Reviewerly/exHarmony huggingface dataset:
Calculating “small” solutions of inhomogeneous relative Thue inequalities

Thue equations and their relative and inhomogeneous extensions are well known in the literature. There exist methods, usually tedious methods, for the complete resolution of these equations. On the other hand our experiences show that such equations usually do not have extremely large solutions. Therefore in several applications it is useful to have a fast algorithm to calculate the “small” solutions of these equations. Under “small” solutions we mean the solutions, say, with absolute values or sizes $\leq 10^{100}$. Such algorithms were formerly constructed for Thue equations, relative Thue equations. The relative and inhomogeneous Thue equations have applications in solving index form equations and certain resultant form equations. It is also known that certain “totally real” relative Thue equations can be reduced to absolute Thue equations (equations over $\mathbb{Z}$). As a common generalization of the above results, in our paper we develop a fast algorithm for calculating “small” solutions (say with sizes $\leq 10^{100}$) of inhomogeneous relative Thue equations, more exactly of certain inequalities that generalize those equations. We shall show that in the “totally real” case these can similarly be reduced to absolute inhomogeneous Thue inequalities. We also give an application to solving certain resultant equations in the relative case.
Question Generation from Code Snippets and Programming Error Messages

For some inexperienced developers, extracting key information from code snippets and programming error messages and turning it into a highly readable question can help them better understand, locate and search for the cause of errors. This paper proposes a copy mechanism guided transformer with pre-trained programming and natural languages representations (CMPPN) to automatically generate questions with high human readability from code snippets and programming error messages. Our CMPPN is pre-trained on a large scale code corpus with code summarization task based on transformer, and incorporated with copying mechanism in the fine-tuning phase. To evaluate our proposed model, we create a new dataset based on Stack Overflow posts, which contains code snippets, programming error messages and corresponding question headlines in 3 programming languages (Java, C# and Python). Extensive experimental results on this dataset verify the effectiveness of our CMPPN compared to baseline methods. Both dataset and model are available on https://github.com/YuiTH/CEMS-SO.
Enhancing Model Robustness via Lexical Distilling

Humans are prone to making typos in writing, which, though, doesn't affect understanding the whole sentence. However, neural models in natural language processing(NLP) would collapse when confronted with such tiny mistakes. This problem results from that neural models incline to entangle information, i.e., replacing a single aspect of the input text leads to significant changes in all components of the representation. Therefore, a trivial noise in a sentence can bring about a dramatic performance drop of the model. In this paper, we propose a novel and general framework to enhance the robustness of a model. The whole framework is trained in an adversarial style, which enables the model to encode the original sentence and the sentence refined by a lexical distiller to a similar sentence representation. We verify the effectiveness of the proposed framework in auto-encoder task. Experimental results show that our framework enhances the robustness of the model in different aspects.
Method for predicting behavior of substation workers based on generative confrontation network

A method for predicting abnormal behaviors of substation workers based on video scenes and using generative confrontation networks to integrate global and local information is proposed. In the substation, this method can be used to issue timely warnings to the transportation and inspection personnel that may trigger dangerous actions during the operation, so as to provide an important guarantee for the life and safety of the transportation and inspection personnel. The human behavior prediction task aims to predict future behavior video frames based on a given behavior video frame. Considering that the human behavior video contains not only relatively stable scene information, but also time-varying and complex human behavior information, this method first uses a global generation confrontation network to generate video scenes and rough human contours; then uses local generation confrontation Network to further optimize the details of human behavior in the video. Experiments show that, compared with the existing methods that only use a single model to achieve pixel-level behavior prediction, the method of combining global and local generation proposed in this paper can better capture the spatial appearance and the timing dynamics of humans in the video.
Perceptual Maps to Aggregate Information from Decision Makers

Understanding different perceptions of human being when using linguistic terms is a crucial issue in human-machine interaction. In this paper, we propose the concept of perceptual maps to model human opinions in a group decision-making context. The proposed approach considers a multi-granular structure using unbalanced hesitant linguistic term sets. An illustrative case is presented in the location decisions made by multinationals enterprises of the energy sector within the European smart city context.
A Semantic Feature Representation Method Based on Dynamic Selection of Sub-word-level and Word-level

Aiming at the problem that low-frequency words or unregistered words are difficult to learn effective word-level feature information due to lack of training samples, which makes the semantic expression of text inaccurate, this paper proposes a semantic feature representation method based on sub-word-level and word-level dynamic selection. First of all, using the bidirectional Long Short-Term Memory network (Bi-LSTM) to capture the characteristics of potential long-distance dependencies, a Bi-LSTM-based sub-word feature representation method is designed on the basis of the Skip-gram method. Then, in order to accurately obtain the semantic feature representation of words, a new gated dynamic selection mechanism is designed, which combines sub-word-level and word-level feature vectors to enrich the effective information of words. The experimental results show that the method proposed in this paper is effective. Compared with the word representation method of related research, the Pearson and Spearman correlation coefficients of this method are significantly improved on the STS dataset and SICK dataset.
Toward autonomous detection of anomalous GNSS data via applied unsupervised artificial intelligence

Artificial intelligence applications within the geo-sciences are becoming increasingly common, yet there are still many challenges involved in adapting established techniques to geoscience data sets. Applications in the realm of volcanic hazards assessment show great promise for addressing such challenges. Here, we describe a Jupyter Notebook we developed that ingests real-time GNSS data streams from the EarthCube CHORDS (Cloud-Hosted Real-time Data Services for the geosciences) portal TZVOLCANO, applies unsupervised learning algorithms to perform automated data quality control ("noise reduction"), and explores autonomous detection of unusual volcanic activity using a neural network. The TZVOLCANO CHORDS portal streams real-time Global Navigation Satellite System (GNSS) positioning data in 1 second intervals from the TZVOLCANO network, which monitors the active volcano Ol Doinyo Lengai in Tanzania, through UNAVCO’s real-time GNSS data services. UNAVCO’s real-time data services provide near-real-time positions processed by the Trimble Pivot system. The positioning data (latitude, longitude, and height) are imported into this Jupyter Notebook in user-defined time spans. The positioning data are then collected in sets by the Jupyter Notebook and processed to extract a useful calculated variable in preparation for the machine learning algorithms, of which we choose the vector magnitude. Unsupervised K-means and Gaussian Mixture machine learning algorithms are then utilized to locate and remove data points ("filter") that are likely caused by noise and unrelated to volcanic signals. We find that both the K-means and Gaussian Mixture machine learning algorithms perform well at identifying regions of high noise within tested GNSS data sets, but the Gaussian Mixtures approach performs better. The filtered data are then used to train an artificial intelligence neural network that predicts volcanic deformation. Our Jupyter Notebook has the potential to be used for detecting potentially hazardous volcanic activity in the form of rapid vertical or horizontal displacement of the Earth’s surface.
Unsupervised Motion Estimation of Vehicles Using ICP

Anticipating the motion of dynamic objects is critical for making intelligent decisions navigating through an environment while avoiding collisions. In this work, we propose a CNN model that estimates 3D motion of objects using sequences of monocular images. We show that we can train this model without using any manual annotations by using Iterative Closest Points (ICP) to align pointclouds of an object at different points in time. We compare our unsupervised approach to a model that was trained using ground truth supervision, on the KITTI tracking dataset. We further improve our model by training our model on a larger dataset, which would otherwise not be possible due to the lack of ground truth data. We also compare our approach with a 3D object detector that estimates motion using a simple tracking scheme.
Abnormal Behavior Detection Based On Optical Flow Features

Crime is inevitable and unpredictable. It can happen everywhere at any given time. Fortunately with the advancement of technology, surveillance devices become more commonly installed in public places. CCTVs are strategically placed in various cities. These CCTVs are usually connected to the city control center. Monitoring all surveillance devices manually is impractical and may not be 100% accurate. There could be some crime activities and crowd incidents that go unnoticed. Thus, we are proposing a method to automatically and continuously detect abnormal, potentially against-the-law behaviours based on visual cues. We will use optical flow algorithms as the feature extractor. Then we will experiment on 3 different classifiers to find the most accurate and suitable classifier for this purpose.
Multi-intent Attention and Top-k Network with Interactive Framework for Joint Multiple Intent Detection and Slot Filling

Multiple intent detection and slot filling are essential components of spoken language understanding. Existing methods treat multiple intent detection as a multi-label classification task. However, multi-label classification methods focus on the correlation between different intents and set the threshold to select the high probability intents. These methods will cause the model to miss part of the correct intents. In this paper, to address this issue, we introduce Multi-Intent Attention and Top-k Network with Interactive Framework (MIATIF) for joint multiple intent detection and slot filling. In particular, we model the multi-intent attention to obtaining the relation between the utterance and intents. Meanwhile, we propose the top-k network to encode the distribution of different intents and accurately predict the number of intents. Experimental results on two publicly available multiple intent datasets show substantial improvement. In addition, our model saves 64%–72% of training time compared to the current state-of-the-art graph-based model.
Map Construction Fusing Environmental Information and Motion Constraints

Robots are expected to replace humans in performing scientific expedition tasks, which greatly reduces the essential impact of human basic survival on scientific expedition activities, and improve the space-time coverage of scientific expedition activities in polar regions. However, the polar regions have complex topographic features, such as the edge of the glacier on the Qinghai-Tibet Plateau is steep and the inland is relatively flat, which undoubtedly brings great challenges to realize the safe and autonomous navigation of unmanned ground vehicles. For glacier area of the Qinghai-Tibet Plateau, accurately converting environmental information into the map that robots can understand is an important part of path planning, that is, mapping. This paper proposes a method of constructing an occupancy grid map for large-scale point cloud data, taking into account the movement constraints and terrain information of the mobile platform. By dividing and rasterizing the sub-point cloud, the state of the raster can be judged according to the normal, elevation and other information. Finally, we carried out path planning based on the A* on the raster map.
Orchestrator for Automating Failure Response in Telecom Carriers

The movement of digital transformation (DX) has reached to telecom carriers. Telecom carriers have long been trying to automate their operation, including assurance works, such as responding to fault alarms. Automating failure response is challenging since the work flows comprises complex flow containing many conditional branches, requiring judgement of skilled operator. Currently, only specific portions are automated, such as verifying construction work and executing specific commands. Therefore, we clarify the evidence accumulation and response process based on system-wide evidence, and propose a fully automated failure response system employing an augmented orchestrator that implements functions equivalent to human judgment. This paper proposes an enhanced orchestrator to automate the entire flow of failure response, such as fault alarm response, which is capable of handling alarms of commercial environment. This paper also describes the problems to automate entire flow of failure response and evaluates implemented proposing orchestrator. Evaluation results show that the aggregation of process succeeded to reduce approximately 82% the amount of in work flow description, and also the orchestrator is capable to handle large amount of alarms in bursts, such as in case of disaster.
Micro-project Design and Teaching Practice Based on Sticky Theory

Project-based learning is a student-centered form of teaching in which students are able to construct their own knowledge and reflect on their own learning projects, thereby increasing learning motivation and self-efficacy. Project-based learning has been explored in different contexts and at different stages of school education, from primary and secondary schools to higher education, and has achieved good teaching results. However, project teaching often lasts for a long period and is quite flexible, so it is difficult to cover most of the course knowledge. In new curriculum system, the class hours are greatly compressed, which makes it difficult for teachers to carry out the project teaching effectively. This paper designed a series of micro-projects based on sticky theory by taking the course “Optical Communication System and Network” as an example to facilitate the core knowledge of the course. The paper first introduces the design methods of micro-projects based on content stickiness, and designs the teaching mode of micro-projects based on pattern stickiness, and then applies the methods and the mode to teaching practice. The teaching results show that students have improved their abilities in some aspects, and gained a great sense of achievement in learning.
Proposal and Evaluation of Automatic Registration Method of Service Information by Distribution from Deployment System to Maintenance System

With the popularization of B2B2X (Business to Business to X, where “X” represents any end user) business, the software for supporting both deployment and maintenance of collaborative services, which combine multiple services, is required. We previously investigated technology for enabling both deployment and maintenance of collaborative services in one stop and achieved automation in every process such as deployment and maintenance. However, to complete a series of processes in one stop without operators, it is necessary to automatically distribute service information (configuration of systems that provide collaborative services) between processes. In this study, we developed a method for automatically distributing service information between a deployment system and a maintenance system, and we evaluated its feasibility. For the evaluation, we implemented the proposed method with current technologies and applied it to the service-maintenance process in a virtual environment.
Ontology Driven Software Development for Better Understanding and Maintenance of Software System

The last few decades have seen tremendous growth in the IT industry where offshoring software has become a general phenomenon. Due to this offshoring, development and maintenance teams are generally located in different time zones along with at distinct locations. This separation of teams at different locations leads to the problem of sharing information in a consistent manner. In this respect, the Semantic Web plays an essential role as it helps in sharing and reusing already available information. Due to the emergence of the Semantic Web, it has been applied in various Software Engineering tasks and issues. Among those various issues, one is to provide a model or system, which can produce automatic documentation for every version of a software system for their better understanding and maintenance. Therefore, this research paper proposes an ontology driven software development approach for better understanding and maintenance of software system. The research approach consists of a multi-phased framework in a cyclic manner that generates the various versions of software and their associated documentation. To implement and demonstrate the research approach, we picked a case study. The results of the implementation demonstrate that the paper successfully generated all versions of the software along with its automatic documentation. Moreover, the documentation is accessible in machine-understandable together with human-understandable form, which leads to the generation of consistent information without any ambiguity. Thus, the research work helps in better understanding and maintenance of software systems.
Designing a Cultural Augmented Reality Application For Educational Purposes

Within the past years, cultural organizations have shown an increasing tendency to adopt technologically innovative solutions in their activities. This shift is attributed to the wider technological evolution, in general, and the manifold capabilities that modern mobile devices have, in particular. Augmented Reality (AR) applications constitute a typical example of an interactive solution which has been already applied in various domains (such as, recreation, education). Motivated by the wider adoption that this technological solution has received, this article discusses the design and the developmental steps followed to create an AR (marker-based) application, which is dedicated to the medieval castle of Arta in Greece. The application has been developed using the Unity 3D platform and the Vuforia Software Development Kit (SDK). As part of our ongoing research and development efforts, our future plans include the empirical evaluation of the developed application. In addition, we hope that the presentation of this work will motivate cultural organisation to consider the adoption and the use of the AR technology as a means to complement and enhance the visitors’ user and, consequently, learning experience.
Métis - An Approach Utilized as Differentiated Authenticity Tool in an IIoT Infrastructure

Security in industrial environments is a growing concern with the integration of Industrial IoT (IIoT). The communication between devices, diverse users, and the volume of digital data transferred increase the vulnerability. Aiming to tackle this challenge, we developed studies related to the application of smart contracts with blockchain support to guarantee the integrity of identity authenticity of the digital data that travels within the Industrial IoT (IIoT) environment. Therefore, in this paper, we present the Métis proposal, which represents a differentiated authenticity approach and which was tested through simulations to provide a security landscape to a real industrial 4.0 project.
A Robust Framework to Estimate the Spatial Resolution of Overhead Images Using Off-the-Shelf Object Detectors

We propose a robust framework for estimating the ground sample distance (GSD) of overhead images using off-the-shelf object detectors. Knowing the GSD of an overhead image is important for many tasks yet images are increasingly being distributed without such meta data due to the advent of drones. We make our framework robust by performing detections over a number of scales and orientations. We derive a reference curve from a large number of training images that relates GSD to object size. This reference curve is used to estimate the GSD of a test image. We demonstrate the effectiveness of our framework on drone imagery ranging from 2 to 32cm GSD using an off-the-shelf car detector.
Modeling hypotactic structure for Chinese-English neural machine translation of complex sentences

The hypotactic structural relation between clauses plays an important role in improving the discourse coherence of document-level translation. However, the standard neural machine translation (NMT) models do not explicitly model the hypotactic relationship between clauses, which usually leads to structurally incorrect translations of long and complex sentences. This problem is particularly noticeable on Chinese-to-English translation task of complex sentences due to the grammatical form distinction between English and Chinese. English is rich in grammatical form (e.g. verb morphological changes and subordinating conjunctions) while Chinese is poor in grammatical form. These linguistic phenomena make it a challenge for NMT to learn the hypotactic structure knowledge from Chinese as well as the structure alignment between Chinese and English. To address these issues, we propose to model the hypotactic structure for Chinese-to-English complex sentence translation by introducing hypotactic structure knowledge. Specifically, we annotate and build a hypotactic structure aligned parallel corpus that provides rich hypotactic structure knowledge for NMT. Moreover, we further propose a structure-infused neural framework to combine the hypotactic structure knowledge with the NMT model through two integrating strategies. In particular, we introduce a specific structure-aware loss to encourage the NMT model to better learn the structure knowledge. Experimental results on WMT17, WMT18 and WMT19 Chinese-to-English translation tasks demonstrate the effectiveness of the proposed methods.
Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture

Deep reinforcement learning (DRL) has been demonstrated to provide promising results in several challenging decision making and control tasks. However, the required inference costs of deep neural networks (DNNs) could prevent DRL from being applied to mobile robots which cannot afford high energy-consuming computations. To enable DRL methods to be affordable in such energy-limited platforms, we propose an asymmetric architecture that reduces the overall inference costs via switching between a computationally expensive policy and an economic one. The experimental results evaluated on a number of representative benchmark suites for robotic control tasks demonstrate that our method is able to reduce the inference costs while retaining the agent’s overall performance.