nils-herrmann/clean_scirepeval huggingface dataset:
A survey of mobile cloud computing: architecture, applications, and approaches

Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed.
Bright Light Therapy : Seasonal Affective Disorder and Beyond

Since the first description of Seasonal Affective Disorder (SAD) by Rosenthal et al. in the 1980s, treatment with daily administration of light, or Bright Light Therapy (BLT), has been proven effective and is now recognized as a first-line therapeutic modality. More recently, studies aimed at understanding the pathophysiology of SAD and the mechanism of action of BLT have implicated shifts in the circadian rhythm and alterations in serotonin reuptake. BLT has also been increasingly used as an experimental treatment in non-seasonal unipolar and bipolar depression and other psychiatric disorders with known or suspected alterations in the circadian system. This review will discuss the history of SAD and BLT, the proposed pathophysiology of SAD and mechanisms of action of BLT in the treatment of SAD, and evidence supporting the efficacy of BLT in the treatment of non-seasonal unipolar major depression, bipolar depression, eating disorders, and ADHD.
Improving Rudder Effectiveness with Sweeping Jet Actuators

The application of active flow control on a vertical tail of a typical twin engine aircraft was investigated. Sweeping jets installed into the rudder surface were used and their effect was assessed by force measurements, flow visualization and local pressure distributions. The airfoil forming the tail is a NACA 0012 with a rudder using 35% of its chord. The tests were carried out at the Lucas Wind Tunnel at the California Institute of Technology at representative Reynolds numbers of up to Re=1.5 million. Multiple flap deflections and spanwise actuator configurations were tested resulting in an increase of up to 50-70% in side force depending on the free stream velocity and momentum input.
Simulation in an SDN network scenario using the POX Controller

Computer networks have evolved due to new trends in the society's needs since their emergence as a way of providing remote access and sharing of computational resources. The architecture inflexibility of the computer networks presents a challenge for researchers, since their experiments can hardly be evaluated in real networks. Thus, in general, tests of new technologies are conducted on network simulators, which imply in a streamline of the reality. The paradigm of Software Defined Networks (SDN) and OpenFlow architecture, offer a way for the implementation of a programmable network architecture, able to be implemented gradually in production networks, which offers the possibility of separating the control mechanisms of the many traffic flows served, so that a scientific experiment can be performed in a real network (adapted for SDN) without interfering with its operation. This paper contextualizes the existing problems in current computer networks, and presents the SDN network as one of the main proposals for the viability of the Internet of the Future. In this context, it is discussed the OpenFlow architecture, which allows the creation of applications for Software Defined Networks. Finally it is presented the network simulator SDN, the Mininet, which implements the OpenFlow interface in a network simulation scenario containing a controller POX with two components, one OpenFlow switch and three nodes. The main objective was to evaluate the communication and bandwidth between nodes.
A hierarchical edge cloud architecture for mobile computing

The performance of mobile computing would be significantly improved by leveraging cloud computing and migrating mobile workloads for remote execution at the cloud. In this paper, to efficiently handle the peak load and satisfy the requirements of remote program execution, we propose to deploy cloud servers at the network edge and design the edge cloud as a tree hierarchy of geo-distributed servers, so as to efficiently utilize the cloud resources to serve the peak loads from mobile users. The hierarchical architecture of edge cloud enables aggregation of the peak loads across different tiers of cloud servers to maximize the amount of mobile workloads being served. To ensure efficient utilization of cloud resources, we further propose a workload placement algorithm that decides which edge cloud servers mobile programs are placed on and how much computational capacity is provisioned to execute each program. The performance of our proposed hierarchical edge cloud architecture on serving mobile workloads is evaluated by formal analysis, small-scale system experimentation, and large-scale trace-based simulations.
Computer science concept inventories: past and future

Computer science concept inventories: past and future C. Taylor, D. Zingaro, L. Porter, K.C. Webb, C.B. Lee & M. Clancy a Department of Computer Science, Oberlin College, Oberlin, OH, USA. b Department of Math and Computer Sciences, University of Toronto, ON, Canada. c Computer Science and Eng. Dept., University of California, San Diego, CA, USA. d Department of Computer Science, Swarthmore College, Swarthmore, PA, USA.  Oct 2014.
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation

We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type, our model requires only a single vector per character type and a fixed set of parameters for the compositional model. Despite the compactness of this model and, more importantly, the arbitrary nature of the form–function relationship in language, our “composed” word representations yield state-of-the-art results in language modeling and part-of-speech tagging. Benefits over traditional baselines are particularly pronounced in morphologically rich languages (e.g., Turkish).
Action Permissibility in Deep Reinforcement Learning and Application to Autonomous Driving

This paper is concerned with deep reinforcement learning (deep RL) in continuous state and action space. It proposes a new method that can drastically speed up RL training for problems that have the property of state-action permissibility (SAP). This property says that after an action at is performed in a state st and the agent reaches the new state st+1, the agent can decide whether the action at is permissible or not permissible in state st . An action is not permissible in a state if the action can never lead to an optimal solution and thus should not have been tried. We incorporate the proposed method into a state-of-the-art deep RL algorithm to guide its training and apply it to solve the lane keeping (steering control) problem in self-driving or autonomous driving. It is shown that the proposed method can help speedup RL training markedly for the lane keeping task as compared to the RL algorithm without exploiting the SAP-based guidance and other baselines that employ constrained action space exploration strategies.
Towards Distributed Machine Learning in Shared Clusters: A Dynamically-Partitioned Approach

Many cluster management systems (CMSs) have been proposed to share a single cluster with multiple distributed computing systems. However, none of the existing approaches can handle distributed machine learning (ML) workloads given the following criteria: high resource utilization, fair resource allocation and low sharing overhead. To solve this problem, we propose a new CMS named Dorm, incorporating a dynamically-partitioned cluster management mechanism and an utilization-fairness optimizer. Specifically, Dorm uses the container-based virtualization technique to partition a cluster, runs one application per partition, and can dynamically resize each partition at application runtime for resource efficiency and fairness. Each application directly launches its tasks on the assigned partition without petitioning for resources frequently, so Dorm imposes flat sharing overhead. Extensive performance evaluations showed that Dorm could simultaneously increase the resource utilization by a factor of up to 2.32, reduce the fairness loss by a factor of up to 1.52, and speed up popular distributed ML applications by a factor of up to 2.72, compared to existing approaches. Dorm's sharing overhead is less than 5% in most cases.
Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles). As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s) with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model) attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.
Navigating from hippocampus to parietal cortex.

The navigational system of the mammalian cortex comprises a number of interacting brain regions. Grid cells in the medial entorhinal cortex and place cells in the hippocampus are thought to participate in the formation of a dynamic representation of the animal's current location, and these cells are presumably critical for storing the representation in memory. To traverse the environment, animals must be able to translate coordinate information from spatial maps in the entorhinal cortex and hippocampus into body-centered representations that can be used to direct locomotion. How this is done remains an enigma. We propose that the posterior parietal cortex is critical for this transformation.
Aquaculture and stress management: a review of probiotic intervention.

To meet the ever-increasing demand for animal protein, aquaculture continuously requires new techniques to increase the production yield. However, with every step towards intensification of aquaculture practices, there is an increase in stress level on the animal as well as on the environment. Feeding practices in aqua farming usually plays an important role, and the addition of various additives to a balanced feed formula to achieve better growth is a common practice among the fish and shrimp culturists. Probiotics, also known as 'bio-friendly agents', such as LAB (Lactobacillus), yeasts and Bacillus sp., can be introduced into the culture environment to control and compete with pathogenic bacteria as well as to promote the growth of the cultured organisms. In addition, probiotics are non-pathogenic and non-toxic micro-organisms, having no undesirable side effects when administered to aquatic organisms. Probiotics are also known to play an important role in developing innate immunity among the fishes, and hence help them to fight against any pathogenic bacterias as well as against environmental stressors. The present review is a brief but informative compilation of the different essential and desirable traits of probiotics, their mode of action and their useful effects on fishes. The review also highlights the role of probiotics in helping the fishes to combat against the different physical, chemical and biological stress.
Cancer Stem Cell Theory and the Warburg Effect, Two Sides of the Same Coin?

Over the last 100 years, many studies have been performed to determine the biochemical and histopathological phenomena that mark the origin of neoplasms. At the end of the last century, the leading paradigm, which is currently well rooted, considered the origin of neoplasms to be a set of genetic and/or epigenetic mutations, stochastic and independent in a single cell, or rather, a stochastic monoclonal pattern. However, in the last 20 years, two important areas of research have underlined numerous limitations and incongruities of this pattern, the hypothesis of the so-called cancer stem cell theory and a revaluation of several alterations in metabolic networks that are typical of the neoplastic cell, the so-called Warburg effect. Even if this specific "metabolic sign" has been known for more than 85 years, only in the last few years has it been given more attention; therefore, the so-called Warburg hypothesis has been used in multiple and independent surveys. Based on an accurate analysis of a series of considerations and of biophysical thermodynamic events in the literature, we will demonstrate a homogeneous pattern of the cancer stem cell theory, of the Warburg hypothesis and of the stochastic monoclonal pattern; this pattern could contribute considerably as the first basis of the development of a new uniform theory on the origin of neoplasms. Thus, a new possible epistemological paradigm is represented; this paradigm considers the Warburg effect as a specific "metabolic sign" reflecting the stem origin of the neoplastic cell, where, in this specific metabolic order, an essential reason for the genetic instability that is intrinsic to the neoplastic cell is defined.
Learning Policies to Forecast Agent Behavior with Visual Data

Reasoning about the future is fundamental to intelligence. In this work, I consider the problem of reasoning about the future actions of an intelligent agent. We find the framework of learning sequential policies beneficial, which poses a set of important design decisions. The focus of this work is the exploration of various policy-learning design decisions, and how these design decisions affect the primary task of forecating agent futures. Throughout this work, I use demonstrations of agent behavior and often use rich visual data to drive learning. I developed forecasting approaches to excel in diverse, realistic, single-agent domains. These include sparse models to generalize from few demonstrations of human daily activity, adaptive models to continuously learn from demonstrations of human daily activity, and high-dimensional generative models learned from demonstrations of human driving behavior. I also explored incentivized forecasting, which encourages an artificial agent that only has access to partial observations of state to learn predictive state representations in order to perform a task better. While powerful and useful in these settings, our answers have only been tested in single agent domains. Yet, many realistic scenarios involve multiple agents undertaking complex behaviors: for instance, cars and people navigating and negotiating at intersections. Therefore, I propose to extend our generative framework to multiagent domains as the first direction of future work. This involves generalizing representations and inputs to multiple agents. Second, in the more difficult multiagent setting where we do not have access to expert demonstrations for one of the agents, our learning system should couple its forecasts of other agents with its own behavior. A third direction of future work is extension of our generative model to the online learning setting. Altogether, our answers will serve as a guiding extensible framework for further development of practical learning-based forecasting systems.
Low Power Wide Area Network Analysis: Can LoRa Scale?

Low power wide area (LPWA) networks are making spectacular progress from design, standardization, to commercialization. At this time of fast-paced adoption, it is of utmost importance to analyze how well these technologies will scale as the number of devices connected to the Internet of Things inevitably grows. In this letter, we provide a stochastic geometry framework for modeling the performance of a single gateway LoRa network, a leading LPWA technology. Our analysis formulates the unique peculiarities of LoRa, including its chirp spread-spectrum modulation technique, regulatory limitations on radio duty cycle, and use of ALOHA protocol on top, all of which are not as common in today’s commercial cellular networks. We show that the coverage probability drops exponentially as the number of end-devices grows due to interfering signals using the same spreading sequence. We conclude that this fundamental limiting factor is perhaps more significant toward LoRa scalability than for instance spectrum restrictions. Our derivations for co-spreading factor interference found in LoRa networks enables rigorous scalability analysis of such networks.
From image to language and back again

We consider retrieving a specific temporal segment, or moment, from a video given a natural language text description. Methods designed to retrieve whole video clips with natural language determine what occurs in a video but not when. To address this issue, we propose the Moment Context Network (MCN) which effectively localizes natural language queries in videos by integrating local and global video features over time. A key obstacle to training our MCN model is that current video datasets do not include pairs of localized video segments and referring expressions, or text descriptions which uniquely identify a corresponding moment. Therefore, we collect the Distinct Describable Moments (DiDeMo) dataset which consists of over 10,000 unedited, personal videos in diverse visual settings with pairs of localized video segments and referring expressions. We demonstrate that MCN outperforms several baseline methods and believe that our initial results together with the release of DiDeMo will inspire further research on localizing video moments with natural language.
Speech-Based Visual Question Answering

This paper introduces speech-based visual question answering (VQA), the task of generating an answer given an image and a spoken question. Two methods are studied: an end-to-end, deep neural network that directly uses audio waveforms as input versus a pipelined approach that performs ASR (Automatic Speech Recognition) on the question, followed by text-based visual question answering. Furthermore, we investigate the robustness of both methods by injecting various levels of noise into the spoken question and find both methods to be tolerate noise at similar levels.
Policy development and implementation in health promotion--from theory to practice: the ADEPT model.

There is a growing interest among health promotion researchers to better understand and influence the policy process. However, at this point in time, theoretical concepts enabling researchers to do so are still rare and underused, suggesting a need for new, easy-to-use concepts to explain successes or failures of health promotion policies. This article presents the ADEPT (Analysis of Determinants of Policy Impact) approach, which aims to explain and influence policy development and policy impact implementation with four determinants: goals, obligations, resources and opportunities. ADEPT provides a detailed operationalization for both quantitative and qualitative use. An empirical test of the ADEPT model using a quantitative survey of 719 policy-makers from four health promotion policy fields and six European nations indicated that both policy outputs and policy outcomes are influenced by the four determinants. The approach has, in the meantime, been successfully utilized to analyze and initiate policy development in a number of health promotion projects. Despite a number of limitations, ADEPT provides an easy-to-use, theory-based and parsimonious tool for understanding and influencing policy processes in health promotion. Moreover, as it identifies potential 'levers of influence' and can easily be connected to existing methods of community development or capacity building, it is a particularly powerful tool for policy development.
Model-Based Design Structure Matrix : Deriving a DSM from an Object-Process Model

We investigate potential benefits of employing Design Structure Matrix (DSM) in the context of Model-Based Systems Engineering (MBSE) for the purposes of analyzing and improving the design of a product-project ensemble. Focusing on process DSM, we present an algorithm for bidirectional transformation frame between a product-project system model and its corresponding Model-Based DSM (MDSM). Using Object-Process Methodology (OPM) as the underlying modeling language, we examine and characterize useful and insightful relationships between the system model and its MDSM. An unmanned aerial vehicle case study demonstrates the semantics of and analogy between various types of relationships as they are reflected in both the OPM system model and the MDSM derived from it. Finally, we conclude with further research direction on showing how clustering of DSM processes can be reflected back as an improvement of the OPM model.
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. Most existing AU detection works often treat face alignment as a preprocessing and handle the two tasks independently. In this paper, we propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared features are learned firstly, and highlevel features of face alignment are fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment features and global features for AU detection. Experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for AU detection.