Reviewerly/exHarmony huggingface dataset:
Multi-level adversarial network for domain adaptive semantic segmentation

Recent progresses in domain adaptive semantic segmentation demonstrate the effectiveness of adversarial learning (AL) in unsupervised domain adaptation. However, most adversarial learning based methods align source and target distributions at a global image level but neglect the inconsistency around local image regions. This paper presents a novel multi-level adversarial network (MLAN) that aims to address inter-domain inconsistency at both global image level and local region level optimally. MLAN has two novel designs, namely, region-level adversarial learning (RL-AL) and co-regularized adversarial learning (CR-AL). Specifically, RL-AL models prototypical regional context-relations explicitly in the feature space of a labelled source domain and transfers them to an unlabelled target domain via adversarial learning. CR-AL fuses region-level AL and image-level AL optimally via mutual regularization. In addition, we design a multi-level consistency map that can guide domain adaptation in both input space (i.e., image-to-image translation) and output space (i.e., self-training) effectively. Extensive experiments show that MLAN outperforms the state-of-the-art with a large margin consistently across multiple datasets.
An Adaptive Pyramid Graph and Variation Residual-Based Anomaly Detection Network for Rail Surface Defects

Anomaly detection is a crucial means to detect unbalanced and multi-class abnormal data in industrial products. The existing anomaly detection model is usually suitable for products with a simple or regular background texture. However, there is a lack of a universal surface defect anomaly detection framework for railway products when facing the challenges: the complex background texture, huge intra-class variation, uneven distribution of inter-class, and weak texture-featured defects. To fill this gap, an innovative generative adversarial network based on adaptive pyramid graph and variation residuals (APGVR-GAN) is proposed, aiming to improve the robustness of anomaly detection in railway products and other complex industrial supplies. First, the adaptive pyramid graph module is embedded in the encoder-decoder-encoder pattern, capturing the correlation description between neighbor regions, which is utilized to enhance the detection of abnormal defects with weak texture. Next, the variation residual module is employed to enhance the expression of various normal samples in the latent space, and improve the identification ability for abnormal samples. Then, the dual-probability prototype loss is proposed to make different normal samples have more concentrated expression and more similar probability distribution centers in latent space. Finally, an adaptive focal-gate loss and a regularized log-likelihood loss are designed to overcome the imbalance problem in training samples with different background information. The effectiveness of the model is verified on three new railway datasets and three other industrial public datasets. The results show that in anomaly detection, APGVR-GAN on the three railway data sets NEU-RSDD-1 dataset, NEU-RSDD-1 dataset and RSDD AUC results can reach 0.89, 0.81, and 0.87, and its detection accuracy reached 0.97, 0.96 and 0.92, which is much higher than other comparative anomaly detection algorithms. In the segmentation experiments on the RSDD dataset, compared with other known rail defect detection methods, the overall evaluation metric F-Score is 83.70 which also better than other methods.
Josephson-Based Scheme for the Detection of Microwave Photons

We propose a scheme for the detection of microwave induced photons through current-biased Josephson junction, from the point of view of the statistical decision theory. Our analysis is based on the numerical study of the zero voltage lifetime distribution in response to a periodic train of pulses, that mimics the absorption of photons. The statistical properties of the detection are retrieved comparing the thermally induced transitions with the distribution of the switchings to the finite voltage state due to the joint action of thermal noise and of the incident pulses. The capability to discriminate the photon arrival can be quantified through the Kumar-Caroll index, which is a good indicator of the Signal-to-Noise-Ratio. The index can be exploited to identify the system parameters best suited for the detection of weak microwave photons.
Hybrid collaborative multi-objective fruit fly optimization algorithm for scheduling workflow in cloud environment

Scheduling the complex workflows in cloud environment have drawn enormous attentions because the distinct features of the cloud resources. Most of the previous approaches ignored the multiple conflicting objectives of workflow scheduling and resources provisioning. In this paper, a novel hybrid collaborative multi-objective fruit fly optimization algorithm (HCMFOA) is developed to optimize both the execution time and cost. In the proposed HCMFOA, a reference points-based cluster strategy is introduced to dynamic divide the swarm into multiple sub-swarms. Moreover, a hybrid initial strategy is designed based on non-linear weight vector and two assignment rules of tasks to initialize the location of all the fruit flies in the problem space. In the collaborative smell-based foraging, three effective problem-specific neighborhood operators are employed to collaborative explore the global scope. In multi-objective vision-based foraging, the sub-swarms based crossover operator is designed to perform exploitation in local region. Finally, an extensive computational experiment is conducted to validate the performance of HCMFOA. The statistical results reveal that HCMFOA significantly outperforms the existing state-of-the-art approaches.
Fuzzy information decomposition incorporated and weighted Relief-F feature selection: When imbalanced data meet incompletion

Data classification is an important computer task in data analysis, which suffers seriously unknown features, imbalanced class, and incomplete data. However, despite their vital yet practical significance, few results have been made on such three distinct issues. To address this problem, we propose a novel feature selection method for the data subject to incomplete data and imbalanced class, namely, improved fuzzy information decomposition (IFID) incorporated and weighted Relief-F (WRelief-F) feature selection. The main idea of the proposed feature selection method is threefold. (1) The proposed IFID algorithm can deal with the imbalanced class and incomplete data at the same time. (2) In IFID, a new membership function is provided to reflect the influence of the observed data on the missing values appropriately. Based on this establishment, a more delicate information decomposition is adopted to make a better recovery than the traditional FID. (3) After using IFID, WRelief-F is put forward to take the relationship of the target instance to inter-class instances and the intra-class instances into consideration in a proper manner. Finally, experiments on the seven public data sets are utilized to show the effectiveness and universal applicability of the proposed feature selection algorithm.
LWT-DCT Based Image Hashing for Tampering Localization via Blind Geometric Correction

There is a tradeoff between hash length and small area tampered localization based on the block-based analysis. In this paper, we have mitigated this issue using only global features. A compact image hash is constructed using LL band of Lifting Wavelet Transform (LWT) followed by Discrete Cosine Transform (DCT). An image map is reconstructed from the hash. Based on the difference of image map obtained from the received hash and received image, tampering regions have been localized. Experimented the blind geometric correction removes the influences of geometric distortion while tampering with localization. While decreasing the hash length, a very small area of tampered regions has been localized using the proposed approach. Due to the blind geometric correction, the proposed method can localize tampering, even if tampering and composite RST geometric transformations occur simultaneously. The experimental results reveal that the proposed technique can upgrade the tampering localization compared to both the block-based and existing global features based state-of-the-art methods.
High-Performance English–Chinese Machine Translation Based on GPU-Enabled Deep Neural Networks with Domain Corpus

The ability to automate machine translation has various applications in international commerce, medicine, travel, education, and text digitization. Due to the different grammar and lack of clear word boundaries in Chinese, it is challenging to conduct translation from word-based languages (e.g., English) to Chinese. This article has implemented a GPU-enabled deep learning machine translation system based on a domain-specific corpus. Our system takes English text as input and uses an encoder-decoder model with an attention mechanism based on Google’s Transformer to translate the text to Chinese output. The model was trained using a simple self-designed entropy loss function and an Adam optimizer on English–Chinese bilingual text sentences from the News area of the UM-Corpus. The parallel training process of our model can be performed on common laptops, desktops, and servers with one or more GPUs. At training time, we not only track loss over training epochs but also measure the quality of our model’s translations with the BLEU score. We also provide an easy-to-use web interface for users so as to manage corpus, training projects, and trained models. The experimental results show that we can achieve a maximum BLEU score of 29.2. We can further improve this score by tuning other hyperparameters. The GPU-enabled model training runs over 15x faster than on a multi-core CPU, which facilitates us having a shorter turn-around time. As a case study, we compare the performance of our model to that of Baidu’s, which shows that our model can compete with the industry-level translation system. We argue that our deep-learning-based translation system is particularly suitable for teaching purposes and small/medium-sized enterprises.
1xN Pattern for Pruning Convolutional Neural Networks

Though network pruning receives popularity in reducing the complexity of convolutional neural networks (CNNs), it remains an open issue to concurrently maintain model accuracy as well as achieve significant speedups on general CPUs. In this paper, we propose a novel 1×N pruning pattern to break this limitation. In particular, consecutive N output kernels with the same input channel index are grouped into one block, which serves as a basic pruning granularity of our pruning pattern. Our 1×N pattern prunes these blocks considered unimportant. We also provide a workflow of filter rearrangement that first rearranges the weight matrix in the output channel dimension to derive more influential blocks for accuracy improvements and then applies similar rearrangement to the next-layer weights in the input channel dimension to ensure correct convolutional operations. Moreover, the output computation after our 1×N pruning can be realized via a parallelized block-wise vectorized operation, leading to significant speedups on general CPUs. The efficacy of our pruning pattern is proved with experiments on ILSVRC-2012. For example, given the pruning rate of 50% and N=4, our pattern obtains about 3.0% improvements over filter pruning in the top-1 accuracy of MobileNet-V2. Meanwhile, it obtains 56.04ms inference savings on Cortex-A7 CPU over weight pruning. Our project is made available at https://github.com/lmbxmu/1xN.
Semantic and explainable research-related recommendation system based on semi-supervised methodology using BERT and LDA models

With the launch of academic search engines such as Google Scholar, Microsoft Academic, and Scopus, researchers are using them to access scholarly materials on a large scale without making any payment. In this optimal research environment, academic libraries or community databases have naturally experienced a rapid increase in volume; however, the excessive quantitative growth of information, or information overload, acts as a double-edged sword that prevents researchers from finding relevant prior studies or researchers with similar interests. Existing keyword and rule-based search systems carry the risk of recommending research literature with a high citation frequency rather than recommending contextually similar documents. Therefore, this study proposes a semi-supervised semantic-based research literature and researcher recommendation system using LDA and BERT. Since a semi-supervised method is used, research literature can be embedded based on contextual and classification information, and the global topic information of the research literature can also be captured. In addition, a research literature information extractor system has been implemented, which comprises classification network of our model and an explainable keywords extractor system implemented using BERT’s self-attention structure. Based on the experimental results, it can be confirmed that the proposed study model shows better performance than other baselines. This model can help users to find information and contextual matching data quickly and accurately from the database. In addition, when implementing an academic library and community database, such a powerful research-related recommendation system and research literature information extractor are expected to have an enormous effect.
Detection of mineralization stages using zonality and multifractal modeling based on geological and geochemical data in the Au-(Cu) intrusion-related Gouzal-Bolagh deposit, NW Iran

The objective of this paper is to detect various gold and copper mineralization stages according to surface lithogeochemical data utilizing zonality index and spectrum-area (S-A) multifractal modeling along with geological data in the Au-(Cu) intrusion-related Gouzal-Bolagh deposit, northwestern Iran. Gold mineralization at Gouzal-Bolagh consists of quartz-sulfide veins within the Paleocene batholithic granitoid complex, which has intruded into the Mesozoic and older metamorphic rocks and shales. Considering the mineralization type, a zonality index was determined as (Au + Cu)/(As + Sb). Au, Cu, and zonality index values were categorized by the S-A multifractal model and the log–log plots. Moreover, multifractal modeling of the zonality index in correlation with geological (specifically mineralographical) data shows that there are three stages for mineralization including Pre-, main and post-mineralization (supergene enrichment). The main stage of mineralization formed from two sub-stages:A (Quartz vein and veinlet sulfidation), B (Quartz sulfide brecciation). In addition, various stages of mineralization contain zonality index values of < 31.8, 31.8–507.3, 507.3–758.3, 758.3–1628.7 and >1628.7 for background, pre-mineralization, sub-stages A, B and supergene, respectively. The background is a granitic batholith containing silicic mineralized veins with a NE-SW trend. Results obtained by this study demonstrate that there are major occurrences of gold and copper mineralization in the sulfidation and brecciation sub-stages, respectively.
An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences

Together with impressive advances touching every aspect of our society, AI technology based on Deep Neural Networks (DNN) is bringing increasing security concerns. While attacks operating at test time have monopolised the initial attention of researchers, backdoor attacks, exploiting the possibility of corrupting DNN models by interfering with the training process, represents a further serious threat undermining the dependability of AI techniques. In a backdoor attack, the attacker corrupts the training data so to induce an erroneous behaviour at test time. Test time errors, however, are activated only in the presence of a triggering event corresponding to a properly crafted input sample. In this way, the corrupted network continues to work as expected for regular inputs, and the malicious behaviour occurs only when the attacker decides to activate the backdoor hidden within the network. In the last few years, backdoor attacks have been the subject of an intense research activity focusing on both the development of new classes of attacks, and the proposal of possible countermeasures. The goal of this overview paper is to review the works published until now, classifying the different types of attacks and defences proposed so far. The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time. As such, the proposed analysis is particularly suited to highlight the strengths and weaknesses of both attacks and defences with reference to the application scenarios they are operating in.
Development of a mobile robot for safe mechanical evacuation of hazardous bulk materials in industrial confined spaces

Abstract Safe mechanical evacuation of hazardous bulk materials in industrial confined spaces with improper conditions (poor light for vision, dust or vapor, high temperature, and toxic fume) and potential of combustion and explosion is high‐risk mission for human. This study deals with development and performance assessments of a robot for this purpose. The robot comprised four major units including control, electro‐hydraulic actuator, communication, and monitoring unit. Detailed descriptions of the units and their main components are beyond the scope of current report. Meanwhile, the robot has movable and stationary parts. The movable part is a wheeled mobile robot which guides suction tube in the confined space. Robotic evacuation operations are accomplished by supervisory remote control at base station with feedback from camera video of the mobile robot. Simulation results showed accurate performance of defined objectives for the units and their main components through designed hydraulic and electronic circuits. Stationary assessments for forward speed (0–30 m/min) and drawbar pull (‐0‐755.6 N) of the mobile robot yielded acceptable results in case of robot mobility. Operational assessments of the robot in two chemical reactors comprised depreciated catalyst and ceramic balls practically approved efficient, safe, feasible, and easily implementable of robotic evacuation operations. Overall, the robot proposed in this study eliminates employment of a human in highly risky conditions of industrial confined spaces.
Sustaining accurate detection of phishing URLs using SDN and feature selection approaches

Phishing is an online fraud that deceives visitors by impersonating a legitimate website to steal their confidential or personal information. This is a well-known form of cybercrime. With the aim of detecting phishing sites, several phishing site detection techniques have recently been created. However, it fails to achieve the desired goal and has a large number of drawbacks, including low accuracy, long learning curve, and low-power embedded hardware. For covering such drawbacks, this work proposes an efficient URLs Phishing detection technique. Our technique depends on Software Defined Network (SDN) technology, clustering and feature method, and Conventional Neural Network (CNN) algorithm. Feature selection technique is based on Recursive Feature Elimination (RFE) with Support Vector Machine (SVM) algorithm. The SDN is used to transfer the URLs phishing detection process out of the user's hardware to the controller layer, continuously train on new data, and then send its outcomes to the SDN-Switches. RFE-SVM and CNN are used to increase accuracy of phishing detection. Therefore, the proposal model does not require retrieving the content of the target website or using any third-party services. It captures the information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features to quickly classify the actual URL. The experimental results showed that our proposal highlighted the robustness and accuracy in distinguishing between phishing and legitimate sites. Our suggestion achieves 99.5% phishing detection accuracy.
Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world.
Video anomaly detection using deep residual-spatiotemporal translation network

Video anomaly detection has gained significant attention in the current intelligent surveillance systems. We propose Deep Residual Spatiotemporal Translation Network (DR-STN), a novel unsupervised Deep Residual conditional Generative Adversarial Network (DR-cGAN) model with an Online Hard Negative Mining (OHNM) approach. The proposed DR-cGAN provides a wider network to learn a mapping from spatial to temporal representations and enhance the perceptual quality of synthesized images from a generator. During DR-cGAN training, we take only the frames of normal events to produce their corresponding dense optical flow. At testing time, we compute the reconstruction error in local pixels between the synthesized and the real dense optical flow and then apply OHNM to remove false-positive detection results. Finally, a semantic region merging is introduced to integrate the intensities of all the individual abnormal objects into a full output frame. The proposed DR-STN has been extensively evaluated on publicly available benchmarks, including UCSD, UMN, and CUHK Avenue, demonstrating superior results over other state-of-the-art methods both in frame-level and pixel-level evaluations. The average Area Under the Curve (AUC) value of the frame-level evaluation for the three benchmarks is 96.73%. The improvement ratio of AUC in the frame level between DR-STN and state-of-the-art methods is 7.6%.