nils-herrmann/clean_scirepeval huggingface dataset:
A review of waste management practices and their impact on human health.

This work reviews (i) the most recent information on waste arisings and waste disposal options in the world, in the European Union (EU), in Organisation for Economic Co-operation and Development (OEDC) countries, and in some developing countries (notably China) and (ii) the potential direct and indirect impact of waste management activities on health. Though the main focus is primarily on municipal solid waste (MSW), exposure to bioaerosols from composting facilities and to pathogens from sewage treatment plants are considered. The reported effects of radioactive waste are also briefly reviewed. Hundreds of epidemiological studies reported on the incidence of a wide range of possible illnesses on employees of waste facilities and on the resident population. The main conclusion of the overall assessment of the literature is that the evidence of adverse health outcomes for the general population living near landfill sites, incinerators, composting facilities and nuclear installations is usually insufficient and inconclusive. There is convincing evidence of a high risk of gastrointestinal problems associated with pathogens originating at sewage treatment plants. In order to improve the quality and usefulness of epidemiological studies applied to populations residing in areas where waste management facilities are located or planned, preference should be given to prospective cohort studies of sufficient statistical power, with access to direct human exposure measurements, and supported by data on health effect biomarkers and susceptibility biomarkers.
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification

Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models. However, in practice, the effect of regular data augmentation, such as random image crop, is limited since it might introduce much uncontrolled background noise. In this paper, we propose WeaklySupervised Data Augmentation Network (WS-DAN) to explore the potential of data augmentation. Specifically, for each training image, we first generate attention maps to represent the object’s discriminative parts by weakly supervised Learning. Next, we randomly choose one attention map to augment this image, including attention crop and attention drop. Weakly-supervised data augmentation network improves the classification accuracy in two folds. On the one hand, images can be seen better since multiple object parts can be activated. On the other hand, attention regions provide spatial information of objects, which can make images be looked closer to further improve the performance. Comprehensive experiments in common fine-grained visual classification datasets show that our method surpasses the state-of-the-art methods by a large margin, which demonstrated the effectiveness of the proposed method.
Multi-agent Diverse Generative Adversarial Networks

We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample. Intuitively, to succeed in this task, the discriminator must learn to push different generators towards different identifiable modes. We perform extensive experiments on synthetic and real datasets and compare MAD-GAN with different variants of GAN. We show high quality diverse sample generations for challenging tasks such as image-to-image translation and face generation. In addition, we also show that MAD-GAN is able to disentangle different modalities when trained using highly challenging diverse-class dataset (e.g. dataset with images of forests, icebergs, and bedrooms). In the end, we show its efficacy on the unsupervised feature representation task.
freedom . js : an Architecture for Serverless Web Applications

Delivering web software as a service has grown into a powerful paradigm for deploying a wide range of Internetscale applications. However for end-users, accessing software as a service is fundamentally at odds with free software, because of the associated cost of maintaining server infrastructure. Users end up paying for the service in one way or another, often indirectly through ads or the sale of their private data. In this paper, we aim to enable a new generation of portable and free web apps by proposing an alternative model to the existing client-server web architecture. freedom.js is a platform for developing and deploying rich multi-user web apps, where application logic is pushed out from the cloud and run entirely on client-side browsers. By shifting the responsibility of where code runs, we can explore a novel incentive structure where users power applications with their own resources, gain the ability to control application behavior and manage privacy of data. For developers, we lower the barrier of writing popular web apps by removing much of the deployment cost and making applications simpler to write. We provide a set of novel abstractions that allow developers to automatically scale their application with low complexity and overhead. freedom.js apps are inherently sandboxed, multi-threaded, and composed of reusable modules. We demonstrate the flexibility of freedom.js through a number of applications that we have built on top of the platform, including a messaging application, a social file synchronization tool, and a peer-to-peer (P2P) content delivery network (CDN). Our experience shows that we can implement a P2P-CDN with 50% fewer lines of application-specific code in the freedom.js framework when compared to a standalone version. In turn, we incur an additional startup latency of 50-60ms (about 6% of the page load time) with the freedom.js version, without any noticeable impact on system throughput.
Helping Users Understand Privacy Notices with Automated Query Answering Functionality : An Exploratory Study

Privacy notices are the default mechanism used to inform users about the data collection and use practices of technologies (e.g., websites, mobile apps, Internet of Things devices) and processes with which they interact. The length of these policies and their often convoluted language have been shown to discourage most users from reading them. Recent progress in natural language processing and machine learning has opened the door to the development of technologies that are capable of automatically extracting statements (or “annotations”) from the text of privacy policies. These technologies could help users quickly identify those elements of a privacy notice they care about without requiring them to read the full text of the notice. In this article, we review the requirements associated with the development of Query Answering functionality that would enable users to ask questions about specific aspects of privacy notices (e.g. Does this app share my location with third parties? Am I able to review the information this website collects about me? Can I delete my account? For how long is my information going to be retained by this company?). We discuss different possible approaches to supporting such functionality and how they relate to recent advances in automatically annotating privacy notices. Initial results obtained with different machine learning/natural language processing techniques are presented, suggesting that Query Answering functionality could be a particularly promising approach to informing users about privacy practices. In particular, in contrast to automated annotation techniques that aim to extract detailed statements from the text of privacy notices, Query Answering functionality could be configured to return short text fragments extracted from privacy notices and rely on the user (rather than the computer) to interpret some of the finer nuances of the text found in these fragments. Such an approach could potentially prove more robust than fully automated annotation techniques, which at least at this time struggle with the interpretation of finer nuances. This article also includes a brief discussion of opportunities and challenges associated with possible extensions of Query Answering functionality in the form of privacy assistants capable of entertaining dialogues with users to clarify some of their questions and help them understand to what extent their concerns are explicitly addressed (or not) by the text of privacy notices. Such functionality could provide for yet greater robustness and usability than fully automated annotation techniques, and could eventually also leverage models of what the user already knows and/or cares about.
Design of omnidirectional mobile robots with ACROBAT wheel mechanisms

In this paper, we study the design of omnidirectional mobile robots with Active-Caster RObotic drive with BAll Transmission (ACROBAT). ACROBAT system has been developed by the authors group which realizes mechanical coordination of wheel and steering motions for creating caster behaviors without computer calculations. A motion in the specific direction relative to a robot body is fully depends on the motion of a specific motor. This feature gives a robot designer to build an omnidirectional mobile robot propelled by active-casters with no redundant actuation with a simple control. A controller of the robot becomes as simple as that for omni-wheeled robotic bases. Namely 3DOF of the omnidirectional robot is controlled by three motors using a simple and constant kinematics. ACROBAT includes a unique dual-ball transmission to transmit traction power to rotate and orient a drive wheel with distributing velocity components to wheel and steering axes in an appropriate ratio. Therefore a sensor for measuring a wheel orientation and calculations for velocity distributions are totally removed from a conventional control system. To build an omnidirectional vehicle by ACROBAT, the significant feature is some multiple drive shafts can be driven by a common motor which realizes non-redundant actuation of the robotic platform. A kinematic model of the proposed robot with ACROBAT is analyzed and a mechanical condition for realizing a non-redundant actuation is derived. Based on the kinematic model and the mechanical condition, computer simulations of the mechanism are performed. A prototype two-wheeled robot with two ACROBATs is designed and built to verify the availability of the proposed system. In the experiments, the prototype robot shows successful omnidirectional motions with a simple and constant kinematics based control.
Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans

Convolutional neural networks (CNNs) have emerged as the most powerful technique for a range of different tasks in computer vision. Recent work suggested that CNN features are generic and can be used for classification tasks outside the exact domain for which the networks were trained. In this work we use the features from one such network, OverFeat, trained for object detection in natural images, for nodule detection in computed tomography scans. We use 865 scans from the publicly available LIDC data set, read by four thoracic radiologists. Nodule candidates are generated by a state-of-the-art nodule detection system. We extract 2D sagittal, coronal and axial patches for each nodule candidate and extract 4096 features from the penultimate layer of OverFeat and classify these with linear support vector machines. We show for various configurations that the off-the-shelf CNN features perform surprisingly well, but not as good as the dedicated detection system. When both approaches are combined, significantly better results are obtained than either approach alone. We conclude that CNN features have great potential to be used for detection tasks in volumetric medical data.
Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes

Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM.
Peer review: a flawed process at the heart of science and journals.

Peer review is at the heart of the processes of not just medical journals but of all of science. It is the method by which grants are allocated, papers published, academics promoted, and Nobel prizes won. Yet it is hard to define. It has until recently been unstudied. And its defects are easier to identify than its attributes. Yet it shows no sign of going away. Famously, it is compared with democracy: a system full of problems but the least worst we have. When something is peer reviewed it is in some sense blessed. Even journalists recognize this. When the BMJ published a highly controversial paper that argued that a new ‘disease’, female sexual dysfunction, was in some ways being created by pharmaceutical companies, a friend who is a journalist was very excited—not least because reporting it gave him a chance to get sex onto the front page of a highly respectable but somewhat priggish newspaper (the Financial Times). ‘But,’ the news editor wanted to know, ‘was this paper peer reviewed?’. The implication was that if it had been it was good enough for the front page and if it had not been it was not. Well, had it been? I had read it much more carefully than I read many papers and had asked the author, who happened to be a journalist, to revise the paper and produce more evidence. But this was not peer review, even though I was a peer of the author and had reviewed the paper. Or was it? (I told my friend that it had not been peer reviewed, but it was too late to pull the story from the front page.
Deep Reinforcement Learning for Continuous Control

Reinforcement learning is a mathematical framework for agents to interact intelligently with their environment. In this field, real-world control problems are particularly challenging because of the noise and the high-dimensionality of input data (e.g., visual input). In the last few years, deep neural networks have been successfully used to extract meaning from such data. Building on these advances, deep reinforcement learning achieved stunning results in the field of artificial intelligence, being able to solve complex problems like Atari games  and Go . However, in order to apply the same methods to real-world control problems, deep reinforcement learning has to be able to deal with continuous action spaces. In this thesis, Deep Deterministic Policy Gradients, a deep reinforcement learning method for continuous control, has been implemented, evaluated and put into context to serve as a basis for further research in the field.
Hashing Over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning

In reinforcement learning (RL) tasks, an efficient exploration mechanism should be able to encourage an agent to take actions that lead to less frequent states which may yield higher accumulative future return. However, both knowing about the future and evaluating the frequentness of states are non-trivial tasks, especially for deep RL domains, where a state is represented by high-dimensional image frames. In this paper, we propose a novel informed exploration framework for deep RL tasks, where we build the capability for a RL agent to predict over the future transitions and evaluate the frequentness for the predicted future frames in a meaningful manner. To this end, we train a deep prediction model to generate future frames given a state-action pair, and a convolutional autoencoder model to generate deep features for conducting hashing over the seen frames. In addition, to utilize the counts derived from the seen frames to evaluate the frequentness for the predicted frames, we tackle the challenge of making the hash codes for the predicted future frames to match with their corresponding seen frames. In this way, we could derive a reliable metric for evaluating the novelty of the future direction pointed by each action, and hence inform the agent to explore the least frequent one. We use Atari 2600 games as the testing environment and demonstrate that the proposed framework achieves significant performance gain over a state-of-the-art informed exploration approach in most of the domains.
Secure clustering and routing for heterogeneous mobile wireless sensor networks with dynamic key management

Secure clustering and routing for heterogeneous mobile wireless sensor networks with dynamic key management V. Thiruppathy Kesavan & S. Radhakrishnan a Department of Computer Science & Engineering, Renganayagi Varatharaj College of Engineering, Sivakasi 626128, Tamil Nadu, India b Department of Computer Science & Engineering, Kalasalingam University, Srivilliputtur 626126, Tamil Nadu, India Published online: 25 Jul 2015.
DCGANs for image super-resolution , denoising and debluring

Advance of computational power and big datasets brings the opportunity of using deep learning methods to do image processing. We used deep convolutional generative adversarial networks (DCGAN) to do various image processing tasks such as super-resolution, denoising and deconvolution. DCGAN allows us to use a single architecture to do different image processing tasks and achieve competitive PSNR scores. While the results of DCGAN shows slightly lower PSNR compared to traditional methods, images produced by DCGAN is more appealing when viewed by human. DCGAN can learn from big datasets and automatically add high-frequency details and features to images while traditional methods can’t. The generatordiscriminator architecture in DCGAN pushes it to generate more realistic and appealing images.
Daily aerobic exercise improves reactive hyperemia in patients with essential hypertension.

The effects of long-term aerobic exercise on endothelial function in patients with essential hypertension remain unclear. To determine whether endothelial function relating to forearm hemodynamics in these patients differs from normotensive subjects and whether endothelial function can be modified by continued physical exercise, we randomized patients with essential hypertension into a group that engaged in 30 minutes of brisk walking 5 to 7 times weekly for 12 weeks (n=20) or a group that underwent no activity modifications (control group, n=7). Forearm blood flow was measured using strain-gauge plethysmography during reactive hyperemia to test for endothelium-dependent vasodilation and after sublingual nitroglycerin administration to test endothelium-independent vasodilation. Forearm blood flow in hypertensive patients during reactive hyperemia was significantly less than that in normotensive subjects (n=17). Increases in forearm blood flow after nitroglycerin were similar between hypertensive and normotensive subjects. Exercise lowered mean blood pressure from 115.7+/-5.3 to 110.2+/-5.1 mm Hg (P<0.01) and forearm vascular resistance from 25.6+/-3.2 to 23. 2+/-2.8 mm Hg/mL per minute per 100 mL tissue (P<0.01); no change occurred in controls. Basal forearm blood flow, body weight, and heart rate did not differ with exercise. After 12 weeks of exercise, maximal forearm blood flow response during reactive hyperemia increased significantly, from 38.4+/-4.6 to 47.1+/-4.9 mL/min per 100 mL tissue (P<0.05); this increase was not seen in controls. Changes in forearm blood flow after sublingual nitroglycerin administration were similar before and after 12 weeks of exercise. Intra-arterial infusion of the nitric oxide synthase inhibitor NG-monomethyl-L-arginine abolished the enhancement of reactive hyperemia induced by 12 weeks of exercise. These findings suggest that through increased release of nitric oxide, continued physical exercise alleviates impairment of reactive hyperemia in patients with essential hypertension.
Counting of People in the Extremely Dense Crowd using Genetic Algorithm and Blobs Counting

Received Nov 13,2012 Revised Jan 05, 2013  Jan 12,2013 In this paper, we have proposed a framework to count the moving person in the video automatically in a very dense crowd situation. Median filter is used to segment the foreground from the background and blob analysis is done to count the people in the current frame. Optimization of different parameters is done by using genetic algorithm. This framework is used to count the people in the video recorded in the mattaf area where different crowd densities can be observed. An overall people counting accuracy of more than 96% is obtained.
Automatic Texts Summarization: Current State of the Art

To facilitate the task of reading and searching information, it became necessary to find a way to reduce the size of documents without affecting the content. The solution is in Automatic text summarization system, it allows, from an input text to produce another smaller and more condensed without losing relevant data and meaning conveyed by the original text. The research works carried out on this area have experienced lately strong progress especially in English language. However, researches in Arabic text summarization are very few and are still in their beginning. In this paper we expose a literature review of recent techniques and works on automatic text summarization field research, and then we focus our discussion on some works concerning automatic text summarization in some languages. We will discuss also some of the main problems that affect the quality of automatic text summarization systems.
Left-Handed People in a Right-Handed World:

The purpose of this research was to explore the experiences of left-handed adults. Four semistructured interviews were conducted with left-handed adults (2 men and 2 women) about their experiences. After transcribing the data, interpretative phenomenological analysis (IPA), which is a qualitative approach, was utilized to analyze the data. The analysis highlighted some major themes which were organized to make a model of life experiences of left-handers. The highlighted themes included Left-handers‟ Development: Interplay of Heredity Basis and Environmental Influences, Suppression of Left-hand, Support and Consideration, Feeling It Is Okay, Left-handers as Being Particular, Physical and Psychological Health Challenges, Agonized Life, Struggle for Maintaining Identity and Transforming Attitude, Attitudinal Barriers to Equality and Acceptance. Implications of the research for parents, teachers, and psychologists are discussed.