Reviewerly/exHarmony huggingface dataset:
Automatic data acquisition for deep learning

Deep learning (DL) has widespread applications and has revolutionized many industries. Although automated machine learning (AutoML) can help us away from coding for DL models, the acquisition of lots of high-quality data for model training remains a main bottleneck for many DL projects, simply because it requires high human cost. Despite many works on weak supervision ( i.e. , adding weak labels to seen data) and data augmentation ( i.e. , generating more data based on seen data), automatically acquiring training data, via smartly searching a pool of training data collected from open ML benchmarks and data markets, is not explored. In this demonstration, we demonstrate a new system, automatic data acquisition (AutoData), which automatically searches training data from a heterogeneous data repository and interacts with AutoML. It faces two main challenges. (1) How to search high-quality data from a large repository for a given DL task? (2) How does AutoData interact with AutoML to guide the search? To address these challenges, we propose a reinforcement learning (RL)-based framework in AutoData to guide the iterative search process. AutoData encodes current training data and feedbacks of AutoML, learns a policy to search fresh data, and trains in iterations. We demonstrate with two real-life scenarios, image classification and relational data prediction, showing that AutoData can select high-quality data to improve the model.
Simulating quantum circuits with ZX-calculus reduced stabiliser decompositions

We introduce an enhanced technique for strong classical simulation of quantum circuits which combines the `sum-of-stabilisers' method with an automated simplification strategy based on the ZX-calculus. Recently it was shown that quantum circuits can be classically simulated by expressing the non-stabiliser gates in a circuit as magic state injections and decomposing them in chunks of 2-6 states at a time, obtaining sums of (efficiently-simulable) stabiliser states with many fewer terms than the naive approach. We adapt these techniques from the original setting of Clifford circuits with magic state injection to generic ZX-diagrams and show that, by interleaving this "chunked" decomposition with a ZX-calculus-based simplification strategy, we can obtain stabiliser decompositions that are many orders of magnitude smaller than existing approaches. We illustrate this technique to perform exact norm calculations (and hence strong simulation) on the outputs of random 50- and 100-qubit Clifford+T circuits with up to 70 T-gates as well as a family of hidden shift circuits previously considered by Bravyi and Gosset with over 1000 T-gates.
Learning Sentence-to-Hashtags Semantic Mapping for Hashtag Recommendation on Microblogs

The growing use of microblogging platforms is generating a huge amount of posts that need effective methods to be classified and searched. In Twitter and other social media platforms, hashtags are exploited by users to facilitate the search, categorization, and spread of posts. Choosing the appropriate hashtags for a post is not always easy for users, and therefore posts are often published without hashtags or with hashtags not well defined. To deal with this issue, we propose a new model, called HASHET ( HAshtag recommendation using Sentence-to-Hashtag Embedding Translation ), aimed at suggesting a relevant set of hashtags for a given post. HASHET is based on two independent latent spaces for embedding the text of a post and the hashtags it contains. A mapping process based on a multi-layer perceptron is then used for learning a translation from the semantic features of the text to the latent representation of its hashtags. We evaluated the effectiveness of two language representation models for sentence embedding and tested different search strategies for semantic expansion, finding out that the combined use of BERT ( Bidirectional Encoder Representation from Transformer ) and a global expansion strategy leads to the best recommendation results. HASHET has been evaluated on two real-world case studies related to the 2016 United States presidential election and COVID-19 pandemic. The results reveal the effectiveness of HASHET in predicting one or more correct hashtags, with an average F -score up to 0.82 and a recommendation hit-rate up to 0.92. Our approach has been compared to the most relevant techniques used in the literature ( generative models , unsupervised models, and attention-based supervised models ) by achieving up to 15% improvement in F -score for the hashtag recommendation task and 9% for the topic discovery task.
Aspect term extraction for opinion mining using a Hierarchical Self-Attention Network

Aspect identification is one of the important sub-tasks in opinion mining and this task can be considered as a token-level sequencing problem. Most recent approaches employ BERT based network to identify the aspect term, which is often complex, consumes a lot of memory, and needs more training time. In this paper, we propose a novel Hierarchical Self-Attention Network (HSAN) which performs well, needs lesser memory and training time. HSAN hierarchically applies a self-attention mechanism to first capture the importance of each word in the context of the overall meaning of the sentence and then it explores the internal dependency of the words in the same sentence to identify interdependent collocated words. A fusion of these two-attention mechanisms helps HSAN to predict multiple aspect terms effectively in the given sentence along with multi-token aspect terms. Our proposed network uses word embeddings, which is a combination of general-purpose embeddings and domain-specific embeddings. We evaluate the performance of HSAN on SemEval-2014 datasets, experimental results demonstrate the efficiency and effectiveness of our model.
Contextualized Keyword Representations for Multi-modal Retinal Image Captioning

Medical image captioning automatically generates a medical description to describe the content of a given medical image. Traditional medical image captioning models create a medical description based on a single medical image input only. Hence, an abstract medical description or concept is hard to be generated based on the traditional approach. Such a method limits the effectiveness of medical image captioning. Multi-modal medical image captioning is one of the approaches utilized to address this problem. In multi-modal medical image captioning, textual input, e.g., expert-defined keywords, is considered as one of the main drivers of medical description generation. Thus, encoding the textual input and the medical image effectively are both important for the task of multi-modal medical image captioning. In this work, a new end-to-end deep multi-modal medical image captioning model is proposed. Contextualized keyword representations, textual feature reinforcement, and masked self-attention are used to develop the proposed approach. Based on the evaluation of an existing multi-modal medical image captioning dataset, experimental results show that the proposed model is effective with an increase of +53.2% in BLEU-avg and +18.6% in CIDEr, compared with the state-of-the-art method. https://github.com/Jhhuangkay/Contextualized-Keyword-Representations-for-Multi-modal-Retinal-Image-Captioning
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection

The advancement in numerous generative models has a two-fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of those images. Thus, it is important to develop a generalized detector for synthesized images of any GAN model or object category, including those unseen during the training phase. However, the conventional methods heavily depend on the training settings, which cause a dramatic decline in performance when tested with unknown domains. To resolve the issue and obtain a generalized detection ability, we propose Bilateral High-Pass Filters (BiHPF), which amplify the effect of the frequency-level artifacts that are generally found in the synthesized images of generative models. Also, to find the properties of the general frequency-level artifacts, we develop an additional method to adversarially extract the artifact compression map. Numerous experimental results validate that our method outperforms other state-of-the-art methods, even when tested with unseen domains.
A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems

The sparrow search algorithm (SSA) is a newly proposed meta-heuristic optimization algorithm based on the sparrow foraging principle.Similar to other meta-heuristic algorithms, SSA has problems such as slow convergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings, a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy (CLSSA) is proposed in this paper.Firstly, in order to balance the exploration and exploitation ability of the algorithm, chaotic mapping is introduced to adjust the main parameters of SSA.Secondly, in order to improve the diversity of the population and enhance the search of the surrounding space, the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally, the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions, and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map, and CLSSA is efficient and useful for engineering problems, which is better than all comparison algorithms.
Second-Generation 3D Automated Breast Ultrasonography (Prone ABUS) for Dense Breast Cancer Screening Integrated to Mammography: Effectiveness, Performance and Detection Rates

In our study, we added a three-dimensional automated breast ultrasound (3D ABUS) to mammography to evaluate the performance and cancer detection rate of mammography alone or with the addition of 3D prone ABUS in women with dense breasts. Our prospective observational study was based on the screening of 1165 asymptomatic women with dense breasts who selected independent of risk factors. The results evaluated include the cancers detected between June 2017 and February 2019, and all surveys were subjected to a double reading. Mammography detected four cancers, while mammography combined with a prone Sofia system (3D ABUS) doubled the detection rate, with eight instances of cancer being found. The diagnostic yield difference was 3.4 per 1000. Mammography alone was subjected to a recall rate of 14.5 for 1000 women, while mammography combined with 3D prone ABUS resulted in a recall rate of 26.6 per 1000 women. We also observed an additional 12.1 recalls per 1000 women screened. Integrating full-field digital mammography (FFDM) with 3D prone ABUS in women with high breast density increases and improves breast cancer detection rates in a significant manner, including small and invasive cancers, and it has a tolerable impact on recall rate. Moreover, 3D prone ABUS performance results are comparable with the performance results of the supine 3D ABUS system.
Real Time Crime Detection Using Deep Learning Algorithm

The Crime Rate and number of Criminals are increasing day by day, so there is great concern about the security issues, so to prevent and identify the crime before it occurs is the primary goal of the police officials. With the help of Recent Technologies, especially CCTV is normally deployed in every private and public area to control crime but it needs human supervision to monitor. It's hard for a human to monitor many screens at the same time. It leads to many errors. To overcome these problems, we proposed Real-Time Crime Detection Technique using a Deep Learning Algorithm which monitors real-time videos and alerts the nearby Cybercrime admin about the occurrence of crime with current location. In this paper, We present YOLO as our object detection algorithm. Our architecture is extremely fast and process image in real-time at 45 frames per second.
Transformer models for enhancing AttnGAN based text to image generation

• A new variant of AttnGAN model is proposed for TTI synthesis. • The proposed AttnGAN TRANS model uses a Transformer based text encoder. • Transformers like BERT, GPT2 and XLNet are employed and analysed. • Experiments validate that AttnGAN TRANS outperforms state-of-the art methods. • Over AttnGAN, AttnGAN TRANS has a 49.9% lower FID and 27.23% higher inception score. Deep neural networks are capable of producing photographic images that depict given natural language text descriptions. Such models have huge potential in applications such as interior designing, video games, editing and facial sketching for digital forensics. However, only a limited number of methods in the literature have been developed for text to image (TTI) generation. Most of them use Generative Adversarial Networks (GAN) based deep learning methods. Attentional GAN (AttnGAN) is a popular GAN based TTI method that extracts meaningful information from the given text descriptions using attention mechanism. In this paper, we investigate the use of different Transformer models such as BERT, GPT2, XLNet with AttnGAN to solve the challenge of extracting semantic information from the text descriptions. Hence, the proposed AttnGAN TRANS architecture has three variants AttnGAN BERT , AttnGAN XL and AttnGAN GPT . The proposed method is successful over the conventional AttnGAN and gives a boosted inception score by 27.23% and a decline of Frechet inception distance by 49.9%. The results in our experiments indicate that the proposed method has the potential to outperform the contemporary state-of-the-art methods and validate the use of Transformer models in improving the performance of TTI generation. The code is made publicly available at https://github.com/sairamkiran9/AttnGAN-trans .
Stochastic Gradient Descent with Polyak’s Learning Rate

Stochastic gradient descent (SGD) for strongly convex functions converges at the rate $$\mathcal {O}(1/k)$$ . However, achieving good results in practice requires tuning the parameters (for example the learning rate) of the algorithm. In this paper we propose a generalization of the Polyak step size, used for subgradient methods, to stochastic gradient descent. We prove a non-asymptotic convergence at the rate $$\mathcal {O}(1/k)$$ with a rate constant which can be better than the corresponding rate constant for optimally scheduled SGD. We demonstrate that the method is effective in practice, and on convex optimization problems and on training deep neural networks, and compare to the theoretical rate.
Re-weighted multi-view clustering via triplex regularized non-negative matrix factorization

Multi-view clustering, which aims at dividing data with similar structures into their respective groups, is a popular research subject in computer vision and machine learning. In recent years, Non-negative matrix factorization (NMF) has received constant concern in multi-view clustering due to its ability to deal with high-dimensional data. However, most existing NMF methods may fail to integrate valuable information from multi-view data adequately, and the local geometry structure in data is also not fully considered. Thus, it’s still a crucial but challenging problem, which effectively extracts multi-view information while maintaining the low-dimensional geometry structure. In this paper, we propose an innovative multi-view clustering method, referred to as re-weighted multi-view clustering via triplex regularized non-negative matrix factorization (SMCTN), which is a unified framework and provides the following contributions: 1) pairwise regularization can extract complementary information between views and is suitable for both homogeneous and heterogeneous perspectives; 2) consensus regularization can process the consistent information between views; 3) graph regularization can preserve the geometric structure of data. Specifically, SMCTN applies a re-weighted strategy to assign suitable weights for multiple views according to their contributions. Besides, an effective iterative updating algorithm is developed to solve the non-convex optimization problem in SMCTN. Extensive experimental results on textual and image datasets indicate that the superior performance of the proposed method.
Simulated Phishing Attack and Embedded Training Campaign

Phishing attacks are costly for both organizations and individuals, yet existing academic research has provided little guidance on how to strategize and implement a combined phishing awareness and training campaign. Drawing on operant conditioning theory, we conduct an in-depth case study on a large phishing awareness campaign and reveal that phishing awareness is a learning process through which individuals' behavior can be strengthened by reinforcement and punishment. Based on the case study findings, we present several propositions for cybersecurity stakeholders. This study contributes to the phishing awareness literature and has implications for research and practice. This paper is useful for organizations planning or in the process of implementing or reviewing a phishing awareness and education program.
A Blockchain-Based Verification System for Academic Certificates

Millions of students complete their education each year and go on to do higher studies or a corporate job. In this case student credentials are verified through a lengthy document verification process. This results in significant overhead as documents are transferred between institutions for verification. There is a need for an automated credential verification system which can reduce the time required for the document verification process. Blockchain Technology can be used to reduce overhead and reduce the time taken for document verification from days to mere seconds. In this work, an attempt has been made to develop a Blockchain-based verification system for academic certificates. With the advent of public Blockchain like Ethereum, DApps (Decentralized Applications) and Smart contracts, scalable and cost effective solutions can be implemented to reduce overhead and make document verification a seamless process. The proposed solution consists of a web app which will have a front-end for registering and requesting verification, along with a backend which will have two modules: An OCR module to extract details from certificates and a Blockchain module to send and verify data stored in the Blockchain.
Policychain: A Decentralized Authorization Service With Script-Driven Policy on Blockchain for Internet of Things

The decentralization mechanism provides manufacturers and distributors with greater customization and flexibility they need through Internet of Things (IoT)-based industrial collaboration systems (IoT-ICS), but it has brought forward security concerns about the shared data-processing tasks and IoT-based access to services and resources. To address them, we propose a practical blockchain solution to achieve decentralized policy management and evaluation on attribute-based access control (ABAC). By offloading the responsibility of ABAC policy administration and decision making to blockchain nodes, a blockchain-based access control framework, called Policychain, is presented to ensure policy with high availability, autonomy, and traceability. To deliver a solid design, we first present a transaction-oriented policy expression scheme with a well-defined syntax and semantics. The scheme can translate ABAC policies into the blockchain transactions with JavaScript object notation (JSON) syntax and script-based logical expression. We further realize a script-driven policy evaluation by extending blockchain inherent scripting instructions to support attribute acquisition of ABAC entities. Furthermore, we propose a policy lifecycle management scheme from policy creation, renovation, to revocation, in which policies are verified by three validation principles at the transaction level. Finally, we provide sophisticated analysis and experiments to show that our framework is secure and practical for decentralized policy management on ABAC in IoT-ICS.
Successive Point-of-Interest Recommendation With Personalized Local Differential Privacy

The fast development of vehicle positioning technologies has paved the way for in-vehicle recommendation systems. Successive Point-of-Interest (POI) recommendation, one of the most common forms of in-vehicle recommendation, can help users choose places where they might visit in the future. Generally, to recommend POIs for users, raw check-in data are collected from in-vehicle devices, which raises users’ privacy concerns. To address this problem, we propose a successive POI recommendation framework with local differential privacy. The proposed framework exploits three types of influence factors for recommendation, including geographical influence, temporal influence, and transition pattern between two POIs. To protect users’ check-in data, we propose a distance-weighted location perturbation mechanism that realizes local differential privacy. Considering that different users may have different privacy requirements, we enhance this perturbation mechanism to provide personalized privacy protection. We use the EM reconstruction method to correct the error caused by perturbation on the server side. To improve the accuracy, we design an individual-weighted EM reconstruction method. Simulation results demonstrate that the proposed methods can provide strong privacy protection for users without hurting the recommendation accuracy too much.
MMU-OCR-21: Towards End-to-End Urdu Text Recognition Using Deep Learning

Optical Character Recognition (OCR) is a technique that generates text from an image. Recognizing the importance of OCR in real-world settings, a plethora of techniques have been developed for Western, as well as Asian languages. Urdu is a prominent South Asian language and a number of different solutions for Urdu OCR have been proposed. However, fewer attempts have been made to develop end-to-end deep learning-based solutions for recognizing printed Urdu text. Furthermore, several benchmark corpora for Urdu OCR have been developed that can be used for training and evaluation of different OCR techniques. However, there are a number of limitations of the existing Urdu corpora: firstly, most of them have either character or word or text images, which are usually rendered using only a single font, Nastaleeq. Secondly, the volume of the existing datasets is so small that it is not suitable for working with the deep-learning techniques that have achieved groundbreaking results for OCRs. To that end, in this study, we have proposed a very large Multi-level and Multi-script Urdu corpus (MMU-OCR-21). It is the largest-ever Urdu corpus of printed text that is effectively suitable to work with deep learning techniques. In total, the corpus is composed of over 602,472 images, including text-line and word images in three prominent fonts, and their respective ground truth. Also, we have performed experiments using multiple state-of-the-art deep learning techniques for text-line and word level images.