Reviewerly/exHarmony huggingface dataset:
Variability Management in a Software Product Line Unaware Company

Software Product Lines (SPL) enable systematic reuse within an organization thus, enabling the reduction of costs, efforts, development time and the average number of defects per product. However, there is little empirical evidence of SPL adoption in the literature, which makes it difficult to strengthen or elaborate adjustments or improvements to SPL frameworks. In this article, we present the first steps towards an empirical evaluation by showing how companies that do not know about of SPL manage variability in their products, pointing out the strengths and weaknesses of their approaches. To this end, we present the design of a case study that we plan to carry out in the future in two companies to evaluate how companies perform variability management when they are not aware of software product lines. Our assumption is that most of the companies manage variability but no many of them are aware of software product lines. In addition, the first preliminary results of the case study applied in a company are presented.
Analysis of hot topics in soil remediation research based on VOSviewer

Abstract Through the cluster analysis of the keywords and draw scientific knowledge map by VOSviewer in the field of soil remediation in order to further explore the hot topics of soil remediation research. The results were analyzed by manual analysis. The key words in the field of soil remediation research were divided into 4 topic groups. They are in the field of heavy metal pollution, chemical remediation technology, bioremediation technology, and electric remediation technology. The hot topic in the field of soil remediation research is mainly “heavy-metals”, “removal”, “bioremediation”, “degradation”, “biodegradation”, “phytoremediation”, “contaminated soil”, “water”, “cadmium”, “polycyclic aromatic-hydrocarbons”, “adsorption”, “lead”, and “sorption”, “bioavailability”, “extraction.
Gender Prediction from Turkish Tweets with Neural Networks

profiling is the characterization of an author through some key attributes such as gender, age, and language.It's an indispensable task especially in security and marketing.In this work, the gender of a Twitter user is predicted using his/her tweets.A model combining a recurrent neural network (RNN) with an attention mechanism is proposed.As far as we know such a predictive analytics is performed in Turkish Twitter dataset for the first time, and the proposed model is tested in Turkish, English, Spanish, and Arabic with accuracy scores of 80.63, 81.73, 78.22, 78.5 respectively.The accuracy values obtained exhibit state-of-the-art in Turkish and competitive performance in the other languages.
Successful application of mivar expert systems for MIPRA – solving action planning problems for robotic systems in real time

For the first time in the world, mivar expert systems were used for solving action planning problems for robotic systems. The KESMI (Wi!Mi) Razumator solution was used to create a mivar-based system called MIPRA (mivar-based intelligent planner of robot actions), which delivers a qualitative (by several orders of magnitude) boost to the process of solving STRIPS-type problems, i. e. constructing algorithms for planning the actions of a robot tasked with relocating blocks in the blocks-world domain. Practical experiments, which any researcher can verify and replicate, have shown that, instead of many hours and powerful multiprocessor servers, MIPRA, runs on an ordinary computer and takes seconds to solve problems involving relocations of the following numbers of blocks: 10 blocks – 0.98 s; 50 blocks – 3.44 s; 100 blocks – 10.57 s; 200 blocks – 38.32 s, and 300 blocks – 84.07 s. The mivar approach implemented in MIPRA allows it to handle tasks with more than three towers on the table and with varied numbers of blocks, even in changing conditions. This creates an opportunity, in the future, to solve a greater variety of action planning problems for different robots, such as, e. g., ensuring logical compliance with traffic regulations for autonomous vehicles, etc.
Human Mesh Recovery from Monocular Images via a Skeleton-disentangled Representation

We describe an end-to-end method for recovering 3D human body mesh from single images and monocular videos. Different from the existing methods try to obtain all the complex 3D pose, shape, and camera parameters from one coupling feature, we propose a skeleton-disentangling based framework, which divides this task into multi-level spatial and temporal granularity in a decoupling manner. In spatial, we propose an effective and pluggable "disentangling the skeleton from the details" (DSD) module. It reduces the complexity and decouples the skeleton, which lays a good foundation for temporal modeling. In temporal, the self-attention based temporal convolution network is proposed to efficiently exploit the short and long-term temporal cues. Furthermore, an unsupervised adversarial training strategy, temporal shuffles and order recovery, is designed to promote the learning of motion dynamics. The proposed method outperforms the state-of-the-art 3D human mesh recovery methods by 15.4% MPJPE and 23.8% PA-MPJPE on Human3.6M. State-of-the-art results are also achieved on the 3D pose in the wild (3DPW) dataset without any fine-tuning. Especially, ablation studies demonstrate that skeleton-disentangled representation is crucial for better temporal modeling and generalization.
Learning Document Embeddings Along With Their Uncertainties

Majority of the text modeling techniques yield only point-estimates of document embeddings and lack in capturing the uncertainty of the estimates. These uncertainties give a notion of how well the embeddings represent a document. We present Bayesian subspace multinomial model (Bayesian SMM), a generative log-linear model that learns to represent documents in the form of Gaussian distributions, thereby encoding the uncertainty in its covariance. Additionally, in the proposed Bayesian SMM, we address a commonly encountered problem of intractability that appears during variational inference in mixed-logit models. We also present a generative Gaussian linear classifier for topic identification that exploits the uncertainty in document embeddings. Our intrinsic evaluation using perplexity measure shows that the proposed Bayesian SMM fits the unseen test data better as compared to the state-of-the-art neural variational document model on (Fisher) speech and (20Newsgroups) text corpora. Our topic identification experiments show that the proposed systems are robust to over-fitting on unseen test data. The topic ID results show that the proposed model outperforms state-of-the-art unsupervised topic models and achieve comparable results to the state-of-the-art fully supervised discriminative models.
Residues of skew rational functions and linearized Goppa codes

This paper constitutes a first attempt to do analysis with skew polynomials. Precisely, our main objective is to develop a theory of residues for skew rational functions (which are, by definition, the quotients of two skew polynomials). We prove in particular a skew analogue of the residue formula and a skew analogue of the classical formula of change of variables for residues. We then use our theory to define and study a linearized version of Goppa codes. We show that these codes meet the Singleton bound (for the sum-rank metric) and are the duals of the linearized Reed-Solomon codes defined recently by Mart{\'i}nez-Pe{\~n}as. We also design efficient encoding and decoding algorithms for them.
Cryptocurrency: Adoption efforts and security challenges in different countries

Abstract This research paper is a holistic review done on the rise of Blockchain and cryptocurrency, then elaborate about the great advantages of having a decentralized finance system. The existing scenarios from a sample of countries were reviewed in regards to their effort to adopt cryptocurrency to find some of the challenges like what are the security challenges this new monetary system faces, and limitations faced by different countries. A detailed analysis was done to answer some of the vital questions as such why cryptocurrency is banned in a few countries when other countries see cryptocurrency as a secured mode of payment transaction or what kind of security is provided by cryptocurrency compared to the traditional payments such as pay by cash, credit, or checks. Lastly, this paper also summarizes a high-level overview to propose recommended solutions to overcome the security concerns associated with the adoption of cryptocurrencies and how does the future of cryptocurrency look.
New Frontiers in Explainable AI: Understanding the GI to Interpret the GO

In this paper we focus on the importance of interpreting the quality of the input of predictive models (potentially a GI, i.e., a Garbage In) to make sense of the reliability of their output (potentially a GO, a Garbage Out) in support of human decision making, especially in critical domains, like medicine. To this aim, we propose a framework where we distinguish between the Gold Standard (or Ground Truth) and the set of annotations from which this is derived, and a set of quality dimensions that help to assess and interpret the AI advice: fineness, trueness, representativeness, conformity, dryness. We then discuss implications for obtaining more informative training sets and for the design of more usable Decision Support Systems.
Effect of Architectures and Training Methods on the Performance of Learned Video Frame Prediction

We analyze the performance of feedforward vs. recurrent neural network (RNN) architectures and associated training methods for learned frame prediction. To this effect, we trained a residual fully convolutional neural network (FCNN), a convolutional RNN (CRNN), and a convolutional long short-term memory (CLSTM) network for next frame prediction using the mean square loss. We performed both stateless and stateful training for recurrent networks. Experimental results show that the residual FCNN architecture performs the best in terms of peak signal to noise ratio (PSNR) at the expense of higher training and test (inference) computational complexity. The CRNN can be trained stably and very efficiently using the stateful truncated backpropagation through time procedure, and it requires an order of magnitude less inference runtime to achieve near real-time frame prediction with an acceptable performance.
Understanding attention in graph neural networks.

We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness. We particularly focus on the ability of attention GNNs to generalize to larger, more complex or noisy graphs. Motivated by insights from the work on Graph Isomorphism Networks, we design simple graph reasoning tasks that allow us to study attention in a controlled environment. We find that under typical conditions the effect of attention is negligible or even harmful, but under certain conditions it provides an exceptional gain in performance of more than 60% in some of our classification tasks. Satisfying these conditions in practice is challenging and often requires optimal initialization or supervised training of attention. We propose an alternative recipe and train attention in a weakly-supervised fashion that approaches the performance of supervised models, and, compared to unsupervised models, improves results on several synthetic as well as real datasets. Source code and datasets are available at this https URL.
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition

This paper studies the task of one-shot fine-grained recognition, which suffers from the problem of data scarcity of novel fine-grained classes. To alleviate this problem, a off-the-shelf image generator can be applied to synthesize additional images to help one-shot learning. However, such synthesized images may not be helpful in one-shot fine-grained recognition, due to a large domain discrepancy between synthesized and original images. To this end, this paper proposes a meta-learning framework to reinforce the generated images by original images so that these images can facilitate one-shot learning. Specifically, the generic image generator is updated by few training instances of novel classes; and a Meta Image Reinforcing Network (MetaIRNet) is proposed to conduct one-shot fine-grained recognition as well as image reinforcement. The model is trained in an end-to-end manner, and our experiments demonstrate consistent improvement over baseline on one-shot fine-grained image classification benchmarks.
ACTSEA: Annotated Corpus for Tamil & Sinhala Emotion Analysis

The purpose of text emotion analysis is to detect and recognize the classification of feeling expressed in text. In recent years, there has been an increase in text emotion analysis studies for English language since data were abundant. Due to the growth of social media large amount data are now available for regional languages such as Tamil and Sinhala as well. However, these languages lack necessary annotated corpus for many NLP tasks including emotion analysis. In this paper, we present our scalable semi-automatic approach to create an annotated corpus named ACTSEA for Tamil and Sinhala to support emotion analysis. Alongside, our analysis on a sample of the produced data and the useful findings are presented for the low resourced NLP community to benefit. For ACTSEA, data were gathered from twitter platform and annotated manually after cleaning. We collected 600280 (Tamil) and 318308 (Sinhala) tweets in total which makes our corpus largest data collection which is currently available for these languages.
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks

Normalization methods play an important role in enhancing the performance of deep learning while their theoretical understandings have been limited. To theoretically elucidate the effectiveness of normalization, we quantify the geometry of the parameter space determined by the Fisher information matrix (FIM), which also corresponds to the local shape of the loss landscape under certain conditions. We analyze deep neural networks with random initialization, which is known to suffer from a pathologically sharp shape of the landscape when the network becomes sufficiently wide. We reveal that batch normalization in the last layer contributes to drastically decreasing such pathological sharpness if the width and sample number satisfy a specific condition. In contrast, it is hard for batch normalization in the middle hidden layers to alleviate pathological sharpness in many settings. We also found that layer normalization cannot alleviate pathological sharpness either. Thus, we can conclude that batch normalization in the last layer significantly contributes to decreasing the sharpness induced by the FIM.
STUDY OF WEB PAGE RANKING ALGORITHMS: A REVIEW

Due to expanding of Web, the Information is also increasing day by day.Many users generally rely on search engine to explore the web.It is a challenge for search engine to provide relevant web page and quality information against the query submitted.This paper deals in the comparative study of various web page ranking algorithms and also its advantages and disadvantages.This paper helps to find out web page ranking algorithms' relative strength and limitations for further research scope.
FLOOD EVACUATION ROUTES MAPPING BASED ON DERIVED- FLOOD IMPACT ANALYSIS FROM LANDSAT 8 IMAGERY USING NETWORK ANALYST METHOD

Abstract. Reported by National Disaster Response Agency of Indonesia (BPBD) as many as 94 dies, 149 injured, and more than 88 thousands homeless caused by floods in 2018. Besides bringing casualties to people and environment, the floods also affect the damages to transportation infrastructures in which vital to disaster emergency response operation e.g. evacuation process. Due to the complex impact of current disaster, the demands of providing a short-term response increases accordingly. Therefore, this research proposes a prototype of flood evacuation route utilizing network analyst method. The network analyst method particularly focus on finding alternative route based on time and distance. This research uses a flood simulation model derived from Landsat 8 imagery and terrain data. Subsequently, the simulation model divides the flood severity based on the depth which consist of &lt; 0.3 m (slight), 0.3-0.5 m (moderate), and &gt; 0.5 m (serious) in order to generate an impact analysis regarding the estimation of damages and casualties. In order to resemble the real situation of flood, barriers (e.g. flood area) are applied into the finding evacuation route procedure. Thereby, the estimated evacuation route can be executed considering the safest and fastest way. Moreover, some comparisons between before and after flood are conducted in order to know the effectiveness of evacuation routes. By such comparison proves that network analyst enables to support disaster management operation with respect to handling the evacuation procedure.
eTranslation’s Submissions to the WMT 2019 News Translation Task

This paper describes the submissions of the eTranslation team to the WMT 2019 news translation shared task.The systems have been developed with the aim of identifying and following rather than establishing best practices, under the constraints imposed by a low resource training and decoding environment normally used for our production systems.Thus most of the findings and results are transferable to systems used in the eTranslation service.Evaluations suggest that this approach is able to produce decent models with good performance and speed without the overhead of using prohibitively deep and complex architectures.
Investigating the effect of digitally augmented toys on young children’s social pretend play

This paper takes an interaction design approach to explore how children perceive augmented toys, assign symbolic meaning, and perform pretence socially in technology-mediated playing. We developed a system with three kinds of toys—each with distinct abstract appearances and audiovisual augmentation—based on several design decisions. An observational user study with thirty-two young children aged 3–7 years revealed that children utilized digital augmentation as a facilitator of pretending behaviour with the possibility for subjective interpretation while substituting an object for another. Digital augmentation was not only a social cue for children to respond by gathering together and negotiating socially for mutual pretence—it was also a hindrance by amplifying conflicts in a shared space. Our study empirically clarifies children’s cognition and behaviour in an interactive system for pretend play and provides broader insight for the design of an interface enriching symbolic interpretation and social interaction.
MVS^2: Deep Unsupervised Multi-view Stereo with Multi-View Symmetry

The success of existing deep-learning based multi-view stereo (MVS) approaches greatly depends on the availability of large-scale supervision in the form of dense depth maps. Such supervision, while not always possible, tends to hinder the generalization ability of the learned models in never-seen-before scenarios. In this paper, we propose the first unsupervised learning based MVS network, which learns the multi-view depth maps from the input multi-view images and does not need ground-truth 3D training data. Our network is symmetric in predicting depth maps for all views simultaneously, where we enforce cross-view consistency of multi-view depth maps during both training and testing stages. Thus, the learned multi-view depth maps naturally comply with the underlying 3D scene geometry. Besides, our network also learns the multi-view occlusion maps, which further improves the robustness of our network in handling real-world occlusions. Experimental results on multiple benchmarking datasets demonstrate the effectiveness of our network and the excellent generalization ability.
Modeling AGI Safety Frameworks with Causal Influence Diagrams.

Proposals for safe AGI systems are typically made at the level of frameworks, specifying how the components of the proposed system should be trained and interact with each other. In this paper, we model and compare the most promising AGI safety frameworks using causal influence diagrams. The diagrams show the optimization objective and causal assumptions of the framework. The unified representation permits easy comparison of frameworks and their assumptions. We hope that the diagrams will serve as an accessible and visual introduction to the main AGI safety frameworks.
Development of a forecast model for the prediction of photovoltaic power using neural networks and validating the model based on real measurement data of a local photovoltaic system

In order to increase the proportion of solar power used to charge electric vehicles via decentralised photovoltaic (PV) systems, a forecast model is required that predicts the energy generated by the system over the next day. For this purpose, the paper presents a model consisting of a forecast model of solar irradiation and a mathematical model for determining the PV power based on solar irradiation. The model has been developed, validated and tested using real measurement data. The forecast model is supposed to be able to predict the solar irradiation in local areas for the next day on the basis of freely available weather forecasts. A neural network has been developed for this purpose, which has been trained and validated on the historical weather data. In the validation, the forecast model reached an accuracy of $60\mathrm{W}/ \mathrm{m}^{2}$ and 6.68 % related to full scale over the entire year 2017. The model for the prognosis of generated decentralised PV power was tested on a PV system on empirical measurement data. Accuracies of mean absolute error of 51.16 W and 3.18 % related to full scale were achieved.
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction

The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, choosing how the pseudo data should be generated or used. In this study, these choices are investigated through extensive experiments, and state-of-the-art performance is achieved on the CoNLL-2014 test set ($F_{0.5}=65.0$) and the official test set of the BEA-2019 shared task ($F_{0.5}=70.2$) without making any modifications to the model architecture.