Miracle-dz/UniFAR-FTUs huggingface dataset:
Product Launches with New Attributes: A Hybrid Conjoint–Consumer Panel Technique for Estimating Demand

The authors propose and empirically evaluate a new hybrid estimation approach that integrates choice-based conjoint with repeated purchase data for a dense consumer panel, and they show that it increases the accuracy of conjoint predictions for actual purchases observed months later. The key innovation lies in combining conjoint data with a long and detailed panel of actual choices for a random sample of the target population. By linking the actual purchase and conjoint data, researchers can estimate preferences for attributes not yet present in the marketplace, while also addressing many of the key limitations of conjoint analysis, including sample selection and contextual differences. Counterfactual product and pricing exercises illustrate the managerial relevance of the approach.
Custom Feedback Selection for Intelligent Tutoring Systems in Ill-Defined Domains

Current medical imaging professional training uses an apprenticeship model with students following an established doctor and viewing their cases, in what is called a practicum. This posses an issue as students are limited to the cases available during their practicum. To resolve this automated instruction can aid in their education promoting both increased depth and breadth. To accomplish this we have created a new Intelligent Tutoring System, Shufti. Shufti makes use of modern gamification and Intelligent Tutoring System designs to augment the learning experience of mammography students. In Shufti we have introduced a new reinforcement learning based technique for use in Intelligent Tutoring System feedback selection in ill-defined domains, and have made use of modern gamification techniques to increase learner engagement.
Obsidian Hydration Rates for Select Sources in the Eastern Great Basin and the Archaic Occupation of Northern Utah

Author(s): Landt, Matthew J. | Abstract: Northern Utah and southern Idaho have numerous sources of obsidian (e.g., Browns Bench, American Falls, Malad, and Wildcat Hills). This article describes a replicable technique for creating regional obsidian hydration chronologies and applies it to prehistoric artifacts recovered during data recovery along the Ruby Pipeline route, which extends eastwest across northern Utah. The method uses regionally-sampled hydration thicknesses to determine a source-specific quadratic equation that is separate from any project-specific data. The estimated hydration rate strongly agrees with radiocarbon and optically stimulated luminescence dates from excavations as well as with common projectile point typologies. The hydration rates, when applied to data from northern Utah, suggest that persistent occupation occurred in the area during the Early Archaic at roughly 8,400 cal B.P.
Effects of Combined Resistance Training and Weightlifting on Injury Risk Factors and Resistance Training Skill of Adolescent Males

Abstract Pichardo, AW, Oliver, JL, Harrison, CB, Maulder, PS, Lloyd, RS, and Kandoi, R. Effects of combined resistance training and weightlifting on injury risk factors and resistance training skill of adolescent males. J Strength Cond Res 35(12): 3370–3377, 2021—The purpose of this study was to investigate the effects of resistance training with or without weightlifting on risk factors for injury and resistance training skill in circa-peak height velocity boys. Sixty-seven boys (aged 12–14 years) from a local secondary school were divided into 3 groups: combined resistance training (CRT), combined resistance training with weightlifting movements (CRT&WL), or a control group (CON). Experimental groups completed twice-weekly training programs over the course of an academic year. The tuck jump assessment, asymmetry measures for single-leg horizontal jump, isometric midthigh pull, and the Star Excursion Balance Test, and resistance training skill were measured pre-, mid-, and post-intervention. Only the CRT group significantly improved tuck jump assessment score pre- to post-test (p = 0.006, −20.4%, d = −0.39) but there were no clear effects on asymmetry measures for any group. Both groups significantly improved resistance training skill from pre- to post-test (CRT&WL: p = 0.002, 17.6%, d = 1.00; CRT: p = 0.026, 9.2%, d = 0.53). This study suggests that a school-based CRT program may provide significant improvements in jump landing kinematics, whereas the inclusion of weightlifting movements may provide greater improvements in resistance training skill.
Improvement of Power Delivery Efficiency of 11KV Power Line using Power Capacitor Placement

Optimal capacitor placement in the distribution network is the most popular technique adopted for the control of power loss and enhancement of Volts Ampere Reactive for effective power delivery. This study, therefore, discusses improvement of distribution efficiency of 11kV power lines through placement of capacitor banks in the network. Power flow in the network, Ado-Ekiti 11kV lines, was carried out using Newton-Rapson iteration method available in MATLAB while the analysis of the network was actualized using the conventional load flow equation modeling. From the analysis of the network, voltage deviation falls within the range of -5.9 to -180.5% which is completely outside the permissible range of ±5%. Voltage drop on the feeders is within the range (1.2 – 1.9) kV before and (0.2 – 0.7) kV after the reactive power of the network was compensated.
Transforming the Archives of the Great East Japan Earthquake into Global Natural Disaster Archives

Soon after the devastation brought by Great East Japan Earthquake (GEJE), a variety of Japanese agencies and organizations began to build digital archives of the disaster. Seven years onwards, over fifty digital archives collect and release millions of earthquake records. These earthquake disaster digital archive created for the Great East Japan Earthquake have the potential to lead the way for more general measures and countermeasures to reduce the impact of future ‘natural’ disasters. 1 We also believe that the records and knowledge of ‘natural’ disasters must be systematized and shared worldwide. As a first step, this research sheds light on examples and subjects of the digital archive of GEJE and report on basic knowledge that serves for the development of digital archive of ‘natural’ disasters’ in the future.
User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio. us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.
Quasi-Experimental Evaluation of Alternative Sample Selection Corrections

Abstract Researchers routinely use datasets where outcomes of interest are unobserved for some cases, potentially creating a sample selection problem. Statisticians and econometricians have proposed many selection correction methods to address this challenge. We use a natural experiment to evaluate different sample selection correction methods’ performance. From 2007, the state of Michigan required that all students take a college entrance exam, increasing the exam-taking rate from 64% to 99% and largely eliminating selection into exam-taking. We apply different selection correction methods, using different sets of covariates, to the selected exam score data from before 2007. We compare the estimated coefficients from the selection-corrected models to those from OLS regressions using the complete exam score data from after 2007 as a benchmark. We find that less restrictive semiparametric correction methods typically perform better than parametric correction methods but not better than simple OLS regressions that do not correct for selection. Performance is generally worse for models that use only a few discrete covariates than for models that use more covariates with less coarse distributions.
LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.
An Unusual Multicystic Case in a Non-syndromic Patient: A Case Report

Aim: The aim of the article is to describe an unusual case of a non-syndromic patient affected by three follicular cysts in the mandible and then by an inflammatory cyst. Furthermore, we describe two different surgical approaches that we have used to manage this case: marsupialization and enucleation. Background: Dentigerous cysts are usually solitary cyst, the presence of multiple cysts is often combined with systemic diseases such as mucopolysaccharidosis, cleidocranial dysplasia, and basal cell nevus syndrome. They are the second cysts in order of frequency. Because of the lack of symptoms, their diagnosis is frequently radiological, and it can be suspected by the failure of tooth eruption. Case description: Two bilateral cysts of the third lower molars and radicular cyst of the right second lower molar were treated. An excisional therapy for the two dentigerous neoformations was performed. While, as a function of the remarkable dimensions which characterized the lesion, a conservative approach by marsupialization for 8 months before, and excision after, were chosen to solve the radicular inflammatory cyst. To preserve the patency of the cystic cavity, an acrylic obturator was used. Conclusion: In the case of multiple dentigerous cysts, a deepen clinical and systematic examination should be done to rule out any associated syndrome. Nowadays in literature, there is not a single and significant orientation based on the results obtained from the various techniques, but this case report shows that it is possible to achieve the expected therapeutic success even if in the same patient. Clinical significance: The case that we have described allows us to observe how a correct study of the lesions allows us to have a more accurate, safe and predictable therapeutic approach that lasts in time.
Semantic Gap Reduction in Content Based Image Retrieval: A Survey

The explosive growth of image data leads to the need of research and development of image retrieval. Content Based Image Retrieval (CBIR) is the approval image retrieval system by which the target image to be retrieved on the basis of useful features of the given image. So, in large collection, users of different domains face a problem of retrieving images relevant to the user query. To overcome this problem, different techniques are adopted. The significance of content based image retrieval system depends on the adopted features to represent images in the knowledge base. Using low-level features cannot give satisfactory results in many cases recovery; especially when high-level concepts in the user’s mind are not easily expressible in terms of low-level features, i.e. semantic gap. The need to improve the precision of image retrieval systems and reduce the semantic gap is high in view of the growing need for image retrieval. We first present semantic extraction methods, and then the key technologies for reducing the semantic gap, i.e. object-ontology, machine learning, generating semantic relevance feedback templates and web image retrieval are discussed
Sketching as a support mechanism for the design and development of shape-changing interfaces

Shape-changing interfaces are a novel computational technology which incorporate physical, tangible, and dynamic surfaces to create a true 3-Dimensional experience. As is often the case with other novel hardware, the current research focus is on iterative hardware design, with devices taking many years to reach potential markets. Whilst the drive to develop novel hardware is vital, this usually occurs without consultation of end-users. Due to the prototypical nature of shape-change, there is no specific current practice of User-Centred Design (UCD). If this is not addressed, the resulting field may consist of undirected, research-focused hardware with little real world value to users. Therefore, the goal of this thesis is to develop an approach to inform the direction of shape-change research, which uses simple, accessible tools and techniques to connect researcher and user. I propose the development of an anticipatory, pre-UCD methodology to frame the field. Sketching is an established methodology. It is also accessible, universal, and provides us with a low-fidelity tool-kit. I therefore propose an exploration of how sketching can support the design and development of shape-changing interfaces. The challenge is approached over five stages: 1) Analysing and categorising shape-changing prototypes to provide the first comprehensive overview of the field; 2) Conducting a systematic review of sketching and HCI research to validate merging sketching, and its associated UCD techniques with highly technological computing research; 3) Using these techniques to explore if non-expert, potential end-users can ideate applications for shape change; 4) Investigating how researchers can utilise subjective sketching for shape-change; 5) Building on ideation and subjective sketching to gather detailed, sketched data from non-expert users with which to generate requirements and models for shape-change. To conclude, I discuss the dialogue between researcher and user, and show how sketching can bring these groups together to inform and elucidate research in this area.
Un estudio empírico preliminar sobre los tests estadísticos más habituales en el aprendizaje automático

Resumen Actualmente no existe un diseño experimental que sea admitido de forma universal por los investigadores en aprendizaje automático. Hay opiniones diversas en lo referente a la proporción de ejemplos de la muestra que se debe reservar para la fase de validación, o acerca de la forma en que se deben seleccionar estos ejemplos, por mencionar algunos puntos controvertidos. En este trabajo se revisa la bibliograf́ıa más relevante al respecto, y se discuten las conclusiones preliminares obtenidas mediante un análisis emṕırico de la potencia de varios tests, usados comúnmente por los investigadores en mineŕıa de datos. El estudio experimental se instrumenta sobre varios conjuntos de datos sintéticos, con propiedades teóricas conocidas.
Economics Of Migration

Migration concerns a variety of private and collective choices, which are of immense value to the functioning of the market economy. This book provides theoretical clarification to the issue with special emphasis on the effects of migration of people. Three types of analytical frameworks are used. The Microeconomic Framework is based on a synthesis of the classical models of labour migration and trade policy. The short-run effects of taxation are examined in close scrutiny. The Macroeconomic Framework applies the Neo-Keynesian macroeconomic model to the analysis of factor migration paying attention to the market adjustment mechanism. In examining the effects of local policies, the long disgraced Adaptive Expectations Hypothesis is given fresh interpretations. In the Club Theoretic Framework, welfare in a locality depends on its size. The competitive dynamics of the system is a product of people’s exit-type migration choices and their collective voice-type choices. The need for centralized policy intervention is also analyzed. The leading intuition of the book is that migration should be a vital element of spatial resource allocation in the real-world context.
The emergence of legislative regulation of lobbying in the United States as an example for Ukraine

The article is devoted to the study of the process of formation of legislative regulation of lobbying in the United States. The main stages of the emergence and reform of the relevant regulatory legislation are considered.The author focuses on the peculiarities of the formation of legislative regulation of lobbying in the United States, due to the specifics of the state and economic processes in this country that took place during the XIX – XX centuries. Regulatory regulation of lobbying in the United States was formed in stages. The antimonopoly and antitrust struggle of the state at the end of the 19th century can be considered as the first elements of such regulation.The next stage was the «new course» of F. Roosevelt, designed to bring the state’s economy out of the great depression, even against the interests of large corporations and efforts to counter the ideological and political influence of the communist and national socialist regimes of the USSR and Germany, respectively.The second half of the twentieth century became the stage of a more mature, modern regulation of lobbying. The most significant changes were made by the Law «On the disclosure of lobbying activities» of 1995. However, taking into account the specifics of the US legal system, a number of precedents of the late XIX and early XX centuries remain in force throughout the entire evolution of legal regulation of lobbyism.Despite the enormous experience of regulating lobbying, even in the United States, the problem of distinguishing lobbying and corruption has not yet been resolved, which makes further scientific research in this area relevant.The article also attempted to consider the prospects of approximation of relevant experience for use in the framework of further regulatory regulation of lobbyism in Ukraine.
Perspectives on Intelligent system techniques used in Data Mining

With the uprising trend of the social media, for marketing purposes and for personal communication, the data mining techniques used in the early decade can no longer handle the data for pattern recognition . It becomes necessity that the data mining techniques become intelligent enough to deal with the volume and variety of the data being searched for recognizing patterns or trends. Thus in this article, I have explored the various neural networks and their techniques being implemented on data to find a particular trend. Various applications of such hybrid intelligent systems have been discussed. Perceptron and it's varieties have been long used for machine learning without the human intervention. Moreover their various features are inspired by the biological strategies endowed by nature on humans to recognize, learn and innovative ideas to solve their problems. Humans have various sensory organs to help them receive their inputs from the surroundings, however it is their brain that helps them to process the large data and get the required data as an output. Neural Networks are based on the Human brain and the nervous system. So we shall explore various intelligent systems of Neural Networks to help in data mining.
A Time Series Forecasting Method

This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.
What Does Free Community College Buy? Early Impacts from the Oregon Promise

This paper examines the Oregon Promise, a state‐level program that exclusively subsidizes in‐state community college attendance. I estimate impacts using a difference‐in‐difference design that links students in states with essentially universal 10th‐grade PSAT coverage to national‐level postsecondary enrollment data. I find that the implementation of the Oregon Promise increased enrollment at two‐year colleges by roughly four to five percentage points for the first two eligible cohorts. In the first year of the program, the increase in community college enrollment comes primarily from students shifting out of four‐year colleges, whereas in the second year the program predominately increases overall postsecondary enrollment.