Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of Movement and Spontaneous Use of The Affected Upper Limb In 8-Year-Old Infantile Hemiplegia After Applying Mirror Therapy. Case Series

Introduction: Children with hemiplegia is a type of Cerebral Palsy where the function of the upper limb is disturbed, with limitation of function in it. The affected hand common characteristics are slowness, abnormal muscle tone, decreased strength and coordination difficulties. And to these problems are often added sensitivity alterations and mirror movements. One of the main problems of hemiplegia is the alteration of bimanual manipulation Based on the concept of mirror neurons, Mirror Therapy (MT) is applied to increase the affected upper limb function. Objective: To assess the increase in the quality of movement and spontaneous use of the affected upper limb in congenital infantile hemiplegia with 8 years old after applying a Mirror Therapy protocol. Materials and Methods: 4 children (8 years old) diagnosed with right congenital infantile hemiplegia are recruited to execute a Mirror Therapy protocol for 5 weeks, 15 minutes per day. Two measurements are carried out for the variables of quality of movement and spontaneous use of the affected upper limb. Results: Increased 5% for spontaneous use assessed with the Shriners Hospital Upper Extremity Evaluation, Shuee Evaluation and 6.5% for the quality of movement measured through the Quality of Upper Extremity Skills Test, Quest scale, produced by a higher score in the final situation of the dissociated movements (increase 11%). Conclusion: Mirror Therapy performed in a time of 15 minutes per day for 5 weeks of treatment would allow improvements in spontaneous use and the quality of movement of the affected upper limb in children diagnosed with congenital infantile hemiplegia with an age of 8 years old.
AN ECONOMIC ENERGY APPROACH FOR QUERIES ON DATA CENTERS

Energy consumption is an issue that involves all of us, both as individuals and as members of a society, and covers all our areas of activity. It is something so broad that its impact has important reflections on our social, cultural and financial structures. The domain of software, and in particular database systems, is not an exception. Although it seems to be a little bit strange to study the energy consumption of just one query, when we consider the execution of a a few thousand queries per second, quickly we see the importance of the querying consumption in the monthly account of any company that has a conventional data center. To demonstrate the energy consumption of queries in data centers, we idealized a small dashboard for monitoring and analyzing the sales of a company, and implemented all the queries needed for populating it and ensuring its operation. The queries were organized into two groups, oriented especially to two distinct database management systems: one relational (MySQL) and one non relational (Neo4J). The goal is to evaluate the energy consumption of different types of queries, and at the same time compare it in terms of relational and non-relational database approaches. This paper relates the process we implemented to set up the energy consumption application scenario, measure the energy consumption of each query, and present our first preliminary results.
Collaborative Filtering and the Netflix Challenge

In collaborative filtering, our task is to predict how a user will rate an item given a history of the user’s ratings of other items, as well as other users’ ratings of items. This paper considers collaborative filtering in the context of the Netflix dataset, a listing of approximately 480,000 users’ ratings of over 17,000 movies. We attempt to construct a novel algorithm for approaching this problem based on the idea that subsets of movies exist in the training set, and that movies within a subset have higher predictive power for one another than for movies outside the subset. We show that such movie sets do in fact exist, although finding high-quality sets is not easy. Further, we demonstrate that predictions can be improved by taking into account a user’s prior rating tendencies. Our results show that our algorithm makes improvements over some of the baseline schemes, and more importantly, that several of the features implemented do improve prediction accuracy of the algorithm. We expect that the insights gained in the course of this paper will lead to better methods for identifying movie subsets and therefore to better predictions in the future.
Automatic UAV image registration using feature detection and matching with satellite imagery

This thesis presents an approach and implementation for using satellite imagery to perform image registration with unprocessed aerial images captured with a UAV. Aerial imagery is used in a large variety of applications including disaster relief, urban planning, crop and vegetation monitoring, and mapping. The common difficulty in utilizing aerial imagery captured by aircraft rather than satellite is that of image registration: transforming data into the image coordinate system. Often, the goal of these applications involves transforming location data into the image coordinate system for object extraction and further processing. Current approaches require hand-labeling of correspondences, the use of ground control points (GCPs), or human analysis to identify objects or locations of interest in the aerial imagery. As such, these methods do not sufficiently scale, generalize, or provide the efficiency required for these applications. The proposed approach to image registration in aerial imagery makes use of raw images captured on consumer-grade UAVs, and uses automatic feature detection and matching to register the imagery. This implementation provides a proof of concept, which was found to succeed on roughly 65% of our images.
Ville Kyrki Title : Segmenting and Sequencing of Compliant Motions Year : 2018 Version : Post print

This paper proposes an approach for segmenting a task consisting of compliant motions into phases, learning a primitive for each segmented phase of the task, and reproducing the task by sequencing primitives online based on the learned model. As compliant motions can “probe” the environment, using the interaction between the robot and the environment to detect phase transitions can make the transitions less prone to positional errors. This intuition leads us to model a task with a non-homogeneous Hidden Markov Model (HMM), wherein hidden phase transition probabilities depend on the interaction with the environment (wrench measured by an F/T sensor). Expectation-maximization algorithm is employed in estimating the parameters of the HMM model. During reproduction, the phase changes of a task are detected online using the forward algorithm, with the parameters learned from demonstrations. Cartesian impedance controller parameters are learned from the demonstrations to reproduce each phase of the task. The proposed approach is studied with a KUKA LWR4+ arm in two setups. Experiments show that the method can successfully segment and reproduce a task consisting of compliant motions with one or more demonstrations, even when demonstrations do not have the same starting position and external forces occur from different directions. Finally, we demonstrate that the method can also handle rotational motions.
CitationAS: A Tool of Automatic Survey Generation Based on Citation Content

Abstract Purpose This study aims to build an automatic survey generation tool, named CitationAS, based on citation content as represented by the set of citing sentences in the original articles. Design/methodology/approach Firstly, we apply LDA to analyse topic distribution of citation content. Secondly, in CitationAS, we use bisecting K-means, Lingo and STC to cluster retrieved citation content. Then Word2Vec, WordNet and combination of them are applied to generate cluster labels. Next, we employ TF-IDF, MMR, as well as considering sentence location information, to extract important sentences, which are used to generate surveys. Finally, we adopt manual evaluation for the generated surveys. Findings In experiments, we choose 20 high-frequency phrases as search terms. Results show that Lingo-Word2Vec, STC-WordNet and bisecting K-means-Word2Vec have better clustering effects. In 5 points evaluation system, survey quality scores obtained by designing methods are close to 3, indicating surveys are within acceptable limits. When considering sentence location information, survey quality will be improved. Combination of Lingo, Word2Vec, TF-IDF or MMR can acquire higher survey quality. Research limitations The manual evaluation method may have a certain subjectivity. We use a simple linear function to combine Word2Vec and WordNet that may not bring out their strengths. The generated surveys may not contain some newly created knowledge of some articles which may concentrate on sentences with no citing. Practical implications CitationAS tool can automatically generate a comprehensive, detailed and accurate survey according to user’s search terms. It can also help researchers learn about research status in a certain field. Originality/value CitaitonAS tool is of practicability. It merges cluster labels from semantic level to improve clustering results. The tool also considers sentence location information when calculating sentence score by TF-IDF and MMR.
Part localization for robotic manipulation

The new generation of collaborative robots allows the use of small robot arms working with human workers, e.g. the YuMi robot, a dual 7-DOF robot arms designed for precise manipulation of small objects. For the further acceptance of such a robot in the industry, some methods and sensors systems have to be developed to allow them to perform a task such as grasping a specific object. If the robot wants to grasp an object, it has to localize the object relative to itself. This is a task of object recognition in computer vision, the art of localizing predefined objects in image sensor data. This master thesis presents a pipeline for object recognition of a single isolated model in point cloud. The system uses point cloud data generated from a 3D CAD model and describes its characteristics using local feature descriptors. These are then matched with the descriptors of the point cloud data from the scene to find the 6-DoF pose of the model in the robot coordinate frame. This initial pose estimation is then refined by a registration method such as ICP. A robot-camera calibration is performed also. The contributions of this thesis are as follows: The system uses FPFH (Fast Point Feature Histogram) for describing the local region and a hypothesize-and-test paradigm, e.g. RANSAC in the matching process. In contrast to several approaches, those whose rely on Point Pair Features as feature descriptors and a geometry hashing, e.g. voting-scheme as the matching process.
Path Planning and Control of Mobile Robot in Road Environments Using Sensor Fusion and Active Force Control

A path planning and control approach of a non-holonomic three-wheeled mobile robot (WMR) for online navigation in road following and roundabout environments is presented in this paper. We proposed a complete navigation algorithm that enables the WMR to autonomously navigate on the road with various scenarios. With such an algorithm, the robot is able to localize itself within the road environment and find a collision free-path starting from a pre-defined start position to a goal point using a novel approach called laser simulator (LS). The path planning and roundabout detection are determined based on LS and sensor fusion of a laser range finder, camera, and odometry measurements. The sensor fusion algorithm is used to remove noises and uncertainties from sensors’ data and provide optimum measurements for path planning. A robot motion control scheme is used for the purpose of controlling the kinematic parameters of WMR using a resolved acceleration control coupled with an active force control for rejecting the disturbances. Experimental results show the capability of the proposed algorithms to robustly drive the robot on the road following and roundabout environments.
Building a Productive Workforce: The Role of Structured Management

In an influential study, Bender et al. [Bender S, Bloom N, Card D, Van Reenen J, Wolter S (2018) Management practices, workforce selection, and productivity. J. Labor Econom. 36(S1):S371–S409] document consistent relationships between management practices, productivity, and workforce composition using administrative data from German firms matched to ratings of their practices from the World Management Survey. We replicate and extend their analysis using comparable data from Brazil. The main conclusions from their study are supported in ours, strengthening the view that more structured practices affect organizational performance through workforce selection across different institutional settings. However, we find that more structured management practices are linked to greater wage inequality in Brazil, relative to greater wage compression in Germany, suggesting that some of the consequences of adopting structured practices are tied to the local context. This paper was accepted by Joshua Gans, business strategy.
Rationale Visualization for Decision Support

We present a method to visually explain the rationale of a reasoning engine that raises an alarm if a certain situation is reached. As a case study we look at the maritime safety and security domain. Based on evidence and a reasoning structure, the engine concludes with a certain probability that, e.g., the vessel is an environmental hazard. This engine is part of a larger safety and security system manned by an operator who makes decisions based on the output of the reasoning engine. To support decision making we visualize the rationale, an abstraction of the reasoning structure that allows users to understand why the conclusion has been reached, and display the evidence in a color-coded matrix that easily reveals if and where observations contradict.
Search Like a Human : Neural Machine Translation for Database Search

With an ever growing amount of data generated every day, the need to simplify the treatment of those data becomes a requirement. We need to develop new tools to allow non-technical users to access and filter data as easily and close to the human language as possible. In this paper, we implement the state-of-theart model for generating an SQL query from a natural language question. The model consists of three parts: a contextualized word model representation (e.g. ELMo, BERT, etc.); a sequence-to-SQL generation decoder; and an ExecutionGuided (EG) decoder to guarantee the consistency of the SQL request. We chose to experiment with a different contextualized word model representation, the recently published GPT-2 model, to test the robustness of the SOTA core model; and change additional features of the model (using Transformers). We observe that this approach, along with NL2SQL suggested by SQLova [11], generates some very promising results and scores the third place on the Salesforce WikiSQL leaderboard. Our implementation with OpenAI GPT-2 gets a 77.5% logical form accuracy and 84.4% execution accuracy on the test set (compared to 83.6% and 89.6% for SQLova, respectively). Our findings prove GPT-2 (small) to be a solid multitasking pre-model, but yet doesn’t produce results as good as BERT (small).
Design of Scale-down Standard Fuel Block Test Section to Validate Core Thermo-fluid Analysis Code for Prismatic Gas-Cooled Reactor

Korea Atomic Energy Research Institute (KAERI) has developed CORONA (Core Reliable Optimization and thermo-fluid Network Analysis) code for thermofluid analysis of prismatic High Temperature Gascooled Reactor (HTGR) core [1]. The CORONA code is aimed to the whole-core thermo-fluid analysis of a prismatic HTGR with fast computation and reasonable accuracy. The key idea for the fast computation is to solve three-dimensional conduction equation combined with one-dimensional fluid flow network equations. The active core of the prismatic gas-cooled reactor consists of the hexagonal graphite blocks stacks stacked vertically in the core. The gaps between the graphite blocks result in the bypass and the cross flow between core gaps and coolant channels. The bypass and the cross flow are the important factor to estimate the thermal margin in the core thermo-fluid design of the prismatic gas-cooled reactor. Most of the code validation data for the prismatic HTGR core are the iso-thermal test results [2-6] using the room temperature air. Inagaki et al. [7] performed thermo-fluid tests on the core of the high temperature using the helium engineering demonstration loop (HENDEL) at the reactor operating condition, but the data cannot be used for CORONA validation because of the annulus channel type with the heated rod. This paper presents the experimental design for the CORONA and the pre-test analysis for the design.
Risk and risk management in organic agriculture : Views of organic farmers

In a series of focus groups during 2001 and 2002, organic farmers from different regions of the United States identified a wide range of risks to their operations. The focus groups were facilitated by the University of Maryland in cooperation with a research team from USDA’s Economic Research Service, to explore the risks faced by organic farmers, how they are managed, and needs for risk management assistance. Contamination of organic production from genetically modified organisms was seen as a major risk, particularly by grain, soybean and cotton farmers. Focus-group participants producing grains and cotton—many of whom knew about and had obtained crop insurance—raised concerns about coverage offered, including the need for insurance to reflect the higher prices received for organic crops. Most fruit and vegetable producers participating in the focus groups had little knowledge of crop insurance. When provided with basic information about crop insurance, operators of small fruit and vegetable farms were skeptical about its usefulness for their type of operation.
Absolute electromagnetic scale modelling and its use in interpretation of TEM response * B .

Scale model studies are often used to determine the distribution of secondary magnetic fields when the nmnerlcal approach Is unreasonably difficult. Reduction in size (L) is compensated by an increase in con· ductlvity (0) or frequency (f), or both. In most cases the results are expressed in a way whereby they are independent of the absolute dimensions of the model and the general modelling relation
Placement of Products and Improvement of Order Picking Process through Association Analysis: A Case Study in Pharmaceutical Warehouse

While taking strategic decisions for the future in today's business world, which has a constantly changing and dynamic structure, several implications can be drawn with the information obtained from databases and the correct processing and analysis of this information. With regard to these implications, various decisions can be taken to improve the processes. Businesses can execute their processes more efficiently and they are able to make better decisions on the future by processing the data that will benefit by using the data mining techniques. In this study, the past orders of a pharmaceutical company were analyzed and an association analysis of the products in these orders was performed and a methodological framework has been presented based on the Apriori Algorithm results to ensure that the products are placed at the optimum level in the warehouse. Therefore, this study contributes to the improvement of order picking process.
spelling variant " Reuter " " Reeter " → query with spelling variant

In previous research, to mimic user queries with typos and abbreviations, a statistical model was used. It was trained to generate spelling variants of address terms that a human would use. A geocoding system enhanced with these spelling variants proved to yield results with higher precision and recall. To train the statistical model, thus far, user queries and their expected results were required to be linked with each other. Such training data is very costly to obtain. In this paper, a novel approach to derive such spelling variants from user queries alone is proposed. A linkage between collected user queries and result addresses is no longer required. The experiment conducted proves that this approach is a reasonable way to observe, derive, and index spelling variants too, allowing to measurably improve the precision and recall metrics of a geocoder.
Numerical study of multiple bubble dynamics with the BEM-ACA approach

Multiple bubble dynamics is analysed involving interactions between bubbles and also between bubbles and adjacent boundaries. In the current work, the adaptive cross approximation (ACA) BEM model is employed to study a “bubble cloud”. Three major factors are discussed, i.e., the presence of an adjacent solid wall and bubble volume fraction. The impact of the cloud on surrounding boundaries is studied.
RESPONSE OF Ocimum basilicum PLANT TO SOME PLANT GROWTH PROMOTING RHIZOBACTERIA

Two field experiments were conducted during the two successive growing seasons of 2011 and 2012 to evaluate the effect of ten inoculation treatments with mixtures of some plant growth promoting rhizobacteria (PGPR); i.e., Paenibacillus polymyxa (P), Azospirillum brasilense (A) and some isolates of growth promoting rhizobacteria; i.e., Pseudomonas synxantha (P1) and Bacillus subtilis 2 (B2) on Ocimum basilicum plant growth and herb yield, and to assess to what extant inoculation treatments reduce the demand of basil plant to nitrogen chemical fertilization. Inoculated plants were received only 75% of the recommended N fertilizer dose (45 kg N/ fad), while control plants were received 100% of the recommended N fertilizer dose (60 kg N/ fad) without PGPR inoculation. Results indicated that inoculation basil plants with bacterial inoculants mixture containing (A+P1+B2) or (A+P1) under the effect of 75% N dose resulted in the tallest plants bearing the highest number of branches/ plant and increased herb fresh and dry weights/ plant and/ fad comparing to control plants and all other inoculation treatments. Also, plants treated with these two the abovementioned inoculation treatments resulted in essential oil %, essential oil yield/ plant and/ fad statistically exceed or equal to that produced from control plants. Respecting essential oil components, 75% N+ (A+P1) treatment increased β-pinene and camphor percentages, while 75% N+ (A+ P1+B2) treatment increased linalool % comparing to their percentages in oil extracted from control plants. In addition, Methyl-chavicol %, which is described as a carcinogen and limits the application of basil oil in therapy, was decreased under the effect of these two treatments. The caused improvement in plant growth, herb yield and essential oil yield and its properties in plants received 75% N and inoculated with inoculant mixture containing (A+P1) or (A+P1+B2) was associated with increases in percentages of total carbohydrates, N, P and K in their tissues. Also, inoculation with all PGPR under this study significantly reduced dehydrogenase enzyme activity, but significantly increased 264 ABEER H. KASSEM et al. nitrogenase enzyme activity in the soil rhisosphere of inoculated basil plants. Conclusively, it could be conclude that inoculation of Ocimum basilicum plant with inoculant bacterial mixture containing equal portions of (Azospirillum brasilense + Pseudomonas synxantha) or (Azospirillum brasilense+ Pseudomonas synxantha + Bacillus subtilis 2) can be compensate the demand of nitrogen mineral fertilization and can improve plant growth, herb yield, essential oil yield and medicinal properties of herb and its essential oil as compare to herb and oil of 100% N fertilized plants without PGPR inoculation.