nils-herrmann/clean_scirepeval huggingface dataset:
Producer's organization and digital technologies for farms' competitiveness

The agricultural sector is characterized by increasing complexity and several factors could have a negative impact on agricultural small and medium sized enterprises' (SMESs) competitiveness. There are two way to improve farms competitiveness: dimensional growth, achievable through the horizontal aggregation of producers (POs) and innovation, achievable through Information and Communication Technologies (ICTs) Although several studies have pointed out the positive influence of ICT tools on SMEs performances growth, there is a lack of works concerning the way POs use ICTs web based in order to improve innovativeness and competitiveness of their small farmers. After a preliminary literature review regarding the main drivers for the adoption of ICT and technological innovations in agriculture, an exploratory analysis was performed through a set of in depth interviews to opinion leaders in associated farms of a PO. This qualitative methodological approach aims to investigate the POs contribution in enhancing process or product innovation, improving competitiveness and transferring knowledge, among the associated farms through the use of ICT tools. Findings confirm the main hypothesis on which this study is based upon and are supported by theoretical implications shown in the literature review.
Process reengineering and improvement for building precast production

Current production methods for the building of precast concrete elements are gradually becoming insufficient to meet industry demands. Most precast factories incorporate fixed molding production lines to produce building structural elements such as beams, trabeculae and pillars. Improvement of current production has reached its limits. The objective of this study is to perform process reengineering so as to create more efficient precast processes. The results of the literature review and advice from 16 experts suggest that the current process used to produce a precast element, which is comprised of 16 steps in a single module, should be redesigned to include 13 steps in 3 modules. Using historical data as input, the Arena software, Version 13, is adopted to simulate both the traditional and proposed methods, and then compare the results for practicability. An overall savings of production time of 24% can be reached by changing the current/traditional method to the flow shop type production method.
An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem

Flexible job shop scheduling problem (FJSP) which is an extension of the classical job shop scheduling problem is a very important problem in the modern manufacturing system. It allows an operation to be processed by any machine from a given set. It has been proved to be a NP hard problem. In this paper, an effective hybrid algorithm (HA) which hybridizes the genetic algorithm (GA) and tabu search (TS) has been proposed for the FJSP with the objective to minimize the makespan. The GA which has powerful global searching ability is utilized to perform exploration, and TS which has good local searching ability is applied to perform exploitation. Therefore, the proposed HA has very good searching ability and can balance the intensification and diversification very well. In order to solve the FJSP effectively, effective encoding method, genetic operators and neighborhood structure are used in this method. Six famous benchmark instances (including 201 open problems) of FJSP have been used to evaluate the performance of the proposed HA. Comparisons among proposed HA and other state of the art reported algorithms are also provided to show the effectiveness and efficiency of proposed method. The computational time of proposed HA also has been compared with other algorithms. The experimental results demonstrate that the proposed HA has achieved significant improvement for solving FJSP regardless of the solution accuracy and the computational time. And, the proposed method obtains the new best solutions for several benchmark problems.
A Tabu Search hyper heuristic strategy for t way test suite generation
Venue: Appl. Soft Comput.

Display Omitted HHH is the first strategy that adopts the hyper heuristic approach for t way test suite generationHHH introduces new approach for the heuristic selection and move acceptance mechanism based on three operators (i.e. improvement operator, diversify operator, and intensify operator) that are integrated into the Tabu search HLH.HHH outperforms existing strategies as far as optimality of test suite is concerned in many benchmarks. This paper proposes a novel hybrid t way test generation strategy (where t indicates interaction strength) called High Level Hyper Heuristic (HHH) HHH adopts Tabu Search as its high level meta heuristic and leverages on the strength of four low level meta heuristics, comprising of Teaching Learning based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm. HHH is able to capitalize on the strengths and limit the deficiencies of each individual algorithm in a collective and synergistic manner. Unlike existing hyper heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable meta heuristic at any particular time. Our results are promising as HHH manages to outperform existing t way strategies on many of the benchmarks.
A hybrid tabu search based heuristic for the periodic distribution inventory problem with perishable goods
Venue: Ann. Oper. Res.

Most of the research on integrated inventory and routing problems ignores the case when products are perishable. However, considering the integrated problem with perishable goods is crucial since any discrepancy between the routing and inventory cost can double down the risk of higher obsolescence costs due to the limited shelf life of the products. In this paper, we consider a distribution problem involving a depot, a set of customers and a homogeneous fleet of capacitated vehicles. Perishable goods are transported from the depot to customers in such a way that out of stock situations never occur. The objective is to simultaneously determine the inventory and routing decisions over a given time horizon such that total transportation cost is minimized. We present a new "arc based formulation" for the problem which is deemed more suitable for our new tabu search based approach for solving the problem. We perform a thorough sensitivity analysis for each of the tabu search parameters individually and use the obtained gaps to fine tune the parameter values that are used in solving larger sized instances of the problem. We solve different sizes of randomly generated instances and compare the results obtained using the tabu search algorithm to those obtained by solving the problem using CPLEX and a recently published column generation algorithm. Our computational experiments demonstrate that the tabu search algorithm is capable of obtaining a near optimal solution in less computational time than the time required to solve the problem to optimality using CPLEX, and outperforms the column generation algorithm for solving the "path flow formulation" of the problem in terms of solution quality in almost all of the considered instances.
Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing tabu search algorithm to solve the symmetrical traveling salesman problem
Venue: Appl. Soft Comput.

Adaptive Hybrid Simulated Annealing Tabu Search Algorithm with Dynamic Neighborhood Based on Circle directed Mutation. Adaptive Hybrid Simulated Annealing Tabu Search Algorithm with 2 opt. This paper develops an adaptive hybrid meta heuristic algorithm that combines simulated annealing and tabu search algorithms with a dynamic neighborhood structure to solve the Traveling Salesman Problem (TSP) The experimental results demonstrated that the proposed algorithm can obtain satisfactory solutions within a reasonable time. Moreover, the proposed algorithm can overcome the individual disadvantages of simulated annealing and tabu search. The new hybrid algorithm provides a fast decrease rate and a clear convergence process, and is not dependent on initial solutions. Furthermore, compared with the classical 2 opt neighborhood, the dynamic neighborhood can significantly reduce computational time by decreasing the number of calculations and simultaneously improve solution quality. Finally, the adaptive parameters are appropriate for solving almost all benchmark test examples.Display Omitted A hybrid simulated annealingtabusearch algorithm is applied to solve the traveling salesman problem.A dynamic neighborhood based on circle directed mutation is developed for the hybrid algorithm.Adaptive parameters are proposed for the hybrid algorithm.Experiments are conducted to verify the accuracy and efficiency of the hybrid algorithm proposed.Experimental results are compared with other algorithms derived from literature. This paper applies a hybrid simulated annealing tabu search algorithm to solve the Traveling Salesman Problem (TSP) Fully considering the characteristics of the hybrid algorithm, we develop a dynamic neighborhood structure for the hybrid algorithm to improve search efficiency by reducing the randomness of the conventional 2 opt neighborhood. A circle directed mutation is developed to achieve this dynamic neighborhood structure. Furthermore, we propose adaptive parameters that can be automatically adjusted by the algorithm based on context specific examples. This negates the need to frequently readjust algorithm parameters. We employ benchmarks obtained from TSPLIB (a library of sample instances for the TSP) to test our algorithm, and find that the proposed algorithm can obtain satisfactory solutions within a reasonable amount of time. The experimental results demonstrate that the proposed hybrid algorithm can overcome the disadvantages of traditional simulated annealing and tabu search methods. The results also show that the dynamic neighborhood structure is more efficient and accurate than the classical 2 opt. Also, adaptive parameters are appropriate for almost all of the numerical examples tested in this paper. Finally, the experimental results are compared with those of other algorithms, to demonstrate the improved accuracy and efficiency of the proposed algorithm.
Optimal fractional order PID design via Tabu Search based algorithm.
Venue: ISA transactions

This paper presents an optimization method based on the Tabu Search Algorithm (TSA) to design a Fractional Order Proportional Integral Derivative (FOPID) controller. All parameter computations of the FOPID employ random initial conditions, using the proposed optimization method. Illustrative examples demonstrate the performance of the proposed FOPID controller design method.
Absence of the celiac trunk and trifurcation of the common hepatic artery: a case report
Venue: Jornal vascular brasileiro

Anatomical variations of the celiac trunk and its branches are particularly important from a surgical perspective due to their relationships with surrounding structures. We report here a particularly rare variant involving absence of the celiac trunk in association with trifurcation of the common hepatic artery. These variations were found in an adult male cadaver. We perform a review of the literature and discuss the clinical and embryological significance of these variations. Recognition of celiac trunk and hepatic artery variations is of utmost importance to surgeons and radiologists because multiple variations can lead to undue complications.
A Qualitative Examination of a New Combined Cognitive Behavioral and Neuromuscular Training Intervention for Juvenile Fibromyalgia
Venue: The Clinical journal of pain

Adolescents with juvenile fibromyalgia (JFM) are typically sedentary despite recommendations for physical exercise, a key component of pain management. Interventions such as cognitive behavior therapy (CBT) are beneficial but do not improve exercise participation. The objective of this study was to obtain preliminary information about the feasibility, safety, and acceptability of a new intervention Fibromyalgia Integrative Training for Teens (FIT Teens) which combines CBT with specialized neuromuscular exercise training modified from evidence based injury prevention protocols. Participants were 17 adolescent females (aged 12 to 18 y) with JFM. Of these, 11 completed the 8 week (16 sessions) FIT Teens program in a small group format with 3 to 4 patients per group. Patients provided detailed qualitative feedback via individual semistructured interviews after treatment. Interview content was coded using thematic analysis. Interventionist feedback about treatment implementation was also obtained. The intervention was found to be feasible, well tolerated, and safe for JFM patients. Barriers to enrollment (50% of those approached) included difficulties with transportation or time conflicts. Treatment completers enjoyed the group format and reported increased self efficacy, strength, and motivation to exercise. Participants also reported decreased pain and increased energy levels. Feedback from participants and interventionists was incorporated into a final treatment manual to be used in a future trial. Results of this study provided initial support for the new FIT Teens program. An integrative strategy of combining pain coping skills via CBT enhanced with tailored exercise specifically designed to improve confidence in movement and improving activity participation holds promise in the management of JFM.
Cost Sensitive Two Stage Depression Prediction Using Dynamic Visual Clues
Venue: ACCV

This paper presents a novel and effective approach to depression recognition in the visual modality of videos, which automatically predicts the depression level through two cost sensitive stages. It delivers an improved solution in two ways compared with other vision based methods. On the one hand, current techniques regard depression recognition as either a classification or a regression problem, which tends to incur overfitting due to the high complexity of the model and the limited number of training samples. To handle such an issue, we propose a two stage framework consisting of a coarse classifier and a fine regressor. The former makes use of a set of linear functions, corresponding to different depression intensities, to approximate the complex non linear model, where a coarse range of the test sample is preliminarily located. The latter then predicts its precise depression level within the given range. On the other hand, depression recognition is different from the general classification and regression tasks, since its analysis is cost sensitive as the diagnosis of heart diseases and cancers. However, this critical cue is not taken into account in the previous investigations, thus making their results problematic. To address this drawback, we embed the indicator of medical risk assessment into both the two stages by constraining the classifier using a weight matrix and loosening the regressor to an expanded range of depression level. The proposed method is evaluated on the Audio and Video Emotion Challenge (AVEC) 2013, and the performance is superior to the best one so far reported using the visual modality. Furthermore, it proves complementary to the audio based methods, and their joint use further ameliorates the accuracy. These facts clearly highlight the effectiveness of the proposed method on depression recognition.
Semisupervised manifold learning for color transfer between multiview images
Venue: 2016 23rd International Conference on Pattern Recognition (ICPR)

In multiview image stitching, the colors of images in a scene might vary when images are taken under different illumination or camera settings. A common way to produce a seamless stitched image is to transform the colors of a target image to match that of a source image. In this paper we present a color transfer method based on two premises: first, pixels in the generated image should have similar colors with their corresponding pixels in the source image. Second, pixels with similar colors should still have similar colors after color transfer. Our method can be considered as a semisupervised manifold learning approach, where the corresponding pixels of the input images serve as the labeled data. Our goal is to learn a final image which not only shares the same colors with the source image but also has the same image structure with the target image. While manifold learning methods aim to find an embedded space to represent the data with minimum structure loss, the proposed method further constrains the solution space using the labeled data. This paper introduces a parametric linear method and a nonparametric nonlinear method to tackle different types of color changes. Experimental results show the effectiveness of our methods both quantitatively and qualitatively.
Learning the Multilingual Translation Representations for Question Retrieval in Community Question Answering via Non Negative Matrix Factorization
Venue: IEEE/ACM Transactions on Audio, Speech, and Language Processing

Community question answering (CQA) has become an increasingly popular research topic. In this paper, we focus on the problem of question retrieval. Question retrieval in CQA can automatically find the most relevant and recent questions that have been solved by other users. However, the word ambiguity and word mismatch problems bring about new challenges for question retrieval in CQA. State of the art approaches address these issues by implicitly expanding the queried questions with additional words or phrases using monolingual translation models. While useful, the effectiveness of these models is highly dependent on the availability of quality parallel monolingual corpora (e.g. question answer pairs) in the absence of which they are troubled by noise issues. In this work, we propose an alternative way to address the word ambiguity and word mismatch problems by taking advantage of potentially rich semantic information drawn from other languages. Our proposed method employs statistical machine translation to improve question retrieval and enriches the question representation with the translated words from other languages via non negative matrix factorization. Experiments conducted on real CQA data sets show that our proposed approach is promising.
The sex of participatory democracy. An analysis of the theoretical approaches and experiences of participatory democracy from a feminist viewpoint

The generalist theory of participatory democracy and the non essentialist feminist approach to forms of democratization have developed in parallel despite sharing an agreement to work towards more shared forms of managing power. The origin of this disunity lies, partly, in the first theoretical body's tendency to present citizen participation as a tool for democratizing the neutral and universal public space. As a result, both theoretical bodies have parallel points of view regarding the elements that structure women's participation. This article explores, through the qualitative analysis of two experiences of participatory democracy in which the feminist viewpoint has been unequally incorporated (Porto Alegre's participatory budgeting Porto Alegre PB and the 15M movement) the relationship between the parallel development of the general proposals of the two theoretical bodies and the elements that restrict the participation of women in the practice of participatory democracy.
Learning Multi domain Convolutional Neural Networks for Visual Tracking
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

We propose a novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network (CNN) Our algorithm pretrains a CNN using a large set of videos with tracking ground truths to obtain a generic target representation. Our network is composed of shared layers and multiple branches of domain specific layers, where domains correspond to individual training sequences and each branch is responsible for binary classification to identify target in each domain. We train each domain in the network iteratively to obtain generic target representations in the shared layers. When tracking a target in a new sequence, we construct a new network by combining the shared layers in the pretrained CNN with a new binary classification layer, which is updated online. Online tracking is performed by evaluating the candidate windows randomly sampled around the previous target state. The proposed algorithm illustrates outstanding performance in existing tracking benchmarks.
Characterising the interaction of individual wheel drives with traction by linear parameter varying model: a method for analysing the role of traction in torsional vibrations in wheel drives and active damping

A model based approach for characterising the interaction of individual wheel drives with traction is contributed in this article. The primary aim is to investigate the influence of traction on torsional vibration behaviour in the drive train. The essence of this approach lies in reformulating the nonlinear traction behaviour into its differential form, which enables an analytical description of this interaction in its linear parameter varying model equivalence. Analytical statements on the vibration behaviour for different driving scenarios are inferred from this model and validated with measurement samples from a high performance electric road vehicle. Subsequent influences of traction on the performance of active damping of torsional vibrations are derived from this model.
A genetic algorithm based heuristic search on graphs with weighted multiple attributes
Venue: 2016 International Conference on Next Generation Intelligent Systems (ICNGIS)

Graphs are efficient data structures used for representing connected real world entities and are very ubiquitously used in various applications. Graphs with multiple attributes describing each node and having non negative weights assigned to these are called as Weighted Multiple Attributed Graphs (WMAGs) WMAGs are able to capture and represent the properties of real world data efficiently. Graphs such as co authorship network, friendship network, communication network etc. belong to this category. Queries on these graphs are complex than traditional graph search. Querying WMAG helps in finding the inherent relations between users in social media, finding closely connected authors who are expertized in various research domains from a co authorship network, etc. This work addresses the problem of graph search on weighted multiple attributed graphs by specifying attribute and structural constraints. In the proposed system, an R Tree based index is used for efficient look ups of nodes which meets the attribute constraints. Finding nodes which spans minimum distance is considered as the structural constraint. The k constraint query remodels the large data graph into a k partite graph. Problem of finding k minimally separated nodes according to k constraints from a WMAG is reduced to generalized minimum spanning tree (GMST) problem. GMST problem is NP Hard and here a heuristic method based on genetic algorithm is proposed as the solution, which is efficient in terms of query processing and execution time.