nils-herrmann/clean_scirepeval huggingface dataset:
Transformation-Grounded Image Generation Network for Novel 3D View Synthesis

We present a transformation-grounded image generation network for novel 3D view synthesis from a single image. Our approach first explicitly infers the parts of the geometry visible both in the input and novel views and then casts the remaining synthesis problem as image completion. Specifically, we both predict a flow to move the pixels from the input to the novel view along with a novel visibility map that helps deal with occulsion/disocculsion. Next, conditioned on those intermediate results, we hallucinate (infer) parts of the object invisible in the input image. In addition to the new network structure, training with a combination of adversarial and perceptual loss results in a reduction in common artifacts of novel view synthesis such as distortions and holes, while successfully generating high frequency details and preserving visual aspects of the input image. We evaluate our approach on a wide range of synthetic and real examples. Both qualitative and quantitative results show our method achieves significantly better results compared to existing methods.
Learning Gaze Transitions from Depth to Improve Video Saliency Estimation

In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing videos that contain a depth map (RGBD) on a 2D screen. Saliency estimation in this scenario is highly important since in the near future 3D video content will be easily acquired yet hard to display. Despite considerable progress in 3D display technologies, most are still expensive and require special glasses for viewing, so RGBD content is primarily viewed on 2D screens, removing the depth channel from the final viewing experience. We train a generative convolutional neural network that predicts the 2D viewing saliency map for a given frame using the RGBD pixel values and previous fixation estimates in the video. To evaluate the performance of our approach, we present a new comprehensive database of 2D viewing eye-fixation ground-truth for RGBD videos. Our experiments indicate that it is beneficial to integrate depth into video saliency estimates for content that is viewed on a 2D display. We demonstrate that our approach outperforms state-of-the-art methods for video saliency, achieving 15% relative improvement.
Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification

The sentiment classification performance relies on high-quality sentiment resources. However, these resources are imbalanced in different languages. Cross-language sentiment classification (CLSC) can leverage the rich resources in one language (source language) for sentiment classification in a resource-scarce language (target language). Bilingual embeddings could eliminate the semantic gap between two languages for CLSC, but ignore the sentiment information of text. This paper proposes an approach to learning bilingual sentiment word embeddings (BSWE) for English-Chinese CLSC. The proposed BSWE incorporate sentiment information of text into bilingual embeddings. Furthermore, we can learn high-quality BSWE by simply employing labeled corpora and their translations, without relying on largescale parallel corpora. Experiments on NLP&CC 2013 CLSC dataset show that our approach outperforms the state-of-theart systems.
Future Directions of Precision Agriculture

Precision Agriculture is advancing but not as fast as predicted 5 years ago. The development of proper decision-support systems for implementing precision decisions remains a major stumbling block to adoption. Other critical research issues are discussed, namely, insufficient recognition of temporal variation, lack of whole-farm focus, crop quality assessment methods, product tracking and environmental auditing. A generic research programme for precision agriculture is presented. A typology of agriculture countries is introduced and the potential of each type for precision agriculture discussed.
SS5G: Collision Resolution Protocol for Delay and Energy Efficient LoRa Networks

Future 5G and Internet of Things (IoT) applications will heavily rely on long-range communication technologies such as low-power wireless area networks (LPWANs). In particular, LoRaWAN built on LoRa physical layer is gathering increasing interests, both from academia and industries, for enabling low-cost energy efficient IoT wireless sensor networks for, e.g., environmental monitoring over wide areas. While its communication range may go up to 20 kilometers, the achievable bit rates in LoRaWAN are limited to a few kilobits per second. In the event of collisions, the perceived rate is further reduced due to packet loss and retransmissions. Firstly, to alleviate the harmful impacts of collisions, we propose a decoding algorithm that enables to resolve several superposed LoRa signals. Our proposed method exploits the slight desynchronization of superposed signals and specific features of LoRa physical layer. Secondly, we design a full MAC protocol enabling collision resolution. The simulation results demonstrate that the proposed method outperforms conventional LoRaWAN jointly in terms of system throughput, energy efficiency as well as delay. These results show that our scheme is well suited for 5G and IoT systems, as one of their major goals is to provide the best trade-off among these performance objectives.
Learning Non-local Image Diffusion for Image Denoising

Image diffusion plays a fundamental role for the task of image denoising. The recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a simple but very effective framework for image denoising. However, as the TNRD model is a local model, whose diffusion behavior is purely controlled by information of local patches, it is prone to create artifacts in the homogenous regions and over-smooth highly textured regions, especially in the case of strong noise levels. Meanwhile, it is widely known that the non-local self-similarity (NSS) prior stands as an effective image prior for image denoising, which has been widely exploited in many non-local methods. In this work, we are highly motivated to embed the NSS prior into the TNRD model to tackle its weaknesses. In order to preserve the expected property that end-to-end training remains available, we exploit the NSS prior by defining a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising. Together with the local filters and influence functions, the non-local filters are learned by employing loss-specific training. The experimental results show that the trained TNLRD model produces visually plausible recovered images with more textures and less artifacts, compared to its local versions. Moreover, the trained TNLRD model can achieve strongly competitive performance to recent state-of-the-art image denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).
I Know What You Want: Semantic Learning for Text Comprehension

Who did what to whom is a major focus in natural language understanding, which is right the aim of semantic role labeling (SRL). Although SRL is naturally essential to text comprehension tasks, it is surprisingly ignored in previous work. This paper thus makes the first attempt to let SRL enhance text comprehension and inference through specifying verbal arguments and their corresponding semantic roles. In terms of deep learning models, our embeddings are enhanced by semantic role labels for more fine-grained semantics. We show that the salient labels can be conveniently added to existing models and significantly improve deep learning models in challenging text comprehension tasks. Extensive experiments on benchmark machine reading comprehension and inference datasets verify that the proposed semantic learning helps our system reach new state-of-the-art.
Programming Intelligent Environments in Natural Language: An Extensible Interactive Approach

The proliferation of Ambient Intelligence (AmI) devices and services and their integration in smart environments creates the need for a simple yet effective way of controlling and communicating with them. Towards that direction, the application of the Trigger -- Action model has attracted a lot of research with many systems and applications having been developed following that approach. This work introduces ParlAmI, a multimodal conversational interface aiming to give its users the ability to determine the behavior of AmI environments, by creating rules using natural language as well as a GUI. The paper describes ParlAmI, its requirements and functionality, and presents the findings of a user-based evaluation which was conducted.
An Efficient Framework for Development of Task-Oriented Dialog Systems in a Smart Home Environment

In recent times, with the increasing interest in conversational agents for smart homes, task-oriented dialog systems are being actively researched. However, most of these studies are focused on the individual modules of such a system, and there is an evident lack of research on a dialog framework that can integrate and manage the entire dialog system. Therefore, in this study, we propose a framework that enables the user to effectively develop an intelligent dialog system. The proposed framework ontologically expresses the knowledge required for the task-oriented dialog system's process and can build a dialog system by editing the dialog knowledge. In addition, the framework provides a module router that can indirectly run externally developed modules. Further, it enables a more intelligent conversation by providing a hierarchical argument structure (HAS) to manage the various argument representations included in natural language sentences. To verify the practicality of the framework, an experiment was conducted in which developers without any previous experience in developing a dialog system developed task-oriented dialog systems using the proposed framework. The experimental results show that even beginner dialog system developers can develop a high-level task-oriented dialog system.
Integrating Visual, Audio and Text Analysis for News Video

In this paper, we present a system developed for content-based broadcasted news video browsing for home users. There are three main factors that distinguish our work from other similar ones. First, we have integrated the image and audio analysis results in identifying news segments. Second, we use the video OCR technology to detect text from frames, which provides a good source of textual information for story classification when transcripts and close captions are not available. Finally, natural language processing (NLP) technologies are used to perform automated categorization of news stories based on the texts obtained from close caption or video OCR process. Based on these video structure and content analysis technologies, we have developed two advanced video browsers for home users: intelligent highlight player and HTML-based video browser.
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation

We combine two of the most popular approaches to automated Grammatical Error Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on Neural Machine Translation (NMT). The hybrid system achieves new state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC system preserves the accuracy of SMT output and, at the same time, generates more fluent sentences as it typical for NMT. Our analysis shows that the created systems are closer to reaching human-level performance than any other GEC system reported so far.
Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning

This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using optimal weight initialization learned from first solving a similar easier environment. We show that incremental learning can produce vastly superior results than standard methods by providing a strong baseline method across ten Dex environments. We finally develop a saliency method for qualitative analysis of reinforcement learning, which shows the impact incremental learning has on network attention.
Moral cognition and its neural constituents

Identifying the neural mechanisms of moral cognition is especially difficult. In part, this is because moral cognition taps multiple cognitive sub-processes, being a highly distributed, whole-brain affair. The assumptions required to make progress in identifying the neural constituents of moral cognition might simplify morally salient stimuli to the point that they no longer activate the requisite neural architectures, but the right experiments can overcome this difficulty. The current evidence allows us to draw a tentative conclusion: the moral psychology required by virtue theory is the most neurobiologically plausible.
Bootstrapping a Blockchain Based Ecosystem for Big Data Exchange

In recent years, data is becoming the most valuable asset. There are more and more data exchange markets on Internet. These markets help data owners publish their datasets and data consumers find appropriate services. However, different from traditional goods like clothes and food, data is a special commodity. For current data exchange markets, it is very hard to protect copyright and privacy. Moreover, maintaining data services requires special IT techniques, which is a difficult job for many organizations who own big datasets, such as hospitals, government departments, planetariums and banks. In this paper, we propose a decentralized solution for big data exchange. This solution aims at cultivating an ecosystem, inside which all participators can cooperate to exchange data in a peer-to-peer way. The core part of this solution is to utilize blockchain technology to record transaction logs and other important documents. Unlike existing data exchange markets, our solution does not need any third-parties. It also provides an convenient way for data owners to audit the use of data, in order to protect data copyright and privacy. We will explain the ecosystem, and discuss the technical challenges and corresponding solutions.
Sentiment Analysis using SVM: A Systematic Literature Review

The world has revolutionized and phased into a new era, an era which upholds the true essence of technology and digitalization. As the market has evolved at a staggering scale, it is must to exploit and inherit the advantages and opportunities, it provides. With the advent of web 2.0, considering the scalability and unbounded reach that it provides, it is detrimental for an organization to not to adopt the new techniques in the competitive stakes that this emerging virtual world has set along with its advantages. The transformed and highly intelligent data mining approaches now allow organizations to collect, categorize, and analyze users’ reviews and comments from micro-blogging sites regarding their services and products. This type of analysis makes those organizations capable to assess, what the consumers want, what they disapprove of, and what measures can be taken to sustain and improve the performance of products and services. This study focuses on critical analysis of the literature from year 2012 to 2017 on sentiment analysis by using SVM (support vector machine). SVM is one of the widely used supervised machine learning techniques for text classification. This systematic review will serve the scholars and researchers to analyze the latest work of sentiment analysis with SVM as well as provide them a baseline for future trends and comparisons. Sentiment analysis; polarity detection; machine learning; support vector machine (SVM); support vector machine; SLR; systematic literature review
Finders , keepers ? Attracting , motivating and retaining knowledge workers

Attracting, motivating and retaining knowledge workers have become important in a knowledge-based and tight labour market, where changing knowledge management practices and global convergence of technology has redefined the nature of work. While individualisation of employment practices and team-based work may provide personal and organisational flexibilities, aligning HR and organisational strategies for competitive advantage has become more prominent. This exploratory study identifies the most and least effective HR strategies used by knowledge intensive firms (KIFs) in Singapore for attracting, motivating and retaining these workers. The most popular strategies were not always the most effective, and there appear to be distinctive ‘bundles’ of HR practices for managing knowledge workers. These vary according to whether ownership is foreign or local. A schema, based on statistically significant findings, for improving the effectiveness of these practices in managing knowledge workers is proposed. Cross-cultural research is necessary to establish the extent of diffusion of these practices.  Frank M. Horwitz, Graduate School of Business, Breakwater Campus, University of Cape Town, Private Bag Rondebosch, Cape Town 7700 South Africa.
Automatic Colorization with Deep Convolutional Generative Adversarial Networks

We attempt to use DCGANs (deep convolutional generative adversarial nets) to tackle the automatic colorization of black and white photos to combat the tendency for vanilla neural nets to ”average out” the results. We construct a small feed-forward convolutional neural network as a baseline colorization system. We train the baseline model on the CIFAR-10 dataset with a per-pixel Euclidean loss function on the chrominance values and achieve sensible but mediocre results. We propose using the adversarial framework as proposed by Goodfellow et al.  as an alternative to the loss function—we reformulate the baseline model as a generator model that maps grayscale images and random noise input to the color image space, and construct a discriminator model that is trained to predict the probability that a given colorization was sampled from data distribution rather than generated by the generator model, conditioned on the grayscale image. We analyze the challenges that stand in the way of training adversarial networks, and suggest future steps to test the viability of the model.
Sequential Data Cleaning: A Statistical Approach

Errors are prevalent in data sequences, such as GPS trajectories or sensor readings. Existing methods on cleaning sequential data employ a constraint on value changing speeds and perform constraint-based repairing. While such speed constraints are effective in identifying large spike errors, the small errors that do not significantly deviate from the truth and indeed satisfy the speed constraints can hardly be identified and repaired. To handle such small errors, in this paper, we propose a statistical based cleaning method. Rather than declaring a broad constraint of max/min speeds, we model the probability distribution of speed changes. The repairing problem is thus to maximize the likelihood of the sequence w.r.t. the probability of speed changes. We formalize the likelihood-based cleaning problem, show its NP-hardness, devise exact algorithms, and propose several approximate/heuristic methods to trade off effectiveness for efficiency. Experiments on real data sets (in various applications) demonstrate the superiority of our proposal.
Cross-lingual sentiment transfer with limited resources

We describe two transfer approaches for building sentiment analysis systems without having gold labeled data in the target language. Unlike previous work that is focused on using only English as the source language and a small number of target languages, we use multiple source languages to learn a more robust sentiment transfer model for 16 languages from different language families. Our approaches explore the potential of using an annotation projection approach and a direct transfer approach using cross-lingual word representations and neural networks. Whereas most previous work relies on machine translation, we show that we can build cross-lingual sentiment analysis systems without machine translation or even high quality parallel data. We have conducted experiments assessing the availability of different resources such as in-domain parallel data, out-of-domain parallel data, and in-domain comparable data. Our experiments show that we can build a robust transfer system whose performance can in some cases approach that of a supervised system.
Tell me a story--a conceptual exploration of storytelling in healthcare education.

The importance of storytelling as the foundation of human experiences cannot be overestimated. The oral traditions focus upon educating and transmitting knowledge and skills and also evolved into one of the earliest methods of communicating scientific discoveries and developments. A wide ranging search of the storytelling, education and health-related literature encompassing the years 1975-2007 was performed. Evidence from disparate elements of education and healthcare were used to inform an exploration of storytelling. This conceptual paper explores the principles of storytelling, evaluates the use of storytelling techniques in education in general, acknowledges the role of storytelling in healthcare delivery, identifies some of the skills learned and benefits derived from storytelling, and speculates upon the use of storytelling strategies in nurse education. Such stories have, until recently been harvested from the experiences of students and of educators, however, there is a growing realization that patients and service users are a rich source of healthcare-related stories that can affect, change and benefit clinical practice. The use of technology such as the Internet discussion boards or digitally-facilitated storytelling has an evolving role in ensuring that patient-generated and experiential stories have a future within nurse education.
From Abstraction to Implementation: Can Computational Thinking Improve Complex Real-World Problem Solving? A Computational Thinking-Based Approach to the SDGs

Utilizing concepts derived from computational thinking—a method of thinking coined by Jeanette Wing—a problem-solving paradigm is presented to demonstrate the applicability of thinking computationally beyond the realm of computer science. The 17 Sustainable Development Goals (SDGs) are used as a set of real world problems to elaborate the function of the proposed four-stage paradigm. The paradigm seeks to provide a method of approaching problems with the aim of finding local and contextualized solutions that reach all members of different societies. This paper also serves as the foundation of further research in the development of computational thinking as a fundamental tool for finding solutions in a broad scope of disciplines and real-world situations.