Miracle-dz/UniFAR-FTUs huggingface dataset:
Lessons We Should Learn from Our Unique Relationship with Dogs: An Ethological Approach

This chapter proposes that the combination of psychology and ethology can contribute to our understanding of dog-human attachment and opens the door to create testable hypotheses and predictions regarding dogs' propensity to make strong 'affectional bonds' with us. The concept of attachment bond can be used to study different types of human relationships, and is also a plausible theoretical ground of developing ways to assess attachment in dog-human relationships, which might be used for studying some other species. Human-animal relationships, including those with dogs, can be interpreted in terms of different social frameworks entailing different research approaches. That is, depending on the attitude towards the species we bring to research, both the conceptual framework and the adopted methods will differ. The chapter provides evidence that dogs of low or restricted contact with humans may retain their ability to form new attachment relationships with humans. Keywords:attachment bond; conceptual framework; dog-human relationships; ethology; psychology
EARNING FROM AND ABOUT OTHER A GENTS IN T ERMS OF SOCIAL M ETAPHORS

We present work that has been conducted in a sociological and psychological context. Our aim is to establish a mechanism that enables agents to cope with environments that contain both selfish and co-operative entities, where the mixture and the behaviour of these entities is previously unknown to all agents. We achieve this by enabling agents to evaluate trust in others, based upon the observations they gather. Using trust, they are able to request observations from others and make use of this possibly manipulated data, therefore enlarging their data base on behaviour of others. Our approach results in significantly faster and better behaviour adaptation. We demonstrate the improvement in performance of agents using trust compared to the performance of others that just use their own observations.
Flower Pollination Algorithm for Multimodal Optimization

This paper proposes a new algorithm called Multimodal Flower Pollination Algorithm (MFPA). Under MFPA, the original Flower Pollination Algorithm (FPA) is enhanced with multimodal capabilities in order to find all possible optima in an optimization problem. The performance of the proposed MFPA is compared to several multimodal approaches considering the evaluation in a set of well-known benchmark functions. Experimental data indicate that the proposed MFPA provides better results over other multimodal competitors in terms of accuracy and robustness.
Predictors of Quality-of-Life Benefit After Percutaneous Coronary Intervention

Background—Improving patients’ quality of life is a primary indication for percutaneous coronary intervention (PCI), yet little is known about patient characteristics associated with greater quality-of-life improvement from the procedure. This study was conducted to identify patient characteristics associated with quality-of-life benefit after PCI. Methods and Results—A consecutive series of 1518 patients undergoing PCI in nonacute myocardial infarction settings were prospectively enrolled into an observational study documenting their postprocedural health status. We examined univariate and multivariable associations between baseline patient characteristics and quality of life 1 year after the procedure using the disease-specific Seattle Angina Questionnaire (SAQ) to quantify the impact of patients’ coronary disease on their quality of life. Baseline angina frequency and physical function were the strongest predictors of quality-of-life improvement 1 year after PCI. In comparing patients without angina to those experiencing monthly, weekly, and daily angina, the quality-of-life improvements (mean±SEM) were 21.4±2.1, 30.7±2.2, and 34.6±2.6 points greater (P<0.001). Patients with mild, moderate, and severe physical limitation improved 13.8±1.9, 20.0±2.1, and 13.5±3.5 points more than those with minimal baseline physical limitation (P<0.001). These findings were maintained in multivariable models correcting for baseline differences in demographic, clinical, disease-severity, and health-status variables. Conclusions—Preprocedural angina frequency is the most important prognostic indicator of quality-of-life improvement after PCI. Although substantial quality-of-life benefits are attained in most patients with preprocedural angina, more careful consideration of the potential benefits and risks of the procedure are needed in asymptomatic patients.
Groundwater and Dependent Ecosystems: Revealing the Impacts of Climate Change

Groundwater resources and groundwater dependent ecosystems are threatened by a changing climate. This paper is an assessment for the current knowledge of the impacts of climate change on these climatic scenarios and the adaptation options for management. Based on the growing body of evidence, given current climate projections, unsteadiness in temperature and rainfall may be the primary threats to groundwater and groundwater dependent ecosystems. Variations of temperature and precipitation will impact these ecosystems by altering recharge of groundwater sources and endangering the survival of biological communities by disrupting key biological processes. Predicting these impacts are just as uncertain as predicting climate trends as they are vary over time and space. Despite these uncertainties, there are numerous attempts at all levels of government and society to develop favorable policy environments, build resilient management institutions, synthesize available science, and increase the consciousness and participation of communities to protect and promote the sustainable use of groundwater and groundwater ecosystems.
Applicability of Energy Efficient Coding Methodology to Address Signal Integrity in 3D NoC Fabrics

Three dimensional (3D) network on chip (NoC) has attracted researchers' attention recently. 3D NoCs are capable of achieving better system throughput and lower latency compared to the corresponding 2D implementations. To fully exploit the performance benefits of 3D architectures, it is imperative to address signal integrity issues in the design phase and its implications on energy dissipation. In this work we show that by incorporating joint crosstalk avoidance and multiple error correction schemes it is possible to enhance the robustness and reduce the energy dissipation simultaneously for both the 3D and more conventional planar NoC architectures. The achievable energy savings in 3D NoCs is significantly more than that in 2D structures.
Natural landmark extraction in cluttered forested environments

In this paper, a new systematical method for extracting tree trunk landmarks from 3D point clouds of cluttered forested environments is proposed. This purely geometric method is established on scene understanding and automatic analysis of trees. The pipeline of our method includes three steps. First, the raw point clouds are segmented by utilizing the circular shape of trees, and segments are grouped into tree sections based on the principle of spatial proximity. Second, circles and axes are extracted from tree sections which are subject to loss of shape information. Third, by clustering and integrating the tree sections resulted from various space inconsistencies, straight tree trunk landmarks are finally formed for future localization. The experimental results from real forested environments are presented.
Investigating of Hybrid Meta-Heuristics to Solve the Large-Scale Multi-Source Weber Problems and Performance Measuring of them with Statistical Tests

The location–allocation problems are a class of complicated optimization problems that requires finding sites for m facilities and to simultaneously allocate n customers to those facilities to minimize the total transportation costs. Indeed, these problems, belonging to the class NP-hard, have a lot of local optima solutions. In this chapter, three hybrid meta-heuristics: genetic algorithm, variable neighborhood search and particle swarm optimization, and a hybrid local search approaches are studied. These are investigated to solve the uncapacitated continuous location-allocation problem (multi-source Weber problem). In this regard, alternate location allocation and exchange heuristics are used to find the local optima of the problem within the framework of hybrid algorithms. In addition, some large-scale problems are employed to measure the effectiveness and efficiency of hybrid algorithms. Obtained results from these heuristics are compared with local search methods and with each other. The experimental results show that the hybrid meta-heuristics produce much better solutions to solve large-scale problems. Moreover, the results of two non-parametric statistical tests detected a significant difference in hybrid algorithms such that the hybrid variable neighborhood search and particle swarm optimization algorithm outperform the others.
Speed evaluation of a freely swimming robotic fish with an artificial lateral line

Artificial lateral line has been drawing an increasing attention recently for its potential applications in robotics. Experiments are usually conducted with a bioinspired robot in a controlled environment, where the sensing platform is held stationary or slowly driven with a simple linear motion. In this paper, we conduct a more practical and challenging study where the robot uses artificial lateral line to evaluate its linear velocity while freely swimming. We use onboard artificial lateral line to measure the pressure profiles over the surface of a robotic fish and employ onboard IMU (inertial measurement unit) to record the motion kinematics of the robot while freely swimming at various speeds. We find that 1) pressure changes are greatest on the head of the robot; 2) pressures increase along with the swimming speed and the oscillation amplitude of angular velocity of the robot. Therefore, we propose a nonlinear prediction model which incorporates distributed pressure and angular velocity to estimate the speed of the robot. Online speed evaluation experiment demonstrates the effectiveness and the accuracy of the proposed model.
Design, Fabrication and Performances of a Biomimetic Robotic Earthworm

This paper presents the design and development of a microrobot which aims to replicate the locomotion principle of earthworms. By investigating the biological field, the authors developed artificial earthworms by mimicking the structures and locomotion principles of real ones. Prototypes with or without micro-legs (which affect the locomotion performance) have been developed. Each prototype has four modules which can be driven independently according to defined undulatory patterns with a typical frequency of 0.5 Hz. Each module is actuated by one or more SMA springs whose configuration has been designed in order to limit the wiring problems and optimizing working frequency. The robots are covered by shaped silicone material which can be used as a platform to insert tiny legs for obtaining differential friction conditions. Preliminary tests demonstrate that the maximum speed of earthworm prototypes can reach 0.22 mm/s without micro-legs and 2.5 mm/s with micro-legs, thus approximating the behavior of biological earthworms
The Community-Reinforcement Approach

The community-reinforcement approach (CRA) is an alcoholism treatment approach that aims to achieve abstinence by eliminating positive reinforcement for drinking and enhancing positive reinforcement for sobriety. CRA integrates several treatment components, including building the client’s motivation to quit drinking, helping the client initiate sobriety, analyzing the client’s drinking pattern, increasing positive reinforcement, learning new coping behaviors, and involving significant others in the recovery process. These components can be adjusted to the individual client’s needs to achieve optimal treatment outcome. In addition, treatment outcome can be influenced by factors such as therapist style and initial treatment intensity. Several studies have provided evidence for CRA’s effectiveness in achieving abstinence. Furthermore, CRA has been successfully integrated with a variety of other treatment approaches, such as family therapy and motivational interviewing, and has been tested in the treatment of other drug abuse.
The challenges of managing a large and geographically dispersed R & D organisation will never go away

How should a global firm manage its network of R&D centres in an effective manner? Some have developed an Integrated Network model in which the R&D centres around the world are tightly integrated into a coherent whole. Others have adopted a LooselyCoupled Network model in which individual “centres of excellence” are given considerable autonomy. In this paper we argue that the way individual R&D units are structured, and the way the entire network is managed, should be based not on administrative heritage, environmental turbulence or management style, but on the basis of the underlying characteristics of the firm’s knowledge assets. In particular we focus on the observability and mobility of the firm’s knowledge assets, the impact these factors have on the way individual R&D centres are structured, and how they relate to one another in the international network. The argument is supported using examples and data from Swedish firms including ABB, Alfa Laval and Ericsson. c 2002 Elsevier Science Ltd. All rights reserved.
De novo assembly and transcriptome analysis of cultivated strawberry (Fragaria × ananassa) receptacles under freezing stress.

The octoploid strawberry (Fragaria × ananassa) is an important species cultivated and consumed around the world for its excellent flavour and nutritional value. Strawberry floral organs are very sensitive to low-temperature stress, which often leads to economic loss. In the current study RNA-Seq method was used to understand the strawberry receptacle response to freezing stress. The results indicated that out of 356,807,838 clean reads obtained from six libraries, 42,365 unigenes were generated through assembling them in a de nove method. These unigenes were further annotated in Pfam, KEGG, Swissprot, String, and NR database. A total of 528 differentially expressed genes were identified in our research. Among these genes, 428 genes were upregulated and 100 genes were downregulated by freezing stress. The GO classification and KEGG enrichment analysis showed that the most highly represented pathways were environmental adaptation and carbohydrate metabolism. Moreover, we identified 3 CBF/DREB transcription factor genes and 1 COR gene in up-regulated DEGs. This analysis reveals the global transcriptomic response of strawberry receptacles to freezing stress and may help to understand the molecular mechanisms involved in freezing stress tolerance in strawberry receptacles. © 2018 Friends Science Publishers
Color Sensitivity Multiple Exposure Fusion using High Dynamic Range Image

In this paper, we present a high dynamic range imaging (HDRI) method using a capturing camera image using normally exposure, over exposure and under exposure. We make three different images from a multiple input image using local histogram stretching. Because the proposed method generated three histogram-stretched images from a multiple input image, ghost artifacts that are the result of the relative motion between the camera and objects during exposure time, are inherently removed. Therefore, the proposed method can be applied to a consumer compact camera to provide the ghost artifacts free HDRI. Experiments with several sets of test images with different exposures show that the proposed method gives a better performance than existing methods in terms of visual results and computation time.
Word Sense Disambiguation in Information Retrieval

The natural language processing has a set of phases that evolves from lexical text analysis to the pragmatic one in which the author’s intentions are shown. The ambiguity problem appears in all of these tasks. Previous works tries to do word sense disambiguation, the process of assign a sense to a word inside a specific context, creating algorithms under a supervised or unsupervised approach, which means that those algorithms use or not an external lexical resource. This paper presents an approximated approach that combines not supervised algorithms by the use of a classifiers set, the result will be a learning algorithm based on unsupervised methods for word sense disambiguation process. It begins with an introduction to word sense disambiguation concepts and then analyzes some unsupervised algorithms in order to extract the best of them, and combines them under a supervised approach making use of some classifiers.
Stunned Myocardium due to Decompensation from Hypovolemic Shock in a Pregnant Woman with Uterine Atony Following Cesarean Section

Transient post ischemic left ventricular dysfunction has been called stunned myocardium. The stunned myocardium can lead cardiac problems such as arrhythmias, left ventricular dysfunction, and a myocardial infarction. Stunned myocardium is a reversible condition. In this case, a 25 year old female patient with a history of mild intermittent asthma became hypovolemic and got into shock after developing uterine atony following a cesarean section. The patient did not respond to rapid volume replacement therapy. Subsequently, the patient had acute pulmonary edema, hypotension, and tachycardia developed. Echocardiogram was done showing severe left ventricular dysfunction (ejection fraction (EF), 25-35%) with left inferobasal wall hypokinesis and no right ventricular dysfunction or severe tricuspid regurgitation or right ventricular hypertrophy. Chest x-ray showed newly diagnosed cardiomegaly and bilateral pulmonary congestion. Initial troponin I was elevated, however, the level of troponin I remained stable with the same baseline value. Patient was at high risk for pulmonary embolism (PE) based on risk assessment, therefore, anticoagulation was started. Chest CT for pulmonary embolism was performed, and the result was negative. Two days after, repeated echocardiogram showed improved EF was 35%. Patient was discharged with beta –blocker to reduce oxygen demand of myocardium. This case supports hypovolemic shock not responding to volume replacement therapy can cause myocardium damage which is able to be diagnosed with stunned myocardium.
Learning distributed power allocation policies in MIMO channels

In this paper1, we study the discrete power allocation game for the fast fading multiple-input multiple-output multiple access channel. Each player or transmitter chooses its own transmit power policy from a certain finite set to optimize its individual transmission rate. First, we prove the existence of at least one pure strategy Nash equilibrium. Then, we investigate two learning algorithms that allow the players to converge to either one of the NE states or to the set of correlated equilibria. At last, we compare the performance of the considered discrete game with the continuous game in [7].
An Efficient Data Mining Framework on Hadoop using Java Persistence API

Data indexing is common in data mining when working with high-dimensional, large-scale data sets. Hadoop, a cloud computing project using the MapReduce framework in Java, has become of significant interest in distributed data mining. A feasible distributed data indexing algorithm is proposed for Hadoop data mining, based on ZSCORE binning and inverted indexing and on the Hadoop SequenceFile format. A data mining framework on Hadoop using the Java Persistence API (JPA) and MySQL Cluster is proposed. The framework is elaborated in the implementation of a decision tree algorithm on Hadoop. We compare the data index-ing algorithm with Hadoop MapFile indexing, which performs a binary search, in a modest cloud environment. The results show the algorithm is more efficient than naïve MapFile indexing. We compare the JDBC and JPA implementations of the data mining framework. The performance shows the framework is efficient for data mining on Hadoop.
An Epidemiological Analysis of Thyroid Cancer in a Spanish Population: Presentation, Incidence and Survival

Accurate statistics on cancer occurrence and outcome are essential both for the purposes of research and for planning and evaluation programmes for cancer control (Parkin, 2006). Although tumours of thyroid account for only 1% of the overall human cancer burden, they represent the most common malignancies of the endocrine system and pose a significant challenge to pathologists, surgeons and endocrinologists. Among epithelial tumors, carcinomas of follicular cell origin far outnumber those of C-cell origin. The vast majority of carcinomas of follicular cell origin are indolent malignancies with 10 year survivals in excess of 90 %.
A New Feature-based Stereo Vision Algorithm using Standard Hardware in a Real Environment

A new feature-based depth mapping and 3D reconstruction al-gorithm is proposed for matching points in two uncalibrated images. The algorithm focuses on intelligence at an early stage to minimize post-processing for false match detection. Features are detected using an iterative procedure for stability in non-ideal lighting conditions. Correlation based stereo matching and a simple voting scheme results in a sparse depth map. A 3D scene approximation is rendered using surface fitting and texture warping. Off-the-shelf PC and imaging hardware is used to implement the algorithm, executing in around 30 seconds and a matching accuracy of better than 90 percent without post-processing. Typically between 100 and 300 points are matched. The algorithm provides a practical method of implementing a stereo system in a harsh environment using standard hardware.
In Pursuit of an MA

In 1950, I submitted “The Happiness Game,” my MA thesis, to the Department of Sociology at Columbia University in fulfillment of the requirements for the degree. Having taken this first baby step into academic social science, I was pleased. So was my adviser, Leo Lowenthal. I remember presenting a paper based on the thesis at the annual meeting of the Eastern Sociological Society. It was then laid to rest.
Wireless virtual network embedding based on spectrum sharing allocation

Wireless network virtualization has gained a lot of interest from academic research and is seen as a promising way to relieve the rigid network problem due to its abstraction and sharing of infrastructure and radio spectrum resources. Some algorithms have been proposed to assign physical resources to virtual networks in an efficient way regardless of the change of user's demand during their entire lifetime. It is a waste to always meet the peak demand of workload especially in consideration of the spectrum crisis. In this paper, we propose a wireless virtual network embedding algorithm based on opportunistic spectrum sharing allocation. Virtual network can implement spectrum sharing enabling increased resource usage and lower rejection rates. The simulation results show that the algorithm we proposed is better than fixed resource allocation VNE in improving accept rates, revenue, resource utilization and lowering the cost of virtual networks.
A Decentralized Linear Programming Approach to Energy-Efficient Event Detection 1 A Decentralized Linear Programming Approach to Energy-Efficient Event Detection

This paper addresses the problem of decentralized event detection in a largescale wireless sensor network (WSN), where without the coordination of any fusion center, sensor nodes autonomously make decisions through information exchange with their neighbors. We formulate the event detection problem as a linear program, and propose a heuristic decentralized linear programming (DLP) approach to solve it. Compared to the existing algorithms which solve decentralized optimization problems, the DLP algorithm requires low communication cost per iteration and shows fast convergence. Convergence property of the DLP algorithm is theoretically analyzed, and then validated through simulation results. We also implement the DLP algorithm in the structural health monitoring application and demonstrate its effectiveness in decentralized event detection.