Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpreting Dust Impact Signals Detected by the STEREO Spacecraft

There is no comprehensive understanding yet of how dust impacts on spacecraft (SC) generate signals detected by antenna instruments. The high sensitivity of the S/WAVES instrument and the large number and high diversity of dust impacts detected make the STEREO mission particularly well suited for a closer investigation. A floating perturbation model (FPP) was recently proposed to explain the characteristic shape of dust impact signals with an overshoot. The FPP model posits that the overshoot is due to the different discharge time constant of the SC and the individual antennas. Kinetic simulations are performed to demonstrate that, contrary to common belief, antennas are inefficient collectors of charged particles from impact plasmas. The collection efficiency is small, only 0.1–1%, varying weakly with the bias potential between the antenna and the SC, and more strongly with impact location. The low recollection efficiencies and an analysis of the shapes and scaling of typical and atypical signals recorded by S/WAVES suggest that, besides the mechanism described by the FPP model, there is another, possibly stronger mechanism that is responsible for generating the characteristic overshoot for most dust impact signals observed by STEREO.
Interfrequency Correlations among Fourier Spectral Ordinates and Implications for Stochastic Ground-Motion Simulation

Models for the interfrequency correlations among Fourier spectral ordinates and variances of these ordinates are presented. These covariances among Fourier spectral ordinates can be used to generate accelerograms within a stochastic simulation framework that offer improvements over traditional approaches that make use of deterministic Fourier amplitude spectra combined with a random phase spectrum. The article demonstrates that the accelerograms generated in this new way result in response spectral ordinates that have variances that are very consistent with those predicted by empirical ground-motion models. In addition, the interperiod correlations among response spectral ordinates obtained from the simulated motions are also consistent with empirically derived response spectral correlations. The study partitions the variance among Fourier spectral ordinates into between-event, between-site and within-event components, and interfrequency correlation models, and variance models are derived for each component. The between-event correlations are found to exhibit a mild degree of magnitude dependence. An important feature of the new correlation and variance models is that they can be used to generate accelerograms that are broadly consistent with conditional response spectra. This new approach has significant implications for response-history analyses within earthquake engineering that make use of conditional spectra as a target.
Augmented Reality From Turkish Researchers' Perspective

Concept of augmented reality (AR) has been attracting the attention of researchers due to its feature of combining physical environment of students and digital presentation materials and offering them to users. AR applications have made a great impression among scientists working in a wide range of fields from engineering sciences to social sciences and become the subject matter of theoretical and experimental research and different kinds of studies in a process starting from the design and programming steps of AR applications to their usage by end-users. In this research, 191 different studies conducted by Turkish scientists on AR were subjected to a document analysis, and the studies were examined according to their fields, types, research designs and population and sample characteristics. These studies were also subjected to a content analysis and scrutinized. According to the research results, AR applications have become the subject matters of product design / development studies particularly in engineering sciences while their different effects have been tested with quantitative research designs in educational sciences.
The African Journal of Information Systems The

This paper interrogated the suitability of Mobile Technologies to facilitate e-commerce in Kenyan Micro and Small Enterprises (MSEs). The study proposed a theoretical model and empirically tested it using a sample selected using proportionate stratified sampling within well-defined geographic clusters. The study revealed that, while there is massive use of Mobile Internet Services (MIS), there is limited use of Mobile Money Transfer Services (MMTS) for B2B and B2C transactions as opposed to C2C and C2B e-commerce transactions. Results also indicated that utilizing MIS and MMTS, positively and significantly influenced organization’s performance through operational, transactional and interactional benefits. On the research model, the results indicated that Appropriateness and Usage directly and significantly affected Organizational Performance while User Acceptance and Appropriateness were significant determinants of Usage. Surprisingly, of the three theorized barriers (Security Risks, Affordability and Performance Risks), only Performance Risks had a significant negative effect on Usage. Finally, the study’s results, theoretical, managerial and policy implications are discussed.
Towards effective spatial data mining : uncertainty, condensity and privacy

Spatial data mining (SDM) is a process of knowledge discovery that the observing data is related to geographical information. It has become an important data mining task due to the explosive growth and pervasive use of spatial data. It is more difficult to extract interesting and useful patterns from spatial datasets due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. Although existing methods can handle the spatial mining task properly, as the arrival of the big data era, new challenges for SDM are arising. Firstly, traditional SDM methods usually focus on deterministic datasets, where spatial events occur affirmatively at precise locations. However, the inherent uncertainty of spatial data makes the mining process more difficult. Classical spatial data mining algorithms are no longer applicable or need delicate modification. Secondly, traditional SDM frameworks produce an exponential number of patterns, which makes it hard for users to understand or apply. To solve the condensity issue, novel techniques such as summarization or representation must be carefully investigated. Thirdly, spatial data usually involves an individual’s location information, which incurs location privacy problem. It would be a challenge to protect location privacy with enhanced data security and improved resulting accuracy. To address the uncertainty issue, we study the problem of discovering co-location patterns in the context of continuously distributed uncertain data, namely Probabilistic Co-location Patterns Mining (PCPM). We develop an effective probabilistic co-location mining framework integrated with optimization strategies to address the challenges. To address the condensity issue, we investigate the problem of Representative Co-location Patterns Mining (RCPM). We define a new measure to quantify the distance between co-location patterns, and develop two efficient algorithms for summarization. To address the privacy issue, we solve the problem of protecting Location Privacy in Spatial Crowdsourcing (LPSC). We propose a secure spatial crowdsourcing framework based on encryption, and devise a novel secure indexing technique for efficient querying. The experimental results demonstrate the effectiveness and efficiency of our proposed solutions. The methods and techniques used in solving concrete SDM tasks can also be applied or extended to other SDM scenarios.
First Impressions

People often draw trait inferences from the facial appearance of other people. We investigated the minimal conditions under which people make such inferences. In five experiments, each focusing on a specific trait judgment, we manipulated the exposure time of unfamiliar faces. Judgments made after a 100-ms exposure correlated highly with judgments made in the absence of time constraints, suggesting that this exposure time was sufficient for participants to form an impression. In fact, for all judgments—attractiveness, likeability, trustworthiness, competence, and aggressiveness—increased exposure time did not significantly increase the correlations. When exposure time increased from 100 to 500 ms, participants' judgments became more negative, response times for judgments decreased, and confidence in judgments increased. When exposure time increased from 500 to 1,000 ms, trait judgments and response times did not change significantly (with one exception), but confidence increased for some of the judgments; this result suggests that additional time may simply boost confidence in judgments. However, increased exposure time led to more differentiated person impressions.
Repositório ISCTE-IUL

This field experiment tested whether inducing common inclusive representations (i.e., one-group, dual-identity) during contact influences intergroup relations differently for ethnic majority and minority children by changing their meta-perceptions and intergroup emotions differently. White (N=113) and Black (N=111) 8 to 10-year old children were exposed to interactive mixed-ethnicity sessions in schools emphasizing either categorization as one-group (national group), dual-identity (national group with ethnic subgroups) or two ethnic groups. Overall, as predicted, for White children, one-group, but not dual-identity perceptions, improved behavioral intentions by influencing meta-perceptions. For Black children, dual-identity, but not one-group perceptions improved behavioral intentions through meta-perceptions. Contrary to the expected, both dual-identity and one-group perceptions were associated with White and Black children’s intergroup emotions.
The African Journal of Information Systems The African

We are told that university students are Digital Natives and the diffusion of digital technologies into education holds prospective advantages. However, will students in Africa be prepared to engage with and accept the technology? This study aimed to determine the applicability of the Unified Theory of Acceptance and Use of Technology (UTAUT) model within a South African higher education setting and to clarify the factors that are influencing students’ intentions to make use of two digital technologies: an eBook and SLMS. 738 ICT students completed a questionnaire to gauge their responses to Performance expectancy (PEx), Effort expectancy (EfEx), Facilitating conditions (FC), Self-efficacy (SE), Anxiety (Anx), Attitude towards using technology (ATT) and Behavioural intention (BI). Statistical analysis was used to develop and assess a model of the factors that are affecting BI. Structural equation modelling was used and the goodness-of-fit test indicated that the model was supported by the data. PEx, FC and EfEx showed high practically significant relationships with BI. SE and ATT as mediators of the model are confirmed, however gender as moderator did not reflect the original findings of UTAUT.
Strategic Flexibility In Information Technology Alliances: The Strategic Flexibility In Information Technology Alliances: The Influence Of Transaction Cost Economics and Social Exchange Influence Of Transaction Cost Economics and Social Exchange Theory Theory

In the literature on strategic alliances, trust is often positioned as the magic ingredient in alliance success. Typically, trust is treated in the literature as a residual term in the statistical models, neither operationalized nor measured. This paper is important because it operationalizes and measures trust as a multidimensional construct, which is both causally antecedent to strategic alliance, flexibility, and the outcome of characteristics of the alliance partners. I believe the field needs more theoretical and empirical precision regarding trust, as exemplified in this paper.
AIS Electronic

Microblog Sentiment Analysis has become a popular research topic extensively examined in the literature. However, microblogging messages are usually short, unstructured, contain less information and much noise, creating a significant challenge for the application of traditional content-based methods. In this study, we propose a novel method, MSA-USSR, where user similarity information and interaction-based social relations information are combined to build sentiment relationships between microblogging data. We employ these microblog–microblog sentiment relations to train the sentiment polarity classifier. The experimental results on two Sina-Weibo datasets show that our model has a better sentiment classification accuracy and F1-score than the Support Vector Machine method and the state-of-the-art supervised model called SANT. In addition, it was proven that the improvement in accuracy brought by interaction-based social relations information is greater than the user similarity information, but MSA-USSR achieved the best performance when incorporating both user similarity information and users’ interaction-based social relations.
COMPARATIVE STUDY OF THE SIGNIFICANCE OF COMPETITION INDICES AND GENOTYPE OF SCOTS PINE TREE REACTIONS TO CHANGES IN METEOROLOGY AND AIR POLLUTION

Changing climatic conditions, increased length of growing season, competition, genotype and pollution are the most important factors for tree productivity and health. That is why only by knowing the impact of these factors on trees we can evaluate and change sylvicultural treatments that are important for the health and productivity of forest in Lithuania. The aim of the present study was to detect the significance of the competition index vs. genotype of Scots pine trees on their reactions to changes in meteorology and air pollution. Tree health, dendrometric parameters and stem basal area increment were chosen as response variables to meet the objectives of the presented study. The investigation was carried out at Scots pine genetic stand located in Aukstaitija national park, Vaisniunai district. Data on meteorology and air pollutants were obtained from ICP Integrated Monitoring Station located there. Genetically related individual groups in the stand were established by the neutral part of the genome of short repeated DNA sequence length polymorphism markers. Trees of distinctive short repeated sequences allele frequencies were grouped by Bayesian cluster analysis methods. Four genetically similar tree groups were separated in the monitored stand based on significance of ΔK value. Seven competition indices were used to determine the competition intensity effect on the tree increment and their dendrometric parameters including crown condition. The obtained results revealed that trees which demonstrated the most intensive BAI formation, best crown condition and the most significant reaction to changes in meteorology and air pollution comprised one tree genetic group while trees with adverse characteristics the second. Notwithstanding this competition intensity had the most significant effect on BAI formation. Keywords: Scots pine, dendrometric parameters, radial increment, crown defoliation, competition index, genetic group, air pollution Article DOI: http://doi.org/10.15544/RD.2017. 168
Improved Bounds for Mixing Rates of Markov Chains and Multicommodity Flow ( Extended Abstract )

In recent years, Markov chain simulation has emerged as a powerful algorithmic paradigm. Its chief application is to the random sampling of combinatorial structures from a specified probability distribution. Such a sampling procedure lies at the heart of efficient probabilistic algorithms for a wide variety of problems, such as approximating the size of combinatorially defined sets, estimating the expectation of certain operators in statistical physics, and combinatorial optimisation by stochastic search. The algorithmic idea is simple. Suppose we wish to sample the elements of a large but finite set X of structures from a distribution lr. First, construct a Markov chain whose states are the elements of X and which converges asymptotically to "the stationary or equilibrium distribution x over X; it is usually possible to do this using as transitions simple random perturbations of the structures in X. Then, starting from an arbitrary state, simulate the chain until it is close to equilibrium: the distribution of the final state will be close to the desired distribution ~r. To take a typical example, let H be a connected graph and X the set of spanning trees of H, and suppose we wish to sample elements of X from a uniform distribution. Consider the Markov chain . /t4C(H) with state space X which, given a spanning tree T E X, makes transitions as follows: select uniformly at random an edge e of H which does not belong to T, add e to T, thereby creating a single cycle C, and finally remove an edge of C uniformly at random to create a new spanning tree T ~. It is not hard to check that this Markov chain converges to the uniform distribution over X. Analysing the efficiency of the above technique in a given application presents a considerable challenge. The key issue is to determine the miring rate of the chain, i.e., the number of simulation steps needed to ensure that it is sufficiently close to its equilibrium distribution lr. An efficient algorithm can result only if this number is reasonably small, which usually means dramatically less than the size of the state space X itself. For example, in the spanning tree problem above we would want . A4C(H) to reach equilibrium in time bounded by some polynomial in n, the size of the problem instance H; however, the number of states IX[ will typically be exponential in n. Informally, we will call
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms (cid:3)

In trying to solve multiobjective optimization problems, many traditional methods scalar-ize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms(GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Scha(cid:11)er and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias towards some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to (cid:12)nd multiple Pareto-optimalpoints simultaneously. The proof-of-principle results obtained on three problemsused by Scha(cid:11)er and others suggest that the proposed method can be extended to higher dimensional and more di(cid:14)cult multiobjective problems. A number of suggestions for extension and application of the algorithm is also discussed.
Analyzing Travelers’ Intention to Accept Travel Information

Advanced traveler information systems (ATIS) cannot improve the traffic environment if travelers do not accept the travel information provided by the system. To understand better why travelers accept or refuse travel information and to explain, predict, and increase travelers’ acceptance of travel information, a research framework based on the technology acceptance model is developed to establish the relationship between travelers’ intention to accept travel information, trust in travel information, perceived usefulness of travel information, perceived ease of its use, and other related variables. Then structural equation modeling is used to examine and analyze the relationship among these variables. The results show that the factors that significantly determine travelers’ intention to accept travel information are trust in travel information, its perceived usefulness, its perceived ease of use, and information attributes. Through an examination of the direct, indirect, and total effects in the model system, it is discovered that perceived ease of use has the largest total effect on intention to accept by a standardized coefficient of 0.522, followed by trust in information (0.348), perceived usefulness (0.199), and information attributes (0.079). These results indicate the practical value of the estimated model for guiding recommendations aimed at increasing travelers’ intention to accept travel information and at improving the service quality of travel information in China.
Optimized spread spectrum watermarking for fading-like collusion attack

An optimized spread spectrum (SS) image watermarking scheme robust against fading-like collusion attack is proposed using genetic algorithms (GA) in multiband (M-band) wavelet decomposition. M-band decomposition of host offers advantages in better scale-space tiling and good energy compactness. This makes watermarking robust against frequency selective fading. On the other hand, GA would determine threshold value for the host coefficients (process gain i.e the length of spreading code) selected for watermark casting along with the respective embedding strengths. An objective function is developed from the weighted combination of local and global embedding distortion (imperceptibility), normalized process gain and robustness measure to balance among conflicting requirements. Simulation results show convergence of GA and validate relative performance gain achieved in this algorithm compared to the existing works.
Key Knowledge Generation

This study examines the association between foreign trade and economic growth in Myanmar over the period 1990-2014. It covers the annual data of GDP, Export and Import of Myanmar from 1980 to 2014. This study adopts two major methodological approaches - exploratory data analysis and descriptive analysis. For the ﬁrst approach, the Augmented Dickey-Fuller (ADF) unit root test and Granger causality test are used under the Vector Autoregressive (VAR) model, which has rarely been studied for the Myanmar case. The second approach includes the analysis of Myanmar’s foreign trade trend with descriptive statistics. The results show that foreign trade did not generate a signiﬁcant effect on the economic growth of Myanmar. Rather, import growth was negatively inﬂuenced by the economic growth of Myanmar. It is found out that the GDP growth had determined exports. Thus, a growth-driven export strategy had been applied between 1990 and 2014. Also, there was only one causal run from export growth to import growth. The results conclude that foreign trade did not signiﬁcantly impact the economic growth of Myanmar over the period of 19902014.
A Human Protein-Protein Interaction Network : A Resource for Annotating the Proteome

Verification of Y2H Interactions by Coimmunoprecipitation and Pull-Down Assays To evaluate the quality of the Y2H data, a representative sample of Y2H interactions was randomly selected for verification assays, because interactions recapitulated independently are unlikely to be experimental false positives (Goehler et al., 2004 ̧ Tewari, 2004 #137). To verify Y2H interactions, we used in vitro pull down assays (Goehler et al., 2004) and a novel membrane coimmunprecipitation assay.
Enterprise Architecture At Work Modelling Communication And Analysis

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