Reviewerly/exHarmony huggingface dataset:
Question answering system for deterministic fault diagnosis of intelligent railway signal equipment

Purpose The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships. Design/methodology/approach This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor. Findings Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain. Originality/value It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.
CrossMap Transformer: A Crossmodal Masked Path Transformer Using Double Back-Translation for Vision-and-Language Navigation

Navigation guided by natural language instructions is particularly suitable for Domestic Service Robots that interacts naturally with users. This task involves the prediction of a sequence of actions that leads to a specified destination given a natural language navigation instruction. The task thus requires the understanding of instructions, such as “Walk out of the bathroom and wait on the stairs that are on the right”. The Visual and Language Navigation remains challenging, notably because it requires the exploration of the environment and at the accurate following of a path specified by the instructions to model the relationship between language and vision. To address this, we propose the CrossMap Transformer network, which encodes the linguistic and visual features to sequentially generate a path. The CrossMap transformer is tied to a Transformer-based speaker that generates navigation instructions. The two networks share common latent features, for mutual enhancement through a double back translation model: Generated paths are translated into instructions while generated instructions are translated into path. The experimental results show the benefits of our approach in terms of instruction understanding and instruction generation.
Medical Named Entity Recognition from Un-labelled Medical Records based on Pre-trained Language Models and Domain Dictionary

Medical named entity recognition (NER) is an area in which medical named entities are recognized from medical texts, such as diseases, drugs, surgery reports, anatomical parts, and examination documents. Conventional medical NER methods do not make full use of un-labelled medical texts embedded in medical documents. To address this issue, we proposed a medical NER approach based on pre-trained language models and a domain dictionary. First, we constructed a medical entity dictionary by extracting medical entities from labelled medical texts and collecting medical entities from other resources, such as the Yidu-N4K data set. Second, we employed this dictionary to train domain-specific pre-trained language models using un-labelled medical texts. Third, we employed a pseudo labelling mechanism in un-labelled medical texts to automatically annotate texts and create pseudo labels. Fourth, the BiLSTM-CRF sequence tagging model was used to fine-tune the pre-trained language models. Our experiments on the un-labelled medical texts, which were extracted from Chinese electronic medical records, show that the proposed NER approach enables the strict and relaxed F1 scores to be 88.7% and 95.3%, respectively.
Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere

Point-to-Hyperplane Nearest Neighbor Search (P2HNNS) is a fundamental yet challenging problem, and it has plenty of applications in various fields. Existing hyperplane hashing schemes enjoy sub-linear query time and achieve excellent performance on applications such as large-scale active learning with Support Vector Machines (SVMs). However, they only conditionally deal with this problem with a strong assumption that all of the data objects are normalized, located at the unit hypersphere. Those hyperplane hashing schemes may be arbitrarily bad without this assumption. In this paper, we introduce a new asymmetric transformation and develop the first two provable hyperplane hashing schemes, Nearest Hyperplane hashing (NH) and Furthest Hyperplane hashing (FH), for high-dimensional P2HNNS beyond the unit hypersphere. With this asymmetric transformation, we demonstrate that the hash functions of NH and FH are locality-sensitive to the hyperplane queries, and both of them enjoy quality guarantee on query results. Moreover, we propose a data-dependent multi-partition strategy to boost the search performance of FH. NH can perform the hyperplane queries in sub-linear time, while FH enjoys a better practical performance. We evaluate NH and FH over five real-life datasets and show that we are around $3 \sim 100 \times$ faster than the best competitor in four out of five datasets, especially for the recall in $[20%, 80%]$. Code is available at \urlhttps://github.com/HuangQiang/P2HNNS.
RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation

Decision-making for self-adaptation approaches need to address different challenges, including the quantification of the uncertainty of events that cannot be foreseen in advance and their effects, and dealing with conflicting objectives that inherently involve multi-objective decision making (e.g., avoiding costs vs. providing reliable service).To enable researchers to evaluate and compare decision-making techniques for selfadaptation, we present the RDMSim exemplar.RDMSim enables researchers to evaluate and compare techniques for decisionmaking under uncertainty that support self-adaptation.The focus of the exemplar is on the domain problem related to Remote Data Mirroring, which gives opportunity to face the challenges described above.RDMSim provides probe and effector components for easy integration with external adaptation managers, which are associated with decision-making techniques and based on the MAPE-K loop.Specifically, the paper presents (i) RDMSim, a simulator for real-world experimentation, (ii) a set of realistic simulation scenarios that can be used for experimentation and comparison purposes, (iii) data for the sake of comparison.
Find it if you can: end-to-end adversarial erasing for weakly-supervised semantic segmentation

Semantic segmentation is a task that traditionally requires a large dataset of pixel-level ground truth labels, which is time-consuming and expensive to obtain. Recent advancements in the weakly-supervised setting show that reasonable performance can be obtained by using only image-level labels. Classification is often used as a proxy task to train a deep neural network from which attention maps are extracted. However, the classification task needs only the minimum evidence to make predictions, hence it focuses on the most discriminative object regions. To overcome this problem, we propose a novel formulation of adversarial erasing of the attention maps. In contrast to previous adversarial erasing methods, we optimize two networks with opposing loss functions, which eliminates the requirement of certain suboptimal strategies; for instance, having multiple training steps that complicate the training process or a weight sharing policy between networks operating on different distributions that might be suboptimal for performance. The proposed solution does not require saliency masks, instead it uses a regularization loss to prevent the attention maps from spreading to less discriminative object regions. Our experiments on the Pascal VOC dataset demonstrate that our adversarial approach increases segmentation performance by 2.1 mIoU compared to our baseline and by 1.0 mIoU compared to previous adversarial erasing approaches.
Domain-Independent Dominance of Adaptive Methods

From a simplified analysis of adaptive methods, we derive AvaGrad, a new optimizer which outperforms SGD on vision tasks when its adaptability is properly tuned. We observe that the power of our method is partially explained by a decoupling of learning rate and adaptability, greatly simplifying hyperparameter search. In light of this observation, we demonstrate that, against conventional wisdom, Adam can also outperform SGD on vision tasks, as long as the coupling between its learning rate and adaptability is taken into account. In practice, AvaGrad matches the best results, as measured by generalization accuracy, delivered by any existing optimizer (SGD or adaptive) across image classification (CIFAR, ImageNet) and character-level language modelling (Penn Treebank) tasks. When training GANs, AvaGrad improves upon existing optimizers. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Towards size-dependent types for array programming

We present a type system for expressing size constraints on array types in an ML-style type system. The goal is to detect shape mismatches at compile-time, while being simpler than full dependent types. The main restrictions is that the only terms that can occur in types are array sizes, and syntactically they must be variables or constants. For those programs where this is not sufficient, we support a form of existential types, with the type system automatically managing the requisite book-keeping. We formalise a large subset of the type system in a small core language, which we prove sound. We also present an integration of the type system in the high-performance parallel functional language Futhark, and show on a collection of 44 representative programs that the restrictions in the type system are not too problematic in practice.
Architectural design decisions that incur technical debt — An industrial case study

During software development, some architectural design decisions incur technical debt, either deliberately or inadvertently. These have serious impact on the quality of a software system, and can cost significant time and effort to be changed. While current research efforts have explored general concepts of architectural design decisions and technical debt separately, debt-incurring architectural design decisions have not been specifically explored in practice. In this case study, we explore debt-incurring architectural design decisions (DADDs) in practice. Specifically, we explore the main types of DADDs, why and how they are incurred in a software system, and how practitioners deal with these types of design decisions. We performed interviews and a focus group with practitioners working in embedded and enterprise software companies, discussing their concrete experience with such architectural design decisions. We provide the following contributions: 1) A categorization for the types of DADDs, which extend a current ontology on architectural design decisions. 2) A process on how deliberate DADDs are made in practice. 3) A conceptual model which shows the relationships between the causes and triggers of inadvertent DADDs. 4) The main factors that influence the way of dealing with DADDs. The results can support the development of new approaches and tools for Architecture Technical Debt management from the perspective of Design Decisions. Moreover, they support future research to capture architecture knowledge related to DADDs.
Exploring Knowledge Distillation of a Deep Neural Network for Multi-script Identification

Multi-lingual script identification is a difficult task consisting of different language with complex backgrounds in scene text images. According to the current research scenario, deep neural networks are employed as teacher models to train a smaller student network by utilizing the teacher model’s predictions. This process is known as dark knowledge transfer. It has been quite successful in many domains where the final result obtained is unachievable through directly training the student network with a simple architecture. In this paper, we explore dark knowledge transfer approach using long short-term memory (LSTM) and CNN based assistant model and various deep neural networks as the teacher model, with a simple CNN based student network, in this domain of multi-script identification from natural scene text images. We explore the performance of different teacher models and their ability to transfer knowledge to a student network. Although the small student network’s limited size, our approach obtains satisfactory results on a well-known script identification dataset CVSI-2015.
A Hybrid Intelligent Text Watermarking and Natural Language Processing Approach for Transferring and Receiving an Authentic English Text Via Internet

Abstract Due to the rapid increase in the exchange of text information via internet networks, the security and the reliability of digital content have become a major research issue. The main challenges faced by researchers are authentication, integrity verification, and tampering detection of the digital contents. In this paper, a Robust English Text Watermarking and Natural Language Processing Approach (RETWNLPA) is proposed based on word mechanism and first level order of Markov model to improve the accuracy of tampering detection of sensitive English text. The RETWNLPA approach embeds and detects the watermark logically without altering the original text document. Based on the hidden Markov model (HMM), the first-level order of word mechanism is used to analyze the interrelationship between English text. The extracted features are used as watermark information and integrated with text zero-watermarking techniques. To detect eventual tampering, RETWNLPA has been implemented and validated with attacked English text. Experiments were performed on four datasets of varying sizes under random locations of common tampering attacks. The simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks. Comparison results show that RETWNLPA outperforms baseline approaches HNLPZWA (an intelligent hybrid of natural language processing and zero-watermarking approach) and ZWAFWMMM (Zero-Watermarking Approach based on Fourth level order of Word Mechanism of Markov Model) in terms of tampering detection accuracy.
Linear correlation analysis of ammunition storage environment based on Pearson correlation analysis

Abstract The problem of the reliability of ammunition storage environment is a hot topic in the field of modern military research, and the key is to find out the main factors that affect the reliability of ammunition sensitive components. Grey relational entropy analysis method can find out the order of influencing factors, but it can’t judge the influence size and correlation. Pearson analysis method can analyze linear correlation factors better. Therefore, this paper proposes that for the complex problems such as the influence factors of ammunition storage environment, Pearson correlation analysis method can be used to find out the linear correlation factors. Then the grey correlation entropy method is used to find out the other factors. It can lay the foundation for the follow-up analysis of the main factors. Through the comparison and analysis of the actual examples, it is found that the ranking of the influencing factors ranking by the two methods has high consistency. But Pearson method can find the linear correlation factors.
Acoustic-based Upper Facial Action Recognition for Smart Eyewear

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.
Video interframe forgery detection: Classification, technique &amp; new dataset

Multimedia communication as well as other related innovations are gaining tremendous growth in the modern technological era. Even though digital content has traditionally proved to be a piece of legitimate evidence. But the latest technologies have lessened this trust, as a variety of video editing tools have been developed to modify the original video. Therefore, in order to resolve this problem, a new technique has been proposed for the detection of duplicate video sequences. The present paper utilizes gray values to extract Hu moment features in the current frame. These features are further used for classification of video as authentic or forged. Afterwards there was also need to validate the proposed technique using training and test dataset. But the scarcity of training and test datasets, however, is indeed one of the key problems to validate the effectiveness of video tampering detection techniques. In this perspective, the Video Forensics Library for Frame Duplication (VLFD) dataset has been introduced for frame duplication detection purposes. The proposed dataset is made of 210 native videos, in Ultra-HD and Full-HD resolution, captured with different cameras. Every video is 6 to 15 seconds in length and runs at 30 frames per second. All the recordings have been acquired in three different scenarios (indoor, outdoor, nature) and in landscape mode(s). VLFD includes both authentic and manipulated video files. This dataset has been created as an initial repository for manipulated video and enhanced with new features and new techniques in future.
The Overlaps and Boundaries Between Service Design and User Experience Design

The ongoing transition within tech industry towards holistic service journey orchestration pushes user experience design (UXD) closer to service design (SD). At the same time, new digital channels for traditional services pushes SD closer to UXD. The increasing interplay between UXD and SD can cause confusion about their relationship, boundaries, and responsibilities. Little research has examined perceptions of these aspects in industry and academia. We surveyed 197 UXD and SD practitioners and academics about their understanding of the overlaps and boundaries between the two design practices. Our findings reveal several conflicting views around the scope of UXD and SD work, the subsumptive relation between UXD and SD, and the theoretical roots and methods each draws upon. We suggest constructive ways for the two design practices to complement each other. Our study shows that UX work is changing, and understanding this change is important to maintain the relevance of UX research.
An Automatic Convolutional Neural Network Optimization Using a Diversity-Guided Genetic Algorithm

Hyperparameters and architecture greatly influence the performance of convolutional neural networks (CNNs); therefore, their optimization is important to obtain the desired results.One of the state-ofthe-art methods to achieve this is the use of neuroevolution that utilizes a genetic algorithm (GA) to optimize a CNN.However, the GA is often trapped into a local optimum resulting in premature convergence.In this study, we propose an approach called the ''diversity-guided genetic algorithm-convolutional neural network (DGGA-CNN)'' that uses adaptive parameter control and random injection to facilitate the search process by exploration and exploitation while preserving the population diversity.The alternation between exploration and exploitation is guided by using an average pairwise Hamming distance.Moreover, the DGGA fully handles the architecture of the CNN by using a novel finite state machine (FSM) combined with three novel mutation mechanisms that are specifically created for architecture chromosomes.Tests conducted on suggestion mining and twitter airline datasets reveal that the DGGA-CNN performs well with valid architectures and a comparison with other methods demonstrates its capability and efficiency.