Miracle-dz/UniFAR-FTUs huggingface dataset:
DecSpace : A Multi-Criteria Decision Analysis Framework

Multi-Criteria Decision Analysis (MCDA) is a field of study that provides a myriad of methods that can be applied to several different domains of use. Using those methods can be a very complex task, which has motivated the development of software solutions to support them. This dissertation has the objective of designing and developing a new web application (called DecSpace) that addresses the usability and user experience issues attached to the use of MCDA methods. This new solution also aims at providing many different MCDA methods and making it easy to add new ones, so that it can support many problems of diverse domains. The most relevant tools, that also support the use of MCDA methods, were studied in order to understand and make use of their greatest aspects and avoid their biggest flaws. A throwaway prototype was built in a previous project as a proof of concepts for the user interaction issues. Its best ideas regarding those issues were perpetuated and adapted to this new solution. After its design and implementation, DecSpace was evaluated in order to comprehend if it was fulfilling its initial objectives. The observed errors and the most useful recommendations made by the users were integrated after the evaluation process ended. DecSpace also went through a validation process, which consisted of the handling and support of two problems that made use of two different MCDA methods. This process had the objective of demonstrating that the new solution can uphold complex issues.
Dominance Concept by A . A . Ukhtomsky and Anticipation

This paper continues investigations of the theory of dominance by A.A. Ukhtomsky (1875-1942). This theory is one of the earliest attempts in the world to scientifically investigate the relationships of life and mind, and human behavior in particlar. A.A. Ukhtomsky anteceded some ideas of cybernetics and synergetics, as it was shown in our previous works. On the other hand, one of main problems analyzed by A.A. Ukhtomsky is the ability to anticipate seen in the behavior of living beings. This work is devoted to the problem of anticipation from the viewpoint of the dominance concept.
Do bankers prefer married couples

Are married couples more credit constrained than unmarried households? If the cost of separation increases the risk of default, banks might be willing to lend to stable couples. In presence of incomplete information, marriage could be used as a signal of the quality of the match. This paper investigates the link between marriage and credit constraints. I use matching methods to evaluate the impact of marriage on credit constraints. I find that married couples are more likely to be approved for their loan, but they bear higher costs of credit. The differences between married and unmarried couples can be attributed to selection in the marriage rather than to discrimination against unmarried couples.
Atomic force microscopy analysis of SasA‐KaiC complex formation involved in information transfer from the KaiABC clock machinery to the output pathway in cyanobacteria

The cyanobacterial clock oscillator is composed of three clock proteins: KaiA, KaiB and KaiC. SasA, a KaiC‐binding EnvZ‐like orthodox histidine kinase involved in the main clock output pathway, exists mainly as a trimer (SasA3mer) and occasionally as a hexamer (SasA6mer) in vitro. Previously, the molecular mass of the SasA‐KaiCDD complex, where KaiCDD is a mutant KaiC with two Asp substitutions at the two phosphorylation sites, has been estimated by gel‐filtration chromatography to be larger than 670 kDa. This value disagrees with the theoretical estimation of 480 kDa for a SasA3mer‐KaiC hexamer (KaiC6mer) complex with a 1:1 molecular ratio. To clarify the structure of the SasA‐KaiC complex, we analyzed KaiCDD with 0.1 mmol/L ATP and 5 mmol/L MgCl2 (Mg‐ATP), SasA and a mixture containing SasA and KaiCDD6mer with Mg‐ATP by atomic force microscopy (AFM). KaiCDD images were classified into two types with height distribution corresponding to KaiCDD monomer (KaiCDD1mer) and KaiCDD6mer, respectively. SasA images were classified into two types with height corresponding to SasA3mer and SasA6mer, respectively. The AFM images of the SasA‐KaiCDD mixture indicated not only KaiCDD1mer, KaiCDD6mer, SasA3mer and SasA6mer, but also wider area “islands,” suggesting the presence of a polymerized form of the SasA‐KaiCDD complex.
INFLUENCE OF PARAMETERIZATION AND OPTIMIZATION METHOD ON THE OPTIMUM AIRFOIL

The potential influence of different combinations of parameterization methods and optimization strategies on the attainable optimum and the optimization cost is evaluated by comparisons of the optimized airfoils. The optimization strategies consist of a single-objective metamodel assisted Genetic Algorithm and multi-objective Differential Evolution both assisted and non-assisted. Bspline and Bézier formulations are used together with a direct and standard description of the airfoil shape. The multi-point optimizations are applied to airfoils in 2D cascades corresponding to propeller geometries.
APPLICATION OF SUSPENSION SYSTEM FOR ROBOTIC GRINDING BY LOW POWER ROBOT

During the grinding operation by a robot in the vertical plane, a large actuator torque is required due to the enormous gravity force effect of the tools and the robot arm. A new grinding strategy has been developed for a low power robot in the vertical plane using a suspension system, where the lifting force of spring balancer compensates the gravity force of the tools and the robot arm. For robotic grinding, to achieve position and force tracking simultaneously, this paper shows the hybrid position/force control strategy with respect to the dynamic behavior of the spring balancer. To show the effectiveness of the proposed system, simulation and experiment have been carried out by a 2 DOF manipulator with a suspension system.
ON THE STABILITY OF TWO NEW RECURSIVE 2 D-ROESSER-BASED DISCRETE MODELS WITH SPATIAL AND TIME DELAYS

This paper presents and discusses from the stability point of view two discrete Roesser-based models whose state evolution has a recursive nature which can be combined in the spatial horizontal and vertical substates and time evolution. A first set of stability results is derived in the absence of controls based on the stability/convergence properties of the matrices defining the dynamics. Later on, two linear control laws are given and the stability properties are reformulated under control feedback. Also, some controllability conditions of certain relevant pairs defining the closed-loop dynamics what allows to prescribe the spectral radii to convenient values to guarantee the stability by means of the synthesis of the controller gains.
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Repairing broken tests in evolving software systems is an expensive and challenging task. One of the main challenges for test repair, in particular, is preserving the intent of the original tests in the repaired ones. To address this challenge, we propose a technique for test repair that models and considers the intent of a test when repairing it. Our technique first uses a search-based approach to generate repair candidates for the broken test. It then computes, for each candidate, its likelihood of preserving the original test intent. To do so, the technique characterizes such intent using the path conditions generated during a dynamic symbolic execution of the tests. Finally, the technique reports the best candidates to the developer as repair recommendations. We implemented and evaluated our technique on a benchmark of 91 broken tests in 4 open-source programs. Our results are promising, in that the technique was able to generate intentpreserving repair candidates for over 79% of those broken tests and rank the intent-preserving candidates as the first choice of repair recommendations for almost 70% of the broken tests.
Merger and Reorganization to Promote Industrial Upgrading: Review and Prospect of Research

In recent years, with the economic restructuring in our country, the traditional mode of development has not adapted to the rapid economic growth. As the key to economic restructuring and upgrading, industrial upgrading has become an inevitable choice for Chinese enterprises to achieve industrial development. However, M & A can optimize the allocation of resources among regions and further promote industrial upgrading and optimization. Based on the viewpoints of M & A, this paper reviews and summarizes the related research results of M & A and industrial upgrading in recent years on the basis of literature review, explores the mechanism and impact of M & A on industrial upgrading, and analyzes the relationship between M & A and industry looking forward to the further study of escalation.
Evaluating Effects of Program Keluarga Harapan (PKH) on School Attendance: Case Study of CCT in Indonesia

Objectives: Program Keluarga Harapan (PKH) is a Conditional Cash Transfer in Indonesia which is aided by the government to improve Indonesia’s human capital quality in order to gain benefits of Indonesia’s demographic bonus in 2020- 2030. The aim of this study are as follows: a. ) to know the size of PKH effect to school attendance. b) to know how huge the impact of children’s food nutrition, which is represented by the quantiity of eating per day, to school attendance Design: Using a cross-sectional data, the authors analyze 4th wave (2007) and 5th wave of IFLS data. While the independent variables are made up of age, children’s educational level, gender, parents’ educational level, children’s eating frequency, origin (rural and urban are), and another dummy variable, PKH which consists of the treatment group, control group and no program group. This model uses a logit estimation. Results: PKH has different effects in 2007 and 2014. In 2007, PKH program raises the school attendance among its beneficiaries. However, in 2014, the effect of PKH was indifferent among its beneficiaries and non-beneficiaries. From the literature reviews, it can be concluded that the problem lies in monitoring and evaluating.
DEVELOPMENT OF ELECTRO-HYDRAULIC EXCAVATOR USED IN HEAVY DUTY APPLICATIONS: A NOVEL VIEW SANTOSH

The increasing trend in the employment of electro-hydraulic systems has become prominent due to their vantages in bettering efficiency. The hybrid system as in whole fuses the electric and hydraulic technology into a compact unit, have been accommodated to an extensive variety of force, speed and torque prerequisites. Hence, these hybrid systems are being exploited in heavy duty applications as they can easily cope up with the environmental problems. This paper examines the difference between hybrid powered automobile vehicle and hybrid powered heavy duty application machinery such as excavator. Normally energy storage source supervises the energy consumption in case of hybrid machines. There is substantial loss of energy as the machine starts and stops frequently which makes the way for the concept of energy regeneration. Hence the design, development and the research guided on the energy regeneration of the excavator and its wide applications are also portrayed.
High-level Cues for Predicting Motivations

The success and effectiveness of deep networks on the popular tasks of image classification [10, 11], object detection [9, 3], etc. has set the foundation for the study of tasks based on high-level image concepts such Visual Question Answering (VQA) [1] and image captioning [7]. Despite the broad scope of required knowledge and successes in scene, action, and attribute prediction, most methods use generic features from the VGG-16 network [11] trained on the ImageNet classification task [10] for solving such tasks. A few recent works [12, 14, 15] have tried to use combine and use a variety of cues to improve performance on the task of VQA. These works claim that using specialized cues helps improves performance on tasks that require higher-level concepts. In this work, we further support this case by using action and scene cues to achieve state-of-the-art performance on predicting ‘motivations’ of humans in images. We propose a simple Canonical Correlation Analysis (CCA) [4] model based on scene and action cue features that achieves significantly better performance compared to prior work on the Motivations dataset [13].
A Human Behavior Recognition Method Based on Latent Semantic Analysis

In this paper, the image sequence of human behavior is regarded as a ”doc-ument”, and the key postures are regarded as ”words” in the ”document”. Then, the latent semantic analysis method, which has obtained very good performance in natural language processing, is utilized to analyze and model the relationships among the image sequences and the key postures. In our method, we ﬁrstly calculate the mesh features of each image in human behavior sequence. Then the mesh features are vector quantized through a rival penalized competitive neural network and the behaviors described by time-sequential images are converted into symbolic sequences. Through above processing, the image sequences of human behavior can be looked as ”documents” composed by the key postures, which is represented by the competitive neurons. Then, we use latent semantic analysis method to model the relationships among the behaviors and the key posture. The redundancy and noise can be eﬀectively removed through singular value decomposition. Finally, the observed behavior is classiﬁed by the maximum posteriori probability criteria. The experiments on Weizmann and KTH datasets demonstrate that our method is eﬀective.
Local Labor Markets and the Persistence of Population Shocks

This article studies the persistence of a large, unexpected and regionally very unevenly distributed population shock, the inflow of eight million ethnic Germans from Eastern Europe to West Germany after World War II. Using detailed census data from 1939 to 1970, we show that the shock proved persistent within local labor markets, but was largely reversed between labor markets. These results show that the choice of spatial units can significantly affect the estimated persistence of population shocks. They can thus help to explain why previous studies on the persistence of population shocks reached conflicting conclusions.
Innovative numerical tools for the simulation of parachutes

The design and evaluation of parachute-payload systems is an important field of applications in which numerical analysis tools can make very important contributions. This work describes new numerical developments carried out at CIMNE in this field, which involve a coupled fluid-structural solver intended for the unsteady simulation of ram-air type parachutes and a set of complementary tools aimed at studying trajectory and control systems effects. For an efficient solution of the aerodynamic problem, an unsteady panel method has been chosen exploiting the fact that large areas of separated flow are not expected under nominal flight conditions of ram-air parachutes. Besides, a dynamic explicit solver based on a finite element technique is chosen for the structure. This approach yields a robust solution even when highly non-linear effects due to large displacements and material asymmetric behaviours are present. The numerical results show considerable accuracy and robustness. An added benefit of the proposed aerodynamic and structural techniques is that they can be easily vectored and thus suitable for use in parallel architectures. The main features of the developed computational tools are described in this work and several numerical examples are provided to illustrate the good performance and potential of the proposed techniques. Further improvements of the methodology being carried out and future lines of investigation are also presented.
A FAST SPARSE RECONSTRUCTION APPROACH FOR HIGH DIMENSIONAL IMAGE-BASED OBJECT SURFACE ANOMALY DETECTION

We propose an approach to resolve two issues in a recent proposed sparse reconstruction based anomaly detection approach as a part of automated visual inspection (AVI). The original approach needs large computation and memory for high-dimensional problem. To solve it, we proposed a twostep sparse reconstruction, 1) the first sparse representation of input image is estimated in a sparse reconstruction with low dimensional downsampled images and 2) the high dimensional residual values is generated in another sparse reconstruction with the sparse representation. The first step provides the flexibility of freely adjusting the computation and the demand of memory storage with small trade-off of detection accuracy. Moreover, an illumination adaptive threshold with morphological operators is used in the anomaly classification. Empirical results show that the proposed approach can effectively replace the original approach with better results.
Event Template Generation for News

In this paper we focus on event extraction from Tamil news article. This system utilizes a scoring scheme for extracting and grouping event-specific sentences. Using this scoring scheme eventspecific clustering is performed for multiple documents. Events are extracted from each document using a scoring scheme based on feature score and condition score. Similarly event specific sentences are clustered from multiple documents using this scoring scheme. The proposed system builds the Event Template based on user specified query. The templates are filled with event specific details like person, location and timeline extracted from the formed clusters. The proposed system applies these methodologies for Tamil news articles that have been enconverted into UNL graphs using a Tamil to UNL-enconverter. The main intention of this work is to generate an event based template. Keywords—Event Extraction, Score based Clustering, Segmentation, Template Generation.
Aalborg Universitet Movie Popularity Classification based on Inherent Movie Attributes using C 4 . 5 , PART and Correlation Coefficient

Abundance of movie data across the internet makes it an obvious candidate for machine learning and knowledge discovery. But most researches are directed towards bi-polar classification of movie or generation of a movie recommendation system based on reviews given by viewers on various internet sites. Classification of movie popularity based solely on attributes of a movie i.e. actor, actress, director rating, language, country and budget etc. has been less highlighted due to large number of attributes that are associated with each movie and their differences in dimensions. In this paper, we propose classification scheme of pre-release movie popularity based on inherent attributes using C4.5 and PART classifier algorithm and define the relation between attributes of post release movies using correlation coefficient. Keywordsmovie; IMDB; data mining; C4.5; PART; correlation coefficient
A New Dimension to Spectrum Management in IoT Empowered 5G Networks

Paving the way for future 5G technologies requires a need to overcome the spectrum crunch, which is one of the major challenges impeding the growth of wireless technology. The issue at hand becomes more pronounced when we consider IoT, where billions of devices require connectivity. This article motivates the need for exploring new spectrum opportunities with reference to the requirements of IoT networks. Millimeter wave (mmWave) spectrum is considered as a panacea for overcoming the spectrum crunch, providing the much needed breathing space for introducing new applications that require higher rates. A network based on CDSA could further improve performance by utilizing mmWave-based DBSs. The CBS operates on the sub-6 GHz single band, while the DBS possesses a dual-band capability. This article presents a new dimension to spectrum heterogeneity by utilizing a dual-band approach at the DBS. One of the unique aspects of this work includes the analysis of a joint radio resource allocation algorithm based on LDD and we compare the proposed algorithm with the maxRx, DSA and JPRA algorithms. The analysis is further expanded by showing an interplay between the utilization of licensed and unlicensed mmWave resources and how dynamic spectrum management could help in their efficient utilization.
Running Title : Translational entropy of DNA melting

Investigation of folding/unfolding DNA duplexes of various size and composition by super-precise calorimetry has revised several long-held beliefs concerning the forces responsible for the formation of the double helix. It was established that: (a) the enthalpy and entropy of duplex unfolding are temperature dependent, increasing with temperature rise and having the same heat capacity increment for CG and AT pairs; (b) the larger stabilizing effect of the CG pair in comparison with AT results not from its larger enthalpic contribution – as expected from its extra hydrogen bond – but from the larger entropic contribution of the AT pair that results from its ability to fix ordered water in the minor groove and release it upon duplex unfolding; (c) the translation entropy, resulting from the appearance of a new kinetic unit on duplex dissociation, determines the dependence of duplex stability on its length and its concentration. It is an order of magnitude smaller than predicted from the statistical mechanics of gases and is fully expressed by the stoichiometric correction term; (d) changes in duplex stability on reshuffling the sequence – the ‘nearest-neighbor effect’ – result from the immobilized water molecules fixed by AT pairs in the minor groove; (e) the evaluated thermodynamic components permit a quantitative expression of DNA duplex stability.
AA – A Software Architecture Aware Environment for Dependable Systems

Explicitly considering software architectural information at all times is now a recognized means for addressing software system dependability. In this paper we propose the basic ideas for AA, an architecture aware environment to improve software system dependability. It builds on ideas from architecting dependable systems, control engineering, and software product lines. AA supports fault tolerance to also take into account global software architectural issues rather than only localized information (or immediate propagation), as well as viable variations in the software architecture. © 2008 University of Newcastle upon Tyne. Printed and published by the University of Newcastle upon Tyne, Computing Science, Claremont Tower, Claremont Road, Newcastle upon Tyne, NE1 7RU, England. Bibliographical details
AI 4 FM : A new project seeking challenges !

The proof obligations generated from many formal me thods tend to be simple and can often be discharged by modern automatic theorem pro vers or SMT systems. However, those proof tasks that need hand -or interactivei nt rvention present a barrier to the use of formal methods. Theorem proving was one of t he earliest challenges addressed by researchers in the area of Artificial Intelligen c and enormous progress has been made in the provision of general purpose heuristics . The approach in the recently started AI4FM project is different: we hope to devi se a system that will learn from an expert user how they tackle one interactive proof a nd then apply the discovered highlevel strategy to other related proof tasks. We are fo tunate in having access to many such problems through the DEPLOY project but are aw are of the dangers of devising an overly specific approach. This short paper appea ls for challenge problems from other sources. © 2010 University of Newcastle upon Tyne. Printed and published by the University of Newcastl e upon Tyne, Computing Science, Claremont Tower, Claremont Road, Newcastle upon Tyne, NE1 7RU, England. Bibliographical details GROV, G. , JONES, C.B. AI4FM: A new project seeking challenges! [By] G. Grov, C.B. Jones Newcastle upon Tyne: University of Newcastle upon T y e: Computing Science, 2010. (University of Newcastle upon Tyne, Computing Scienc e, Technical Report Series, No. CS-TR-1216)