Reviewerly/exHarmony huggingface dataset:
Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network

Some recent studies have described deep convolutional neural networks to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts.One of the best techniques does two transfer learnings: the first uses a model trained on natural images to create a "patch classifier" that categorizes small subimages; the second uses the patch classifier to scan the whole mammogram and create the "single-view whole-image classifier".We propose to make a third transfer learning to obtain a "two-view classifier" to use the two mammographic views: bilateral craniocaudal and mediolateral oblique.We use EfficientNet as the basis of our model.We "end-to-end" train the entire system using CBIS-DDSM dataset.To ensure statistical robustness, we test our system twice using: (a) 5-fold cross validation; and (b) the original training/test division of the dataset.Our technique reached an AUC of 0.934 using 5-fold cross validation (accuracy, sensitivity and specificity are 85.13% at the equal error rate point of ROC).Using the original dataset division, our technique achieved an AUC of 0.8483, as far as we know the highest reported AUC for this problem, although the subtle differences in the testing conditions of each work do not allow for an accurate comparison.The inference code and model are available at https://github.com/dpetrini/two-views-classifier.
Harnessing optoelectronic noises in a photonic generative network

Integrated optoelectronics is emerging as a promising platform of neural network accelerator, which affords efficient in-memory computing and high bandwidth interconnectivity. The inherent optoelectronic noises, however, make the photonic systems error-prone in practice. It is thus imperative to devise strategies to mitigate and, if possible, harness noises in photonic computing systems. Here, we demonstrate a photonic generative network as a part of a generative adversarial network (GAN). This network is implemented with a photonic core consisting of an array of programable phase-change memory cells to perform four-element vector-vector dot multiplication. The GAN can generate a handwritten number (“7”) in experiments and full 10 digits in simulation. We realize an optical random number generator, apply noise-aware training by injecting additional noise, and demonstrate the network’s resilience to hardware nonidealities. Our results suggest the resilience and potential of more complex photonic generative networks based on large-scale, realistic photonic hardware.
Principles of Sustainable Economic Growth and Development: A Call to Action in a Post-COVID-19 World

The analysis of sustainable economic growth and development often focuses on how to control the market process through coercive state intervention. While state interventionism may play a significant role in countries’ progress, entrepreneurship is the driving force behind sustainable growth and development. Entrepreneurship is the people’s judgment on ideas, plans, and projects, which promises profit in uncertain times. Its effects are the creation and transmission of information and social coordination as a dynamic process of identifying and solving human problems. Sustainable development is the widening range of entrepreneurial alternatives open to people, and sustainable growth is a phase of sustainable development that depends on genuine savings to finance increasingly capital-intensive production structures. The degree to which people are entrepreneurs and the direction genuine savings take depend on institutional arrangements. Some institutions are more conducive to sustainable growth and development than others. After reviewing principles of growth and development sustainability, how coercive state intervention influences economic performance is discussed, proposing novel policy conclusions and research avenues to cultivate entrepreneurship and genuine savings in a post-COVID-19 world.
Cross-Content Recommendation between Movie and Book using Machine Learning

Machine learning-driven recommendation systems are widely used in today’s growing digital world. Existing movie and book recommender systems work using a collaborative approach, which can result in a lack of fresh and diverse content and a reduced surprise factor. There is also no platform providing recommendations across different contents, such as recommendations for books from movies and vice versa. In this paper, our main goal is to introduce a cross-content recommendation system based on the descriptions of movies and books and identifying similarities using natural language processing and machine learning algorithms. We processed a combined dataset of the two different types of contents, generated a TF-IDF vector of the descriptions and apply three different algorithms: K-means clustering, hierarchical clustering, and cosine similarity. There being no known cross-content recommendation research and no similar dataset with ground truth labels, we applied subjective reasoning to evaluate the results of our system.
The Use of Blockchain Technology in e-Government Services

e-Government services have evolved significantly over the last decade, from a paper-based bureaucratic procedure to digital services. Electronically processed transactions require limited physical interaction with the public administration, and provide reduced response times, increased transparency, confidentiality and integrity. Blockchain technology enhances many of the above properties as it facilitates immutability and transparency for the recorded transactions and can help establish trust among participants. In this paper, we conduct a literature review on the use of blockchain technology in e-government applications to identify e-government services that can benefit from the use of blockchains, types of technologies that are chosen for the proposed solutions, and their corresponding maturity levels. The aim is to demonstrate blockchain’s potential and contribution to the field, provide useful insights to governments who are considering investing in this innovative technology, and facilitate researchers in their future activities in blockchain-enabled e-government services.
Review of object instance segmentation based on deep learning

As a challenging task in computer vision, instance segmentation has attracted extensive attention in recent years. Able to obtain very rich and refined object information, this technology shows important application value in many fields, such as intelligent driving, medical health, and remote sensing detection. Instance segmentation technology should not only identify the positions of objects but should also accurately mark the boundary of any single instance, which can be defined as solving object detection and semantic segmentation at the same time. Our study gives a detailed introduction to the background of instance segmentation technology, its development and the common datasets in this field, and further deeply discusses key issues appearing in the development of this field, with the future development direction of instance segmentation technology proposed. Our study provides an important reference for future research on this technology
The Path of Film and Television Animation Creation Using Virtual Reality Technology under the Artificial Intelligence

The purpose is to provide a more reliable human-computer interaction (HCI) guarantee for animation works under virtual reality (VR) technology. Inspired by artificial intelligence (AI) technology and based on the convolutional neural network—support vector machine (CNN-SVM), the differences between animation works under VR technology and traditional animation works are analyzed through a comprehensive analysis of VR technology. The CNN-SVM gesture recognition algorithm using the error correction strategy is designed based on HCI recognition. To have better recognition performance, the advantages of depth image and color image are combined, and the collected information is preprocessed including the relations between the times of image training iterations and the accuracy of different methods in the direction of the test set. After experiments, the maximum accuracy of the preprocessed image can reach 0.86 showing the necessity of image preprocessing. The recognition accuracy of the optimized CNN-SVM is compared with other algorithm models. Experiments show that the accuracy of the optimized CNN-SVM has an upward trend compared with the previous CNN-SVM, and the accuracy reaches 0.97. It proves that the designed algorithm can provide good technical support for VR animation, so that VR animation works can interact well with the audience. It is of great significance for the development of VR animation and the improvement of people’s artistic life quality.
Dense Tissue Pattern Characterization Using Deep Neural Network

Abstract Breast tumors are from the common infections among women around the world. Classifying the various types of breast tumors contribute to treating breast tumors more efficiently. However, this classification task is often hindered by dense tissue patterns captured in mammograms. The present study has been proposed a dense tissue pattern characterization framework using deep neural network. A total of 322 mammograms belonging to the mini-MIAS dataset and 4880 mammograms from DDSM dataset have been taken, and an ROI of fixed size 224 × 224 pixels from each mammogram has been extracted. In this work, tedious experimentation has been executed using different combinations of training and testing sets using different activation function with AlexNet , ResNet-18 model. Data augmentation has been used to create a similar type of virtual image for proper training of the DL model. After that, the testing set is applied on the trained model to validate the proposed model. During experiments, four different activation functions ‘ sigmoid ’, ‘ tanh ’, ‘ ReLu ’, and ‘ leakyReLu ’ are used, and the outcome for each function has been reported. It has been found that activation function ‘ ReLu ’ perform always outstanding with respect to others. For each experiment, classification accuracy and kappa coefficient have been computed. The obtained accuracy and kappa value for MIAS dataset using ResNet-18 model is 91.3% and 0.803, respectively. For DDSM dataset, the accuracy of 92.3% and kappa coefficient value of 0.846 are achieved. After the combination of both dataset images, the achieved accuracy is 91.9%, and kappa coefficient value is 0.839 using ResNet-18 model. Finally, it has been concluded that the ResNet-18 model and ReLu activation function yield outstanding performance for the task.
The VoicePrivacy 2020 Challenge: Results and findings

This paper presents the results and analyses stemming from the first VoicePrivacy 2020 Challenge which focuses on developing anonymization solutions for speech technology. We provide a systematic overview of the challenge design with an analysis of submitted systems and evaluation results. In particular, we describe the voice anonymization task and datasets used for system development and evaluation. Also, we present different attack models and the associated objective and subjective evaluation metrics. We introduce two anonymization baselines and provide a summary description of the anonymization systems developed by the challenge participants. We report objective and subjective evaluation results for baseline and submitted systems. In addition, we present experimental results for alternative privacy metrics and attack models developed as a part of the post-evaluation analysis. Finally, we summarize our insights and observations that will influence the design of the next VoicePrivacy challenge edition and some directions for future voice anonymization research.
GAN-Based Image Colorization for Self-Supervised Visual Feature Learning

Large-scale labeled datasets are generally necessary for successfully training a deep neural network in the computer vision domain. In order to avoid the costly and tedious work of manually annotating image datasets, self-supervised learning methods have been proposed to learn general visual features automatically. In this paper, we first focus on image colorization with generative adversarial networks (GANs) because of their ability to generate the most realistic colorization results. Then, via transfer learning, we use this as a proxy task for visual understanding. Particularly, we propose to use conditional GANs (cGANs) for image colorization and transfer the gained knowledge to two other downstream tasks, namely, multilabel image classification and semantic segmentation. This is the first time that GANs have been used for self-supervised feature learning through image colorization. Through extensive experiments with the COCO and Pascal datasets, we show an increase of 5% for the classification task and 2.5% for the segmentation task. This demonstrates that image colorization with conditional GANs can boost other downstream tasks' performance without the need for manual annotation.
Controllable Natural Language Generation with Contrastive Prefixes

To guide the generation of large pretrained language models (LM), previous work has focused on directly fine-tuning the language model or utilizing an attribute discriminator.In this work, we propose a novel lightweight framework for controllable GPT2 (Radford et al., 2019) generation, which utilizes a set of small attribute-specific vectors, called prefixes (Li and Liang, 2021), to steer natural language generation.Different from Li and Liang (2021), where each prefix is trained independently, we take the relationship among prefixes into consideration and train multiple prefixes simultaneously, as illustrated in Figure 1.We propose a novel supervised method and also an unsupervised method to train the prefixes for single-aspect control while the combination of these two methods can achieve multi-aspect control.Experimental results on both singleaspect and multi-aspect control show that our methods can guide generation towards the desired attributes while keeping high linguistic quality.
Performance Study on Extractive Text Summarization Using BERT Models

The task of summarization can be categorized into two methods, extractive and abstractive. Extractive summarization selects the salient sentences from the original document to form a summary while abstractive summarization interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches in the literature, including statistical-, graph-, and deep learning-based approaches. Deep learning has achieved promising performances in comparison to the classical approaches, and with the advancement of different neural architectures such as the attention network (commonly known as the transformer), there are potential areas of improvement for the summarization task. The introduction of transformer architecture and its encoder model “BERT” produced an improved performance in downstream tasks in NLP. BERT is a bidirectional encoder representation from a transformer modeled as a stack of encoders. There are different sizes for BERT, such as BERT-base with 12 encoders and BERT-larger with 24 encoders, but we focus on the BERT-base for the purpose of this study. The objective of this paper is to produce a study on the performance of variants of BERT-based models on text summarization through a series of experiments, and propose “SqueezeBERTSum”, a trained summarization model fine-tuned with the SqueezeBERT encoder variant, which achieved competitive ROUGE scores retaining the BERTSum baseline model performance by 98%, with 49% fewer trainable parameters.
A COINTEGRATION ANALYSIS FOR THE VALIDITY OF PURCHASING POWER PARITY: EVIDENCE FROM MIDDLE EAST COUNTRIES

This paper examines the validity of Purchasing Power Parity between Jordan and its major trading partners namely, Turkey, Qatar, Iraq, United Arab Emirates and Saudi Arabia. Unit root tests, Johansen cointegration test were employed to test the data covering the period of 2000Q1-2020Q4. The unit root tests demonstrated that all variables are integrated of order one. The results of cointegration tests showed that there exists a cointegrating relationship between exchange rate, domestic and foreign price levels for the selected countries does have a cointegration relationship. This suggests that whenever there is a deviation from the equilibrium cointegrating relationship, exchange rate interacts in a dynamic fashion in adjusting to restore long-run equilibrium. As a conclusion, these results provide evidence on Purchasing Power Parity model hold in the long run and the Jordanian economy is integrated with these countries.
A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Transfer learning is one of the recent emerging AI-based techniques that allow rapid learning progress and improve medical imaging diagnosis performance. Although deep learning classification for breast cancer has been widely covered, certain obstacles still remain to investigate the independency among the extracted high-level deep features. This work tackles two challenges that still exist when designing effective CAD systems for breast lesion classification from mammograms. The first challenge is to enrich the input information of the deep learning models by generating pseudo-colored images instead of only using the input original grayscale images. To achieve this goal two different image preprocessing techniques are parallel used: contrast-limited adaptive histogram equalization (CLAHE) and Pixel-wise intensity adjustment. The original image is preserved in the first channel, while the other two channels receive the processed images, respectively. The generated three-channel pseudo-colored images are fed directly into the input layer of the backbone CNNs to generate more powerful high-level deep features. The second challenge is to overcome the multicollinearity problem that occurs among the high correlated deep features generated from deep learning models. A new hybrid processing technique based on Logistic Regression (LR) as well as Principal Components Analysis (PCA) is presented and called LR-PCA. Such a process helps to select the significant principal components (PCs) to further use them for the classification purpose. The proposed CAD system has been examined using two different public benchmark datasets which are INbreast and mini-MAIS. The proposed CAD system could achieve the highest performance accuracies of 98.60% and 98.80% using INbreast and mini-MAIS datasets, respectively. Such a CAD system seems to be useful and reliable for breast cancer diagnosis.
CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment

CLIP has shown a remarkable zero-shot capability on a wide range of vision tasks.Previously, CLIP is only regarded as a powerful visual encoder.However, after being pretrained by language supervision from a large amount of image-caption pairs, CLIP itself should also have acquired some few-shot abilities for vision-language tasks.In this work, we empirically show that CLIP can be a strong vision-language few-shot learner by leveraging the power of language.We first evaluate CLIP's zero-shot performance on a typical visual question answering task and demonstrate a zero-shot cross-modality transfer capability of CLIP on the visual entailment task.Then we propose a parameter-efficient fine-tuning strategy to boost the few-shot performance on the vqa task.We achieve competitive zero/fewshot results on the visual question answering and visual entailment tasks without introducing any additional pre-training procedure.
Detection of SQL Injection Attack Using Machine Learning Techniques: A Systematic Literature Review

An SQL injection attack, usually occur when the attacker(s) modify, delete, read, and copy data from database servers and are among the most damaging of web application attacks. A successful SQL injection attack can affect all aspects of security, including confidentiality, integrity, and data availability. SQL (structured query language) is used to represent queries to database management systems. Detection and deterrence of SQL injection attacks, for which techniques from different areas can be applied to improve the detect ability of the attack, is not a new area of research but it is still relevant. Artificial intelligence and machine learning techniques have been tested and used to control SQL injection attacks, showing promising results. The main contribution of this paper is to cover relevant work related to different machine learning and deep learning models used to detect SQL injection attacks. With this systematic review, we aims to keep researchers up-to-date and contribute to the understanding of the intersection between SQL injection attacks and the artificial intelligence field.
Measurement-induced phase transitions in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi>d</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:math> -dimensional stabilizer circuits

The interplay between unitary dynamics and local quantum measurements results in unconventional nonunitary dynamical phases and transitions. In this paper we investigate the dynamics of $(d+1)$-dimensional hybrid stabilizer circuits, for $d=1,2,3$. We characterize the measurement-induced phases and their transitions using large-scale numerical simulations focusing on entanglement measures, purification dynamics, and wave-function structure. Our findings demonstrate the measurement-induced transition in $(d+1)$ spatiotemporal dimensions is conformal and has critical properties close to the percolation transition in $(d+1)$ spatial dimensions.
SeqTrack: Sequence to Sequence Learning for Visual Object Tracking

In this paper, we present a new sequence-to-sequence learning framework for visual tracking, dubbed SeqTrack. It casts visual tracking as a sequence generation problem, which predicts object bounding boxes in an autoregressive fashion. This is different from prior Siamese trackers and transformer trackers, which rely on designing complicated head networks, such as classification and regression heads. SeqTrack only adopts a simple encoder-decoder transformer architecture. The encoder extracts visual features with a bidirectional transformer, while the decoder generates a sequence of bounding box values autoregressively with a causal transformer. The loss function is a plain cross-entropy. Such a sequence learning paradigm not only simplifies tracking framework, but also achieves competitive performance on benchmarks. For instance, SeqTrack gets 72.5% AUC on LaSOT, establishing a new state-of-the-art performance. Code and models are available at https://github.com/microsoft/VideoX.