nils-herrmann/clean_scirepeval huggingface dataset:
The role of BRCA1 and BRCA2 mutations in prostate, pancreatic and stomach cancers
Venue: Hereditary cancer in clinical practice
Year: 2015

The association of germline mutations in the breast cancer susceptibility gene 1 (BRCA1) and the breast cancer susceptibility gene 2 (BRCA2) with the development of breast and ovarian cancers have been widely researched and recognised. It is known that these genes function at multiple sites in the body. Research has subsequently evolved into the connection of BRCA1/2 with cancers at other sites within the body. This review examines the association of BRCA1/2 germline gene mutations with prostate, pancreatic and stomach cancers. An extensive literature search revealed conflicting findings regarding the association of BRCA1/2 gene mutations with these cancers. Most studies suggest that there is an association between BRCA1/2 mutations and carcinoma of the prostate, pancreas and stomach, but some reports propose that such a correlation may be due to factors other than possessing a mutated BRCA1/2 gene, and other associations may be revealed as further epidemiological information becomes available. The review concludes that as more knowledge arises about the mechanisms of BRCA1/2 gene mutations, it should pave the way for future screening programmes to be applied effectively.
Emotional response inhibition in children with attention deficit/hyperactivity disorder: neural and behavioural data
Venue: Psychological Medicine
Year: 2015

Although both emotion and response inhibition are thought to be important in attention deficit/hyperactivity disorder (ADHD) little is known about the neural mechanisms that underlie the interaction between these two processes in patients with this disorder. This study aimed at examining how emotional contexts affect inhibitory control in children with ADHD.  A total of 24 ADHD children and 24 healthy comparison subjects performed a modified go/no go task during three different emotionally laden contexts: negative, neutral and positive. To explore the timing and the underlying neural substrates of emotion modulated response inhibition, event related potentials were measured and further analysed both at the scalp and at the voxel level.  Patients with ADHD showed greater activation of inhibition related neural mechanisms (i.e. no go P3 amplitudes and orbitofrontal cortex activity) to maintain a similar level of performance as healthy comparison subjects, especially during the emotionally arousing contexts (negative and positive)  This study provides plausible neural mechanisms for the difficulty that ADHD children have in controlling their behaviour in highly emotional situations. Such emotional contexts might increase the need for top down inhibitory control and put ADHD children at greater risk for impulsive behaviours and emotional dysregulation.
Motivation and Engagement in Learning
Year: 2019

Motivation and engagement are firmly implicated in students' participation in class, educational aspirations, enjoyment of school, academic learning, and academic achievement. Motivation refers to an individual's inclination, energy, direction, and drive with respect to learning and achievement. Engagement involves the thoughts, actions, and emotions that reflect this inclination, energy, and drive. There are numerous theories articulating the key elements of students' motivation and engagement. These theories describe how and why motivation and engagement are important for educational outcomes in their own right, as well as how and why motivation and engagement are important means to other desirable educational outcomes. Given the vast array of different theories in the areas of motivation and engagement, researchers have made calls to integrate the body of knowledge that has amassed. The Motivation and Engagement Wheel is an example of a multidimensional framework that traverses salient motivation and engagement factors from major theory and research. These factors encompass positive motivation constructs (self efficacy, valuing, and mastery orientation) positive engagement constructs (planning, task management, and persistence) negative motivation constructs (anxiety, uncertain control, and failure avoidance) and negative engagement constructs (self handicapping and disengagement) A broad body of research provides support for the salience of the factors in the Wheel in relation to a range of other variables (e.g. background characteristics, cultural factors) and through a variety of research designs and approaches. Importantly, there are additional exciting directions in motivation and engagement research, such as real time investigations, the use of biomarkers, the interface with teachers, and intervention research, that are relevant to optimizing students' academic development through school and beyond.
Real Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re Identification
Venue: 2018 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2018

Online multi object tracking is a fundamental problem in time critical video analysis applications. A major challenge in the popular tracking by detection framework is how to associate unreliable detection results with existing tracks. In this paper, we propose to handle unreliable detection by collecting candidates from outputs of both detection and tracking. The intuition behind generating redundant candidates is that detection and tracks can complement each other in different scenarios. Detection results of high confidence prevent tracking drifts in the long term, and predictions of tracks can handle noisy detection caused by occlusion. In order to apply optimal selection from a considerable amount of candidates in real time, we present a novel scoring function based on a fully convolutional neural network, that shares most computations on the entire image. Moreover, we adopt a deeply learned appearance representation, which is trained on large scale person re identification datasets, to improve the identification ability of our tracker. Extensive experiments show that our tracker achieves real time and state of the art performance on a widely used people tracking benchmark.
The Finite Element Method in Structural Mechanics: Principles And Practice Of Design Of Field Consistent Elements For Structural And Solid Mechanics
Year: 2010

Preface. 1.  2. Shear Flexible Beam Elements. 3. Simple Curved Beam Elements. 4. Non Uniform Mapping and the Quadratic Beam Element. 5. Variationally Incorrect Formulations. 6. General Curved Beam Element. 7. Plane Stress Elements. 8. Plate Elements. 9. Brick Elements. 10. Shell Elements. 11. Varying Modulus,  a Philosophical Summing Up.
Joint Power Control and User Association Strategy in Green HetNets Using Deep Q Network with LSTM
Venue: 2019 IEEE Global Communications Conference (GLOBECOM)
Year: 2019

With the proliferation of users in wireless communication networks and the increase in traffic, heterogeneous networks have emerged. Minimizing the total energy consumption of base stations is an important issue for environmental reasons. Therefore, the concept of a green heterogeneous network was proposed. However, the deployment of a large number of small base stations also poses problems for user management. In order to better improve the decision making and self organizing ability of the network, the paper also proposes an user energy consumption prediction model based on Recurrent Neural Network. After that, the article also proposes a power control scheme through a Deep Reinforcement Learning algorithm with Long Short Term Memory to improve the quality of user service. Finally, a joint optimization scheme of network power control and user management is realized. The experimental results show that the joint optimization effect using this method can converge to better solution and reduce energy consumption compared with traditional deep reinforcement learning.
Use data Mining Techniques to Predict Users' Engagement on the Social Network Posts in The Period Before, During and After Ramadan
Year: 2017

Social media has become ubiquitous and main requirement for individuals lives, its used for socialization sharing information and recently for marketing and advertising. The content of the posts has a significant impact on attracting users and total user's engagement. Companies have adopted social media as an essential tool for realizing potential customer needs and to ensure optimal customer satisfaction. Analyzing the content, type and the best time of posting can effectively impact and benefit a business. Data mining (also referred to as Data Knowledge Discovery) is the practice of examining a dataset to establish the hidden patterns and knowledge in order to represent the results in an understandable format. In this thesis, the effectiveness of using a data mining classifier helps to predict users' engagement with a social media post before publishing and anticipating the best type of media for the post. Different classification algorithms were applied to the dataset using the Rapidminer tool. The results of the classification considered as a baseline for this research. In contrast to traditional approaches on this topic, this dissertation seeks to analyze the depth of users' engagement with social media posts and discuss the basis for a predictive model that to predict the total engagement of a post before publishing. At the beginning, a data set was collected from Crowdbabble online tool and the collected dataset presented different periods and different social media networks to examine the user behavior at different times. Before Ramadan presented a typical month, During Ramadan related to the special month for Muslims and after Ramadan referred to the beginning of summer vacation. Facebook, Twitter and Instagram were the main platforms examined as these are popular in the Arab world. This study focused on dataset from the Arab world to investigate Arab users' behaviors and interests. The collected dataset was analyzed from various perspectives to study the post characteristics and the effect on users' engagement. Each platform (Facebook, Instagram, Twitter) has a primary role in users' engagement, Facebook is the top used application tool compared to Instagram and Twitter. The results showed that Post type (Status, Photo, Link, Video) has an important role in attracting users and the analysis confirms that video and photo posts create the maximum levels of engagement. Post fields also had impact on engaging users, whereby the categories of Beauty, Fashion and Celebrity were the most attractive page types.
Green Infrastructure Solutions to Health Impacts of Climate Change: Perspectives of Affected Residents in Detroit, Michigan, USA
Venue: Sustainability
Year: 2019

Cities worldwide are incorporating green infrastructure to mitigate climate change and achieve health cobenefits. However, green infrastructure projects are often distributed inequitably based on race and class. Residents' perspectives are necessary to develop and enact effective and equitable 'green' strategies to address climate change and its health impacts. This study reports findings from interviews and ethnographic observations with diverse residents of Detroit, Michigan, USA, who have experience with both green infrastructure projects and intense weather events (flooding) Residents expressed widespread support for green infrastructure solutions, while also sharing concerns about unintended health consequences from unsatisfactory governance of green spaces and climate change itself. Residents also held differing perspectives regarding their responsibility for, and capacity to enact, these solutions compared to businesses, city government, and nonprofit organizations. These findings illuminate key factors that city governments and partnering institutions should incorporate into planning processes with residents to achieve greater environmental justice through green infrastructure strategies to mitigate climate change and related health impacts.
The Influence of Employee Promotion, Upward Downward Communication and Work Environment on Job Satisfaction of PT. Holcim Indonesia, Tbk
Year: 2020

This study aims to determine the influence of employee promotions, upward downward communication, and work environment on employee job satisfaction. This research uses a quantitative approach using a survey method (questionnaire) Path analysis is used to analyse the data collected. The population in this study were all employees of PT. Holcim Indonesia, Tbk, who have a working period of 10 years and over, totalling 510 people spread across all plants in Indonesia. The number of samples taken in this study were 225 people who were randomly selected. The results showed that job satisfaction was directly influenced by employee promotions, upward downward communication, and work environment. The study also found that the work environment was influenced by employee promotion and upward downward communication. This study also found that job satisfaction is indirectly influenced by the promotion and upward downward communication through the work environment. Based on these findings it can be concluded that any changes that occur in employee job satisfaction are influenced by employee promotion, upward downward communication, and work environment. Therefore employee job satisfaction, employee promotions, upward downward communication, and work environment can be taken into consideration in making strategic planning for human resource development.
Multi Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Venue: 2018 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2018

Learning based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi task domain adaptation framework for instance grasping in cluttered scenes by utilizing simulated robot experiments. Our neural network takes monocular RGB images and the instance segmentation mask of a specified target object as inputs, and predicts the probability of successfully grasping the specified object for each candidate motor command. The proposed transfer learning framework trains a model for instance grasping in simulation and uses a domain adversarial loss to transfer the trained model to real robots using indiscriminate grasping data, which is available both in simulation and the real world. We evaluate our model in real world robot experiments, comparing it with alternative model architectures as well as an indiscriminate grasping baseline.
More unlabelled data or label more data? A study on semi supervised laparoscopic image segmentation
Venue: DART/MIL3ID@MICCAI
Year: 2019

Improving a semi supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications. With a laparoscopic liver image segmentation application, we investigate the performance impact by altering the quantities of labelled and unlabelled training data, using a semi supervised segmentation algorithm based on the mean teacher learning paradigm. We first report a significantly higher segmentation accuracy, compared with supervised learning. Interestingly, this comparison reveals that the training strategy adopted in the semi supervised algorithm is also responsible for this observed improvement, in addition to the added unlabelled data. We then compare different combinations of labelled and unlabelled data set sizes for training semi supervised segmentation networks, to provide a quantitative example of the practically useful trade off between the two data planning strategies in this surgical guidance application.
A directional distance function approach to regional environmental economic assessments
Year: 2010

Numerous difficulties await those creating regional scale environmental assessments, from data having inconsistent spatial or temporal scales to poorly understood environmental processes and indicators. Including socioeconomic variables further complicates assessments. While statistical or process based regional environmental assessment models may be computationally or financially expensive, we propose a simple nonparametric outcomes based approach using a directional distance function from the efficiency and productivity analysis literature. The regional environmental economic directional distance function characterizes the relative efficiency of geographic units in combining multiple inputs to produce multiple desirable and undesirable socioeconomic and environmental outputs. This function makes no assumptions about the functional relationships among variables, but by quantifying the extent to which desirable outputs can be expanded and inputs and undesirable outputs contracted, the function can help decisionmakers identify the most important broad scale management and restoration opportunities across a heterogeneous region. A case study involving 134 watersheds in the Mid Atlantic region of the USA indicates that, depending on which outputs are specified as desirable in the models, 25% 33% of the watersheds are efficient in producing desirable outputs while minimizing inputs and undesirable outputs. Models including socioeconomic indicators exhibit increased watershed efficiency compared to models using only environmental indicators. Efficiency levels appear to be correlated with ecoregions.
Users` Moving Patterns Analysis for Personalized Product Recommendation in Offline Shopping Malls
Year: 2006

Most systems in ubiquitous computing analyze context information of users which have similar propensity with demographics methods and collaborative filtering to provide personalized recommendation services. The systems have mostly used static context information such as sex, age, job, and purchase history. However the systems have limitation to analyze users` propensity accurately and to provide personalized recommendation services in real time, because they have difficulty in considering users situation as moving path. In this paper we use users` moving path of dynamic context to consider users situation. For the prediction accuracy we complete with a path completion algorithm to moving path which is inputted to RSOM. We train the moving path to be completed by RSOM, analyze users` moving pattern and predict a future moving path. Then we recommend the nearest product on the prediction path with users` high preference in real time. As the experimental result, MAE is lower than 0.5 averagely and we confirmed our method can predict users moving path correctly.
Unanticipated safety outcomes: Shiftwork and drive in, drive out workforce in Queensland's Bowen Basin
Year: 2007

Extended work shifts (more than 8 hours) and a marked increase in the use of a contractor workforce has resulted in significant productivity gains for employers. These developments we argue have resulted in an unanticipated outcome that has OHS implications: the emergence of a drive in drive out workforce (DIDOW) in the case we studied, a workforce that is permanently based outside Queensland's Bowen Basin but travels to and from their place of work. The concern with a DIDOW is that the workers are at greater risk of driver sleepiness as a function of the work schedule compounded by a long distance commute. These factors place the driver and the community at a greater accident risk. The results suggested some drivers commenced travel at 0200 and drove up to 1300 km to work. Driving in the early morning and traveling longer distances were associated with significantly higher levels of self reported sleepiness. Some 13% of drivers reported falling asleep when driving to commence day shift compared to 23% following night shift. Driver sleepiness is a significant safety risk factor in a DIDOW. We anticipate that the planned expansion of the coal industry will result in more employees joining the DIDOW, putting more people at risk of accident.
Finite/Fixed Time Stabilization for Nonlinear Interconnected Systems With Dead Zone Input
Venue: IEEE Transactions on Automatic Control
Year: 2017

The decentralized control problem is considered for a class of nonlinear time varying interconnected systems. Each subsystem is with the dead zone input and unmodeled dynamics. The interconnections are bounded by time varying nonlinear functions, which relaxes the commonly used linear condition or time invariant condition. Based on the recursive method, we design a new decentralized finite time controller such that all the state variables reach zero in finite time. The reaching time is based on the design parameters and initial value of system state. Furthermore, the fixed time stability conditions are given and the constructed controller can guarantee the fixed time stabilization of the system, in which the reaching time only depends the control design parameters. Finally, simulation results are presented to illustrate the effectiveness of proposed method.
Extending Fuzzy Cognitive Maps with Tensor Based Distance Metrics
Year: 2020

Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners.