Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on kinematics and attitude control model of a surgical interventional catheter

To solve the problems of poor versatility and inactivity of the traditional interventional catheters, a forward kinematic model of multi-segment catheters in series is established by using D-H parameter method, which is based on the geometrical structure of the designed catheters. In order to ensure the decoupling control of the driver’s length, we look into the relationship between the driver’s length and the posture of the catheter unit. The control model of the catheter’s posture is further presented, in which the characteristics of driver is equivalent to the arc shape. Finally, the fuzzy Proportional-Integral-Differential Control (PID) control is designed to control the catheter unit which greatly improves the precision of the control model. The results show that the relationship between the predicted driver length and the catheter attitude angle is basically consistent with the experimental results, which verifies the effectiveness of the variable universe fuzzy PID control.
VLSI Architecture for Systolic-Like Modular Multipliers over GF (2m) Build on Irreducible All-One Polynomials

: By using irreducible AOP, an effective recursive formulation is proposed implies systolic implementation of these finite field multiplications over GF (2 m ). Here, a recursive algorithm derived for the multiplication and used it in designing a systematic and localized bit-linear dependence graph for computing systolic multiplication. This dependence graph is altered to a fine-grained dependence graph (DG) by using node splitting method. This parallel systolic architecture is mapped from fine-grained DG. Compared to other structures, it doesn’t include any global communication for reducing the modules. The proposed architecture has same time compared to other existent bit-parallel systolic structure and includes registers to a lesser extent. This proposed structure has an ascendable latency of l+ [log 2 s] +1 cycles which is minimum compared with existing designs. This structure is proposed specifically for hardware complexity in the structure and throughput scalability to meet the area-time tradeoff by maintaining the overall latency in resource-constrained application.
Tuberculosis surveillance and monitoring in Europe 2012.

Completeness of DRS data* Yes Completeness of HIV data** Case-linked data reporting Yes Cases with DST results 523 (100.0%) Estimated MDR N, (best-low-high) 11-3-20 Pulmonary MDR cases 7 (2.2%) of which XDR cases 2 (28.6%) All MDR cases 8 (1.5%) of which XDR cases 2 (25.0%) TB cases tested for HIV HIV-positive TB cases of these on antiretroviral therapy * National coverage 100% or culturing 90%, C+/All TB cases 50%, DST done for C+ 75%, EQA 95% ** More than 50% of TB cases tested for HIV. Treatment outcome monitoring
Physical Exercise: Knowledge, attitude and participation of senior secondary school students of selected private schools in Ibadan North Local Government Area, Oyo State (Nigeria)

Background: Studies suggest that young adulthood is a critical time for physical exercise interventions. This study assessed knowledge, attitude and participation in physical exercise by students of privately owned secondary schools in Ibadan North East Local Government Area, Ibadan, Oyo State, Nigeria. Materials and methods: This study is a cross sectional survey. Informed consent was obtained from the schools and individual participants. Multi-stage sampling technique was used in selecting participants for the study. Each participant was guided in completing the questionnaire. Data on knowledge, attitude and participation in physical exercise was obtained from students. Data were analyzed using frequency, percentages and chi square. Results: Five hundred and sixty questionnaires were administered to respondents and 520 (92.9%) were completed and returned. Out of the 520 participants 258 (49.62%) were males and 262 (50.38%) were females. Out of 520 participants, 97.88% had good knowledge about physical exercise (49.23% males and 48.65% females). Out of 520 participants, 410 (78.85%) had good attitude towards physical exercise (40.96% males and 37.89% females). Male participants were better in knowledge and attitude than female participants. Participation in physical exercise was poor because participants were not putting in enough time in physical exercise. Conclusions: Boys had better knowledge about physical exercise and better attitude to physical exercise than girls. Girls actually participate more in physical exercise, though both boys and girls were not putting in enough time in participating in physical exercise. School curriculum that will encourage physical exercise should be adopted with more time allocated for physical exercises
Analyzing trajectories of learning processes through behaviour-based entropy

ABSTRACT Over recent decades, it has been asserted that the essence of developmental learning processes is change through learning. However, capturing the essence of change in learning processes remains an open question. To study learning processes, we take up a maze problem and conduct an experiment in which a participant draws a route for the maze problem over 10 sessions. To understand how a participant learns to draw a route, we draw learning curves by plotting, for each session, the number of mazes for which a participant succeeds in drawing correct routes. To analyze the learning process, we introduce a new metric called behaviour-based entropy, which quantifies the extent of how intensively a participant is devoted to drawing a route. A crucial finding is that substantial improvement in performance is preceded by a few sessions (plateau) during which the behaviour-based entropy is quite high. We run a program that simulates drawing of routes, and thereby obtain a learning curve based on the simulation. The resultant learning curves turn out to coincide roughly with the corresponding learning curves based on the experiment, which demonstrates the plausibility of the computational model for the simulation.
Voter Responses to Fiscal Austerity

Abstract Governments have great difficulties designing politically sustainable responses to rising public debt. These difficulties are grounded in a limited understanding of the popular constraints during periods of fiscal pressure. For instance, an influential view claims that fiscal austerity does not entail significant political risk. But this research potentially underestimates the impact of austerity on votes because of strategic selection bias. This study addresses this challenge by conducting survey experiments in Spain, Portugal, Italy, the UK and Germany. In contrast to previous findings, the results show that a government's re-election chances greatly decrease if it proposes austerity measures. Voters object particularly strongly to spending cuts and, to a lesser extent, to tax increases. While voters also disapprove of fiscal deficits, they weight the costs of austerity policies more than their potential benefits for the fiscal balance. These findings are inconsistent with the policy recommendations of international financial institutions.
An End-to-End Deep Neural Network for Facial Emotion Classification

Facial emotional expression is a nonverbal communication medium in human-human communication. Facial expression recognition (FER) is a significantly challenging task in computer vision. With the advent of deep neural networks, facial expression recognition has transitioned from lab-controlled settings to more neutral environments. However, deep neural networks (DNNs) suffer from overfitting the data and biases towards specific categorical distribution. The number of samples in each category is heavily imbalanced, and overall the number of samples is much less than the full number of samples representing all emotions. In this paper, we propose an end-to-end convolutional-self attention framework for classifying facial emotions. The convolutional neural network (CNN) layers can capture the spatial features in a given frame. Here we apply a convolutional-self-attention mechanism to obtain the spatiotemporal features and perform context modelling. The AffectNet database is used to validate the framework. The AffectNet database has a large number of image samples in the wild settings, which makes this database very challenging. The result shows a 30% improvement in accuracy from the CNN baseline.
Survey On A New Approach of Energy Efficient Virtual Machine allocation in Cloud Computing

- Rapid growth of the demand for computational power has led to the creation of large-scale data centers. They consume enormous amounts of electrical power resulting in high operational costs and carbon dioxide emissions. Moreover, modern rational costs and carbon dioxide emissions. Moreover, modern Cloud computing environments have to provide high Quality of Service (QoS) for their customers resulting in the necessity to deal with power-performance trade-off. So, better virtual machine allocation, CPU Power management is necessary . Scheduling of virtual machine in a way to optimize for energy saving and performance. This paper presents the different techniques to reduce energy consumption and proper allocation of virtual machine.
Variability in above- and belowground carbon stocks in a Siberian larch watershed

Abstract. Permafrost soils store between 1330 and 1580 Pg carbon (C), which is 3 times the amount of C in global vegetation, almost twice the amount of C in the atmosphere, and half of the global soil organic C pool. Despite the massive amount of C in permafrost, estimates of soil C storage in the high-latitude permafrost region are highly uncertain, primarily due to undersampling at all spatial scales; circumpolar soil C estimates lack sufficient continental spatial diversity, regional intensity, and replication at the field-site level. Siberian forests are particularly undersampled, yet the larch forests that dominate this region may store more than twice as much soil C as all other boreal forest types in the continuous permafrost zone combined. Here we present above- and belowground C stocks from 20 sites representing a gradient of stand age and structure in a larch watershed of the Kolyma River, near Chersky, Sakha Republic, Russia. We found that the majority of C stored in the top 1 m of the watershed was stored belowground (92 %), with 19 % in the top 10 cm of soil and 40 % in the top 30 cm. Carbon was more variable in surface soils (10 cm; coefficient of variation (CV) = 0.35 between stands) than in the top 30 cm (CV = 0.14) or soil profile to 1 m (CV = 0.20). Combined active-layer and deep frozen deposits (surface – 15 m) contained 205 kg C m−2 (yedoma, non-ice wedge) and 331 kg C m−2 (alas), which, even when accounting for landscape-level ice content, is an order of magnitude more C than that stored in the top meter of soil and 2 orders of magnitude more C than in aboveground biomass. Aboveground biomass was composed of primarily larch (53 %) but also included understory vegetation (30 %), woody debris (11 %) and snag (6 %) biomass. While aboveground biomass contained relatively little (8 %) of the C stocks in the watershed, aboveground processes were linked to thaw depth and belowground C storage. Thaw depth was negatively related to stand age, and soil C density (top 10 cm) was positively related to soil moisture and negatively related to moss and lichen cover. These results suggest that, as the climate warms, changes in stand age and structure may be as important as direct climate effects on belowground environmental conditions and permafrost C vulnerability.
MAKING SENSE OF DIGITAL TRANSFORMATION: How sensemaking is used to drive early stage change

This thesis investigates sensemaking in an IT company that undergo change in connection with digital transformation. The study focuses on the managerial perspective in the early stage of change where the main challenge often is to motivate and lead people. The theoretical framework with eight sensemaking mechanisms by Iveroth and Hallencreutz (2016) is used as an analytical lens throughout the study. Empirical data in forms of indepth interviews, informal observations and documents were analyzed in order to identify how different mechanisms of sensemaking is used by managers in the organization when they communicate the change message. Out of the eight mechanisms, we identified that change through logic of attraction, provide a direction, translate as well as stay in motion is essential during the early stage of change, where they can be used by the change leader to mobilize support from the employees. The findings can guide managers to lead when they prepare for change. Further, the results contribute to the body of knowledge about sensemaking and how it is used during early stages of change processes. Our conclusion is that the framework by Iveroth and Hallencreutz is useful when applied to understand a sensemaking process.
A Case Study on Swarm Intelligence based Senor Network Optimization

-Wireless senor network technology was the promising technology applied for many applications. The performance of network is highly depends on architecture, routing algorithm and protocols. There has been a rapid growth in Research on swarm intelligence and many of the researcher has been selected swarm intelligence concept for senor network optimization. This research paper is a review of swarm intelligence were applied in the wireless senor network optimization
Corruption in Malaysia: A Review

Objective: This study presents a review of the studies on corruption in Malaysia that have been conducted from different approaches. Methodology: A literature search was done in the English language relating to corruption and its related terminologies. Google Scholar, Science Direct, Wiley Online Library, and SAGE are the four data sources that were selected to conduct the literature search. Fifteen papers met the predetermined criteria. The selected online published materials were between the years 2000 and 2017. Only expert opinion materials, editorials, reports, reviews, and grey literature were considered for this review. Findings: As a result of this review, many studies discussed the various government initiated anti-corruption actions over the past decades; several studies criticized the failures and limitations of the government’s anti-corruption actions, while some postulated the reasons behind corruption. One study surmised the problems in the judicial system when handling bribery and corruption cases. Conclusion: Gaps identified from these studies suggest a need for researches focusing on psychological and criminological aspects of corruption as roots of the problem. In addition, focusing on psychological and criminological aspects of corruption can become foundations for effective and proactive community crime prevention measures that can be implemented by policy makers, and governments and non-government agencies.
Volatility and Liquidity on High-frequency Electricity Futures Markets: Empirical Analysis and Stochastic Modeling

This paper investigates the relationship between volatility and liquidity on the German electricity futures market based on high-frequency intraday prices. We estimate volatility by the time-weighted realized variance acknowledging that empirical intraday prices are not equally spaced in time. Empirical evidence suggests that volatility of electricity futures decreases as time approaches maturity, while coincidently liquidity increases. Established continuous-time stochastic models for electricity futures prices involve a growing volatility function in time and are thus not able to capture our empirical findings a priori. In Monte Carlo simulations, we demonstrate that incorporating increasing liquidity into the established models is key to model the decreasing volatility evolution.
Data visualization and toss related analysis of IPL teams and batsmen performances

Sports play a very significant role in the development of the human persona. Getting involved in games like Cricket and other various sports help us to build character, discipline, confidence and physical fitness. Indian Premier League, IPL provides the most successful form of cricket as it gives opportunities to young and talented players to show case their talents on various pitch. Decision-makers are the utmost customers for all fundamentals in the sports analytics framework. Sports analytics has been a smash hit in shaping success for many players and teams in various sports. Sports analytics and data visualization can play a crucial role in selecting the best players for a team. This paper is about the Toss Related analysis and the breadth of data visualization in supporting the decision makers for identifying inherent players for their teams.
No Distributional Learning in Adults from Attended Listening to Non-Speech

Distributional learning is a perceptual process hypothesized to underlie the phenomena of phonetic recalibration and selective adaptation, as well as infant speech sound category learning. However, in order to be conclusively tied to the earliest stages of speech sound category development, that is, the formation of novel perceptual categories, distributional learning must be shown to operate on stimuli for which there are no pre-existing categories. We investigated this in a previous study, finding no evidence of distributional learning in adults from unattended listening to non-speech. Since attention to stimuli impacts distributional learning, the present study focused on distributional learning from attended listening to non-speech. The same paradigm was used as in the previous study, except that participants’ attention was directed towards stimuli by means of a cover task. Non-speech stimuli were spectrally rotated vowels and the mismatch negativity was used to measure perceptual categorization. No distributional learning was found, that is, no effect of attention on distributional learning was demonstrated. This could mean that the distributional learning process does not operate on stimuli where perceptual categories do not already exist, or that the mismatch negativity measure does not capture the earliest stages of perceptual category development.
Extracting Flooded Roads by Fusing GPS Trajectories and Road Network

Urban roads are the lifeline of urban transportation and satisfy the commuting and travel needs of citizens. Following the acceleration of urbanization and the frequent extreme weather in recent years, urban waterlogging is occurring more than usual in summer and has negative effects on the urban traffic networks. Extracting flooded roads is a critical procedure for improving the resistance ability of roads after urban waterlogging occurs. This paper proposes a flooded road extraction method to extract the flooding degree and the time at which roads become flooded in large urban areas by using global positioning system (GPS) trajectory points with driving status information and the high position accuracy of vector road data with semantic information. This method uses partition statistics to create density grids (grid layer) and uses map matching to construct a time-series of GPS trajectory point density for each road (vector layer). Finally, the fusion of grids and vector layers obtains a more accurate result. The experiment uses a dataset of GPS trajectory points and vector road data in the Wuchang district, which proves that the extraction result has a high similarity with respect to the flooded roads reported in the news. Additionally, extracted flooded roads that were not reported in the news were also found. Compared with the traditional methods for extracting flooded roads and areas, such as rainfall simulation and SAR image-based classification in urban areas, the proposed method discovers hidden flooding information from geospatial big data, uploaded at no cost by urban taxis and remaining stable for a long period of time.
Freeze and Bid-Ask Spread in the Sovereign Bond Market

We describe a model for two market-makers in the sovereign bond market. The paper gives special importance to the way payments are decided and reflects the cost of securities issuance and trading, a usually small and neglected component of the bond price in normal times. The model generates a dynamic of the bid-ask spread which depends on the discount rate of utility of the market-makers. The novelty of the paper consists in modelling the discount rate of utility as a function depending on fundamentals and the possible changes in the overall risk aversion in the market. The latter aspect is modelled as a martingale process and called sentiment process. We then obtain a solution for our model that can qualitatively mimic the decrease of turnover and the related increase of spread observed in periods of financial uncertainty.
Optimizing the Topology of Bayesian Network Classifiers by Applying Conditional Entropy to Mine Causal Relationships Between Attributes

Due to the excellent classification performance and expressivity, the study of Bayesian network classifiers (BNCs) has attracted great attention ever since the success of Naive Bayes (NB). Information theory has established mathematical basis for the rapid development of BNC. In this paper we propose the definition of entropy function <inline-formula> <tex-math notation="LaTeX">$H_{\mathcal {B}}(D)$ </tex-math></inline-formula>, which corresponds to the optimal number of bits encoded in the network structure <inline-formula> <tex-math notation="LaTeX">$\mathcal {B}$ </tex-math></inline-formula> and can roughly measure the amount of information implicated in training data <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>. Each factor in <inline-formula> <tex-math notation="LaTeX">$H_{\mathcal {B}}(D)$ </tex-math></inline-formula> explicitly represents statements about causal relationships. An efficient heuristic search strategy is introduced to minimize <inline-formula> <tex-math notation="LaTeX">$H_{\mathcal {B}}(D)$ </tex-math></inline-formula> and explore the optimal topology of BNC. Our extensive experimental evaluation on 40 datasets reveals that this out-of-core algorithm achieves competitive classification performance compared to state-of-the-art learners such as tree augmented Naive Bayes, <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-dependence Bayesian classifier, support vector machine, logistic regression and neural network.
A Learning-Based Data Placement Framework for Low Latency in Data Center Networks

Low-latency data service is an increasingly critical challenge for data center applications. In modern distributed storage systems, proper data placement helps reduce the data movement delay, which can contribute to the service latency reduction tremendously. Existing data placement solutions have often assumed the prior distribution of data requests or discovered it via trace analysis. However, data placement is a difficult online decision-making problem faced with dynamic network conditions and time-varying user request patterns. The conventional static model-based solutions are less effective to handle the dynamic system. With an overall consideration of data movement and analytical latency, we develop a reinforcement learning-based framework DataBot+, automatically learning the optimal placement policies. DataBot+ adopts neural networks, trained with a variant of <inline-formula><tex-math notation="LaTeX">$Q$</tex-math><alternatives><mml:math><mml:mi>Q</mml:mi></mml:math><inline-graphic xlink:href="huang-ieq1-2940953.gif"/></alternatives></inline-formula>-learning, whose input is the real-time data flow measurements and whose output is a value function estimating the near-future latency. For instantaneous decision making, DataBot+ is decoupled into two asynchronous production and training components, ensuring that the training delay will not introduce extra overheads to handle the data flows. Evaluation results driven by real-world traces demonstrate the effectiveness of our design.