Miracle-dz/UniFAR-FTUs huggingface dataset:
The Chronic and Unpredictable Stress Suppressed Kisspeptin Expression during Ovarian Cycle in Mice

Chronic and unpredictable stress can disrupt the female reproductive system by suppression for secretion of gonadotrophin-releasing hormone (GnRH) and gonadotrophin, resulted in ovarian malfunction and infertility. In the recent days, kisspeptin has been highly highlighted as a hypothalamic peptide which directly stimulates synthesis and release for GnRH. However, in spite of the key role of kisspeptin in the female reproductive system, little information is still available on the changes of its expression during ovarian cycle under stressed condition. Therefore, we induced chronic and unpredictable stress series to the female mice to analyze kisspeptin expression in the brain and ovary. Stressed mice exhibited changes of behavior and body weight gain during the stress assessment, which suggested that the present stress model in mice was successfully established. In the brain level, kisspeptin expression was attenuated than control. In the ovary level, the stressed mice displayed irregularly shrunk oocytes with broken zona pellucida throughout the follicle stages, pyknotic granulosa cells, decreased number of developing follicles and increased number of atretic follicles than the control. In case of kisspeptin expression in the whole ovary tissue, the expression level was decreased in the stressed mice. In detail, the less intensity of kisspeptin expression in the antral follicles phase was observed in the stressed mice than control mice, indicating that local function of kisspeptin during ovary cycle is highly associated with development of ovarian follicles. We expect that the present study has important implications for the fields of reproductive biology.
Soft sensors for social sensing in cultural heritage

This paper presents a work in progress concerning a soft sensor approach for social sensing in the context of cultural heritage. The approach analyzes public posts and comments on the British Museum Facebook page and tries to give an overall measurement regarding the sentiment and the emotions arising from a post. This can help museums to better address their resources in order to improve the effectiveness of their divulgation action.
Analyzing information transfer in time-varying multivariate data

Effective analysis and visualization of time-varying multivariate data is crucial for understanding complex and dynamic variable interaction and temporal evolution. Advances made in this area are mainly on query-driven visualization and correlation exploration. Solutions and techniques that investigate the important aspect of causal relationships among variables have not been sought. In this paper, we present a new approach to analyzing and visualizing time-varying multivariate volumetric and particle data sets through the study of information flow using the information-theoretic concept of transfer entropy. We employ time plot and circular graph to show information transfer for an overview of relations among all pairs of variables. To intuitively illustrate the influence relation between a pair of variables in the visualization, we modulate the color saturation and opacity for volumetric data sets and present three different visual representations, namely, ellipse, smoke, and metaball, for particle data sets. We demonstrate this information-theoretic approach and present our findings with three time-varying multivariate data sets produced from scientific simulations.
A no-reference image enhancement quality metric and fusion technique

A no-reference image enhancement quality metric is proposed in this paper that uses three factors to score images: lightness, contrast, and noise. It has been shown in literature that certain ideal ranges for lightness and contrast exist, and image enhancement techniques tend to push an image towards these. The proposed metric gives each pixel in an image a score based on its neighborhood statistics, and these pixel scores are averaged to get an overall image quality score. An image fusion technique is also proposed that fuses multiple enhanced images into one based on the local scores obtained from the no-reference metric. It is shown that fused images score higher using the no-reference metric and also have superior visual quality.
Exploiting value locality for secure-energy aware communication

Minimizing energy consumption is important for prolonging the life of battery-powered sensor nodes. A major portion of a sensor power budget in a distributed sensor network is consumed by communication with adjacent nodes. Another issue with the communication of data among the sensor nodes is the possibility of leaking confidential sensed data to adversaries. The adoption of standard encryption techniques for protecting the transmitted data is not attractive in this environment as it is costly in terms of energy consumption. We present a new energy-efficient and secure communication protocol that exploits the locality of data transfer in distributed sensor networks. Our evaluation reveals that the proposed technique is specifically effective in sensor applications that involve frequent communications with a high data locality.
Optimal coefficients for channel-coded linear physical layer network coding

This paper investigates the linear network coding map in a Physical-layer Network Coding (PNC) operated two-way relay network. To realize the full potential of Physical-layer Network Coding in high SNR regimes, we need to use high-order signaling beyond BPSK/QPSK. In a PNC system with high-order signaling, we can have many different choices for the network coding (NC) map. For linear network coding, the network-coding map is realized by coefficients corresponding to the weights of the linearly-combined network-coded message. Prior work on channel-coded linear PNC adopted the same network-coding coefficients as in non-channel-coded linear PNC. We find that the optimal coefficients for such uncoded systems are far from optimal for coded systems. We also show that the coefficients that maximize the computation rate in the “compute-and-forward” framework, which adopts nested lattice codes, are near optimal for linear PNC that adopts the LDPC codes.
MRI brain tumor recognition using Modified Shuffled Frog Leaping Algorithm

This paper presents a novel optimal algorithm for MRI brain tumor recognition. To do this, we use the newly developed meta-heuristic MSFLA (Modified Shuffled Frog Leaping Algorithm). Otherwise, a suitable choice of the fitness function ensures faster time of research with greater chance of convergence to the optimal value. The calculation of the used fitness function is linked to the image. The image must be scanned to calculate this function. For this, this function assists to quickly discover the adequate area modeling the tumor. Computer simulation results illustrate the effectiveness of the developed algorithm.
An Improved Estimation Using Polya-Gamma Augmentation for Bayesian Structural Equation Models with Dichotomous Variables

Abstract Bayesian algorithms have been used successfully in the social and behavioral sciences to analyze dichotomous data particularly with complex structural equation models. In this study, we investigate the use of the Polya-Gamma data augmentation method with Gibbs sampling to improve estimation of structural equation models with dichotomous variables. An empirical example is provided to illustrate the performance of different estimation approaches followed by a simulation study to evaluate the proposed method. The Polya-Gamma method is shown to provide stable results with larger effective sample size than standard Gibbs sampling.
IISR Crosslink Approach at NTCIR 9 CLLD Task

In this paper, we describe our approach to the English-Korean Cross-Lingual Link Discovery (CLLD) task in NTCIR 9. We propose a simple and effective approach to discover the links. Our method comprises preprocessing steps, anchor-target link mapping, and the ranking steps. For discovering the links, we use the English anchor names, the inter-language links, and the translation by the Google Translate as features and extract the possible links with the exactly matching among them. Our method also ranks the anchor candidates by the Wikipedia category sets and the PageRank method, and we select the Korean target pages with the mutual information between English anchors and Korean titles of Wikipedia articles. The official file-to-file evaluation with the manual assessment of our system is achieved from 0.6 to 0.7 in P10 precision, which shows that our approach can achieve satisfactory results.
Optimal Reactive Power Dispatching for Automatic Voltage Control of Hydropower Station Based on an Improved Genetic Algorithm

-This paper presents recent work on the control strategy and algorithm for automatic voltage control (AVC) of hydropower station with multiple units and large capacities. Under the conditions that no dynamic voltage problems are considered, a method intended primarily to control steady state voltage of HV bus by economically regulating the reactive power generation of hydropower station is proposed. An improved Genetic Algorithm (GA) is used for optimal reactive power dispatching in automatic voltage control of hydropower station. Compared with the classic GA, three basic operators in GA which are the sorting selection operator, the pattern crossover operator and the quasi-gradient mutation operator are amended in this paper. Furthermore, the result of GA is modified by mountain-climbing method. The simulating results show that the compatibility of the improved GA is better than normal optimal algorithms and classic GA, and the algorithm can also obtain global optimization and reduce the net-loss to the most extent. The numerical simulations in off-line environment on practical system with 5 units and 13 units demonstrate that the processing speed and the accuracy are completely acceptable in practical project.
Joint Optimization of Computing and Cooling Energy: Analytic Model and a Machine Room Case Study

Total energy minimization in data centers (including both computing and cooling energy) requires modeling the interactions between computing decisions (such as load distribution) and heat transfer in the room, since load acts as heat sources whose distribution in space affects cooling energy. This paper presents the first closed-form analytic optimal solution for load distribution in a machine rack that minimizes the sum of computing and cooling energy. We show that by considering actuation knobs on both computing and cooling sides, it is possible to reduce energy cost comparing to state of the art solutions that do not offer holistic energy optimization. The above can be achieved while meeting both throughput requirements and maximum CPU temperature constraints. Using a thorough evaluation on a real test bed of 20 machines, we demonstrate that our simple model adequately captures the thermal behavior and energy consumption of the system. We further show that our approach saves more energy compared to the state of the art in the field.
Time-predictable Cache Organization

Caches are a mandatory feature of current processors to deliver instructions and data to a fast processor pipeline. However, standard cache organizations are designed to increase the average case performance. They are hard to model for worst-case execution time (WCET) analysis. Unknown abstract cache states during the analysis result in conservative WCET bounds. Therefore, we propose to adapt the cache organization to simplify the analysis. The data cache is split into several independent caches for the stack, static data, constants, and heap allocated data.
The Relationship Between Transformational Leadership And Innovative Work Behaviour

This study focusing on the relationship between Transformational Leadership acts as independent variable and Innovative Work Behaviour acts as dependent variable. This study was conducted in Universiti Teknologi Mara, Bandaraya Melaka.There are three main objectives for this study which are i) To identify the frequent transformational leadership behaviour in organization,ii) To identify the level of innovative work behaviour in organization and iii)To study the relationship between transformational leadership and innovative work behaviour in organization.For this study,the sample was collected from 86 respondents that are working as non–academic staffs at Universiti Teknologi MARA (UiTM) which located at Bandaraya Melaka by using the survey method. Questionnaire sample was developed by Bass (1985) and rematches by Dong I.Jung and John J.Sosik (2002) used to measure Transformational Leadership and Innovative Work Behaviour.The data from the survey was analyzed by using the descriptive statistics and inferences statistic. Result show Transformational Leadership style and Innovative Work Behaviour have been implemented nicely in Universiti Teknologi Mara.Besides,this study show significant result between Transformational Leadership and Innovative Work Behaviour which value r2 = 0.511, P < 0.05.
An Algorithm For Selection of Best Orthonormal

This contribution deals with the problem of structure determination for generalized orthonormal basis models used in system identiication. The model structure is parameterized by a pre-speciied set of poles. Given this structure and experimental data a model can be estimated using linear regression techniques. Since the variance of the estimated model increases with the number of estimated parameters, the objective is to nd structures that are as compact/parsimonious as possible. A natural approach would be to estimate the poles, but this leads to nonlinear optimization with possible local minima. In this paper, a best basis algorithm is derived for the generalized orthonormal rational bases. Combined with linear regression and thresh-olding this leads to compact transfer function representations .
Structured Auto-Encoder with application to Music Genre Recognition

In this work, we present a technique that learns discriminative audio features for Music Information Retrieval (MIR). The novelty of the proposed technique is to design auto-encoders that make use of data structures to learn enhanced sparse data representations. The data structure is borrowed from the Manifold Learning field, that is data are supposed to be sampled from smooth manifolds, which are here represented by graphs of proximities of the input data. As a consequence, the proposed auto-encoders finds sparse data representations that are quite robust w.r. t. perturbations. The model is formulated as a non-convex optimization problem. However, it can be decomposed into iterative sub-optimization problems that are convex and for which well-posed iterative schemes are provided in the context of the Fast Iterative Shrinkage-Thresholding (FISTA) framework. Our numerical experiments show two main results. Firstly, our graph-based auto-encoders improve the classification accuracy by 2% over the auto-encoders without graph structure for the popular GTZAN music dataset. Secondly, our model is significantly more robust as it is 8% more accurate than the standard model in the presence of 10% of perturbations.
Top-k graph pattern matching over large graphs

There exist many graph-based applications including bioinformatics, social science, link analysis, citation analysis, and collaborative work. All need to deal with a large data graph. Given a large data graph, in this paper, we study finding top-k answers for a graph pattern query (kGPM), and in particular, we focus on top-k cyclic graph queries where a graph query is cyclic and can be complex. The capability of supporting kGPM provides much more flexibility for a user to search graphs. And the problem itself is challenging. In this paper, we propose a new framework of processing kGPM with on-the-fly ranked lists based on spanning trees of the cyclic graph query. We observe a multidimensional representation for using multiple ranked lists to answer a given kGPM query. Under this representation, we propose a cost model to estimate the least number of tree answers to be consumed in each ranked list for a given kGPM query. This leads to a query optimization approach for kGPM processing, and a top-k algorithm to process kGPM with the optimal query plan. We conducted extensive performance studies using a synthetic dataset and a real dataset, and we confirm the efficiency of our proposed approach.
Programming with Verification Conditions

This paper contains an exposition of the method of programming with verification conditions. Although this method has much in common with the one discussed by Dijkstra in A Disciplne of Programming, it is shown to have the advantage in simplicity and flexibility. The simplicity is the result of the method's being directly based on Floyd's inductive assertions The method is flexible becasue of the way in which the program is constructed in two stages. In the first stage, a set of verification conditions is collected which corresponds to a program in "flowgraph" form. In this stage sequencing control is of no concern to the progmmer. Control is introduced in the second stage, which consists of automatable applications of translation and optimization rules, resulting in conventional code. Although our method has no use for the sequencing primitives of "structured programming," it is highly secure and systematic.
I/O-Efficient Bundled Range Aggregation

This paper studies bundled range aggregation, which is conceptually equivalent to running a range aggregate query separately on multiple datasets, returning the query result on each dataset. In particular, the queried datasets can be arbitrarily chosen from a large number (hundreds or even thousands) of candidate datasets. The challenge is to minimize the query cost no matter how many and which datasets are selected. We propose a fully-dynamic data structure called aggregate bundled B-tree (aBB-tree) to settle bundled range aggregation. Specifically, the aBB-tree requires linear space, answers any query in O(logB N) I/Os, and can be updated in O(logB N) I/Os (where N is the total size of all the candidate datasets, and B the disk page size), under the circumstances where the number of datasets is O(B). The practical efficiency of our technique is demonstrated with extensive experiments.
The Value of Market Research When a Firm is Learning: Real Option Pricing and Optimal Filtering

In this paper we model the value to a firm of undertaking market research into a particular product opportunity. The way in which information about the potential of the project arrives and knowledge evolves during the life of the project is modeled using the theory of optimal filtering. The value of the project and optimal entrance decision rule is then derived as the solution to a partial differential equation, using boundary conditions which reflect the structure of the project.
AN IMPROVED TEXT CLASSIFICATION METHOD BASED ON GINI INDEX

In text classification, the purity of the Gini inde x can be used. When purity value is greater, the characteristic of the information contained in the attribute is higher, and the feature distinguishing capability is stronger. But using the Gini purity f ormula on feature weight, the classification result is not very good, one of the main reasons is those rare wo ds nly appearing in one category and not appearin g in other categories can not be strictly differentiated . In order to solve this problem, On the basis of G ini index, an improved feature weight method based on Gini ind ex has proposed. By introducing the approximation quality of features term in the categories, accordi ng to the category distinguishing ability adjust te rm weight, using the purity formula feature weight com parison, the above problem is well solved, which ca n effectively improve the performance of text classif ication. The experiments have verified the feasibil ity of the proposed method.
Predicting Short-Term Defect Inflow in Large Software Projects - An Initial Evaluation

Predicting a defect inflow is important for project planning and monitoring purposes. For project planning purposes and for quality management purposes, an important measure is the trend of defect inflow in the project - i.e. how many defects are reported in a particular stage of the project. Predicting the defect inflow provides a mechanism of early notification whether the project is going to meet the set goals or not. In this paper we present and evaluate a method for predicting defect inflow for large software projects: a method for short-term predictions for up to three weeks in advance on a weekly basis. The contribution of this paper is the fact that our model is based on the data from project planning, status monitoring, and current trends of defect inflow and produces results applicable for large projects. The method is evaluated by comparing it to existing defect inflow prediction practices (e.g. expert estimations) at one of the large projects at Ericsson. The results show that the method provides more accurate predictions (in most cases) while decreasing the time required for constructing the predictions using current practices in the company.
Do we speak the same language?: design goals and culture clashes in an online forum for young people

This is a case study exploring the social scene created on a newly-developed online service for increasing the study motivation of 16--18-year-old students in vocational education in Finland. The developers wished to motivate participation by the addition of a communal chat space to engender a sense of community on the site. The analysis shows that the students appropriated the communal chat space for uses in line with their prior experience of online interaction, while the developers had based their design on a very different experience. However, the developers were able to respond flexibly to encourage interaction rather than limiting topics of conversation to those desired in the original design. As a result, the communal page could be seen to fulfil some of the expectations of the developers in unexpected ways. The case offers learning points for developers and administrators who wish to create online social spaces with a particular aim.
Comparison pattern matching and creative simile recognition

Comparisons are phrases that express the likeness of two entities. They are usually marked by linguistic patterns, among which the most discussed are X is like Y and as X as Y . We propose a simple slot-based dependency-driven description of such patterns that reﬁnes the phrase structure approach of Niculae and Yaneva (2013). We introduce a simple similarity-based approach that proves useful for measuring the degree of ﬁgurativeness of a comparison and therefore in simile (ﬁgu-rative comparison) identiﬁcation. We pro-pose an evaluation method for this task on the VUAMC metaphor corpus.
The Open University ’ s repository of research publications and other research outputs Modeling document features for expert finding Conference Item

We argue that expert finding is sensitive to multiple document features in an organization, and therefore, can benefit from the incorporation of these document features. We propose a unified language model, which integrates multiple document features, namely, multiple levels of associations, PageRank, indegree, internal document structure, and URL length. Our experiments on two TREC Enterprise Track collections, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios.