only focus on transforming structural descriptions of software systems while semantical specifications are neglected. However, to preserve also the semantical integrity rules we propose a Query Code Generation Framework that enables Model-Driven Integrity Engineering. This framework allows for mapping UML models to arbitrary data schemas and for mapping OCL invariants to sentences in corresponding declarative query languages, enforcing semantical data integrity on implementation level. This supersedes the manual translation of integrity constraints and, thus, decreases development costs while increasing software quality.
I nternational J ournal of I nnovative R esearch in S cience, E ngineering and T echnology

-To authenticate a user, classification based on mouse operating behavior is proposed. The data includes co-ordinate axes, time Stamp value and mouse operations. The holistic and procedural features are extracted from the mouse behavior data. The feature distance vector is calculated using Manhattan and Dynamic Time Warping method based on feature vector for representing the original mouse feature space. Kernel PCA method is used to reduce the dimensionality of the feature distance vector. Then the one-class Support Vector Machine classifier is applied on the distance-based eigenspace feature to analyze whether the input sample is legitimate user or an imposter. The performance of the proposed method is measured by False Acceptance Rate and False Rejection Rate. The test result proves that, the proposed method KPCA with one-class Support Vector Machine provides low error rates with good accuracy than the existing method PCA with classifier.
STIC - Proposition de Sujets de Thèse 2019

General objective: Temporal action detection in untrimmed videos (long video containing several actions) is an important task for monitoring patients, building robots for assisting and other healthcare applications. Although several approaches, including the Deep Convolutional Neural Networks (CNNs), have significantly improved performance on action classification, they still struggle to achieve precise spatio-temporal action localization in untrimmed videos. Temporal action detection aims at not only recognizing the action category but also detecting the beginning and ending of an action instance. Most temporal action detection frameworks consist of two parts: action boundary proposition and action classification. The algorithm that we want to develop will be deployed in real life settings, to help senior people and their relatives to feel safer at home since video analytics intends to detect potentially
Experiments with the Negotiated Boolean Queries of the TREC 2008 Legal Track

Abstract : We analyze the results of several experimental runs submitted for the TREC 2008 Legal Track. In the Ad Hoc task, we found that rank-based merging of vector results with the reference Boolean results produced a statistically significant increase in mean F1@K and Recall@B compared to just using the reference Boolean results. In the Relevance Feedback task, we found that the investigated relevance feedback technique, when merged with the reference Boolean results, produced some substantial increases in Recall@Br without any substantial decreases on individual topics.
Exploring the Effects of Cultural Attrition on HIV and AIDS: The Case Study of Selected Community Members of from Alice Townships, Nkonkobe District, Eastern Cape Province, South Africa

Abstract Cultural attrition is a cankerworm, recipe of moral decadence and a weakening of cultural norms. The paper explored the effects of cultural attrition on the prevalence of HIV/AIDS in Alice townships of Eastern Cape. The research was explorative, descriptive and adopted qualitative design. An interview guide was the instrument utilised, and 33 samples were investigated. Findings indicate that the culture of sexual permissiveness was increasingly becoming a norm; inadequate retention and preservation of cultural values; and that cultural attrition was a recipe of modernization and apartheid. The paper recommends to the government and other pragmatists to support cultural renaissance, renovation and resuscitation.
Assessment of trace elements and its influence on physico-chemical and biological properties in coastal agroecosystem soil, Puducherry region

Abstract The concentration of trace elements Ba, Sr, V, Cr, Ni, Pb, Cu, Zn, As, Mn, and Co were assed in the surface soil of agricultural lands, Puducherry region. The ranking order of occurrence of the heavy metals in 10 farms soils was Ba > Sr > Mn > Cr > V > Zn > Ni > Cu > Pb > Co > As indicating that Ba concentration was high. Trace elements such as, Cu, Cr, V, Zn, Pb, Ni, Co, and As enrichment levels were high in current years. Most of the farming soil showed metals Ba and Sr were higher than maximum permissible limit. Organic farming (farm 3, 6, and 9) soils were containing higher amount of plant available nutrients, sol microbial population and β-glucosidase activity. Cr, Zn, Cu, Pb, Co, and As trace elements were stronger inhibitory effects on soil biological properties. The enrichment of these trace elements in agriculture soil samples conforms their higher input of trace element contaminated organic manures, synthetic fertilizer, and fungicides by the farmers.
RichTags: A Social Semantic Tagging System

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Learning aspect models with partially labeled data

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Modeling the Helpful Opinion Mining of Online Consumer Reviews as a Classification Problem

The paper addresses an opinion mining problem: how to find the helpful reviews from online consumer reviews via the quality of the content. Since there are too many reviews, efficiently identifying the helpful ones earlier can benefit both consumers and companies. Consumers can read only the helpful opinions from helpful reviews before they purchase a product, while companies can acquire the true reasons a product is liked or hated. A system is built to assess the difficulty of the problem. The experimental results show that helpful reviews can be distinguished from unhelpful ones with high precision.
Two-way relationships between slowness and offset azimuths in layered triclinic media

The theory for high-order, azimuthally varying, normal moveout analysis in horizontally layered general anisotropic (e.g., triclinic) media, has been recently well established for all types of pure-mode and converted waves. The moveout can be presented in both slownessazimuth and offset-azimuth domains. In this work we present the two-way relationships between these two different azimuths, considering only the cases of symmetric moveout: triclinic layers for pure-mode waves and monoclinic layers for pure-mode and converted waves.
Online Information Privacy: Measuring the Cost-Benefit Trade-Off

Concern over information privacy is widespread and rising. However, prior research is silent about the value of information privacy and the benefit of privacy protection. We conducted a conjoint analysis to explore individuals’ trade-offs between the benefits and costs of providing personal information to Websites. We find that economic incentives (monetary reward and future convenience) do affect individuals’ preferences over Websites with differing privacy policies. For instance, the disallowance of secondary use of personal information is worth between $39.83 and $49.78. Surprisingly, we find that cost-benefit trade-offs did not vary with personal characteristics including gender, contextual knowledge, individualism, and trust propensity.
Building an Annotated Japanese-Chinese Parallel Corpus - A Part of NICT Multilingual Corpora

We are constricting a Japanese-Chinese parallel corpus, which is a part of the NICT Multilingual Corpora. The corpus is general domain, of large scale of about 40,000 sentence pairs, long sentences, annotated with detailed information and high quality. To the best of our knowledge, this will be the first annotated JapaneseChinese parallel corpus in the world. We created the corpus by selecting Japanese sentences from Mainichi Newspaper and then manually translating them into Chinese. We then annotated the corpus with morphological and syntactic structures and alignments at word and phrase levels. This paper describes the specification in human translation and the scheme of detailed information annotation, and the tools we developed in the corpus construction. The experience we obtained and points we paid special attentions are also introduced for share with other researches in corpora construction.