nils-herrmann/clean_scirepeval huggingface dataset:
Solid-State Color Night Vision: Fusion of Low-Light Visible and Thermal Infrared Imagery

■ We describe an apparatus and methodology to support real-time color imaging for night operations. Registered imagery obtained in the visible through nearinfrared band is combined with thermal infrared imagery by using principles of biological opponent-color vision. Visible imagery is obtained with a Gen III image intensifier tube fiber-optically coupled to a conventional charge-coupled device (CCD), and thermal infrared imagery is obtained by using an uncooled thermal imaging array. The two fields of view are matched and imaged through a dichroic beam splitter to produce realistic color renderings of a variety of night scenes. We also demonstrate grayscale and color fusion of intensified-CCD/FLIR imagery. Progress in the development of a low-light-sensitive visible CCD imager with high resolution and wide intrascene dynamic range, operating at thirty frames per second, is described. Example low-light CCD imagery obtained under controlled illumination conditions, from full moon down to overcast starlight, processed by our adaptive dynamic-range algorithm, is shown. The combination of a low-light visible CCD imager and a thermal infrared microbolometer array in a single dualband imager, with a portable image-processing computer implementing our neuralnet algorithms, and color liquid-crystal display, yields a compact integrated version of our system as a solid-state color night-vision device. The systems described here can be applied to a large variety of military operations and civilian needs.
Deep Feature Flow for Video Recognition

Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable. We present deep feature flow, a fast and accurate framework for video recognition. It runs the expensive convolutional sub-network only on sparse key frames and propagates their deep feature maps to other frames via a flow field. It achieves significant speedup as flow computation is relatively fast. The end-to-end training of the whole architecture significantly boosts the recognition accuracy. Deep feature flow is flexible and general. It is validated on two recent large scale video datasets. It makes a large step towards practical video recognition. Code would be released.
Deep Learning in Semantic Kernel Spaces

Kernel methods enable the direct usage of structured representations of textual data during language learning and inference tasks. Expressive kernels, such as Tree Kernels, achieve excellent performance in NLP. On the other side, deep neural networks have been demonstrated effective in automatically learning feature representations during training. However, their input is tensor data, i.e., they cannot manage rich structured information. In this paper, we show that expressive kernels and deep neural networks can be combined in a common framework in order to (i) explicitly model structured information and (ii) learn non-linear decision functions. We show that the input layer of a deep architecture can be pre-trained through the application of the Nyström low-rank approximation of kernel spaces. The resulting “kernelized” neural network achieves state-of-the-art accuracy in three different tasks.
Hate Speech Detection Using Natural Language Processing Techniques

Hate speech is currently of broad and current interest in the domain of social media. The anonymity and flexibility afforded by the Internet has made it easy for users to communicate in an aggressive manner. And as the amount of online hate speech is increasing, methods that automatically detect hate speech is very much required. Moreover, these problems have also been attracting the Natural Language Processing and Machine Learning communities a lot. Therefore, the goal of this paper is to look at how Natural Language Processing applies in detecting hate speech. Furthermore, this paper also applies a current technique in this field on a dataset. As neural network approaches outperforms existing methods for text classification problems, a deep learning model has been introduced, namely the Convolutional Neural Network. This classifier assigns each tweet to one of the categories of a Twitter dataset: hate, offensive language, and neither. The performance of this model has been tested using the accuracy, as well as looking at the precision, recall and F-score. The final model resulted in an accuracy of 91%, precision of 91%, recall of 90% and a F-measure of 90%. However, when looking at each class separately, it should be noted that a lot of hate tweets have been misclassified. Therefore, it is recommended to further analyze the predictions and errors, such that more insight is gained on the misclassification.
Predicting Information Spreading in Twitter

We present a new methodology for predicting the spread of inf rmation in a social network. We focus on the Twitter network, where informa tion is in the form of 140 character messages called tweets, and information is spread by users forwarding tweets, a practice known as retweeting. Using data of who and what was retweeted, we train a probabilistic collaborative filte r model to predict future retweets. We find that the most important features for predic tion are the identity of the source of the tweet and retweeter. Our methodology is q uite flexible and be used as a basis for other prediction models in social network s.
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling

Street scene understanding is an essential task for autonomous driving. One important step toward this direction is scene labeling, which annotates each pixel in the images with a correct class label. Although many approaches have been developed, there are still some weak points. First, many methods are based on the hand-crafted features whose image representation ability is limited. Second, they cannot label foreground objects accurately due to the data set bias. Third, in the refinement stage, the traditional Markov random filed inference is prone to over smoothness. For improving the above problems, this paper proposes a joint method of priori convolutional neural networks at superpixel level (called as “priori s-CNNs”) and soft restricted context transfer. Our contributions are threefold: 1) a priori s-CNNs model that learns priori location information at superpixel level is proposed to describe various objects discriminatingly; 2) a hierarchical data augmentation method is presented to alleviate data set bias in the priori s-CNNs training stage, which improves foreground objects labeling significantly; and 3) a soft restricted MRF energy function is defined to improve the priori s-CNNs model’s labeling performance and reduce the over smoothness at the same time. The proposed approach is verified on CamVid data set (11 classes) and SIFT Flow Street data set (16 classes) and achieves a competitive performance.
Efficient Progressive Neural Architecture Search

This paper addresses the difficult problem of finding an optimal neural architecture design for a given image classification task. We propose a method that aggregates two main results of the previous state-of-the-art in neural architecture search. These are, appealing to the strong sampling efficiency of a search scheme based on sequential modelbased optimization (SMBO) , and increasing training efficiency by sharing weights among sampled architectures [18]. Sequential search has previously demonstrated its capabilities to find state-of-the-art neural architectures for image classification. However, its computational cost remains high, even unreachable under modest computational settings. Affording SMBO with weight-sharing alleviates this problem. On the other hand, progressive search with SMBO is inherently greedy, as it leverages a learned surrogate function to predict the validation error of neural architectures. This prediction is directly used to rank the sampled neural architectures. We propose to attenuate the greediness of the original SMBO method by relaxing the role of the surrogate function so it predicts architecture sampling probability instead. We demonstrate with experiments on the CIFAR-10 dataset that our method, denominated Efficient progressive neural architecture search (EPNAS), leads to increased search efficiency, while retaining competitiveness of found architectures.
A novel JXTA-based architecture for implementing heterogenous Networks of Things

This paper presents EmbJXTAChord, a novel peer-to-peer (P2P) architecture that integrates the good features of different sources, such as JXTA, EXI, CoAP, combining and augmenting them to provide a framework that is specifically devised for developing IoT applications over heterogeneous networks. EmbJXTAChord provides for several interesting properties, such as, distributed and fault-tolerant resource discovery, transparent routing over subnetworks, application protocol independence from the transport protocol in narrowband Wireless Sensor Networks, thus eliminating the need for using dedicated software or configuring custom gateways to achieve these functionalities. Moreover, EmbJXTAChord offers native support not only for TCP/HTTP, but also for Bluetooth RFCOMM and 6LoWPAN, thus opening to a broad range of IoT devices in supernetworks composed of networks using different interconnection technologies, not necessarily IP-based. In addition, EmbJXTAChord offers security over heterogeneous networks providing support for secure peergroups (even nested) and for group encryption, thus allowing for unicast and multicast communication between groups of objects sharing the same resources. The users of the proposed architecture will benefit from an integrated solution and the applications developed on the proposed framework will be able to reconfigure themselves, adapting automatically to the network topology of the execution environment. Finally, EmbJXTAChord provides jxCOAP-E, a new CoAP implementation that leverages on the transport mechanisms for heterogeneous networks offered by EmbJXTAChord. jxCOAP-E enables to realize a RESTful service architecture for peer-topeer narrowband or broadband networks composed of devices connected via Ethernet, Wi-Fi, Bluetooth, BLE or IEEE 802.15.4. Differently from CoAP, jxCOAP-E provides a distributed and fault-tolerant service discovery mechanism and support for secure multicast communications. The paper presents EmbJXTAChord, discusses all the relevant design challenges and presents a comparative experimental performance assessment with state-of-the-art solutions on commercial-off-the-shelf devices.  The formal version of the work is available at https://doi.org/10.1016/j.comcom.2017.11.002  2017 ar X iv :1 71 1. 08 21 8v 1 [ cs .N I] 2 2 N ov 2 01 7
A Multimodal Deep Learning Network for Group Activity Recognition

Several studies focused on single human activity recognition, while the classification of group activities is still under-investigated. In this paper, we present an approach for classifying the activity performed by a group of people during daily life tasks at work. We address the problem in a hierarchical way by first examining individual person actions, reconstructed from data coming from wearable and ambient sensors. We then observe if common temporal/spatial dynamics exist at the level of group activity. We deployed a Multimodal Deep Learning Network, where the term multimodal is not intended to separately elaborate the considered different input modalities, but refers to the possibility of extracting activity-related features for each group member, and then merge them through shared levels. We evaluated the proposed approach in a laboratory environment, where the employees are monitored during their normal activities. The experimental results demonstrate the effectiveness of the proposed model with respect to an SVM benchmark.
Cloud Computing : Overview & Current Research Challenges

Cloud computing is a set of IT services that are provided to a customer over a network on a leased basis and with the ability to scale up or down their service requirements. Usually Cloud Computing services are delivered by a third party provider who owns the infrastructure.Cloud Computing holds the potential to eliminate the requirements for setting up of high-cost computing infrastructure for IT-based solutions and services that theindustry uses. It promises to provide a flexible IT architecture, accessible through internet from lightweight portable devices.This would allow multi-fold increase in the capacity and capabilities of the existing and new software.This new economic model for computing has found fertile ground and is attracting massive global investment. Many industries, such as banking, healthcare and education are moving towards the cloud due to the efficiency of services provided by the pay-per-use pattern based on the resources such as processing power used, transactions carried out, bandwidth consumed, data transferred, or storage space occupied etc.In a cloud computing environment, the entire data resides over a set of networked resources, enabling the data to be accessed through virtual machines. Despite the potential gains achieved from the cloud computing, the organizations are slow in accepting it due to security issues and challenges associated with it. Security is one of the major issues which hamper the growth of cloud. There are various research challenges also there for adopting cloud computing such as well managed service level agreement (SLA), privacy, interoperability and reliability.This research paper presents what cloud computing is, the various cloud models and the overview of the cloud computing architecture. This research paper also analyzes the key research challenges present in cloud computing and offers best practices to service providers as well as enterprises hoping to leverage cloud service to improve their bottom line in this severe economic climate.
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train such a deep network with limited training data for brain segmentation, we seamlessly integrate multi-modality and multi-level contextual information into our network, so that the complementary information of different modalities can be harnessed and features of different scales can be exploited. Furthermore, an auto-context version of the VoxResNet is proposed by combining the low-level image appearance features, implicit shape information, and high-level context together for further improving the segmentation performance. Extensive experiments on the well-known benchmark (i.e., MRBrainS) of brain segmentation from 3D magnetic resonance (MR) images corroborated the efficacy of the proposed VoxResNet. Our method achieved the first place in the challenge out of 37 competitors including several state-of-the-art brain segmentation methods. Our method is inherently general and can be readily applied as a powerful tool to many brain-related studies, where accurate segmentation of brain structures is critical.
Mood state effects of chocolate.

Chocolate consumption has long been associated with enjoyment and pleasure. Popular claims confer on chocolate the properties of being a stimulant, relaxant, euphoriant, aphrodisiac, tonic and antidepressant. The last claim stimulated this review.



We review chocolate's properties and the principal hypotheses addressing its claimed mood altering propensities. We distinguish between food craving and emotional eating, consider their psycho-physiological underpinnings, and examine the likely 'positioning' of any effect of chocolate to each concept.



Chocolate can provide its own hedonistic reward by satisfying cravings but, when consumed as a comfort eating or emotional eating strategy, is more likely to be associated with prolongation rather than cessation of a dysphoric mood.



This review focuses primarily on clarifying the possibility that, for some people, chocolate consumption may act as an antidepressant self-medication strategy and the processes by which this may occur.



Any mood benefits of chocolate consumption are ephemeral.
A Random Walk Model for Item Recommendation in Social Tagging Systems

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users’ preferences on an item similarity graph and spreading items’ influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation.
Zero-Shot Learning on Semantic Class Prototype Graph

Zero-Shot Learning (ZSL) for visual recognition is typically achieved by exploiting a semantic embedding space. In such a space, both seen and unseen class labels as well as image features can be embedded so that the similarity among them can be measured directly. In this work, we consider that the key to effective ZSL is to compute an optimal distance metric in the semantic embedding space. Existing ZSL works employ either euclidean or cosine distances. However, in a high-dimensional space where the projected class labels (prototypes) are sparse, these distances are suboptimal, resulting in a number of problems including hubness and domain shift. To overcome these problems, a novel manifold distance computed on a semantic class prototype graph is proposed which takes into account the rich intrinsic semantic structure, i.e., semantic manifold, of the class prototype distribution. To further alleviate the domain shift problem, a new regularisation term is introduced into a ranking loss based embedding model. Specifically, the ranking loss objective is regularised by unseen class prototypes to prevent the projected object features from being biased towards the seen prototypes. Extensive experiments on four benchmarks show that our method significantly outperforms the state-of-the-art.
On Evaluation of Outlier Rankings and Outlier Scores

Outlier detection research is currently focusing on the development of new methods and on improving the computation time for these methods. Evaluation however is rather heuristic, often considering just precision in the top k results or using the area under the ROC curve. These evaluation procedures do not allow for assessment of similarity between methods. Judging the similarity of or correlation between two rankings of outlier scores is an important question in itself but it is also an essential step towards meaningfully building outlier detection ensembles, where this aspect has been completely ignored so far. In this study, our generalized view of evaluation methods allows both to evaluate the performance of existing methods as well as to compare different methods w.r.t. their detection performance. Our new evaluation framework takes into consideration the class imbalance problem and offers new insights on similarity and redundancy of existing outlier detection methods. As a result, the design of effective ensemble methods for outlier detection is considerably enhanced.
Oral feeding readiness in the neonatal intensive care unit.

Oral feeding is a complex sensorimotor process that is influenced by many variables, making the introduction and management of oral feeding a challenge for many health care providers. Feeding practice guided by tradition or a trial-and-error approach may be inconsistent and has the potential to delay the progression of oral feeding skills. Oral feeding initiation and management should be based on careful, individualized assessment of the NICU infant and requires an understanding of neonatal physiology and neurodevelopment. The purpose of this article is to help the health care provider with this complex process by (a) defining oral feeding readiness, (b) describing the importance of oral feeding in the NICU and the physiology of feeding, and (c) providing a review of the literature regarding the transition from gavage to oral feeding in the NICU.
Business culture and dishonesty in the banking industry

Trust in others’ honesty is a key component of the long-term performance of firms, industries, and even whole countries. However, in recent years, numerous scandals involving fraud have undermined confidence in the financial industry. Contemporary commentators have attributed these scandals to the financial sector’s business culture, but no scientific evidence supports this claim. Here we show that employees of a large, international bank behave, on average, honestly in a control condition. However, when their professional identity as bank employees is rendered salient, a significant proportion of them become dishonest. This effect is specific to bank employees because control experiments with employees from other industries and with students show that they do not become more dishonest when their professional identity or bank-related items are rendered salient. Our results thus suggest that the prevailing business culture in the banking industry weakens and undermines the honesty norm, implying that measures to re-establish an honest culture are very important.
DATA INTEGRITY IN CLOUD COMPUTING SECURITY

Cloud computing requires comprehensive security sol utions based upon many aspects of a large and loosely integrated system. The application software and databases in cloud computing are moved to the centralized large data centers, where the managemen t of the data and services may not be fully trustwo r hy. Threats, vulnerabilities and risks for cloud comput ing are explained, and then, we have designed a clo ud computing security development lifecycle model to a chieve safety and enable the user to take advantage of this technology as much as possible of security and f ce the risks that may be exposed to data. A data integrity checking algorithm; which eliminates the third party auditing, is explained to protect stati c and dynamic data from unauthorized observation, modific ation, or interference.