nils-herrmann/clean_scirepeval huggingface dataset:
Machine Learned Resume Job Matching Solution
Venue: ArXiv
Year: 2016

Job search through online matching engines nowadays are very prominent and beneficial to both job seekers and employers. But the solutions of traditional engines without understanding the semantic meanings of different resumes have not kept pace with the incredible changes in machine learning techniques and computing capability. These solutions are usually driven by manual rules and predefined weights of keywords which lead to an inefficient and frustrating search experience. To this end, we present a machine learned solution with rich features and deep learning methods. Our solution includes three configurable modules that can be plugged with little restrictions. Namely, unsupervised feature extraction, base classifiers training and ensemble method learning. In our solution, rather than using manual rules, machine learned methods to automatically detect the semantic similarity of positions are proposed. Then four competitive "shallow" estimators and "deep" estimators are selected. Finally, ensemble methods to bag these estimators and aggregate their individual predictions to form a final prediction are verified. Experimental results of over 47 thousand resumes show that our solution can significantly improve the predication precision current position, salary, educational background and company scale.
Location of Aerial Targets Based on Particle Swarm Optimization
Year: 2007

On the condition of being disturbed, the radar is hard to fix the accurate position of the aerial target. Aiming at this problem, it puts forward the particle swarm optimization to solve the nonlinear optimization problem based on TDOA to locate the aerial target when the multi base radar receiving stations distributed randomly in the space. Firstly, the algorithm initializes a random particle swarm, then alters the velocity and position of the particles in according with the fitness, searches the optimal coordinates through iterative searching method. The experimental results show that if the parameters are assumed reasonably, the algorithm is stable and can find out the global optimal solution.
Contact and Human Dynamics from Monocular Video
Venue: SCA
Year: 2020

Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors that violate physical constraints, such as feet penetrating the ground and bodies leaning at extreme angles. In this paper, we present a physics based method for inferring 3D human motion from video sequences that takes initial 2D and 3D pose estimates as input. We first estimate ground contact timings with a novel prediction network which is trained without hand labeled data. A physics based trajectory optimization then solves for a physically plausible motion, based on the inputs. We show this process produces motions that are significantly more realistic than those from purely kinematic methods, substantially improving quantitative measures of both kinematic and dynamic plausibility. We demonstrate our method on character animation and pose estimation tasks on dynamic motions of dancing and sports with complex contact patterns.
Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12
Venue: ArXiv
Year: 2017

In June 2016, Apple announced that it will deploy differential privacy for some user data collection in order to ensure privacy of user data, even from Apple. The details of Apple's approach remained sparse. Although several patents have since appeared hinting at the algorithms that may be used to achieve differential privacy, they did not include a precise explanation of the approach taken to privacy parameter choice. Such choice and the overall approach to privacy budget use and management are key questions for understanding the privacy protections provided by any deployment of differential privacy. In this work, through a combination of experiments, static and dynamic code analysis of macOS Sierra (Version 10.12) implementation, we shed light on the choices Apple made for privacy budget management. We discover and describe Apple's set up for differentially private data processing, including the overall data pipeline, the parameters used for differentially private perturbation of each piece of data, and the frequency with which such data is sent to Apple's servers. We find that although Apple's deployment ensures that the (differential) privacy loss per each datum submitted to its servers is $1$ or $2$ the overall privacy loss permitted by the system is significantly higher, as high as $16$ per day for the four initially announced applications of Emojis, New words, Deeplinks and Lookup Hints. Furthermore, Apple renews the privacy budget available every day, which leads to a possible privacy loss of 16 times the number of days since user opt in to differentially private data collection for those four applications. We advocate that in order to claim the full benefits of differentially private data collection, Apple must give full transparency of its implementation, enable user choice in areas related to privacy loss, and set meaningful defaults on the privacy loss permitted.
Deep Learning Based Proactive Caching for Effective WSN Enabled Vision Applications
Venue: Complex.
Year: 2019

Wireless Sensor Networks (WSNs) have a wide range of applications scenarios in computer vision, from pedestrian detection to robotic visual navigation. In response to the growing visual data services in WSNs, we propose a proactive caching strategy based on Stacked Sparse Autoencoder (SSAE) to predict content popularity (PCDS2AW) Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network. Then, the SSAE network structure parameters and network model parameters are optimized through training. The proactive cache strategy implementation procedure is divided into four steps. (1) The SDN controller is responsible for dynamically collecting user request data package information in the WSNs network. (2) The SSAEs predicts the packet popularity based on the SDN controller obtaining user request data. (3) The SDN controller generates a corresponding proactive cache strategy according to the popularity prediction result. (4) Implement the proactive caching strategy at the WSNs cache node. In the simulation, we compare the influence of spatiotemporal data on the SSAE network structure. Compared with the classic caching strategy Hash LRU, Betw LRU, and classic prediction algorithms SVM and BPNN, the proposed PCDS2AW proactive caching strategy can significantly improve WSN performance.
Race, Class, and the Limits of the Analogical Imagination: Pier Paolo Pasolini's African America
Year: 2020

Pier Paolo Pasolini's tireless opposition to neocapitalism throughout the 1960s and early '70s took "Africa" as the allegory and instantiation of political resistance tout court a fact not surprising given the number of countries which began wresting back their sovereignty from European colonizers during these years. Yet there is another dimension of Africanness bound up with the continent's history yet simultaneously alien to it that figures prominently in Pasolini's aesthetics throughout the period: the African American community and its particular cultural and counter cultural expressions. By virtue of the United States' fraught racial politics, sprawling ghettoes, and imperialist ambitions, the country figured prominently into Pasolini's "third world" imaginary in a variety of media and genres representations relatively overlooked in his influential oeuvre, and which this article examines in detail.
Inverse Open Loop Noncooperative Differential Games and Inverse Optimal Control
Venue: IEEE Transactions on Automatic Control
Year: 2020

We consider the problem of computing parameters of player cost functionals such that given state and control trajectories constitute an open loop Nash equilibrium for a noncooperative differential game. We propose two methods for solving this inverse differential game problem and novel conditions under which our methods compute unique cost functional parameters. Our conditions are analogous to persistence of excitation conditions in adaptive control and parameter estimation. The efficacy of our methods is illustrated in simulations.
A Framework on the Performance Analysis of Dual Hop Mixed FSO RF Cooperative Systems
Venue: IEEE Transactions on Communications
Year: 2019

Free space optical (FSO) communication systems provide line of sight wireless connectivity in the unlicensed optical spectrum and achieve higher data rates in comparison to their radio frequency (RF) counterparts. FSO systems are particularly attractive for the last mile access problem by establishing a connectivity bridge between optical fiber based backbone and RF access networks. To address this practical deployment scenario, there has been an increasing attention on the so called mixed (dual hop) FSO RF systems where FSO transmission is utilized at one hop followed by RF transmission at the other one. In this paper, we investigate the performance of mixed FSO RF transmission systems where the FSO link is modeled by the double generalized Gamma distribution with generalized pointing error impairments and the RF link experiences the extended generalized K shadowed fading. For both amplify and forward and decode and forward relaying, we derive closed form expressions for outage probability, bit error probability, and ergodic capacity in terms of bivariate Fox H function. Monte Carlo simulation results are provided to verify the accuracy of derived expressions. We further provide an asymptotic analysis and discuss the achievable diversity orders of mixed FSO RF systems.
Self Assisted Upper Limb Rehabilitation Devices, A Comprehensive Review
Year: 2016

For the purpose of Upper Limb rehabilitation, the use of mechanical and robot assisted rehabilitation therapies are not a new concept. Such systems allow patients to perform therapies with minimal or no assistance. The complexity of such systems vary from being as simple as a custom made bars to highly sophisticated robotic exoskeletons. The advancement of robotics and motor control mechanisms has fuelled interest in this domain and we see a lot of development in the field of robot assisted rehabilitation with in a comparatively short span of time. This paper presents a review and tabulates the results of a few selected rehabilitation devices. The devices include mechanical as well as intelligent robotics based rehabilitation devices.
Involvement of ROS mediated mitochondrial dysfunction and SIRT3 down regulation in tris(2 chloroethyl)phosphate induced cell cycle arrest.
Venue: Toxicology research
Year: 2016

Tris(2 chloroethyl)phosphate (TCEP) is a flame retardant in plastics. It is bio accumulative and persistent in the environment and has been detected in ambient and indoor air, surface and groundwater, food, house dust, and consumer products. Studies showed that TCEP can cause damage to the liver and kidneys of rats. However, the mechanisms underlying TCEP remain unclear. To investigate the effect of TCEP on mitochondrial function and cell fate, Chang liver cells were treated with TCEP (3.12, 12.50, 50.00, and 200.00 mg L 1) for 24 and 48 h. The results showed that TCEP increased mitochondrial reactive oxygen species production, disrupted mitochondrial integrity and caused mitochondrial dysfunction, representing increased intercellular free Ca2+ levels, decreased mitochondrial membrane potential and mitochondrial DNA copies as well as reduced ATP synthesis, and G2/M cell cycle arrest with down regulation of SIRT3, forkhead box O3a and manganese superoxide dismutase proteins. The findings suggest that TCEP caused cell cycle arrest through down regulation of SIRT3 is involved in mitochondrial oxidative stress.
Can sustainable restaurant practices enhance customer loyalty? The roles of value theory and environmental concerns
Year: 2020

Attracting diner participation in sustainable restaurant practices, such as waste reduction, are highly significant for the environment and in reducing the carbon footprint of food consumption. However, there are concerns as to whether the adoption of sustainable practices in restaurant settings is favored by consumers. To examine such issues we aim to identify, by applying value theory, whether sustainable restaurant practices increase diner loyalty. To do this, this research develops and tests an integrated theoretical model on relationships between sustainable restaurant practices, consumer values (hedonic and utilitarian) environmental concern, and diner behavior. Results reveal that sustainable restaurant practices as a second order construct of food sustainability and waste reduction influence hedonic/utilitarian values. Sustainable restaurant practices also positively influence diner behavior as a second order construct of participation in waste reduction practices and loyalty to sustainable restaurants. Diner behavior is affected by hedonic/utilitarian values on waste reduction, which are moderated by environmental concern.
Reusing Newspaper Kiosks for Last Mile Delivery in Urban Areas
Year: 2020

The current increase in e commerce is generating growing problems in urban areas in terms of both traffic flow (increasing traffic, no parking spaces) and environmental issues (noise, atmospheric pollution, etc. In parallel, an iconic element of historic districts is disappearing: more and more newspaper kiosks are closing their business as their work dwindles. In this scenario, the objective of this paper is to propose a model for last mile parcel delivery that exploits the current available newspaper kiosk network by using them as parcel lockers. To demonstrate the benefits of this proposal, we map the kiosk network of the city of Valladolid (Spain) and compare the environmental impact of a traditional (door to door) delivery and the proposed model which reuses old kiosks as parcel lockers. The necessary steps to carry out simulations are described in detail so that experiments can be replicated in other cities that face the same issues.
Who Tweets About Diabetic Foot on Twitter and Which Tweets Are More Attractive?
Venue: The international journal of lower extremity wounds
Year: 2020

Diabetic foot is a serious problem for health care systems. Twitter can provide communication between people and it might be an informative tool for health care management. The purpose of this study is detecting the people or organizations that tweet about diabetic foot and analyze the interactions of these tweets on Twitter. All tweets containing the keyword "diabetic foot" in April 2019 were collected. The users were separated into 7 groups: patients with diabetes, health care providers, nongovernmental organizations, information sites and communication media, private companies, medical students, and others. Health care professionals and nonprofessionals were evaluated in likes, mentions, and retweets. The major group was health care providers. By 2 group comparisons of professionals and nonprofessionals, all likes, mentions, and retweets were significantly different (P .02, P .04, P .001, respectively) We concluded that the tweets of health care professionals get more interaction than others. Twitter might be a useful tool to distinguish accurate information about diabetic foot. Also, health care professionals should use for making people aware of the diabetic foot and shed light on society.
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi View Convolutional Networks
Venue: IEEE Transactions on Medical Imaging
Year: 2016

We propose a novel Computer Aided Detection (CAD) system for pulmonary nodules using multi view convolutional networks (ConvNets) for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2 D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2 D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi view ConvNets is highly suited to be used for false positive reduction of a CAD system.
Automatic documentation generation via source code summarization of method context
Venue: ICPC 2014
Year: 2014

A documentation generator is a programming tool that creates documentation for software by analyzing the statements and comments in the software's source code. While many of these tools are manual, in that they require specially formatted metadata written by programmers, new research has made inroads towards automatic generation of documentation. These approaches work by stitching together keywords from the source code into readable natural language sentences. These approaches have been shown to be effective, but carry a key limitation: the generated documents do not explain the source code's context. They can describe the behavior of a Java method, but not why the method exists or what role it plays in the software. In this paper, we propose a technique that includes this context by analyzing how the Java methods are invoked. In a user study, we found that programmers benefit from our generated documentation because it includes context information.
Adaptive Deep Learning of Cross Domain Loss in Collaborative Filtering
Venue: ArXiv
Year: 2019

Nowadays, users open multiple accounts on social media platforms and e commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content that users interact with, such as movies, music, clothing and retail products. In this paper, we propose an adaptive deep learning strategy for cross domain recommendation, referred to as ADC. We design a neural architecture and formulate a cross domain loss function, to compute the non linearity in user preferences across domains and transfer the knowledge of users' multiple behaviors, accordingly. In addition, we introduce an efficient algorithm for cross domain loss balancing which directly tunes gradient magnitudes and adapts the learning rates based on the domains' complexities/scales when training the model via backpropagation. In doing so, ADC controls and adjusts the contribution of each domain when optimizing the model parameters. Our experiments on six publicly available cross domain recommendation tasks demonstrate the effectiveness of the proposed ADC model over other state of the art methods. Furthermore, we study the effect of the proposed adaptive deep learning strategy and show that ADC can well balance the impact of the domains with different complexities.
FAMILIA, ESCUELA Y CLASE SOCIAL: SOBRE LOS EFECTOS PERVERSOS DE LA IMPLICACION FAMILIAR
Year: 2014

Para la pedagogia moderna, una educacion de calidad requiere de la implicacion de las familias en las escuelas. Algunas investigaciones sociologicas sostienen que favorece los resultados academicos, lo cual ha sido asumido por los poderes publicos. Sin embargo, no existe consenso sobre los efectos de esta participacion, ni sobre las causas que la motivan. Varios teoricos indican que el efecto positivo sobre el rendimiento del alumno depende en gran medida del origen socioeconomico. Si la implicacion esta desigualmente recompensada, las relaciones entre familia y escuela podrian reforzar las diferencias de clase en el sistema educativo. Ademas, vincular el bajo rendimiento a la falta de implicacion familiar acaba estigmatizando a las familias de clase obrera, ignorando los factores estructurales y simplificando las causas del fracaso escolar. Palabras clave: Implicacion familiar, clase social, diferencias de rendimiento.  According to modern pedagogy, a quality education requires parent involvement in schools. Some sociological researches maintain that it favors academic achievement, which has also been assumed by public authorities. However, there is not an agreement about the effects of this participation or over the causes which motivate them. Several theorists indicate that the positive effect on student's achievement depends in great measure to their socioeconomic background. If parental involvement is unequally rewarded, the family school relationships could reinforce the class achievement gap in education system. Furthermore, linking low achievement to the lack of parental involvement ends up stigmatizing working class families, ignoring the structural facts and simplifying scholar failure causes.  Parental involvement, social class, achievement gap
An Empirical Study on Forensic Analysis of Urdu Text Using LDA Based Authorship Attribution
Venue: IEEE Access
Year: 2019

In the recent years, text based digital forensic has evolved into a major research domain that supports digital investigation. A piece of text can be a critical source of information that is written by somebody with respect to writing style, usage of typical vocabulary, and so on. In this paper, we present a unified approach for intelligent association analysis of text of how much a piece of text is related to a person with respect to his stylometric writing features. The latent Dirichlet allocation (LDA) based approach emphasizes on instance based and profile based classification of an author's text. Here, LDA suitably handles the high dimensional and sparse data by allowing more expressive representation of text. The presented approach is an unsupervised computational methodology that can handle the heterogeneity of the dataset, diversity in writing styles of authors, and the inherent ambiguity of Urdu language text. A large corpus was collected for performance testing of the presented approach. The results of the experiments show the superiority of the proposed approach over the state of the art representations and other algorithms used for authorship attribution. Manifold contributions of the presented paper are use of improved sqrt cosine similarity with LDA topics to measure similarity in vectors of text documents for the forensic analysis purpose, construction of a large data set of 6000 documents of articles, and achievement of (92% f1 measure) results on articles without using any labels for authorship attribution task.