nils-herrmann/clean_scirepeval huggingface dataset:
Order-Planning Neural Text Generation From Structured Data

Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, researchers use neural language models and encoder-decoder frameworks for table-to-text generation. However, these neural network-based approaches typically do not model the order of content during text generation. When a human writes a summary based on a given table, he or she would probably consider the content order before wording. In this paper, we propose an order-planning text generation model, where order information is explicitly captured by link-based attention. Then a self-adaptive gate combines the link-based attention with traditional content-based attention. We conducted experiments on the WIKIBIO dataset and achieve higher performance than previous methods in terms of BLEU, ROUGE, and NIST scores; we also performed ablation tests to analyze each component of our model.
Monte Carlo Tree Search

Monte Carlo Tree Search is a method that depends on decision theory in taking actions/ decisions, when other traditional methods failed on doing so, due to lots of factors such as uncertainty, huge problem domain, or lack in the knowledge base of the problem. Before using this method, several problems remained unsolved including some famous AI games like GO. This method represents a revolutionary technique where a Monte Carlo method has been applied to search tree technique, and proved to be successful in areas thought for a long time as impossible to be solved. This paper highlights some important aspects of this method, and presents some areas where it worked well, as well as enhancements to make it even more powerful.
A comparison of neutral landscapes - NK, NKp and NKq

Recent research in molecular evolution has raised awareness of the importance of selective neutrality. Several different models of neutrality have been proposed based on Kauffman’s well-known NK landscape model. Two of these models, NKp and NKq, are investigated and found to display significantly different structural proper ties. The fitness distr ibutions of these neutral landscapes reveal that their levels of cor relation with non-neutral landscapes are significantly different, as are the distr ibutions of neutral mutations. In this paper we descr ibe a ser ies of simulations of a hill climbing search algor ithm on NK, NKp and NKq landscapes with varying levels of epistatic interaction. These simulations demonstrate differences in the way that epistatic interaction affects the ‘searchability’ of neutral landscapes. We conclude that the method used to implement neutrality has an impact on both the structure of the resulting landscapes and on the per for mance of evolutionary search algor ithms on these landscapes. These model-dependent effects must be taken into consideration when modelling biological phenomena.
Algorithmic brands: A decade of brand experiments with mobile and social media

Algorithmic brands: A decade of brand experiments with mobile and social media Nicholas Carah, University of Queensland Published in New Media and Society here: http://nms.sagepub.com/content/early/2015/09/10/1461444815605463.abstract  Carah, N. (2015). Algorithmic brands: a decade of brand experiments with mobile and social media, New Media and Society, 1-16.  This article examines how brands have iteratively experimented with mobile and social media. The activities of brands – including Coca-Cola, Virgin and Smirnoff – at music festivals in Australia since 2005 are used as an instructive case. The article demonstrates how these brands imagined social media, attempted to instruct consumers to use mobile devices, and used cultural events to stimulate image production tuned to the decision-making of social media algorithms. The article contributes to debate by articulating how brands are important actors in the development of algorithmic media infrastructure and devices. Accounts of algorithmic media need to examine how the analytic capacities of social and mobile media are interdependent with orchestrating the creative participation of users.
Adversarial nets with perceptual losses for text-to-image synthesis

Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated images. Differentiated from previous work, our synthetic image generator optimizes on perceptual loss functions that measure pixel, feature activation, and texture differences against a natural image. We present visually more compelling synthetic images of birds and flowers generated from text descriptions in comparison to some of the most prominent existing work.
Deep Learning Techniques for Music Generation - A Survey

This paper is a survey and an analysis of different ways of using deep learning to generate musical content. We propose a methodology based on five dimensions:  What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file).  What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot.  What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenges - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and propose some tentative multidimensional typology which is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and used to exemplify the various choices of objective, representation, architecture, challenges and strategies. The last part of the paper includes some discussion and some prospects. This is a simplified version (weak DRM) of the book: Briot, J.-P., Hadjeres, G. and Pachet, F.-D. (2019) Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer.
The Rey Auditory Verbal Learning Test: alternate forms equivalency and reliability for the Iranian adult population (Persian version).

Increasing demand for memory assessment in clinical settings in Iran, as well as the absence of a comprehensive and standardized task based upon the Persian culture and language, requires an appropriate culture- and language-specific version of the commonly used neuropsychological measure of verbal learning and memory, the Rey Auditory Verbal Learning Test (RAVLT).



The Persian adapted version of the original RAVLT and two other alternate word lists were generated based upon criteria previously set for developing new word lists. A total of 90 subjects (three groups of 30 persons), aged 29.75±7.10 years, volunteered to participate in our study and were tested using the original word list. The practice effect was assessed by retesting the first and second groups using the same word list after 30 and 60 days, respectively. The test-retest reliability was evaluated by retesting the third group of participants twice using two new alternate word lists with an interval of 30 days.



The re-administration of the same list after one or even two months led to significant practice effects. However, the use of alternate forms after a one-month delay yielded no significant difference across the forms. The first and second trials, as well as the total, immediate, and delayed recall scores showed the best reliability in retesting by the alternate list.



The difference between the generated forms was minor, and it seems that the Persian version of the RAVLT is a reliable instrument for repeated neuropsychological testing as long as alternate forms are used and scores are carefully chosen.
A Time-sensitive Networking (TSN) Simulation Model Based on OMNET++

Industrial and automation control systems require that data be delivered in a highly predictable manner in terms of time. Time-sensitive Networking (TSN), an extension of the Ethernet, is a set of protocols developed and maintained by the IEEE 802.1 Task Group; the protocols deal with time synchronization, traffic scheduling, and network configuration, etc. TSN yields promising solutions for real-time and deterministic networks. Here, we develop a TSN simulation model based on OMNET++; we model a TSN-enabled switch that schedules traffic using gate control lists (GCLs). Simulation verified that the model guaranteed deterministic end-to-end latency.
A Recommendation Engine Using Apache Spark

The volume of structured and unstructured data has grown at exponential scale in recent days. As a result of this rapid data growth, we are always inundated with plethora of choices in any product or service. It is very natural to get lost in the amazon of such choices and finding hard to make decisions. The project aims at addressing this problem by using entity recommendation. The two main aspects that the project concentrates on are implementing and presenting more accurate entity recommendations to the user and another is dealing with vast amount of data. The project aims at presenting recommendation results according to user’s query with efficiency and accuracy. Project makes use of ListNet ranking algorithm to rank the recommendation results. Query independent features and query dependent features are used to come up with ranking scores. Ranking scores decide the order in which the recommendation results are presented to the user. Project makes use of Apache Spark, a distributed big­data processing framework. Spark gives the advantage of handling iterative and interactive algorithms with efficiency and minimal processing time as compared to traditional map­reduce paradigm. We performed the experiments for recommendation engine using DBPedia as the dataset and tested the results for movie domain. We used both query­independent (pagerank) and query­dependent (click­logs) features for ranking purposes. We observed that ListNet algorithm performs really well by making use of Apache Spark as
Protocol: The effect of whole-grain dietary intake on non-communicable diseases: A systematic review, multivariate meta-analysis and dose-response of prospective cohorts, cross-sectional, case-control and intervention studies

The proposed protocol is for a systematic review and meta-analysis on the effects of whole-grains (WG) on non-communicable diseases such as type 2 diabetes, cardiovascular disease, hypertension and obesity. The primary objectives is to explore the mechanisms of WG intake on multiple biomarkers of NCDs such as fasting glucose, fasting insulin and many others. The secondary objective will look at the dose-response relationship between these various mechanisms. The protocol outlines the motive and scope for the review, and methodology including the risk of bias, statistical analysis, screening and study criteria.
Visualizing Workload and Emotion Data in Air Traffic Control - An Approach Informed by the Supervisors Decision Making Process

The work of a supervisor, working in an area control center in air traffic control, seems to be rarely addressed by research. Within this paper, we present a preliminary study that deals with the supervisors decision making process, current practices and interfaces. Based on this study, we derived design guidelines to the development of a visualization, displaying the air traffic controllers’ workload and emotional state, thus supporting the supervisors decision making. Keywords–air traffic control supervisor; visualization, workload data, emotional data, decision making
An attention-based effective neural model for drug-drug interactions extraction

Drug-drug interactions (DDIs) often bring unexpected side effects. The clinical recognition of DDIs is a crucial issue for both patient safety and healthcare cost control. However, although text-mining-based systems explore various methods to classify DDIs, the classification performance with regard to DDIs in long and complex sentences is still unsatisfactory. In this study, we propose an effective model that classifies DDIs from the literature by combining an attention mechanism and a recurrent neural network with long short-term memory (LSTM) units. In our approach, first, a candidate-drug-oriented input attention acting on word-embedding vectors automatically learns which words are more influential for a given drug pair. Next, the inputs merging the position- and POS-embedding vectors are passed to a bidirectional LSTM layer whose outputs at the last time step represent the high-level semantic information of the whole sentence. Finally, a softmax layer performs DDI classification. Experimental results from the DDIExtraction 2013 corpus show that our system performs the best with respect to detection and classification (84.0% and 77.3%, respectively) compared with other state-of-the-art methods. In particular, for the Medline-2013 dataset with long and complex sentences, our F-score far exceeds those of top-ranking systems by 12.6%. Our approach effectively improves the performance of DDI classification tasks. Experimental analysis demonstrates that our model performs better with respect to recognizing not only close-range but also long-range patterns among words, especially for long, complex and compound sentences.
Information Seeking, Information Sharing, and going mobile: Three bridges to informal learning

This paper introduces a new perspective on information behavior in Web 2.0 environments, including the role of mobile access in bridging formal to informal learning. Kuhlthau’s (1991, 2007) Information Search Process (ISP) model is identified as a theoretical basis for exploring Information Seeking attitudes and behaviors, while social learning and literacy concepts of Vygotsky (1962, 1978), Bruner (1962, 1964) and Jenkins (2010) are identified as foundations for Information Sharing. The Guided Inquiry Spaces model (Maniotes, 2005) is proposed as an approach to bridging the student’s informal learning world and the curriculum-based teacher’s world. Research within this framework is operationalized through a recently validated Information and Communications Technology Learning (ICTL) survey instrument measuring learners’ preferences for self-expression, sharing, and knowledge acquisition interactions in technology-pervasive environments. Stepwise refinement of ICTL produced two reliable and valid psychometric scales, Information Sharing (alpha = .77) and Information Seeking (alpha = .72). Cross-validation with an established Mobile Learning Scale (Khaddage & Knezek, 2013) indicates that Information Sharing aligns significantly (p < .05) with Mobile Learning. Information Seeking, Information Sharing, and mobile access are presented as important, complimentary components important, complimentary components in the shift along the formal to informal learning continuum. Therefore, measures of these constructs can assist in understanding students’ preferences for 21st century learning.
Probabilistic Neural Programs

We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling the use of deep neural networks. Probabilistic neural programs combine a computation graph for specifying a neural network with an operator for weighted nondeterministic choice. Thus, a program describes both a collection of decisions as well as the neural network architecture used to make each one. We evaluate our approach on a challenging diagram question answering task where probabilistic neural programs correctly execute nearly twice as many programs as a baseline model.
High-performance graphene-based transparent flexible heaters.

We demonstrate high-performance, flexible, transparent heaters based on large-scale graphene films synthesized by chemical vapor deposition on Cu foils. After multiple transfers and chemical doping processes, the graphene films show sheet resistance as low as ∼43 Ohm/sq with ∼89% optical transmittance, which are ideal as low-voltage transparent heaters. Time-dependent temperature profiles and heat distribution analyses show that the performance of graphene-based heaters is superior to that of conventional transparent heaters based on indium tin oxide. In addition, we confirmed that mechanical strain as high as ∼4% did not substantially affect heater performance. Therefore, graphene-based, flexible, transparent heaters are expected to find uses in a broad range of applications, including automobile defogging/deicing systems and heatable smart windows.
Graph R-CNN for Scene Graph Generation

We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images. Our model contains a Relation Proposal Network (RePN) that efficiently deals with the quadratic number of potential relations between objects in an image. We also propose an attentional Graph Convolutional Network (aGCN) that effectively captures contextual information between objects and relations. Finally, we introduce a new evaluation metric that is more holistic and realistic than existing metrics. We report state-of-the-art performance on scene graph generation as evaluated using both existing and our proposed metrics.
Loss-aware Weight Quantization of Deep Networks

The huge size of deep networks hinders their use in small computing devices. In this paper, we consider compressing the network by weight quantization. We extend a recently proposed loss-aware weight binarization scheme to ternarization, with possibly different scaling parameters for the positive and negative weights, and m-bit (where m > 2) quantization. Experiments on feedforward and recurrent neural networks show that the proposed scheme outperforms state-of-the-art weight quantization algorithms, and is as accurate (or even more accurate) than the full-precision network.
Anatomy of an online misinformation network

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.
Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching

Convolutional neural networks(CNN) have been shown to perform better than the conventional stereo algorithms for stereo estimation. Numerous efforts focus on the pixel-wise matching cost computation, which is the important building block for many start-of-the-art algorithms. However, those architectures are limited to small and single scale receptive fields and use traditional methods for cost aggregation or even ignore cost aggregation. Differently we take them both into consideration. Firstly, we propose a new multi-scale matching cost computation sub-network, in which two different sizes of receptive fields are implemented parallelly. In this way, the network can make the best use of both variants and balance the trade-off between the increase of receptive field and the loss of detail. Furthermore, we show that our multi-dimension aggregation sub-network which containing 2D convolution and 3D convolution operations can provide rich context and semantic information for estimating an accurate initial disparity. Finally, experiments on challenging stereo benchmark KITTI demonstrate that the proposed method can achieve competitive results even without any additional post-processing.
From information security to cyber security

The term cyber security is often used interchangeably with the term information security. This paper argues that, although there is a substantial overlap between cyber security and information security, these two concepts are not totally analogous. Moreover, the paper posits that cyber security goes beyond the boundaries of traditional information security to include not only the protection of information resources, but also that of other assets, including the person him/herself. In information security, reference to the human factor usually relates to the role(s) of humans in the security process. In cyber security this factor has an additional dimension, namely, the humans as potential targets of cyber attacks or even unknowingly participating in a cyber attack. This additional dimension has ethical implications for society as a whole, since the protection of certain vulnerable groups, for example children, could be seen as a societal responsibility. a 2013 Elsevier Ltd. All rights reserved.