nils-herrmann/clean_scirepeval huggingface dataset:
IoT based smart irrigation monitoring and controlling system

Interconnection of number of devices through internet describes the Internet of things (IoT). Every object is connected with each other through unique identifier so that data can be transferred without human to human interaction. It allows establishing solutions for better management of natural resources. The smart objects embedded with sensors enables interaction with the physical and logical worlds according to the concept of IoT. In this paper proposed system is based on IoT that uses real time input data. Smart farm irrigation system uses android phone for remote monitoring and controlling of drips through wireless sensor network. Zigbee is used for communication between sensor nodes and base station. Real time sensed data handling and demonstration on the server is accomplished using web based java graphical user interface. Wireless monitoring of field irrigation system reduces human intervention and allows remote monitoring and controlling on android phone. Cloud Computing is an attractive solution to the large amount of data generated by the wireless sensor network. This paper proposes and evaluates a cloud-based wireless communication system to monitor and control a set of sensors and actuators to assess the plants water need.
Deep De-Aliasing for Fast Compressive Sensing MRI

Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience. This can also potentially increase the image quality by reducing the motion artefacts and contrast washout. However, once an image field of view and the desired resolution are chosen, the minimum scanning time is normally determined by the requirement of acquiring sufficient raw data to meet the Nyquist–Shannon sampling criteria. Compressive Sensing (CS) theory has been perfectly matched to the MRI scanning sequence design with much less required raw data for the image reconstruction. Inspired by recent advances in deep learning for solving various inverse problems, we propose a conditional Generative Adversarial Networks-based deep learning framework for de-aliasing and reconstructing MRI images from highly undersampled data with great promise to accelerate the data acquisition process. By coupling an innovative content loss with the adversarial loss our de-aliasing results are more realistic. Furthermore, we propose a refinement learning procedure for training the generator network, which can stabilise the training with fast convergence and less parameter tuning. We demonstrate that the proposed framework outperforms state-of-the-art CS-MRI methods, in terms of reconstruction error and perceptual image quality. In addition, our method can reconstruct each image in 0.22ms–0.37ms, which is promising for real-time applications.
SICK through the SemEval glasses. Lesson learned from the evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment

Training and testing data were subsets of the SICK (Sentences Involving Compositional Knowledge) data set. SICK was developed with the aim of providing a proper benchmark to evaluate compositional semantic systems, though task participation was open to systems based on any approach. Taking advantage of the SemEval experience, in this paper we analyze the SICK data set, in order to evaluate the extent to which it meets its design goal and to shed light on the linguistic phenomena that are still challenging for state-of-the-art computational semantic systems. Qualitative and quantitative error analyses show that many systems are quite sensitive to changes in the proportion of sentence pair types, and degrade in the presence of additional lexico-syntactic complexities which do not affect human judgements. More compositional systems seem to perform better when the task proportions are changed, but the effect needs further confirmation.
DeepSearch: A Fast Image Search Framework for Mobile Devices

Content-based image retrieval (CBIR) is one of the most important applications of computer vision. In recent years, there have been many important advances in the development of CBIR systems, especially Convolutional Neural Networks (CNNs) and other deep-learning techniques. On the other hand, current CNN-based CBIR systems suffer from high computational complexity of CNNs. This problem becomes more severe as mobile applications become more and more popular. The current practice is to deploy the entire CBIR systems on the server side while the client side only serves as an image provider. This architecture can increase the computational burden on the server side, which needs to process thousands of requests per second. Moreover, sending images have the potential of personal information leakage. As the need of mobile search expands, concerns about privacy are growing. In this article, we propose a fast image search framework, named DeepSearch, which makes complex image search based on CNNs feasible on mobile phones. To implement the huge computation of CNN models, we present a tensor Block Term Decomposition (BTD) approach as well as a nonlinear response reconstruction method to accelerate the CNNs involving in object detection and feature extraction. The extensive experiments on the ImageNet dataset and Alibaba Large-scale Image Search Challenge dataset show that the proposed accelerating approach BTD can significantly speed up the CNN models and further makes CNN-based image search practical on common smart phones.
A fully pipelined hardware architecture for convolutional neural network with low memory usage and DRAM bandwidth

As a typical deep learning model, Convolutional Neural Network (CNN) has shown excellent ability in solving complex classification problems. To apply CNN models in mobile ends and wearable devices, a fully pipelined hardware architecture adopting a Row Processing Tree (RPT) structure with small memory resource consumption between convolutional layers is proposed. A modified Row Stationary (RS) dataflow is implemented to evaluate the RPT architecture. Under the the same work frequency requirement for these two architectures, the experimental results show that the RPT architecture reduces 91% on-chip memory and 75% DRAM bandwidth compared with the modified RS dataflow, but the throughput of the modified RS dataflow is 3 times higher than the our proposed RPT architecture. The RPT architecture can achieve 121fps at 100MHZ while processing a CNN including 4 convolutional layers.
Interpretable Intuitive Physics Model

Humans have a remarkable ability to use physical commonsense and predict the effect of collisions. But do they understand the underlying factors? Can they predict if the underlying factors have changed? Interestingly, in most cases humans can predict the effects of similar collisions with different conditions such as changes in mass, friction, etc. It is postulated this is primarily because we learn to model physics with meaningful latent variables. This does not imply we can estimate the precise values of these meaningful variables (estimate exact values of mass or friction). Inspired by this observation, we propose an interpretable intuitive physics model where specific dimensions in the bottleneck layers correspond to different physical properties. In order to demonstrate that our system models these underlying physical properties, we train our model on collisions of different shapes (cube, cone, cylinder, spheres etc.) and test on collisions of unseen combinations of shapes. Furthermore, we demonstrate our model generalizes well even when similar scenes are simulated with different underlying properties.
Using Images to Improve Machine-Translating E-Commerce Product Listings

In this paper we study the impact of using images to machine-translate user-generated ecommerce product listings. We study how a multi-modal Neural Machine Translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attentional NMT and a Statistical Machine Translation (SMT) model. User-generated product listings often do not constitute grammatical or well-formed sentences. More often than not, they consist of the juxtaposition of short phrases or keywords. We train our models end-to-end as well as use text-only and multimodal NMT models for re-ranking n-best lists generated by an SMT model. We qualitatively evaluate our user-generated training data also analyse how adding synthetic data impacts the results. We evaluate our models quantitatively using BLEU and TER and find that (i) additional synthetic data has a general positive impact on text-only and multi-modal NMT models, and that (ii) using a multi-modal NMT model for re-ranking n-best lists improves TER significantly across different n-
Evolutionary game theory and multi-agent reinforcement learning

In this paper we survey the basics of Reinforcement Learning and (Evolutionary) Game Theory, applied to the field of Multi-Agent Systems. This paper contains three parts. We start with an overview on the fundamentals of Reinforcement Learning. Next we summarize the most important aspects of Evolutionary Game Theory. Finally, we discuss the state-of-the-art of Multi-Agent Reinforcement Learning and the mathematical connection with Evolutionary Game Theory.
Heavy metal removal from wastewater using various adsorbents : a review

Heavy metals are discharged into water from various industries. They can be toxic or carcinogenic in nature and can cause severe problems for humans and aquatic ecosystems. Thus, the removal of heavy metals fromwastewater is a serious problem. The adsorption process is widely used for the removal of heavy metals from wastewater because of its low cost, availability and eco-friendly nature. Both commercial adsorbents and bioadsorbents are used for the removal of heavy metals fromwastewater, with high removal capacity. This review article aims to compile scattered information on the different adsorbents that are used for heavy metal removal and to provide information on the commercially available and natural bioadsorbents used for removal of chromium, cadmium and copper, in particular.
Intelligent Vehicle-Trust Point: Reward based Intelligent Vehicle Communication using Blockchain

The Intelligent vehicle (IV) is experiencing revolutionary growth in research and industry, but it still suffers from many security vulnerabilities. Traditional security methods are incapable to provide secure IV communication. The major issues in IV communication, are trust, data accuracy and reliability of communication data in the communication channel. Blockchain technology works for the crypto currency, Bit-coin, which is recently used to build trust and reliability in peer-topeer networks having similar topologies as IV Communication. In this paper, we are proposing, Intelligent Vehicle-Trust Point (IV-TP) mechanism for IV communication among IVs using Blockchain technology. The IVs communicated data provides security and reliability using our proposed IV-TP. Our IV-TP mechanism provides trustworthiness for vehicles behavior, and vehicles legal and illegal action. Our proposal presents a reward based system, an exchange of some IV-TP among IVs, during successful communication. For the data management of the IVTP, we are using blockchain technology in the intelligent transportation system (ITS), which stores all IV-TP details of every vehicle and is accessed ubiquitously by IVs. In this paper, we evaluate our proposal with the help of intersection use case scenario for intelligent vehicles communication.  Blockchain, intelligent vehicles, security, component; ITS
Broad Learning based Social Community Detection

In the real-world online social networks, users tend to form different social communities. Due to its extensive applications, community detection in online social networks has been a hot research topic in recent years. In this chapter, we will focus on introducing the social community detection problem in online social networks. To be more specific, we will take the hard community detection problem as an example to introduce the existing models proposed for conventional (one single) homogeneous social network, and the recent broad learning based (multiple aligned) heterogeneous social networks respectively.  Community Detection; Social Media; Aligned Heterogeneous Networks; Broad Learning
Deep learning for network analysis: Problems, approaches and challenges

The analysis of social, communication and information networks for identifying patterns, evolutionary characteristics and anomalies is a key problem for the military, for instance in the Intelligence community. Current techniques do not have the ability to discern unusual features or patterns that are not a priori known. We investigate the use of deep learning for network analysis. Over the last few years, deep learning has had unprecedented success in areas such as image classification, speech recognition, etc. However, research on the use of deep learning to network or graph analysis is limited. We present three preliminary techniques that we have developed as part of the ARL Network Science CTA program: (a) unsupervised classification using a very highly trained image recognizer, namely Caffe; (b) supervised classification using a variant of convolutional neural networks on node features such as degree and assortativity; and (c) a framework called node2vec for learning representations of nodes in a network using a mapping to natural language processing.
Network of Information (NetInf) - An information-centric networking architecture

0140-3664/$ see front matter 2013  The research leading to these results has been conducted in the SAIL project and received funding from the European Community’s Seventh Framework Program (2013) under Grant Agreement No. 257448. ⇑ Corresponding author.  +49 5251 60 5385; fax: +49 5251 60 5377. E-mail addresses: cdannewitz@upb.de (C. Dannewitz), Dirk.Kutscher@neclab.eu (D. Kutscher), Borje.Ohlman@ericsson.com (B. Ohlman), stephen.farrell@cs.tcd.ie (S. Farrell), bengta@sics.se (B. Ahlgren), hkarl@upb.de (H. Karl). 1 <http://www.cisco.com/web/solutions/sp/vni/vni_mobile_forecast_highlights/ index.html>. Christian Dannewitz , Dirk Kutscher b,⇑, Börje Ohlman , Stephen Farrell , Bengt Ahlgren , Holger Karl a
Automatic foveation for video compression using a neurobiological model of visual attention

We evaluate the applicability of a biologically-motivated algorithm to select visually-salient regions of interest in video streams for multiply-foveated video compression. Regions are selected based on a nonlinear integration of low-level visual cues, mimicking processing in primate occipital, and posterior parietal cortex. A dynamic foveation filter then blurs every frame, increasingly with distance from salient locations. Sixty-three variants of the algorithm (varying number and shape of virtual foveas, maximum blur, and saliency competition) are evaluated against an outdoor video scene, using MPEG-1 and constant-quality MPEG-4 (DivX) encoding. Additional compression radios of 1.1 to 8.5 are achieved by foveation. Two variants of the algorithm are validated against eye fixations recorded from four to six human observers on a heterogeneous collection of 50 video clips (over 45 000 frames in total). Significantly higher overlap than expected by chance is found between human and algorithmic foveations. With both variants, foveated clips are, on average, approximately half the size of unfoveated clips, for both MPEG-1 and MPEG-4. These results suggest a general-purpose usefulness of the algorithm in improving compression ratios of unconstrained video.
Reconstituting knowledge management

The purpose of the paper is to improve traditional knowledge management models in light of complexity theory, emphasizing the importance of moving away from hierarchical relationships among data, information, knowledge, and wisdom.  Traditional definitions and models are critically reviewed and their weaknesses highlighted. A transformational perspective of the traditional hierarchies is proposed to highlight the need to develop better perspectives. The paper demonstrates the holistic nature of data, information, knowledge, and wisdom, and how they are all based on an interpretation of existence.  Existing models are logically extended, by adopting a complexity-based perspective, to propose a new model – the E2E model – which highlights the non-linear relationships among existence, data, information, knowledge, wisdom, and enlightenment, as well as the nature of understanding as the process that defines the differences among these constructs. The meaning of metas (such as meta-data, meta-information, and meta-knowledge) is discussed, and a reconstitution of knowledge management is proposed.  The importance of understanding as a concept to create useful metaphors for knowledge management practitioners is emphasized, and the crucial importance of the metas for knowledge management is shown.  A new model of the cognitive system of knowledge is proposed, based on application of complexity theory to knowledge management. Understanding is identified as the basis of the conversion process among an extended range of knowledge constructs, and the scope of knowledge management is redefined.
SwapNet: Image Based Garment Transfer

We present Swapnet, a framework to transfer garments across images of people with arbitrary body pose, shape, and clothing. Garment transfer is a challenging task that requires (i) disentangling the features of the clothing from the body pose and shape and (ii) realistic synthesis of the garment texture on the new body. We present a neural network architecture that tackles these sub-problems with two task-specific sub-networks. Since acquiring pairs of images showing the same clothing on different bodies is difficult, we propose a novel weaklysupervised approach that generates training pairs from a single image via data augmentation. We present the first fully automatic method for garment transfer in unconstrained images without solving the difficult 3D reconstruction problem. We demonstrate a variety of transfer results and highlight our advantages over traditional image-to-image and anal-
Understanding Participation in Sharing Economy: the Roles of Convenience, Risk, and regulatory FOC I

In recent years, sharing economy has been growing rapidly. Meanwhile, understanding why people participate in sharing economy emerges as a rising concern. Given that research on sharing economy is scarce in the information systems literature, this paper aims to enrich the theoretical development in this area by testing different dimensions of convenience and risk that may influence people’s participation intention in sharing economy. We will also examine the moderate effects of two regulatory foci (i.e., promotion focus and prevention focus) on participation intention. The model will be tested with data of Uber users. Results of the study will help researchers and practitioners better understand people’s behavior in sharing economy.
Gesture's role in speaking, learning, and creating language.

When speakers talk, they gesture. The goal of this review is to investigate the contribution that these gestures make to how we communicate and think. Gesture can play a role in communication and thought at many timespans. We explore, in turn, gesture's contribution to how language is produced and understood in the moment; its contribution to how we learn language and other cognitive skills; and its contribution to how language is created over generations, over childhood, and on the spot. We find that the gestures speakers produce when they talk are integral to communication and can be harnessed in a number of ways. (a) Gesture reflects speakers' thoughts, often their unspoken thoughts, and thus can serve as a window onto cognition. Encouraging speakers to gesture can thus provide another route for teachers, clinicians, interviewers, etc., to better understand their communication partners. (b) Gesture can change speakers' thoughts. Encouraging gesture thus has the potential to change how students, patients, witnesses, etc., think about a problem and, as a result, alter the course of learning, therapy, or an interchange.  By watching how children and adults who do not already have a language put those blocks together, we can observe the process of language creation. Our hands are with us at all times and thus provide researchers and learners with an ever-present tool for understanding how we talk and think.
Detecting Pneumonia in Chest X-Rays with Supervised Learning

Physicians often use chest X-rays to quickly and cheaply diagnose disease associated with the area. However, it is much more difficult to make clinical diagnoses with chest X-rays than with other imaging modalities such as CT or MRI. With computer-aided diagnosis, physicians can make chest X-ray diagnoses more quickly and accurately. Pneumonia is often diagnosed with chest X-Rays and kills around 50,000 people each year . With computeraided diagnosis of pneumonia specifically, physicians can more accurately and efficiently diagnose the disease. In this project, we hope to train a model using the dataset described below to help physicians in making diagnoses of pneumonia in chest X-Rays. Our problem is thus a binary classification where the inputs are chest X-ray images and the output is one of two classes: pneumonia or non-pneumonia.
Fuzzy Logic in Control Systems : Fuzzy Logic

During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of fuzzy set theory, especially in the realm of industrial processes, which do not lend themselves to control by conventional methods because of a lack of quantitative data regarding the input-output relations. Fuzzy control is based on fuzzy logic-a logical system that is much closer in spirit to human thinking and natural language than traditional logical systems. The fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy. A survey of the FLC is presented ; a general methodology for constructing an FLC and assessing its performance is described; and problems that need further research are pointed out. In particular, the exposition includes a discussion of fuzzification and defuzzification strategies, the derivation of the database and fuzzy control rules, the definition of fuzzy implication, and an analysis of fuzzy reasoning mechanisms. A may be regarded as a means of emulating a skilled human operator. More generally, the use of an FLC may be viewed as still another step in the direction of model-ing human decisionmaking within the conceptual framework of fuzzy logic and approximate reasoning. In this context, the forward data-driven inference (generalized modus ponens) plays an especially important role. In what follows, we shall investigate fuzzy implication functions, the sentence connectives and and also, compositional operators, inference mechanisms, and other concepts that are closely related to the decisionmaking logic of an FLC. In general, a fuzzy control rule is a fuzzy relation which is expressed as a fuzzy implication. In fuzzy logic, there are many ways in which a fuzzy implication may be defined. The definition of a fuzzy implication may be expressed as a fuzzy implication function. The choice of a fuzzy implication function reflects not only the intuitive criteria for implication but also the effect of connective also. I) Basic Properties of a Fuuy Implication Function: The choice of a fuzzy implication function involves a number of criteria, which are discussed in considered the following basic characteristics of a fuzzy implication function: fundamental property, smoothness property, unrestricted inference, symmetry of generalized modus ponens and generalized modus tollens, and a measure of propagation of fuzziness. All of these properties are justified on purely intuitive grounds. We prefer to say …