Miracle-dz/UniFAR-FTUs huggingface dataset:
Load and locational marginal pricing prediction in competitive electrical power environment using computational intelligence

This paper presents a comprehensive study of forecasting a day-ahead of load and locational marginal pricing (LMP) using artificial intelligent systems. An artificial neural network (ANN) is trained with a stochastic optimization technique called particle swarm optimization (PSO). This training algorithm works to adjust the network weights and biases as to minimize the error function. Wavelet transformed data is fed into neural network as preprocessing stage in order to get a better price pattern that will be reliable for forecasting. The proposed models were trained and tested using real data consists of historical load and LMP and corresponding influence variables such as weather information and marginal losses cost (MLC). The data used is from NYISO and Weather Source Stations, Buffalo, New York over a period of three years (2001–2003). Simulation results are compared with that of conventional back-propagation (BP) neural network and radial basis function network (RBFN) and provided highly accurate generalization capability.
Targeted integration of genes into the Clostridiurn acetobutylicum c hromosorne

A number of non-replicative, integrational plasmids have been introduced into the chromosome of the NCIMB 8052 strain of CIostridium acetobutykum. Integration occurred via homologous recombination, apparently by a Campbell-like mechanism. For plasmids containing internal fragments of gutD or spoOA, insertion was mutagenic leading, respectively, to an inability to use sorbitol as sole source of carbon for energy and growth or an inability to form endospores. One insertion was used to determine the map location of a randomly cloned DNA fragment and another underwent amplification in the bacterial chromosome. These results suggest that integrational plasmids will be very useful tools for genetic analysis in Clostridium acetobutylicum. To our knowledge, this is the first demonstration of gene transfer involving homologous recombination with the chromosome of any species of Clostridium .
Characterising a Heterogeneous System for Person Detection in Video using Histograms of Oriented Gradients : Power vs . Speed vs . Accuracy

This paper presents a new implementation, with complete analysis, of the processing operations required in a widely-used pedestrian detection algorithm (the Histogram of Oriented Gradients detector) when run in various configurations on a heterogeneous platform suitable for use as an embedded system. The platform consists of FPGA, GPU and CPU and we detail the advantages of such an image processing system for real-time performance. We thoroughly analyse the consequent tradeoffs made between power consumption, latency and accuracy for each possible configuration. We thus demonstrate that prioritisation of each of these factors can be made by selecting a specific configuration. These separate configurations may then be changed dynamically to respond to changing priorities of a real-time system, e.g. on a moving vehicle. We compare the performance of real-time implementations of linear and kernel SVMs in HOG and evaluate the entire system against the stateof-the-art in real-time person detection. We also show that our FPGA implementation detects pedestrians more accurately than existing implementations, and that a heterogeneous configuration which performs image scaling on the GPU, and histogram extraction and classification on the FPGA, produces a good compromise between power and speed.
Comparing Numbered Heads Together with and without Peer-Led Opportunities to Respond: A Case Study

Abstract:The current case study compared the effects of a Numbered Heads Together strategy with and without peer-led opportunities to respond during a post-secondary classroom management class. Results indicated that the student with high-functioning autism had a higher percentage of intervals of on-task behavior and weekly quiz scores during the Heads Together condition with peer-led opportunities to respond. Student satisfaction ratings suggested that Heads Together with peer-led opportunities to respond was a preferred strategy to Heads Together alone. A discussion of study limitations, implications, and future research directions is included.
Corrugated antipodal Vivaldi antenna using spiral shape negative index metamaterial for ultra-wideband application

This paper describes a corrugated antipodal Vivaldi antenna with improved performance by using negative index metamaterial (NIM) of the Archimedean spiral design. A single layer NIM piece is placed perpendicular middle of the two arm of the proposed antenna. The antenna size is 30×60×0.787 mm3 operating at 8GHz. The simulated results of NEVI corrugated antipodal Vivaldi antenna show that the gain and directivity has increased up to 1.2dB and 1dB respectively. The HPBW is increased by 90 with the reflection coefficient less than DIO dB from 4.7 GHz to 11 GHz for UWB application.
Fake News, Conspiracies and Myth Debunking in Social Media - A Literature Survey Across Disciplines

Since the 2016 U.S. presidential election, the problem of fake news on social media gained renewed public attention. Consequently, a huge amount of research literature concerning this matter is published each year. With this study, we seek to identify and integrate the findings of relevant and high quality studies across several research disciplines and provide insights concerning the current state-of-the-art literature on fake news detection. For this goal, systematically gathered detailed information as well as an overview of existing fake news detection features and methods are presented, including studies on spread mechanisms of fake news on social media. Finally, methodological limitations and future research directions are discussed.
Model-based detection and segmentation of vehicles for intelligent transportation system

In this paper we present a method to detect vehicles on the road for intelligent transport application. In the proposed method, statistical property of background pixels are defined, where foreground objects of a current frame are detected by subtracting the current frame w.r. t. background color model and background edge model. Color and edge are considered here as these two features provide complementary advantages. On top of this pixel-based step of subtracting pixel w.r. t. background models, region property is incorporated, where segmentation of a foreground is also subject to its appearance in spatial domain. Clustering is performed to capture local areas with high entropy indicating the presence of a foreground. A region- based speed function is proposed to evolve an active contour for a final segmentation based on region property within the contour. We observe that our strategy of integrating pixel-based and region-based modeling yields robust performance in handling challenging scenarios. The experiment of this work reports results of the proposed method when evaluating it on surveillance videos recorded by a live system.
Enhanced aperiodic responsiveness by multi budget bandwidth preserving server

This paper presents multi budget bandwidth preserving server (MBBPS) to improve the quality of service in terms of better responsiveness of the aperiodic task by utilizing the concept of multi budget and multi priority of the server while ensure the feasibility of periodic task at the same time. Feasibility analysis is done at offline by assigning the priority of periodic tasks as well as server to service the aperiodic tasks by fixed priority rate monotonic first algorithm. Theorem has been formulated to ensure the feasibility of the periodic tasks with enhanced budget. Besides providing better responsiveness to the aperiodic tasks, this work also improves the quality of service (QoS) by accepting aperiodic tasks that were rejected by the existing deferrable server. The complexity of proposed MBBPS algorithm is same as deferrable server. The extensive examples and simulation results illustrate that our approach can effectively reduce the average response time of aperiodic tasks as well as reduce the rejection ratio while guaranteeing the periodic tasks at the same time.
Two Perspectives on Learning Rich Representations from Robot Experience

This position paper describes two approaches towards the representations that a robot can learn from its experience. In the first approach, the robot learns models for reasoning about human-interpretable aspects of the environment, for example models of space and objects. In the second approach, the robot incrementally learns predictions for the consequences of performing policies, where a policy is any experimental procedure that the robot can perform. These two approaches correspond closely to the ideas of a scientific model and an experimental prediction, and ideally the benefits of both can be accessible to a robot.
Thermal conductivity of silicon nanomeshes: Effects of porosity and roughness

We theoretically investigate thermal conductivity in silicon nanomeshes using Monte Carlo simulations of phonon transport. Silicon membranes of 100 nm thickness with randomly located pores of 50 nm diameter are considered. The effects of material porosity and pore surface roughness are examined. Nanomesh porosity is found to have a strong detrimental effect on thermal conductivity. At room temperature, a porosity of 50% results in ∼80% reduction in thermal conductivity. Boundary roughness scattering further degrades thermal conductivity, but its effect is weaker. Thermal transport can additionally be affected by the specific arrangement of the pores along the transport direction.
Enhancing Medical Named Entity Recognition with Features Derived from Unsupervised Methods

A study of the usefulness of features extracted from unsupervised methods is pro- posed. The usefulness of these features will be studied on the task of performing named entity recognition within one clinical sub-domain as well as on the task of adapting a named entity recognition model to a new clinical sub-domain. Four named entity types, all very relevant for clinical information extraction, will be studied: Disorder, Finding, Pharmaceutical Drug and Body Structure. The named entity recognition will be performed using conditional random fields. As unsupervised features, a clustering of the semantic representation of words obtained from a ran- dom indexing word space will be used.
Planning social actions through the others' eyes for emergent storytelling

Stories have become an important element of games, since they can increase their immersion level by giving the players the context and the motivation to play. However, despite the interactive nature of games, their stories usually do not develop considering every decision and/or action the players are capable of, because depending on the game size, it would take too much effort to author alternative routes for all of them. To make these alternatives viable, an interesting solution would be to procedurally generate them, which could be achieved by using the story generation approaches already developed by many works of the storytelling field. Some of these approaches are based on the simulation of virtual worlds, in which the stories are generated by making the characters that inhabit the worlds act trying to reach their goals. The resulting actions and the world's reactions compose the final story. Since the actions are the building blocks of the stories, the characters' acting capabilities are determinant features of the generation potential of simulations. For instance, it is only possible to generate stories with deception if the characters are capable of deceiving each other. To allow the generation of stories where the characters are capable of manipulation, cooperation and other social behaviors by actively using what the others will do based on what they know and see, we propose a recursive planning approach that deals with the uncertainty of the others' knowledge and with a purposely error-prone perception simulation. To test our proposal we developed a story generation system and designed an adaptation of Little Red Riding Hood world as test scenario. With our approach, the system was capable of generating coherent story variations with deceptive actions.
A Statistical Model of the Gross Analysis of Transaction Flows Econo-metrica 28

This model is applicable to the flow of any kind of quantifiable transactions, and to matrices from three to more than one hundred actors, utilizing a 650 IBM electronic computer or similar equipment. The method develops a matrix of expected or baseline data from assumptions of complete indifference among the actors, and measures the plus or minus differences between this baseline value and the actual amount of transactions in each direction for every pair of actors. It thus removes gross size effects and permits tentative inferences about the distribution of preferences among pairs of larger groups of actors; about degrees of clustering or integration among actors; and about changes over time, if several matrices are used. It thus locates interesting pairs or groups for further study. Import-export data are used as an example to show the detailed application of the method. For a given year and a group of countries, the importexport data can be arranged like a contingency table, except the diagonal cells are zero and the other entries are quantities of money, a continuous variable instead of a discrete variable. A model describing the data and techniques for the statistical analysis are presented. This is a "null model" in the sense that the departures from it are of primary interest. The North Atlantic Area (1928) is used as an illustration.
Using Program Slicing to Identify Faults in Software

This study explores the relationship between program slices and faults. The aim is to investigate whether the characteristics of program slices can be used to identify fault- prone software components. Slicing metrics and dependence clusters are used to characterise the slicing profile of a software component, then the relationship between the slicing profile of the component and the faults in that component are then analysed. Faults can increase the likelihood of a system becoming unstable causing problems for the development and evolution of the system. Identifying faultprone components is difficult and reliable predictors of fault- proneness not easily identifiable. Program slicing is an established software engineering tech- nique for the detection and correction of specific faults. An investigation is carried out into whether the use of program slicing can be extended as a reliable tool to predict fault-prone software components. Preliminary results are promising suggesting that slicing may offer valuable insights into fault-proneness.
THE ROLE OF MIX, DENSITY AND LOCATION OF NEW HOUSING IN ACHIEVING SUSTAINABILITY AND AFFORDABILITY

In the last decade housing and planning policies in UK have tended to result in a concentration of much new housing development in central and urban locations, with a marked shift towards more use of apartment development at higher densities. This paper examines the issues of housing type mix and density from the viewpoint of the key housing policy goal of ‘affordability’ and the key planning goal of ‘sustainability’, while also considering the essential practical requirement of economic viability. Building on hedonic house price models and models of development costs, the different patterns of price/value and cost and the resulting viability calculation are mapped across a selection of city-regions in England using a range of model housing schemes involving different mix and density. The affordability outcomes are assessed using estimates of local income distributions. Wider social sustainability outcomes are informed by relationships established on national and local survey datasets. In the light of these findings the scope and case for planning to shape the form of new housing development in different parts of the city regions, under different market conditions will be assessed.
The Use of Evolutionary Algorithms in the Analysis of Economics Experiments

We report on the application of an evolutionary algorithm in the analysis of data from experiments in economics. The algorithm was used in conjunction with regression analysis to evaluate various variable subsets and find those sets best capable of explaining the experimental outcomes. This evolutionary computation approach is offered as a supplemental method to generate optimal parsimonious subsets according to user specified criteria, or to validate subsets generated by more traditional means such as step-
Statistical Script Learning with Recurrent Neural Nets

Statistical Scripts are probabilistic models of sequences of events. For example, a script model might encode the information that the event “Smith met with the President” should strongly predict the event “Smith spoke to the President. ” We present a number of results improving the state of the art of learning statistical scripts for inferring implicit events. First, we demonstrate that incorporating multiple arguments into events, yielding a more complex event representation than is used in previous work, helps to improve a co-occurrence-based script system’s predictive power. Second, we improve on these results with a Recurrent Neural Network script sequence model which uses a Long Short-Term Memory component. We evaluate in two ways: first, we evaluate systems’ ability to infer held-out events from documents (the “Narrative Cloze” evaluation); second, we evaluate novel event inferences by collecting human judgments. We propose a number of further extensions to this work. First, we propose a number of new probabilistic script models leveraging recent advances in Neural Network training. These include recurrent sequence models with different hidden unit structure and Convolutional Neural Network models. Second, we propose integrating more lexical and linguistic information into events. Third, we propose incorporating discourse relations between spans of text into event co-occurrence models, either as output by an off-the-shelf discourse parser or learned automatically. Finally, we propose investigating the interface between models of event co-occurrence and coreference resolution, in particular by integrating script information into general coreference systems.
Dual governance and traditional communities : the case study of the Mamaila (Kolobetona) traditional community.

This study explores how the existence of traditional leadership alongside democratic governance within the local sphere of government affects traditional communities. It focuses on the relationships between traditional leadership and other key governance institutions at the local sphere; that is local government, administration of justice; community safety and land administration and how these relationships are translated in traditional communities. The key elements focused on include the roles of the different institutions, the role and position of the traditional community in governance, the need for institutions of governance to uphold their Constitutional mandates including the principles of democracy enshrined in the Bill of Rights within the Constitution. Through the use of the case study method of research, focusing on the Mamaila (Kolobetona) Traditional Community and the Lemondokop Village in particular, the study affirms the position held by some scholars that there is dual governance within the local sphere of government where the institution of traditional leadership exists. Furthermore, this study reveals that the relationships between traditional leadership and the other institutions of governance vary depending on the mandate of such institutions within the traditional community. The question is how such varied relationships affect the traditional community. To answer the above question I separated my findings into two chapters, focusing on perceptions and experiences of community leadership and community members respectively. This study shows that while dual governance is entrenched within the traditional community, and the key actors have found a way of accommodating each other and balancing their roles, the traditional community is at the mercy of traditional leadership because of the authority over communal land. The traditional community is not well conversant with the rights they have over the land and hence their development needs are driven by an elite who is more concerned about entrenching his authority than promoting community development.
Value of magnetic resonance cholangiopancreatography in the diagnosis of biliary abnormalities in postcholecystectomy patients: A probabilistic cost-effectiveness analysis of diagnostic strategies

Background: Endoscopic retrograde cholangiopancreatography (ERCP) is considered the gold standard for imaging of the biliary tract but is associated with complications. Less invasive imaging techniques, such as magnetic resonance cholangiopancreatography (MRCP), have a much lower complication rate. The accuracy of MRCP is comparable to that of ERCP, and MRCP may be more effective and cost-effective, particularly in cases for which the suspected prevalence of disease is low and further intervention can be avoided. A model was constructed to compare the effectiveness and cost-effectiveness of MRCP and ERCP in patients with a previous history of cholecystectomy, presenting with abdominal pain and/or abnormal liver function tests. Methods: Diagnostic accuracy estimates came from a systematic review of MRCP. A decision analytic model was constructed to represent the diagnostic and treatment pathway of this patient group. The model compared the following two diagnostic strategies: (i) MRCP followed with ERCP if positive, and then management based on ERCP; and (ii) ERCP only. Deterministic and probabilistic analyses were used to assess the likelihood of MRCP being cost-effective. Sensitivity analyses examined the impact of prior probabilities of common bile duct stones (CBDS) and test performance characteristics. The outcomes considered were costs, quality-adjusted life years (QALYs), and cost per additional QALY. Results: The deterministic analysis indicated that MRCP was dominant over ERCP. At prior probabilities of CBDS, less than 60 percent MRCP was the less costly initial diagnostic test; above this threshold, ERCP was less costly. Similarly, at probabilities of CBDS less than 68 percent, MRCP was also the more effective strategy (generated more QALYs). Above this threshold, ERCP became the more effective strategy. Probabilistic sensitivity analyses indicated that, in this patient group for which there is a low to moderate probability of CBDS, there was a 59 percent likelihood that MRCP was cost-saving, an 83 percent chance that MRCP was more effective with a higher quality adjusted survival, and an 83 percent chance that MRCP had a cost-effectiveness ratio more favorable than $50,000 per QALY gained. Conclusions: Costs and cost-effectiveness are dependent upon the prior probability of CBDS. However, probabilistic analysis indicated that, with a high degree of certainty, MRCP was the more effective and cost-effective initial test in postcholecystectomy patients with a low to moderate probability of CBDS.