Reviewerly/exHarmony huggingface dataset:
DeepReview: Automatic Code Review Using Deep Multi-instance Learning

Code review, an inspection of code changes in order to identify and fix defects before integration, is essential in Software Quality Assurance (SQA). Code review is a time-consuming task since the reviewers need to understand, analysis and provide comments manually. To alleviate the burden of reviewers, automatic code review is needed. However, this task has not been well studied before. To bridge this research gap, in this paper, we formalize automatic code review as a multi-instance learning task that each change consisting of multiple hunks is regarded as a bag, and each hunk is described as an instance. We propose a novel deep learning model named DeepReview based on Convolutional Neural Network (CNN), which is an end-to-end model that learns feature representation to predict whether one change is approved or rejected. Experimental results on open source projects show that DeepReview is effective in automatic code review tasks. In terms of F1 score and AUC, DeepReview outperforms the performance of traditional single-instance based model TFIDF-SVM and the state-of-the-art deep feature based model Deeper.
A Deep-Language Mathematical Analysis of Gospels, Acts and Revelation

The paper aims at casting some light on the interrelations among the Gospels, Acts and Revelation. We do not consider words ranking and their frequency of occurrence, as largely done in the literature, but we analyze, statistically, some mathematical aspects of the texts, which the authors were not conscious of. We use mathematical methods developed for specifically studying deep-language parameters of literary texts, such as the number of words per sentence, the number of characters per word, the number of words per interpunctions, the number of interpunctions per sentence, all very likely peculiar to the writer’s style, after having recalled the punctuation in classical languages. First, we consider the full texts of the canonical Gospels, Acts and Revelation, then the partial texts attributable to the Triple Tradition (Matthew, Mark and Luke), to the Double Tradition (Matthew and Luke), Own Tradition (Matthew and Luke) and Q source. The mathematical/statistical tool used confirms and reinforces some general results concerning the Gospels, Acts, Revelation and Q source, but also evidences some interesting differences concerning the number of words per sentence and words per interpunctions, likely casting some light on the capacity of the short-term memory of the readers/listeners of these texts. All these New Testament writings fit very well in the larger Greek literature of the time. The existence of a proto gospel seems more probable than other possible hypotheses.
Single View 3D Face Reconstruction with Landmark Updating

3D face reconstruction is a long-term and challenging problem, which has wide application scenarios, such as animation, recognition. Inspired by recent works in face landmark marching, we develop a method for 3D face reconstruction using a novel landmark updating optimization strategy. Our method is also based on minimization of the landmark projection error for calculation speed. To achieve more accuracy, contour landmarks and self-occluded landmarks need to be updated. For contour landmarks, the detected landmarks on 2D image are to updated by finding a nearest position on contour curve. For self-occluded landmarks, we render the model onto image plane, extract the edge of projected area, and generate new correspondence landmarks according to the edge pixels. We test our method quantitatively with ground truth data, and provide qualitative example reconstructions, show that our method performs better with experiments on MICC dataset.
Remote sensing satellite imagery for prospecting geothermal systems in an aseismic geologic setting: Yankari Park, Nigeria

The aim of this investigation is to evaluate the applicability of multispectral satellite imagery for hydrothermal alteration mineral mapping and thermal anomaly detection as a proxy for the characterization of subtle geothermal systems in an aseismic geologic setting. The Yankari Park, an area in northeastern Nigeria characterized by several surface manifestations of hotsprings and hydrothermally altered rock deposits was selected for this study. The study evaluates the effectiveness of the Independent Component Analysis and Mixture Tuned Matched Filtering (MTMF) algorithm for target detection of hydrothermal alteration zones and the Single Channel Algorithm for thermal anomaly detection associated with subtle geothermal systems using spectral bands of Landsat-8, ASTER and Landsat-7, respectively. The results indicated that the use of verified image endmember spectra gives more accurate results compared to that from library spectra by implementing MTMF. Results of mapping thermally anomalous pixels did not conform to known locations of the thermal springs, however, examining the spatial correlation of the anomaly areas with the major fault-fracture systems from the geological map of the study area indicates a close affinity between them and with previously reported thermal gradients within heat conserving sedimentary formations. This investigation has significant implications as it signifies the need for the integrative use of alteration mineral mapping and thermal anomaly detection for cost-effective prefeasibility stage mapping of geothermal systems in an aseismic geologic setting located in African plate and other regions around the world.
A Neural Network for Automated Quality Screening of Ground Motion Records from Small Magnitude Earthquakes

The ambitious scopes of recent earthquake ground motion studies are generating a need for more high-quality ground motion records. As the number of deployed sensors is rapidly growing through improved accessibility and cost (e.g., ground motion stations, low-cost accelerometers, smart phones), an exponentially increasing amount of data are being generated. Previously, quality-assured ground motion data sets for engineering applications were generated using both manual and automated quality screening methodologies. More recently, new techniques have emerged that potentially offer both improved classification accuracy and computational expediency. This work presents a machine learning–oriented method to facilitate and accelerate the quality classification of ground motion records from small magnitude earthquakes. Feedforward neural networks are selected for their ability to efficiently recognize patterns and are trained on two New Zealand data sets. An application to physics-based ground motion simulation validation indicates that the proposed approach delivers results that are comparable with manual quality selection. Robust automatic ground motion quality screening allows a significant increase in data set size for development, calibration, and validation of ground motion models.
Mutual Authentication for IoT in the Context of Fog Computing

The authentication of restricted memory devices has major problems because memory consumption is high when applied with other protocols that have the purpose of mutual authentication. This article proposes an authentication model that validates mutually the parties in an Internet of Things environment applied in the context of Fog Computing. Through the hypothetic-deductive methodology, the deductions and hypotheses are presented for the formal proof of the new proposed method. The results achieved in the research show that the approach of the authentication mechanism is optimized, the prototype was validated in the AVISPA protocol evaluation tool and in a restricted memory controlled environment in a context of Fog Computing. However, this work is more appropriate in Fog Computing environments because there are no route changes as in Cloud Computing.
From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.
A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity

Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target response, greatly reducing the utility of the system. Reinforcement learning rules have been developed for reservoir computing, but we find that they fail to converge on complex motor tasks. Current theories of biological motor learning pose that early learning is controlled by dopamine-modulated plasticity in the basal ganglia that trains parallel cortical pathways through unsupervised plasticity as a motor task becomes well learned. We developed a novel learning algorithm for reservoir computing that models the interaction between reinforcement and unsupervised learning observed in experiments. This novel learning algorithm converges on simulated motor tasks on which previous reservoir computing algorithms fail and reproduces experimental findings that relate Parkinson's disease and its treatments to motor learning. Hence, incorporating biological theories of motor learning improves the effectiveness and biological relevance of reservoir computing models.
A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction

Duy-Cat Can, Hoang-Quynh Le, Quang-Thuy Ha, Nigel Collier. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Image unmosaicing without location information using stacked GAN

Image mosaicing is an image processing technique that is most commonly used to conceal identities of sensitive objects. The authors’ research features recovering the mosaiced parts in an image, especially focusing on facial parts. While recent image completion methods based on deep learning have shown promising results on recovering damaged parts in an image, they have not addressed the problem of image unmosaicing. Moreover, all those methods necessitate the location information of damaged parts to tackle the recovery problem. They formulate unmosaicing as an image‐to‐image translation problem, and propose a two‐stage method using generative adversarial network (GAN): stage‐I GAN generates a coarse prediction followed by stage‐II GAN which produces a final unmosaiced image with finer information. A combination of low‐level loss and high‐level structural similarity loss is used to attain visually plausible and semantically consistent output. They have evaluated their method on the CelebA dataset and achieved better results than state‐of‐the‐art image completion methods without explicitly exploiting the location information of mosaiced parts.
Accurate calibration method for a fringe projection system by projecting an adaptive fringe pattern

In fringe projection profilometry, the lens distortion of a commercial projector may introduce larger error when testing large objects and can become one of the major error sources. In this paper, a calibration method for a fringe projection system is adapted in order to remove the influence of projector lens distortion without the need to calibrate the projector. In the proposed method, the accuracy of the fringe projection system has a considerable improvement because an adaptive fringe pattern is calculated in order to compensate for all possible distortions generated by the projector.
STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings

This paper introduces STRASS: Summarization by TRAnsformation Selection and Scoring.It is an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces.Our method creates an extractive summary by selecting the sentences with the closest embeddings to the document embedding.The model learns a transformation of the document embedding to minimize the similarity between the extractive summary and the ground truth summary.As the transformation is only composed of a dense layer, the training can be done on CPU, therefore, inexpensive.Moreover, inference time is short and linear according to the number of sentences.As a second contribution, we introduce the French CASS dataset, composed of judgments from the French Court of cassation and their corresponding summaries.On this dataset, our results show that our method performs similarly to the state of the art extractive methods with effective training and inferring time.
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing

Neural semantic parsers utilize the encoderdecoder framework to learn an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation.To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens.We instead to propose an adaptive decoding method to avoid such intermediate representations.The decoder is guided by model uncertainty and automatically uses deeper computations when necessary.Thus it can predict tokens adaptively.Our model outperforms the state-of-the-art neural models and does not need any expertise like predefined grammar or sketches in the meantime.
Zenodo, an Archive and Publishing Repository: A tale of two herbarium specimen pilot projects

Zenodo (https://zenodo.org) is an open-access repository operated by CERN (European Organization for Nuclear Research), which provides researchers with an easy and stable platform to archive and publish their data and other output, such as software tools, manuals and project reports. In the context of the ICEDIG (Innovation and Consolidation for Large scale Digitisation of Natural Heritage) project, Zenodo was investigated for its usability as a platform where digitized images of collection specimens could be archived and published. In a production digitization pipeline, we foresee the automated archiving of daily image production. If Zenodo could be used for this purpose, such a process would also immediately mean that data and images are published FAIR-ly (Findable, Accessible, Interoperable and Reusable) within hours of their creation. To evaluate performance of the system, we first used a test dataset of 1800 herbarium specimen images, which was uploaded using Zenodo's API (Application Programming Interface) (Dillen et al. 2019). This dataset includes lossless TIFF images, label-segmented overlays and JSON-LD (JavaScript Object Notation for Linked Data) metadata using DwC (Darwin Core) terminology, constituting over 208 gigabytes of data. In addition, for all individual digital specimens the data about the specimen (in DwC) as well as metadata about its deposition on Zenodo (in Zenodo's internal data model) were available in multiple machine-readable formats. All data in DwC were provided as linked data with their DwC identifiers (e.g. http://rs.tdwg.org/dwc/terms/basisOfRecord). All individual specimens received minted DOIs (Digital Object Identifiers). A second upload of 280,000 herbarium JPEG images from a single institution (ca. 1 terabyte of data) with limited metadata (but using the same approach) was launched as well. In this presentation, the workflow for proper usage of the API will be described as well as some performance metrics, flexibilities and functionalities of the platform. Some issues and potential developments to tackle them will be discussed. Currently, the rate of ingestion into Zenodo seems only fast enough for small scale digitization pipelines. However, a modest improvement in transfer rate would make this a realistic proposition for large volume usage.
Digital Skills and New Media and Information Literacy in the Conditions of Digitization

Objectives of the article are: (1) to explore digital skills and media and information literacy, representing different aspects of the digital person who is responsible for how technology is used. (2) to present that these skills contribute to the development of identity, thinking and decision-making skills, and values. The article is based on several surveys: (1) “Cultural Universals in Academic Environment”, (2) “Fundamental transformations in media and communication systems and its reflections in society”, DN 05-11 2016/2019, team leader D. Peicheva; (3) “Digital Media Literacy in the context of “Knowledge Society”: state and challenges” KП-06-H25/4, team leader V. Milenkova. Students, who are respondents in surveys indicated that they use the Internet anywhere and feel confident in creating different digital content and on-line products. They are aware of the new dangers of emerging hybrid media wars and see the role of the digital media education and literacy in this direction.
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations

In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot.We propose four neural systems and combine them to further improve the results.We show that our neural ensemble systems can successfully distinguish three emotions (SAD, HAPPY, and ANGRY), and separate them from the rest (OTHERS) in a highly-imbalanced scenario.Our best system achieved a 0.77 F 1 -score and was ranked fourth out of 165 submissions.
WestSearch Plus

We present a non-factoid QA system that provides legally accurate, jurisdictionally relevant, and conversationally responsive answers to user-entered questions in the legal domain. This commercially available system is entirely based on NLP and IR, and does not rely on a structured knowledge base. WestSearch Plus aims to provide concise one sentence answers for basic questions about the law. It is not restricted in scope to particular topics or jurisdictions. The corpus of potential answers contains approximately 22M documents classified to over 120K legal topics.
Evolving dota 2 shadow fiend bots using genetic programming with external memory

The capacity of genetic programming (GP) to evolve a 'hero' character in the Dota 2 video game is investigated. A reinforcement learning context is assumed in which the only input is a 320-dimensional state vector and performance is expressed in terms of kills and net worth. Minimal assumptions are made to initialize the GP game playing agents - evolution from a tabula rasa starting point - implying that: 1) the instruction set is not task specific; 2) end of game performance feedback reflects quantitive properties a player experiences; 3) no attempt is made to impart game specific knowledge into GP, such as heuristics for improving navigation, minimizing partial observability, improving team work or prioritizing the protection of specific strategically important structures. In short, GP has to actively develop its own strategies for all aspects of the game. We are able to demonstrate competitive play with the built in game opponents assuming 1-on-1 competitions using the 'Shadow Fiend' hero. The single most important contributing factor to this result is the provision of external memory to GP. Without this, the resulting Dota 2 bots are not able to identify strategies that match those of the built-in game bot.
Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model

In recent years, multi-object tracking has attracted more and more attention, both in academia and engineering, but most of the recent works do not pay attention to the speed of the algorithm and only pursue the accuracy. In this paper, we propose an online multi-pedestrian tracking algorithm, taking into account both the accuracy and the speed. First, the motion models of the targets are established by the Kalman filter. At the same time, the appearance models of the targets are extracted by the convolutional neural network. Moreover, a data association algorithm is proposed, which integrates the motion information, including scale, intersection-over-union, and distance, and the appearance information, including the current appearance model and the long-term appearance model. With the data association algorithm, the matching between detections and tracklets is realized, and the goal of tracking by detection is achieved. We compare the proposed algorithm with other algorithms on the MOT15 benchmark and the MOT16 benchmark. The experiment results show that the algorithm has high accuracy and good real-time performance.
Discriminating between

This paper describes the language identification systems used by the SUKI team in the Discriminating between the Mainland and Taiwan variation of Mandarin Chinese (DMT) and the German Dialect Identification (GDI) shared tasks which were held as part of the third VarDial Evaluation Campaign. The DMT shared task included two separate tracks, one for the simplified Chinese script and one for the traditional Chinese script. We submitted three runs on both tracks of the DMT task as well as on the GDI task. We won the traditional Chinese track using Naive Bayes with language model adaptation, came second on GDI with an adaptive version of the HeLI 2.0 method, and third on the simplified Chinese track using again the adaptive Naive Bayes.
Generic Constructions of Five-Valued Spectra Boolean Functions

Whereas the design and properties of bent and plateaued functions have been frequently addressed during the past few decades, there are only a few design methods of the so-called five-valued spectra Boolean functions whose Walsh spectra take the values in {0, ±2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ1</sup> , ±2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ2</sup> }. Moreover, these design methods mainly regard the specification of these functions in their algebraic normal form (ANF) domain. In this paper, we give a precise characterization of this class of functions in their spectral domain using the concept of a dual of plateaued functions. Both necessary and sufficient conditions on the Walsh support of these functions are given, which then connects their design (in the spectral domain) to a family of the so-called totally (non-overlap) disjoint spectra plateaued functions. We identify some suitable families of plateaued functions having this property, thus providing some generic methods in the spectral domain. Furthermore, we also provide an extensive analysis of their constructions in the ANF domain and provide several generic design methods. The importance of this class of functions is manifolded, where apart from being suitable for some cryptographic applications, we emphasize their property of being constituent functions in the so-called four-bent decomposition.