Miracle-dz/UniFAR-FTUs huggingface dataset:
Putting critical realism to use in ICT4D research: Reflections on practice

This paper presents my reflections on adopting critical realist (CR) assumptions in information and communication technologies for development (ICT4D) research over the last decade. Critical realism, and its notion of generative mechanisms as contingent causality, offers one potential way to frame and engage in research that is compatible with the contextual nature of developing ICT4D theory. However, CR can be difficult to understand and operationalize. This paper provides practical insights on how to engage in ICT4D research underpinned by the CR philosophy of science. As such, the bulk of this paper presents insights and implications of accepting CR assumptions in the research process, looking at different elements of research (eg, including forming research questions, theory building, engaging in research). I use examples in ICT4D research to illustrate these implications.
Non-Gaussian cosmic microwave background temperature fluctuations from peculiar velocities of clusters

We use numerical simulations of a (480 Mpc h−1)3 volume to show that the distribution of peak heights in maps of the temperature fluctuations from the kinematic and thermal Sunyaev–Zeldovich (SZ) effects will be highly non-Gaussian, and very different from the peak-height distribution of a Gaussian random field. We then show that it is a good approximation to assume that each peak in either SZ effect is associated with one and only one dark matter halo. This allows us to use our knowledge of the properties of haloes to estimate the peak-height distributions. At fixed optical depth, the distribution of peak heights resulting from the kinematic effect is Gaussian, with a width that is approximately proportional to the optical depth; the non-Gaussianity comes from summing over a range of optical depths. The optical depth is an increasing function of halo mass and the distribution of halo speeds is Gaussian, with a dispersion that is approximately independent of halo mass. This means that observations of the kinematic effect can be used to put constraints on how the abundance of massive clusters evolves, and on the evolution of cluster velocities. The non-Gaussianity of the thermal effect, on the other hand, comes primarily from the fact that, on average, the effect is larger in more massive haloes, and the distribution of halo masses is highly non-Gaussian. We also show that because haloes of the same mass may have a range of density and velocity dispersion profiles, the relation between halo mass and the amplitude of the thermal effect is not deterministic, but has some scatter.
Mutual Summaries: Unifying Program Comparison Techniques

In this paper, we formalize mutual summaries as a contract mechanism for comparing two programs, and provide a method for checking such contracts modularly. We show that mutual summary checking generalizes equivalence checking, conditional equivalence checking and translation validation. More interestingly, it enables comparing programs where the changes are interprocedural. We have prototyped the ideas in SymDiff, a Boogie based language-independent infrastructure for comparing programs.
Glacier and lake-level variations in west-central Europe over the last 3500 years

On the basis of glacier and lake-level records, this paper attempts, for the first time, a comparison between high-resolution palaeohydrological and palaeoglaciological data in west-central Europe over the past 3500 years. A data set of tree-ring width, radiocarbon and archaeological data, in addition to historical sources, were used to reconstruct fluctuations of the Great Aletsch, the Gorner and the Lower Grindelwald glaciers in the Swiss Alps. The three ice-streams experienced nearly synchronous advances at c. 1000-600 BC and AD 500-600, 800-900, 1100-1200 and 1300-1860. These glacier fluctuations show strong correspondence with lake-level variations reconstructed in eastern France (Jura mountains and Pre-Alps) and on the Swiss Plateau. This supports the hypothesis of climatically driven fluctuations. Historical data available for the period since AD 1550 reveal, in detail, various meteorological conditions behind the successive glacier advances. However, in agreement with the general trend shown by the historical data, the synchroneity between glacier advances and periods of higher lake level suggests the impact of general winter cooling and an increase in summer moisture as responsible for reinforced feeding of both glaciers and lakes in west-central Europe over the past 3500 years. Finally, a comparison between the Great Aletsch glacier and the residual 14C records supports the hypothesis that variations in solar activity were a major forcing factor of climatic oscillations in west-central Europe during the late Holocene.
Evolving models for meso-scale structures

Real world complex networks are scale free and possess meso-scale properties like core-periphery and community structure. We study evolution of the core over time in real world networks. This paper proposes evolving models for both unweighted and weighted scale free networks having local and global core-periphery as well as community structure. Network evolves using topological growth, self growth, and weight distribution function. To validate the correctness of proposed models, we use K-shell and S-shell decomposition methods. Simulation results show that the generated unweighted networks follow power law degree distribution with droop head and heavy tail. Similarly, generated weighted networks follow degree, strength, and edge-weight power law distributions. We further study other properties of complex networks, such as clustering coefficient, nearest neighbor degree, and strength degree correlation.
Cost-Sensitive Parsimonious Linear Regression

We examine linear regression problems where some features may only be observable at a cost (e.g., in medical domains where features may correspond to diagnostic tests that take time and costs money). This can be important in the context of data mining, in order to obtain the best predictions from the data on a limited cost budget. We define a parsimonious linear regression objective criterion that jointly minimizes prediction error and feature cost. We modify least angle regression algorithms commonly used for sparse linear regression to produce the ParLiR algorithm, which not only provides an efficient and parsimonious solution as we demonstrate empirically, but it also provides formal guarantees that we prove theoretically.
Improving Vehicle Detection Accuracy Based on Vehicle Shadow andSuperposition Elimination

Vehicle shadow and superposition have a great influence on the accuracy of vehicles detection in traffic video. Many background models have been proposed and improved to deal with detection moving object. This paper presented a method which improves Gaussian mixture model to get adaptive background. The HSV color space was used to eliminate vehicle shadow, it was based on a computational colour space that makes use of our shadow model. Vehicle superposition elimination was discussed based on vehicle contour dilation and erosion method. Experiments were performed to verify that the proposed technique is effective for vehicle detection based traffic surveillance systems. Detection results showed that our approach was robust to widely different background and illuminations.
A Bio-inspired Behavior Based Bipedal Locomotion Control {--} {B4LC} Method for Bipedal Upslope Walking

Though over decades’ development of bipedal robots, the terrains that bipedal robots can walk remains limited compared to what human can accomplish. To increase bipedal transversality on upslope terrain, this paper studies the biomechanical and biological aspects of human walking on inclined slopes with underlying the motor skills and reexive systems. The control strategy can be divided into low and high gradient upslope walking. The strategy for the low gradient uphill walking is generated on the basis of an existing B4LC system. Furthermore, investigating the human walking on high gradient upslope terrain thoroughly unveils a new control strategy for bipedal walking on high gradient slope. Through validating the suggested method on a simulated biped upon dierent upslope terrains, the bipedal robot shows a naturally looking walking gait, achieving uphill walking up to 15 inclination which can compete with most advanced bipedal robots in the world.
European Conference on Information Systems ( ECIS ) 5-8-2012 COMMERCIAL EXPLOITATION OF FACEBOOK : HOW COMPANIES ADOPT FACEBOOK AS A MARKETING TOOL

As the population of Facebook users is growing fast, companies are paying more attention to the influence of social networks to promote brands and are using this rising platform to carry out many marketing activities, such as operating a Facebook Page, and hosting on-line events via such pages. However, few studies have discussed the benefit, which brands could attain by operating on Facebook Pages. This study took place in Taiwan where Facebook has been very popular and aims to understand the motivation and results regarding brand experience of consumers when using social networks and the reasons affecting consumers’ willingness to use companies’ Facebook Pages. In doing so, we propose a model to discuss the influence from intrinsic and extrinsic motivations: brand experience and E-Word of Mouth (E-WOM). Our results show that perceived ease of use and perceived usefulness positively influence brand experience; brand love and customer effort both directly influence brand experience. Yet, brand experience influences E-WOM directly and through Facebook pages such as loyalty. We believe that our results could be useful for companies looking to exploit Facebook as a marketing tool.
Silent Polymorphisms: Can the tRNA Population Explain Changes in Protein Properties?

Silent mutations are being intensively studied. We previously showed that the estrogen receptor alpha Ala87’s synonymous polymorphism affects its functional properties. Whereas a link has been clearly established between the effect of silent mutations, tRNA abundance and protein folding in prokaryotes, this connection remains controversial in eukaryotic systems. Although a synonymous polymorphism can affect mRNA structure or the interaction with specific ligands, it seems that the relative frequencies of isoacceptor tRNAs could play a key role in the protein-folding process, possibly through modulation of translation kinetics. Conformational changes could be subtle but enough to cause alterations in solubility, proteolysis profiles, functional parameters or intracellular targeting. Interestingly, recent advances describe dramatic changes in the tRNA population associated with proliferation, differentiation or response to chemical, physical or biological stress. In addition, several reports reveal changes in tRNAs’ posttranscriptional modifications in different physiological or pathological conditions. In consequence, since changes in the cell state imply quantitative and/or qualitative changes in the tRNA pool, they could increase the likelihood of protein conformational variants, related to a particular codon usage during translation, with consequences of diverse significance. These observations emphasize the importance of genetic code flexibility in the co-translational protein-folding process.
Learning the Sentiment of Soccer Fans from Data on Bets and Social Nets

In this paper we propose a Hidden Markov Model in order to predict the sentiment of soccer fans based on information regarding the result of matches. The model was constructed by data collected from a social network where fans of a soccer team periodically expressed feelings towards their team. We show that the choice of a HMM is justified due to the fact that the change in a fan's sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation will be performed between variations of the proposed models and also between the most accurate of them and classification algorithms. Second order HMM, considering the match results and fan's gambling information, is the most accurate model.
An approach to visualise and reconcile use case descriptions from multiple viewpoints

The Unified Modelling Language (UML) is a visual approach to the analysis and design of object-oriented computer systems. However, when it comes to specifying requirements through use case descriptions we see two weaknesses in the current approach. The descriptions are textual (the success and acceptance of UML is largely due to its visual nature) and the group process for acquiring descriptions can result in a small number of stakeholders having their views represented. To address these issues the RECOCASE methodology captures use case descriptions from multiple viewpoints and automatically generates a visualisation of the individual and shared viewpoints to assist identification and resolution of conflicts. We briefly describe the approach and results from various initial evaluation studies.
Metamorphic Testing: A New Approach for Generating Next Test Cases

In software testing, a set of test cases is constructed according to some predefined selection criteria. The software is then examined against these test cases. Three interesting observations have been made on the current artifacts of software testing. Firstly, an error-revealing test case is considered useful while a successful test case which does not reveal software errors is usually not further investigated. Whether these successful test cases still contain useful information for revealing software errors has not been properly studied. Secondly, no matter how extensive the testing has been conducted in the development phase, errors may still exist in the software [5]. These errors, if left undetected, may eventually cause damage to the production system. The study of techniques for uncovering software errors in the production phase is seldom addressed in the literature. Thirdly, as indicated by Weyuker in [6], the availability of test oracles is pragmatically unattainable in most situations. However, the availability of test oracles is generally assumed in conventional software testing techniques. In this paper, we propose a novel test case selection technique that derives new test cases from the successful ones. The selection aims at revealing software errors that are possibly left undetected in successful test cases which may be generated using some existing strategies. As such, the proposed technique augments the effectiveness of existing test selection strategies. The technique also helps uncover software errors in the production phase and can be used in the absence of test oracles.
Automatic detection of performance deviations in the load testing of Large Scale Systems

Load testing is one of the means for evaluating the performance of Large Scale Systems (LSS). At the end of a load test, performance analysts must analyze thousands of performance counters from hundreds of machines under test. These performance counters are measures of run-time system properties such as CPU utilization, Disk I/O, memory consumption, and network traffic. Analysts observe counters to find out if the system is meeting its Service Level Agreements (SLAs). In this paper, we present and evaluate one supervised and three unsupervised approaches to help performance analysts to 1) more effectively compare load tests in order to detect performance deviations which may lead to SLA violations, and 2) to provide them with a smaller and manageable set of important performance counters to assist in root-cause analysis of the detected deviations. Our case study is based on load test data obtained from both a large scale industrial system and an open source benchmark application. The case study shows, that our wrapper-based supervised approach, which uses a search-based technique to find the best subset of performance counters and a logistic regression model for deviation prediction, can provide up to 89% reduction in the set of performance counters while detecting performance deviations with few false positives (i.e., 95% average precision). The study also shows that the supervised approach is more stable and effective than the unsupervised approaches but it has more overhead due to its semi-automated training phase.
A Least Median of Squares Method Based on Fuzzy Reinforcement Learning for Modeling of Computer Vision Applications

The computer vision approach involves a lot of modeling problems in preventing noise caused by sensing units such as cameras and projectors. In order to improve computer vision modeling performance, a robust modeling technique must be developed for essential models in the system. The RANSAC and least median of squares (LMedS) algorithms have been widely applied in such issues. However, the performance deteriorates as the noise ratio increases and the modeling time for algorithms tends to increase in actual applications. In this study, we propose a new LMedS method based on fuzzy reinforcement learning concept for modeling of computer vision applications. The performance of the algorithm is evaluated by modeling synthetic data and camera homography experiments. Their results found the method to be effective in improving calculation time, model optimality, and robustness in modeling performance.
Efficiency of a POD‐based reduced second‐order adjoint model in 4D‐Var data assimilation

Order reduction strategies aim to alleviate the computational burden of the four‐dimensional variational data assimilation by performing the optimization in a low‐order control space. The proper orthogonal decomposition (POD) approach to model reduction is used to identify a reduced‐order control space for a two‐dimensional global shallow water model. A reduced second‐order adjoint (SOA) model is developed and used to facilitate the implementation of a Hessian‐free truncated‐Newton (HFTN) minimization algorithm in the POD‐based space. The efficiency of the SOA/HFTN implementation is analysed by comparison with the quasi‐Newton BFGS and a nonlinear conjugate gradient algorithm. Several data assimilation experiments that differ only in the optimization algorithm employed are performed in the reduced control space. Numerical results indicate that first‐order derivative methods are effective during the initial stages of the assimilation; in the later stages, the use of second‐order derivative information is of benefit and HFTN provided significant CPU time savings when compared to the BFGS and CG algorithms. A comparison with data assimilation experiments in the full model space shows that with an appropriate selection of the basis functions the optimization in the POD space is able to provide accurate results at a reduced computational cost. The HFTN algorithm benefited most from the order reduction since computational savings were achieved both in the outer and inner iterations of the method. Further experiments are required to validate the approach for comprehensive global circulation models. Copyright © 2006 John Wiley & Sons, Ltd.
Three-mobile-beacon assisted weighted centroid localization method in wireless sensor networks

Localization is one of the key technologies in wireless sensor networks, and the mobile beacon assisted localization method is promising to reduce the cost. The methods with only one beacon introduce collinearity problem which degrades performance. This paper proposes a weighted centroid localization method using three mobile beacons. These beacons preserve a special formation while traversing the network deployment area, and broadcast their positions periodically. The sensor nodes to be localized estimate the distances to these beacons, and utilize weighted centroid localization scheme to calculate its position. Simulation shows that this method is superior to trilateration and weighted centroid algorithm with single mobile beacon.
Lexicon Generation by Extraction of Context Patterns

Semantic browser technologies such as Magpie require the construction of lexicons to support the identification of terms in Web pages which are linked to a user’s chosen ontology. We frame the generation of such lexicons from ontologies as a problem of finding synonyms and hyponyms. Synonym finding using the hypothesis of semantic substitutability relies upon the discovery of patterns in which the target word occurs. Information extraction has the potential to find a range of patterns in text. We present a methodology for finding synonyms for inclusion in lexicons in this way and preliminary tests of the method using standard tools.
Analytical approximation for the double-stance phase of a walking robot

This paper introduces an approximate analytical solution to the otherwise non-integrable double-stance dynamics of the bipedal spring-loaded inverted pendulum (SLIP). Despite the apparent structural simplicity of the SLIP, the exact analytical solution to its stance dynamics cannot be found. Approximate maps have been proposed for the monoped SLIP runner (encompassing a single-stance phase). Still, even in an approximate form, a solution to the double-stance dynamics of the bipedal SLIP walker remained an open problem. We propose a double-stance map that can be readily utilized especially in the design of control systems for active dynamic walking. The accuracy of the derived map over a feasible range of locomotion properties is analyzed numerically, and a control application based on this solution is presented. Simulations for an arbitrary chosen energy level reveals that the devised controller enlarges the stable walking domain of the standard SLIP considerably.
Coevolution of neoplastic epithelial cells and multilineage stroma via polyploid giant cells during immortalization and transformation of mullerian epithelial cells

Stromal cells are generally considered to be derived primarily from the host's normal mesenchymal stromal cells or bone marrow. However, the origins of stromal cells have been quite controversial. To determine the role of polyploidy in tumor development, we examined the fate of normal mullerian epithelial cells during the immortalization and transformation process by tracing the expression of SV40 large T antigen. Here we show that immortalized or HRAS-transformed mullerian epithelial cells contain a subpopulation of polyploid giant cells that grow as multicellular spheroids expressing hematopoietic markers in response to treatment with CoCl2. The immortalized or transformed epithelial cells can transdifferentiate into stromal cells when transplanted into nude mice. Immunofluorescent staining revealed expression of stem cell factors OCT4, Nanog, and SOX-2 in spheroid, whereas expression of embryonic stem cell marker SSEA1 was increased in HRAS-transformed cells compared with their immortalized isogenic counterparts. These results suggest that normal mullerian epithelial cells are intrinsically highly plastic, via the formation of polyploid giant cells and activation of embryonic stem-like program, which work together to promote the coevolution of neoplastic epithelial cells and multiple lineage stromal cells.