Reviewerly/exHarmony huggingface dataset:
Enhancing representation in the context of multiple-channel spam filtering

This study addresses the usage of different features to complement synset-based and bag-of-words representations of texts in the context of using classical ML approaches for spam filtering (Ferrara, 2019). Despite the existence of a large number of complementary features, in order to improve the applicability of this study, we have selected only those that can be computed regardless of the communication channel used to distribute content. Feature evaluation has been performed using content distributed through different channels (social networks and email) and classifiers (Adaboost, Flexible Bayes, Naïve Bayes, Random Forests, and SVMs). The results have revealed the usefulness of detecting some non-textual entities (such as URLs, Uniform Resource Locators) in the addressed distribution channels. Moreover, we also found that compression properties and/or information regarding the probability of correctly guessing the language of target texts could be successfully used to improve the classification in a wide range of situations. Finally, we have also detected features that are influenced by specific fashions and habits of users of certain Internet services (e.g. the existence of words written in capital letters) that are not useful for spam filtering.
A new framework of designing iterative techniques for image deblurring

In this work we present a framework of designing iterative techniques for image deblurring in inverse problem. The new framework is based on two observations about existing methods. We used Landweber method as the basis to develop and present the new framework but note that the framework is applicable to other iterative techniques. First, we observed that the iterative steps of Landweber method consist of a constant term, which is a low-pass filtered version of the already blurry observation. We proposed a modification to use the observed image directly. Second, we observed that Landweber method uses an estimate of the true image as the starting point. This estimate, however, does not get updated over iterations. We proposed a modification that updates this estimate as the iterative process progresses. We integrated the two modifications into one framework of iteratively deblurring images. Finally, we tested the new method and compared its performance with several existing techniques, including Landweber method, Van Cittert method, GMRES (generalized minimal residual method), and LSQR (least square), to demonstrate its superior performance in image deblurring.
Prediction of recommendations for employment utilizing machine learning procedures and geo-area based recommender framework

With increment in the utilization of Internet, the pace of increment of social networks is getting ubiquitous in recent years. This paper focuses on the job portal websites. The research objective of this paper is that the recommender framework takes the abilities from the website and makes suggestion to the candidates with the jobs whose descriptions are coordinating with their profiles the most. This paper additionally presents a short presentation on recommender framework and talks about different categories of this framework. From the start, information is cleaned by expelling the filthy information as extra space and duplicates. Then the job recommendations are made to the target applicants on the basis of their preferences. It utilizes different Machine Learning procedures which results show that Random Forest Classifier (RFC) gives the most noteworthy expectation accuracy when contrasted with different procedures. Finally, the optimization technique is utilized to get the most exact outcome. The advantage of recommender framework in career orientation is expressed. Geo-area based recommendation framework is utilized to find the organization's position which can assist the ideal applicants with reaching their destination. This examination shows that the utilization of job recommender system can assist with improving the recommendation of appropriate employment for work searchers.
Long Short-Term Memory-based Sentiment Classification of Cloud Dataset

Text Sentiment Classification is a crucial task for various decision-making processes in many organizations. It identifies the polarity of texts positively and negatively and highlights the opinions and views hidden within the comments or reviews of a product or service. Performing it on big data from social media and related sources is quite tricky and time-consuming. Nowadays, Deep Learning (DL) is widely used for sentiment analysis due to its high performance. In this paper, Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) approach is applied to perform sentiment analysis of a cloud review dataset. The cloud review dataset contains cloud consumer reviews regarding the services provided by different cloud service providers and the dataset is achieved as a result of the Harvesting-as-a-Service (HaaS) framework. The study focuses on observing the behaviour of the deep learning RNN-LSTM approach on a cloud dataset. Results of the experiment are evaluated using various evaluation and performance metrics. The approach tends to achieve 95 % accuracy.
Unsupervised Anomaly Detection in Multivariate Time Series through Transformer-based Variational Autoencoder

Modern industrial devices often use multiple sensors to detect the status of system, which produce a large amount of multivariate time series. Due to the complex temporal dependency of intra-channel and inter-correlations among different channels, few of proposed algorithms have addressed these challenges for anomaly detection in multivariate time series. Besides, previous work does not consider future dependency, which has been shown to be critical for sequential data modeling. In this paper, we develop an unsupervised anomaly detection algorithm TransAnomaly, which integrates Transformer, variational autoencoder (VAE) and nonlinear state space model. TransAnomaly not only reduces the computational complexity and allows for more parallelization but also provides explainable insights. To the best of our knowledge, it is the first model that combines VAE and Transformer for multivariate time series anomaly detection. Extensive experiments on several public real-world datasets show that TransAnomaly outperforms state-of-the-art baseline methods while training cost is reduced by nearly 80%.
Discriminative deep attributes for generalized zero-shot learning

We indirectly predict a class by deriving user-defined (i.e., existing) attributes (UA) from an image in generalized zero-shot learning (GZSL). High-quality attributes are essential for GZSL, but the existing UAs are sometimes not discriminative. We observe that the hidden units at each layer in a convolutional neural network (CNN) contain highly discriminative semantic information across a range of objects, parts, scenes, textures, materials, and color. The semantic information in CNN features is similar to the attributes that can distinguish each class. Motivated by this observation, we employ CNN features like novel class representative semantic data, i.e., deep attribute (DA). Precisely, we propose three objective functions (e.g., compatible, discriminative, and intra-independent) to inject the fundamental properties into the generated DA. We substantially outperform the state-of-the-art approaches on four challenging GZSL datasets, including CUB, FLO, AWA1, and SUN. Furthermore, the existing UA and our proposed DA are complementary and can be combined to enhance performance further.
A thorough review of models, evaluation metrics, and datasets on image captioning

Image captioning means generate descriptive sentences from a query image automatically. It has recently received widespread attention from the computer vision and natural language processing communities as an emerging visual task. Currently, both components have evolved considerably by exploiting object regions, attributes, attention mechanism methods, entity recognition with novelties, and training strategies. However, despite the impressive results, the research has not yet come to a conclusive answer. This survey aims to provide a comprehensive overview of image captioning methods, from technical architectures to benchmark datasets, evaluation metrics, and comparison of state-of-the-art methods. In particular, image captioning methods are divided into different categories based on the technique adopted. Representative methods in each class are summarized, and their advantages and limitations are discussed. Moreover, many related state-of-the-art studies were quantitatively compared to determine the recent trends and future directions in image captioning. The ultimate goal of this work is to serve as a tool for understanding the existing literature and highlighting future directions in the area of image captioning for Computer Vision and Natural Language Processing communities may benefit from.
Radio Frequency Fingerprint Identification for Security in Low-Cost IoT Devices

Radio frequency fingerprint identification (RFFI) can uniquely classify wireless devices by analyzing the received signal distortions caused by the intrinsic hardware impairments. The state-of-the-art deep learning techniques such as convolutional neural network (CNN) have been adopted to classify IoT devices with high accuracy. However, deep learning-based RFFI requires input data of a fixed size. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the low SNR RFFI is rarely investigated. In this paper, the state-of-the-art transformer model is used as the classifier, which can process signals of variable length. Data augmentation is adopted to improve low SNR RFFI performance. A multi-packet inference approach is further proposed to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low SNR RFFI performance by up to 50% and multi-packet inference can further increase it by over 20%.
Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust Classification

Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.
HSR: Hyperbolic Social Recommender

With the prevalence of online social media, users' social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we explore the use of hyperbolic geometry for social recommendation. We present the Hyperbolic Social Recommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic space, HSR can learn high-quality user and item representations to better model user-item interaction and user-user social relations. Through extensive experiments on four real-world datasets, we show that our proposed HSR outperforms its Euclidean counterpart and state-of-the-art social recommenders in click-through rate prediction and top-K recommendation, demonstrating the effectiveness of social recommendation in the hyperbolic space.
Line Art Colorization Based on Explicit Region Segmentation

Abstract Automatic line art colorization plays an important role in anime and comic industry. While existing methods for line art colorization are able to generate plausible colorized results, they tend to suffer from the color bleeding issue. We introduce an explicit segmentation fusion mechanism to aid colorization frameworks in avoiding color bleeding artifacts. This mechanism is able to provide region segmentation information for the colorization process explicitly so that the colorization model can learn to avoid assigning the same color across regions with different semantics or inconsistent colors inside an individual region. The proposed mechanism is designed in a plug‐and‐play manner, so it can be applied to a diversity of line art colorization frameworks with various kinds of user guidances. We evaluate this mechanism in tag‐based and reference‐based line art colorization tasks by incorporating it into the state‐of‐the‐art models. Comparisons with these existing models corroborate the effectiveness of our method which largely alleviates the color bleeding artifacts. The code is available at https://github.com/Ricardo-L-C/ColorizationWithRegion .
LETA: A lightweight exchangeable-track accelerator for efficientnet based on FPGA

Lightweight convolutional neural networks (CNNs) have become increasingly popular due to their lower computational complexity and fewer memory accesses with equivalent accuracy compared to previous CNN models. However, the newly proposed networks bring new challenges to efficient hardware design, such as, in EfficientNet, depthwise convolution, squeeze-and-excitation (SE) module, and swish/sigmoid functions. Although individual engine architecture could achieve a high computing efficiency for the standard convolution or the depth-wise convolution, it is still not efficient for EfficientNet because the workload imbalance between two types of convolutional engines causes inevitable idling. To overcome this problem, we present a lightweight reconfigurable computational kernel based on FPGA with an exchangeable-track datapath scheme. In addition, a low-accuracy-loss function replacement strategy is proposed for swish/sigmoid functions. Furthermore, the low-cost hardware architecture to implement the replaced functions is designed. The proposed accelerator (LETA) can implement EfficientNet on Xilinx XCVU37P with a 300 MHz system clock and a 600 MHz kernel clock. The linear growth of resource usage in the 4-kernel implementation in 1 super logic region (SLR) with the same clock frequencies justifies the scalability of LETA. The experimental results show that LETA can achieve 2× throughput/DSP compared to the latest FPGA-based accelerator with 1.6% (0.7%) top-1 (top-5) accuracy loss on EfficientNet-B3.
The Impact of Learning Rate Decay and Periodical Learning Rate Restart on Artificial Neural Network

There is no denying that learning rate is one of the most important hyper-parameter for model training. In this paper, two typical strategies, namely learning rate decay and periodical learning rate restart are tested in artificial neural networks (ANN) and compared with the fixed learning rate. Experiments demonstrate that learning rate adjustment strategies surpass fixed learning rate in model training, including fast convergence, high validation accuracy and low training loss. Besides, periodical learning rate restart strategy tends to take fewer epochs than learning rate decay to get the same accuracy. Thus, increasing the learning rate appropriately will better fit the model and achieve excellent performance.
Fuzzy Elman Wavelet Network: Applications to function approximation, system identification, and power system control

Fuzzy Wavelet Neural Networks (FWNNs) have recently gained popularity as a powerful tool for various applications. Although the literature contains several effective FWNNs, there is still scope for improvement. This research proposes and implements a novel modification called the Fuzzy Elman Wavelet Network (FEWN), which combines the appealing properties of Elman Neural Networks (ENNs), wavelet functions, and fuzzy membership functions (MFs). The integration suggests the use of interval type-2 fuzzy MFs and wavelet functions with self-recurrent and ENN's cross-coupled feedback loops to handle system uncertainties while accurately representing the intrinsic cross-coupled interferences of real dynamic nonlinear systems. However, it is worth noting that the proposed integration has no detrimental effect on the computational network load, which is critical for online applications. Furthermore, a thorough stability analysis is conducted, and the novel network is implemented and tested in various applications. Finally, the effectiveness of the proposed novel network is evaluated through extensive simulation studies using well-known benchmark functions and dynamic systems. These studies demonstrate the proposed FEWN's efficacy in function approximation, system identification, and as a damping controller for two benchmark large-scale nonlinear power systems.
Automatic creation of a 3D cartoon from natural language story

The automatic creation of 3D animation from natural language text is used in many fields. The main target of this paper is to produce a 3D cartoon from a text input. Therefore, we need to analyze the input corpus to extract useful information by employing theories and tools from linguistics and natural language processing in addition to computer graphics for human language visualization. The system operates through two phases. The NLP phase, in which input text passes first through a coreference resolution solver in order to remove pronouns and substitute them with their corresponding nouns followed by a dependency parser in order to detect subject-action-object (SAO) relations in the resolved text. The sequence of SAOs resulting from the NLP phase is passed to the graphics phase. In the graphics phase a 3D animated video cartoon is generated by visualizing each SAO extracted in the NLP phase and Storytelling using the Unity game engine platform. The main contribution of this work is that the input does not have to be a screenplay. It is also demonstrated that performing coreference resolution before dependency parsing resulted in a more compact sequence of SAOs.
GIS-KG: building a large-scale hierarchical knowledge graph for geographic information science

An organized knowledge base can facilitate the exploration of existing knowledge and the detection of emerging topics in a domain. Knowledge about and around Geographic Information Science and its associated system technologies (GIS) is complex, extensive and emerging rapidly. Taking the challenge, we built a GIS knowledge graph (GIS-KG) by (1) merging existing GIS bodies of knowledge to create a hierarchical ontology and then (2) applying deep-learning methods to map GIS publications to the ontology. We conducted several experiments on information retrieval to evaluate the novelty and effectiveness of the GIS-KG. Results showed the robust support of GIS-KG for knowledge search of existing GIS topics and potential to explore emerging research themes.
Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-Based Reranking

The goal of local citation recommendation is to recommend a missing reference from the local citation context and optionally also from the global context. To balance the tradeoff between speed and accuracy of citation recommendation in the context of a large-scale paper database, a viable approach is to first prefetch a limited number of relevant documents using efficient ranking methods and then to perform a fine-grained reranking using more sophisticated models. In that vein, BM25 has been found to be a tough-to-beat approach to prefetching, which is why recent work has focused mainly on the reranking step. Even so, we explore prefetching with nearest neighbor search among text embeddings constructed by a hierarchical attention network. When coupled with a SciBERT reranker fine-tuned on local citation recommendation tasks, our hierarchical Attention encoder (HAtten) achieves high prefetch recall for a given number of candidates to be reranked. Consequently, our reranker requires fewer prefetch candidates to rerank, yet still achieves state-of-the-art performance on various local citation recommendation datasets such as ACL-200, FullTextPeerRead, RefSeer, and arXiv.
A New Full-Reference Image Quality Metric for Motion Blur Profile Characterization

Motion blur is common in images captured by handheld devices, arising from hand, device and/or object motion. To restore sharp images from the images degraded by the motion, it is extremely important to assess the quality of the captured image and its corresponding blur profile as accurately as possible. In image deblurring, the perceived image quality is usually assessed by the SSIM and the PSNR metric. These methods have certain limitations and the objective image quality assessed by these methods can be contradictory to the subjectively perceived image quality. We propose a new reference image based objective blur level (BL) metric by utilizing point spread function/blur kernel analysis in this paper. In our experiments, we found our BL metric describes the perceived image quality of motion blurred images better than SSIM and PSNR in most cases. Additionally, our method performs well in low light and low texture images, where SSIM and PSNR metrics are prone to failure in describing blurriness/sharpness of the image.
A Context-Aware Approach for Extracting Hard and Soft Skills

The continuous growth in the online recruitment industry has made the candidate screening process costly, labour intensive, and time-consuming. Automating the screening process would expedite candidate selection. In recent times, recruiting is moving towards skill-based recruitment where candidates are ranked according to the number of skills, skill’s competence level and skill’s experience. Therefore it is important to create a system which can accurately and automatically extract hard and soft skills from candidates’ resume and job descriptions. The task is less complex for hard skills which in some cases could be named entities but much more challenging for soft skills which may appear in different linguistic forms depending on the context. In this paper, we propose a context-aware sequence classification and token classification model for extracting both hard and soft skills. We utilized the most recent state-of-the-art word embedding representations as textual features for various machine learning classifiers. The models have been validated by evaluating them on a publicly available job description dataset. Our results indicated that the best performing sequence classification model used BERT embeddings in addition with POS and DEP tags as input for a logistic regression classifier. The best performing token classification model used fine-tuned BERT embeddings with a support vector machine classifier.