nils-herrmann/clean_scirepeval huggingface dataset:
Mobile Edge Computing: Survey and Research Outlook

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.
Building Rome on a Cloudless Day

This paper introduces an approach for dense 3D reconstruction from unregistered Internet-scale photo collections with about 3 million images within the span of a day on a single PC (“cloudless”). Our method advances image clustering, stereo, stereo fusion and structure from motion to achieve high computational performance. We leverage geometric and appearance constraints to obtain a highly parallel implementation on modern graphics processors and multi-core architectures. This leads to two orders of magnitude higher performance on an order of magnitude larger dataset than competing state-of-the-art approaches.
Effects of hippotherapy and therapeutic horseback riding on postural control or balance in children with cerebral palsy: a meta-analysis.

This research review and meta-analysis presents an overview of the effects of hippotherapy and therapeutic horseback riding (THR) on postural control or balance in children with cerebral palsy (CP).



To synthesize previous research findings, a systematic review and meta-analysis were undertaken. Relevant studies were identified by systematic searches of multiple online databases from the inception of the database through to May 2010. Studies were included if they fulfilled the following criteria: (1) quantitative study design, (2) investigation of the effect of hippotherapy or THR on postural control or balance, and (3) the study group comprised children and adults with CP. The selected articles were rated for methodological quality. The treatment effect was coded as a dichotomous outcome (positive effect or no effect) and quantified by odds ratio (OR). The pooled treatment effect was calculated using a random-effects model. Meta-regression of the effect size was performed against study covariates, including study size, publication date, and methodological quality score.



From 77 identified studies, 10 met the inclusion criteria. Two were excluded because they did not include a comparison group. Therapy was found to be effective in 76 out of 84 children with CP included in the intervention groups. The comparison groups comprised 89 children: 50 non-disabled and 39 with CP. A positive effect was shown in 21 of the children with CP in the comparison group regardless of the activity undertaken (i.e. physiotherapy, occupational therapy, sitting on a barrel or in an artificial saddle). The pooled effect size estimate was positive (OR 25.41, 95% CI 4.35, 148.53), demonstrating a statistically significant effectiveness of hippotherapy or THR in children with CP (p<0.001). Meta-regression of study characteristics revealed no study-specific factors.



The eight studies found that postural control and balance were improved during hippotherapy and THR. Although the generalization of our findings may be restricted by the relatively small sample size, the results clearly demonstrate that riding therapy is indicated to improve postural control and balance in children with CP.
Can machine learning aid in delivering new use cases and scenarios in 5G?

5G represents the next generation of communication networks and services, and will bring a new set of use cases and scenarios. These in turn will address a new set of challenges from the network and service management perspective, such as network traffic and resource management, big data management and energy efficiency. Consequently, novel techniques and strategies are required to address these challenges in a smarter way. In this paper, we present the limitations of the current network and service management and describe in detail the challenges that 5G is expected to face from a management perspective. The main contribution of this paper is presenting a set of use cases and scenarios of 5G in which machine learning can aid in addressing their management challenges. It is expected that machine learning can provide a higher and more intelligent level of monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of the future 5G network.
Density-Based Data Pruning Method for Deep Reinforcement Learning

We present a density-based Data Pruning method for Deep Reinforcement Learning (DRL) to improve learning stability and long-term memory in rare situations. The method controls density distribution in the experience pool by discarding high correlation data and preserving rare and unique data. We apply our method to Deep Q-networks (DQN) and Deep Deterministic Policy Gradients (DDPG) for testing in discrete and continuous action space, respectively. We evaluate our method on path following tasks in a simulated physical environment. Compared to other conventional methods such as First-In-First-Out (FIFO), our method provides a significant improvement in performance and learning stability, the average cumulative reward is increased by up to 21% and the standard deviation of the cumulative reward over multiple trials is reduced by 80%. In addition, long-term memory improvement is shown as the agent can remember and perform a behavior corresponding to a past rare event.
Deep Neural Networks for High Dimension, Low Sample Size Data

Deep neural networks (DNN) have achieved breakthroughs in applications with large sample size. However, when facing high dimension, low sample size (HDLSS) data, such as the phenotype prediction problem using genetic data in bioinformatics, DNN suffers from overfitting and high-variance gradients. In this paper, we propose a DNN model tailored for the HDLSS data, named Deep Neural Pursuit (DNP). DNP selects a subset of high dimensional features for the alleviation of overfitting and takes the average over multiple dropouts to calculate gradients with low variance. As the first DNN method applied on the HDLSS data, DNP enjoys the advantages of the high nonlinearity, the robustness to high dimensionality, the capability of learning from a small number of samples, the stability in feature selection, and the end-to-end training. We demonstrate these advantages of DNP via empirical results on both synthetic and real-world biological datasets.
High-Q tunable dielectric resonator filters

Tunable dielectric resonator filters can potentially address wireless and satellite applications that require very high Q values (4,000 and up) with a limited tuning range (less than 15%). Such high Q requirements cannot be met by any other known non-superconductor tunable filter technology at the present time. The intent of this paper is to provide newcomers and end users with the current status and prospective of using dielectric resonators for tunable filters. It is an enabling technology for high-Q tunable filter applications. A key challenge, however, is to increase the tuning range without degrading the Q value. While several techniques have been reported to demonstrate the feasibility of tuning dielectric resonators, the tunable dielectric resonator filter technology is still in its infancy. Very limited research effort has been dedicated to explore the potential for improving the tuning range. Most of the work reported thus far has focused on the use of TE01delta modes and standard shape resonators demonstrating a narrow tuning range. We believe that the tuning range can be increased while maintaining reasonably high Q values by exploring the use of other modes and by the use of non-standard-shape dielectric resonators.
Flower species recognition system using convolution neural networks and transfer learning

Automatic identification and recognition of medicinal plant species in environments such as forests, mountains and dense regions is necessary to know about their existence. In recent years, plant species recognition is carried out based on the shape, geometry and texture of various plant parts such as leaves, stem, flowers etc. Flower based plant species identification systems are widely used. While modern search engines provide methods to visually search for a query image that contains a flower, it lacks in robustness because of the intra-class variation among millions of flower species around the world. Hence in this proposed research work, a Deep learning approach using Convolutional Neural Networks (CNN) is used to recognize flower species with high accuracy. Images of the plant species are acquired using the built-in camera module of a mobile phone. Feature extraction of flower images is performed using a Transfer Learning approach (i.e. extraction of complex features from a pre-trained network). A machine learning classifier such as Logistic Regression or Random Forest is used on top of it to yield a higher accuracy rate. This approach helps in minimizing the hardware requirement needed to perform the computationally intensive task of training a CNN. It is observed that, CNN combined with Transfer Learning approach as feature extractor outperforms all the handcrafted feature extraction methods such as Local Binary Pattern (LBP), Color Channel Statistics, Color Histograms, Haralick Texture, Hu Moments and Zernike Moments. CNN combined with Transfer Learning approach yields impressive Rank-1 accuracies of 73.05%, 93.41% and 90.60% using OverFeat, Inception-v3 and Xception architectures, respectively as Feature Extractors on FLOWERS102 dataset.
Online and Linear-Time Attention by Enforcing Monotonic Alignments

Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-tosequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a quadratic time complexity. Based on the insight that the alignment between input and output sequence elements is monotonic in many problems of interest, we propose an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time. We validate our approach on sentence summarization, machine translation, and online speech recognition problems and achieve results competitive with existing sequence-tosequence models.
Information quality in information fusion

Designing fusion systems for decision support in complex dynamic situations calls for an integrated human-machine information environment, in which some processes are best executed automatically while for others the judgment and guidance of human experts and end-users are critical. Thus decision making in such environment requires constant information exchange between human and automated agents that utilize operational data, data obtained from sensors, intelligence reports, and open source information. The quality of decision making strongly depends on the success of being aware of, and compensating for, insufficient information quality at each step of information exchange. Designing the methods of representing and incorporating information quality into this environment is a relatively new and a rather difficult problem. The paper discusses major challenges and suggests some approaches to address this problem.
Improved Redirection with Distractors: A large-scale-real-walking locomotion interface and its effect on navigation in virtual environments

Users in virtual environments often find navigation more difficult than in the real world. Our new locomotion interface, Improved Redirection with Distractors (IRD), enables users to walk in larger-than-tracked space VEs without predefined waypoints. We compared IRD to the current best interface, really walking, by conducting a user study measuring navigational ability. Our results show that IRD users can really walk through VEs that are larger than the tracked space and can point to targets and complete maps of VEs no worse than when really walking.
Learning to search: Functional gradient techniques for imitation learning

Programming robot behavior remains a challenging task. While it is often easy to abstractly define or even demonstrate a desired behavior, designing a controller that embodies the same behavior is difficult, time consuming, and ultimately expensive. The machine learning paradigm offers the promise of enabling “programming by demonstration” for developing high-performance robotic systems. Unfortunately, many “behavioral cloning” (Bain & Sammut, 1995; Pomerleau, 1989; LeCun et al., 2006) approaches that utilize classical tools of supervised learning (e.g. decision trees, neural networks, or support vector machines) do not fit the needs of modern robotic systems. These systems are often built atop sophisticated planning algorithms that efficiently reason far into the future; consequently, ignoring these planning algorithms in lieu of a supervised learning approach often leads to myopic and poor-quality robot performance. While planning algorithms have shown success in many real-world applications ranging from legged locomotion (Chestnutt et al., 2003) to outdoor unstructured navigation (Kelly et al., 2004; Stentz, 2009), such algorithms rely on fully specified cost functions that map sensor readings and environment models to quantifiable costs. Such cost functions are usually manually designed and programmed. Recently, a set of techniques has been developed that explore learning these functions from expert human demonstration. These algorithms apply an inverse optimal control approach to find a cost function for which planned behavior mimics an expert’s demonstration. The work we present extends the Maximum Margin Planning (MMP) (Ratliff et al., 2006a) framework to admit learning of more powerful, non-linear cost functions. These algorithms, known collectively as LEARCH (LEArning to seaRCH ), are simpler to implement than most existing methods, more efficient than previous attempts at non-linearization (Ratliff et al., 2006b), more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the function’s form. We derive and discuss the framework both mathematically and intuitively, and demonstrate practical realworld performance with three applied case-studies including legged locomotion, grasp planning, and autonomous outdoor unstructured navigation. The latter study includes hundreds of kilometers of autonomous traversal through complex natural environments. These case-studies address key challenges in applying the algorithm in practical settings that utilize state-of-the-art planners, and which may be constrained by efficiency requirements and imperfect expert demonstration.
Recurrent Neural Aligner: An Encoder-Decoder Neural Network Model for Sequence to Sequence Mapping

We introduce an encoder-decoder recurrent neural network model called Recurrent Neural Aligner (RNA) that can be used for sequence to sequence mapping tasks. Like connectionist temporal classification (CTC) models, RNA defines a probability distribution over target label sequences including blank labels corresponding to each time step in input. The probability of a label sequence is calculated by marginalizing over all possible blank label positions. Unlike CTC, RNA does not make a conditional independence assumption for label predictions; it uses the predicted label at time t−1 as an additional input to the recurrent model when predicting the label at time t. We apply this model to end-to-end speech recognition. RNA is capable of streaming recognition since the decoder does not employ attention mechanism. The model is trained on transcribed acoustic data to predict graphemes and no external language and pronunciation models are used for decoding. We employ an approximate dynamic programming method to optimize negative log likelihood, and a sampling-based sequence discriminative training technique to fine-tune the model to minimize expected word error rate. We show that the model achieves competitive accuracy without using an external language model nor doing beam search decoding.
Consumers ’ attitude towards online shopping : Factors influencing employees of crazy domains to shop online

E-commerce offers many online marketing opportunities to companies worldwide and along with high rapid growth of online shopping; it has impressed many retailers to sell products and services through online channel to expand their market. Online shopping or marketing is the use of technology (i.e., computer, internet) for better marketing performance. And retailers are mixing strategies to meet the demand of online shoppers; they are busy in studying consumer in the field of online shopping, to see the consumer attitudes towards online shopping and specifically studying the factors influencing consumers to shop online. In this study, the multiple regression analysis was employed to measure the relationship between 9 independent variables and receptivity to online shopping. The score of Beta weight presented that all 9 independent variables had positive statistical significant effect to Internet users to accept online shopping. Among the 9 factors, the strongest influencers from highest to lowest were Price, Refund, Convenience, Auction websites, Security, Brand, Search engines, Promotion and Online shopping malls. According to independent t-test analysis for gender, there was significant different means between males and females for online shopping malls and Auctions websites factors to receptivity on online shopping. The means of female significant higher than male for these two factors. This study might contribute not only to a better understanding on what and how strongly the factors are involved in online consumer purchasing decisions but also this study provides e-retailer’s standpoint such the effectively manage and recommendations. However, eretailers should keep in mind that consumer behavior might change in time to time especially in online market so the e-retailer should investigate the consumer behavior in time to time and adapt the products and services to serve as the customer requirements.
Autonomous Object Manipulation Using a Soft Planar Grasping Manipulator

This article presents the development of an autonomous motion planning algorithm for a soft planar grasping manipulator capable of grasp-and-place operations by encapsulation with uncertainty in the position and shape of the object. The end effector of the soft manipulator is fabricated in one piece without weakening seams using lost-wax casting instead of the commonly used multilayer lamination process. The soft manipulation system can grasp randomly positioned objects within its reachable envelope and move them to a desired location without human intervention. The autonomous planning system leverages the compliance and continuum bending of the soft grasping manipulator to achieve repeatable grasps in the presence of uncertainty. A suite of experiments is presented that demonstrates the system's capabilities.
Data Warehouse Life-Cycle and Design

The term data warehouse life-cycle is used to indicate the phases (and their relationships) a data warehouse system goes through between when it is conceived and when it is no longer available for use. Apart from the type of software, life-cycles typically include the following phases: requirements analysis, design (including modeling), construction, testing, deployment, operation, maintenance and retirement. On the other hand, different life-cycles differ in the relevance and priority with which the phases are carried out, that can vary according to the implementation constraints (i.e. economic constraints, time constraints, etc.) and the software specificities and complexity; in particular, the specificities in the data warehouse life cycle derive from the presence of the operational database that feeds the system and by the extent of this kind of system that must be considered in order to keep under control the cost and the complexity of the project. Although the design phase is only a step within the overall life-cycle, the identification of a proper lifecycle model and the adoption of a correct design methodology are strictly related since each one influences the other.
Field classification, modeling and anomaly detection in unknown CAN bus networks

This paper describes a novel domain-aware anomaly detection system for in-car CAN bus network traffic. Through inspection of real CAN bus communication, we were able to split the messages into fields and identify the field types, without any prior knowledge of the message formats. We discovered the presence of Constant fields, Multi-Value fields and Counter or Sensor fields. Next we developed a classifier that automatically identifies the boundaries and types of these fields. In its learning phase, our anomaly detection system uses the classifier to characterize the fields and build a model for the messages, based on their field types. The model is based on Ternary Content-Addressable Memory (TCAM), that can run efficiently in either software or hardware. During the enforcement phase our system detects deviations from the model. We evaluated our system on simulated CAN bus traffic, and achieved very encouraging results: a median false positive rate of 1% with a median of only 89.5 TCAMs.
CEET: A Compressed Encrypted & Embedded Technique for Digital Image Steganography

In this information era, digital information sharing and transfer plays a vital role and their use has increased exponentially with the development of technology. Thus providing security of data is a topic of concern. Data hiding is a powerful tool that provides mechanism for securing data over insecure channel by concealing information within information. Steganography inherits data hiding concept and passes information through host files in such a way that the existence of the embedded secret is unknown. This paper presents a joint application of compression, encryption and embedding techniques for implementing digital image steganography. Compression technique generates noise in the image. Inorder to retain the noise distortion to a minimum level, LSB insertion method is used for embedding purpose where the bits are inserted at the last 2 LSB’s of the image. In this proposed technique the secret information is initially compressed and then the resultant bits are encrypted. Finally these encrypted bits are embedded into an image. The main objective is to develop an application that increases the steganographic capacity and enhances the stego image quality while keeping the security intact.