Reviewerly/exHarmony huggingface dataset:
Secure and Robust Image Watermarking using Discrete Wavelet and Arnold Transforms

Visual information transmitted over a network is prone to copyright violation or forgery. Therefore, authenticating and protecting sensitive information by embedding a digital watermark is one of the most effective ways of solving this problem. A watermark is normally embedded onto visual data for ownership identification and copyright protection. Thus far, its robustness against malicious attacks can be further strengthen. This paper thus proposes a double secure key in digital watermarking scheme, using Discrete Wavelet (DWT) and Arnold transforms. Unlike other existing wavelet-based watermarking, the proposed scheme used Arnold transform to switch embedding positions. The experimental results on embedding a binary watermark onto both binary and color images demonstrated that it was not only difficult to decrypt but also stronger against attacks, while maintaining the most information in the original content.
A Hybrid Model for Spatio-Temporal Information Recognition in COVID-19 Trajectory Text

Since the outbreak of the COVID-19 epidemic at the end of 2019, the normalization of epidemic prevention and control has become one of the core tasks of the entire country. Health self-examination by checking the trajectory of diagnosed patients has gradually become everyone's basic necessity and essential to epidemic prevention. The COVID-19 patient's spatio-temporal information helps to facilitate the self-inspection of the masses of whether their trajectory overlaps with the confirmed cases, which promotes the epidemic prevention work. This paper, proposes a named entity recognition model to automatically identify the time and place information in the COVID-19 patient trajectory text. The model consists of an ALBERT layer, a Bi-GRU layer, and a GlobalPointer layer. The previous two layers jointly focus on extracting the context's characteristics and the semantic dependencies. And the GlobalPointer layer extracts the corresponding named entities from a global perspective, which improves the recognition ability for the long-nested place and time entities. Compared to the conventional name entity recognition models, our proposed model has high effectiveness because it has a smaller parameter scale and faster training speed. We evaluate the proposed model using a dataset crawled from the official COVID-19 trajectory text. The F1-score of the model has reached 92.86%, which outperforms four traditional named entity recognition models.
Data Augmentation for Multi-Image Super-Resolution

Super-resolution reconstruction consists in generating a high-resolution image from a single low-resolution image or multiple images presenting the same area of interest. Existing state-of-the-art approaches to single-image and multi-image super-resolution are based on deep learning that requires large amounts of training data. They are commonly obtained by simulating low-resolution images from an original image treated as a high-resolution reference, but such simulation may not reflect the real-life operating conditions. Therefore, a serious obstacle in deploying super-resolution in real-world cases results from the lack of training data that would encompass real low-resolution images coupled with a real high-resolution reference. In this paper, we propose a new data augmentation technique underpinned with learning the relation between high and low resolution. This helps reduce the requirements concerned with the amount of real-life data necessary to train a super-resolution network, while providing higher-quality data for training, compared with the simulated low-resolution images. Our initial experimental results reported in the paper confirm that the proposed approach is suitable for multi-image super-resolution.
Combating Multi-level Adversarial Text with Pruning based Adversarial Training

Despite significant advancements of deep learning-based models for natural language processing (NLP) tasks, previous efforts have shown that numerous models, including deep neural networks (DNNs), suffer from moderate to significant performance degradation with adversarial examples. Adversary crafts malicious text by adding, deleting, modifying chars, words, and sentences, to fool the DNN models. Therefore, adversarial training and model enhanced methods are proposed to combat the adversarial attack. However, both methods are lack generalization due to the overfitting intrinsic of neural networks. In this paper, we propose a novel framework to combat text adversarial examples, namely DisPAT, which consists an adversarial text discriminator and a robust pruned text classifier. First, we explore the adversarial examples and benign examples distribution in embedding space, indicating the feasibility of a DNN-based discriminator. To get multi-level adversarial texts, we deploy a generator, and a discriminator to identify adversarial perturbations. Notably, in the inference stage, our pipeline places the well-trained discriminator in front of the text classifier to distinguish the char-level adversarial text. Finally, we apply neuron-salience-based pruning to specifically improve the classifier performance of adversarial text. Experimental results show that our approach outperforms state-of-the-art baselines in combating both char-level and word-level adversarial text. Moreover, DisPAT achieves a very close to or even higher accuracy than that of the standard model.
Subarea Division for Urban Road Network Based on Causality

In this paper, a data-driven causal inference method used for intersection relationship detection of urban road network is proposed. Based on the detected causal relationship between intersections, a directed weighted logical network of the urban road network is constructed. Furthermore, a subarea division method based on modularity division is proposed. This method can divide the urban road network into any number of subareas by maximizing the degree of network modularity. The internal links of each subarea network reflect the close logical relationship between intersections, which may in turn facilitate urban road network planning in different areas.
Classification of Infectious Diseases in Chickens Based on Feces Images Using Deep Learning

Artificial intelligence technology in deep learning is one of the popular classification methods. The development of deep learning technology is expected to assist farmers in identifying the types of infectious diseases that attack chickens based on feces images so as to increase production yields. Several infectious diseases that attack chickens can be identified through their feces, including newcastle disease caused by a virus, pullorum caused by bacteria, and coccidiosis caused by parasites. To identify, it is necessary to classify the types of diseases that attack by using images of chicken feces. With deep learning based on Convolutional Neural Network (CNN) in Keras/TensorFlow, the percentage of images that were accurately predicted according to the classification of infectious diseases suffered by chickens was 95.28% and less accurate was 4.72%.
Self-adaptive Context Reasoning Mechanism for Text Sentiment Analysis

With the rapid development of contemporary e-commerce and social media, the need of better understanding and exploring users' evaluations on e-commercial products is becoming urgent and crucial, which results in the emergence of a new research hot-spot aiming at analysing and mining latent features of customer reviews on e-commercial products. In order to analyse the associations among semantic features with different lengths in e-commercial reviews, a text sentiment analysis method, named as ALBERT-SACR, is proposed based on self-adaptive context reasoning mechanism in this paper. Firstly, the global contextual features are extracted using the Transformer blocks of ALBERT. Then, semantic features with different lengths are extracted on the basis of multi-channel CNN combined with self-attention mechanism to perform context reasoning and adaptive adjustment of relational weights. Finally, a fully connected neural network is used for sentiment classification. public Chinese datasets Waimai and Shopping, where the performance of our method is qualitatively compared with five other methods. Simulation results verify both the effectiveness and efficiency of our proposed ALBERT-SACR, and the adaptive nature of our proposed SACR is effective in contextual inference for semantic features of different lengths.
Colorization for in situ Marine Plankton Images

Underwater imaging with red-NIR light illumination can avoid phototropic aggregation-induced observational deviation of marine plankton abundance under white light illumination, but this will lead to the loss of critical color information in the collected grayscale images, which is non-preferable to subsequent human and machine recognition. We present a novel deep networks-based vision system IsPlanktonCLR for automatic colorization of in situ marine plankton images. IsPlanktonCLR uses a reference module to generate self-guidance from a customized palette, which is obtained by clustering in situ plankton image colors. With this self-guidance, a parallel colorization module restores input grayscale images into their true color counterparts. Additionally, a new metric for image colorization evaluation is proposed, which can objectively reflect the color dissimilarity between comparative images. Experiments and comparisons with state-of-the-art approaches are presented to show that our method achieves a substantial improvement over previous methods on color restoration of scientific plankton image data.
ASR for Speech based Search in Hindi using Attention based Model

The experiment with automated speech recognition (ASR) in the domain of speech-based searching in Hindi language is described in this paper. Starting with the Listen-Attend-Spell (LAS) training algorithm i.e., a neural network (NN) that, unlike typical DNN-HMM models, directly transcribe spoken utterances, we build on and enhance the attention mechanisms as well as design of the model to add advanced methods such as location-based attention, multi-pass training (single pass, two pass and three pass), and external rescoring utilising language models and losses based on phonemes. Using these improvements, we show a comparative improvement in the error rates over state-of-the-art LAS models as well as the Connectionist Temporal Classification (CTC) approach. In addition, the paper gives insight of the many aspects which may easily be tweaked in a LAS-based system for better accuracy. The best Word Error Rate (WER) i.e., 10.83, is observed in LAS with 3 passes with addition of regularization technique.
From Research to Crisis Management: Multiagent Simulation for Local Governments

During the COVID-19 pandemic, a rise of (agent-based) simulation models for predicting future developments and assessing intervention scenarios has been observed. At the same time, dashboarding has become a popular way to aggregate and visualise large quantities of data. The AScore Pandemic Management Cockpit brings together multiagent-based simulation (MABS) and analysis functionalities for crisis managers. It combines the presentation of data and forecasting on the effects of containment measures in a modular, reusable architecture that streamlines the process of use for these non-researcher users. In this paper, the most successful features and concepts for the simplification of simulation usage are presented: definition of scenarios, limitation of parameters, and integrated result visualisation, all bundled in a web-based service to offer a low-barrier entry to the usage of MABS in decision-making processes.
New Configurations of Grain Ciphers: Security Against Slide Attacks

eSTREAM brought to the attention of the cryptographic community a number of stream ciphers including Grain v0 and its revised version Grain v1. The latter was selected as a finalist of the competition’s hardware-based portfolio. The Grain family includes two more instantiations, namely Grain-128 and Grain-128a. The scope of our paper is to provide an insight on how to obtain secure configurations of the Grain family of stream ciphers. We propose different variants for Grain and analyze their security with respect to slide attacks. More precisely, as various attacks against initialization algorithms of Grain were discussed in the literature, we study the security impact of various parameters which may influence the LFSR’s initialization scheme.
Managing Dataset Shift by Adversarial Validation for Credit Scoring

The inconsistency between the distribution of training data and the data that need to be predicted is very common in credit scoring scenarios, which is called dataset shift. The macroeconomic environment and risk control strategies are likely to evolve over time, and the behavior patterns of borrowers may also change. The model trained with past data may not be applicable to the recent stage. Although dataset shift can cause poor model performance, the vast majority of studies do not take this into account. In this study, we propose a method based on adversarial validation, in which partial training set samples with the closest distribution to the predicted data are selected for cross-validation to ensure generalization performance. In addition, the remaining training samples with inconsistent distribution are also involved in the training process, but not in the validation, which makes full use of all the data and further improves the model performance. To verify the effectiveness of the proposed method, comparative experiments with several other data split methods are conducted with the Lending Club dataset. The experimental results demonstrate the importance of dataset shift problem in the field of credit scoring and the superiority of the proposed method.
RoBiGAN: A bidirectional Wasserstein GAN approach for online robot fault diagnosis via internal anomaly detection

Complex robots in challenging scenarios require constant monitoring of their state and adaptation of their behavior to ensure robustness, reliability and longevity. While known possible errors can be specifically surveilled, other prob-lems can be fully unforeseen, requiring detection systems able to identify novel faults. We detect possible faults as anomalies on various internal sensor data, utilizing unsupervised learning techniques. A bidirectional Wasserstein GAN approach for anomaly detection on multivariate, highly dependent time-series data is implemented and trained on a small amount of non-anomalous robot sensor data. This model is then used for inference on the on-board hardware of a robot without parallel processing units. We evaluate multiple variants of the architecture using manually introduced anomalies in the form of different weights attached to the robot's legs. Overall we are able to show that RoBiGAN is able to consistently detect and localize small anomalies in an online scenario, with little to no robot specific modeling needed.
Efficient Flicker-Free Tone mapping of HDR Videos

Tone mapping is necessary for Low Dynamic Range (LDR) devices to display High Dynamic Range (HDR) images and videos. Multiple video Tone Mapping Operators (vTMOs) have been devised for HDR videos. The majority of vTMOs apply an image TMO to each video frame. This is followed by pre/post filtering to ensure temporal coherence. However, this destroys the natural temporal variation in intensity that is inherent in a changing scene. Furthermore, in these methods computational complexity of an image TMO is scaled up in proportion to the number of frames. We propose an efficient method to extend an image TMO to video TMO. The proposed method is general and takes care of temporally coherent intensity variation between frames while addressing the well-known issue of flickering in tone mapped video. Additionally, it lowers computational complexity as a new tone mapping curve (TMC) is not generated on per frame basis. The proposed vTMO can be used to extend any state-of-the-art global TMO that is deemed to generate a TMC. Fresh TMC is generated only when a hard cut in video is detected or the global change in illumination in HDR video becomes large enough. Visual comparisons and objective evaluations with three well known image TMOs demonstrate that the suggested extension method generates high quality LDR video generally at low computational cost when compared to existing vTMOs. Further work on efficient implementation of embedding an image TMO in our vTMO algorithm is expected to yield even better computational efficiency.
Research into Digital Oil Painting Restoration Algorithm Based on Image Acquisition Technology

In this study, the damaged oil painting is digitally collected by image acquisition technology, and the micro repair is realized by deconstructing the oil painting image. The integration of digital technology can more scientifically reveal the oil painting techniques and oil painting materials used, so as to provide a more scientific technical basis for the aging and damage repair of oil paintings and even oil painting cultural relics. Under this technical background, this research is based on the image acquisition technology to repair the damaged oil painting area, and uses the digital repair technology to collect the image of the specific damaged area of the oil painting. Then, the original pixels of the damaged area are identified by digital algorithm, and finally the pixel extension is obtained to realize the image repair of the damaged area. This repair is more accurate and has no coloring coverage, so it has wider applicability and better effect than the traditional repair.
Generating and Evaluating Text Summarisations using Text-representing Centroids(TRC)

Short abstracts and text summarisation are gaining increasing the importance of tools for filtering out the most relevant articles due to the increased documents and information. The following article uses TRCs to support text summaries’ generation and evaluation. A new method of document summarisation has been introduced based on text-representing centroids (TRC). TRC-based similarity measure delivers good similarity estimations for both methods, which are in the excellent range of 75 percent on average.
The More Secure, The Less Equally Usable: Gender and Ethnicity (Un)fairness of Deep Face Recognition along Security Thresholds

Face biometrics are playing a key role in making modern smart city applications more secure and usable. Commonly, the recognition threshold of a face recognition system is adjusted based on the degree of security for the considered use case. The likelihood of a match can be for instance decreased by setting a high threshold in case of a payment transaction verification. Prior work in face recognition has unfortunately showed that error rates are usually higher for certain demographic groups. These disparities have hence brought into question the fairness of systems empowered with face biometrics. In this paper, we investigate the extent to which disparities among demographic groups change under different security levels. Our analysis includes ten face recognition models, three security thresholds, and six demographic groups based on gender and ethnicity. Experiments show that the higher the security of the system is, the higher the disparities in usability among demographic groups are. Compelling unfairness issues hence exist and urge countermeasures in real-world high-stakes environments requiring severe security levels.
Effort Estimation using Neural Network and Metaheuristic Optimizer

One of the most essential factors in software development is to have an accurate forecast of workforce. In a project, the project management team must ensure accurate effort as well cost estimation in order to develop a high quality project which satisfies the customer's requirements. Inaccuracy in effort estimation of a project may lead to rejection of its development. Prognosis of effort forecasting involves prediction goals, different mechanisms for measurements, updating and refining techniques, and the processes used for execution. In the course of past few years, researches have examined various distinct problems for effort forecasting and have given newer models and techniques to minimize the errors involved. A lot of different models and approaches were surveyed to find out the trend in effort estimation and it was concluded that meta-heuristic-based models perform better as compared to other techniques proposed. So, in order to find a technique that can improve the effort accuracy, the work presented in this paper proposes a Neural Network based model which performs weight optimization using a meta-heuristic algorithm called Dragonfly algorithm. Performance of the proposed model had been evaluated using COCOMO81, Albrecht and Desharnais datasets and its performance had been compared against the effort estimation models developed using traditional machine learning algorithms viz. Random Forest and Regression Tree. The values of the performance parameters show the superior performance of the proposed meta-heuristic model as compared to the machine learning models.
Innovative the Hybrid Teaching Model of Computer Courses Based on the Concept of Deep Learning Under the Background of Big Data

Under the background of the era of big data (BD), blended teaching is the goal of education and teaching. Comprehensive integration of big data technology (BDT) and teaching, blended teaching has become an significant path of education, providing a broad space for students to realize deep learning (DL). How to effectively carry out blended teaching with the support of BD technology has become an important proposition for the development of education. Therefore, this study takes computer courses as the experimental object and uses BDT to construct a computer course hybrid teaching model (HTM) based on the concept of DL. According to the results of teaching evaluation, of 75% teachers and 63.33% of students recognize the model. Hold a positive attitude, and this mode has played a certain role in promoting the teaching consequent.
Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection

This paper aims to develop an acoustic signal-based unsupervised anomaly detection method for automatic machine monitoring. Existing approaches such as deep autoencoder (DAE), variational autoencoder (VAE), conditional variational autoencoder (CVAE) etc. have limited representation capabilities in the latent space and, hence, poor anomaly detection performance. Different models have to be trained for each different kind of machines to accurately perform the anomaly detection task. To solve this issue, we propose a new method named as hierarchical conditional variational autoencoder (HCVAE). This method utilizes available taxonomic hierarchical knowledge about industrial facility to refine the latent space representation. This knowledge helps model to improve the anomaly detection performance as well. We demonstrated the generalization capability of a single HCVAE model for different types of machines by using appropriate conditions. Additionally, to show the practicability of the proposed approach, (i) we evaluated HCVAE model on different domain and (ii) we checked the effect of partial hierarchical knowledge. Our results show that HCVAE method validates both of these points, and it outperforms the baseline system on anomaly detection task by utmost 15 % on the AUC score metric.