Reviewerly/exHarmony huggingface dataset:
Development and operation of a digital platform for sharing pathology image data

Abstract Background Artificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing and sharing digital pathology data to AI researchers, and highlight challenges to overcome in operating a sustainable platform in conjunction with pathologists. Methods Over 3000 pathological slides from five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) in histologically confirmed tumor cases by pathology departments at three hospitals were selected for the dataset. After digitalizing the slides, tumor areas were annotated and overlaid onto the images by pathologists as the ground truth for AI training. To reduce the pathologists’ workload, AI-assisted annotation was established in collaboration with university AI teams. Results A web-based data sharing platform was developed to share massive pathological image data in 2019. This platform includes 3100 images, and 5 pre-processing algorithms for AI researchers to easily load images into their learning models. Discussion Due to different regulations among countries for privacy protection, when releasing internationally shared learning platforms, it is considered to be most prudent to obtain consent from patients during data acquisition. Conclusions Despite limitations encountered during platform development and model training, the present medical image sharing platform can steadily fulfill the high demand of AI developers for quality data. This study is expected to help other researchers intending to generate similar platforms that are more effective and accessible in the future.
Fair Representation Learning for Heterogeneous Information Networks

Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness. A growing body of research has identified unfair AI systems and proposed methods to debias them, yet many challenges remain. Representation learning methods for Heterogeneous Information Networks (HINs), fundamental building blocks used in complex network mining, have socially consequential applications such as automated career counseling, but there have been few attempts to ensure that it will not encode or amplify harmful biases, e.g. sexism in the job market. To address this gap, we propose a comprehensive set of de-biasing methods for fair HINs representation learning, including sampling-based, projection-based, and graph neural networks (GNNs)-based techniques. We systematically study the behavior of these algorithms, especially their capability in balancing the trade-off between fairness and prediction accuracy. We evaluate the performance of the proposed methods in an automated career counseling application where we mitigate gender bias in career recommendation. Based on the evaluation results on two datasets, we identify the most effective fair HINs representation learning techniques under different conditions.
A Research on HMM based Speech Recognition in Spoken English

Background: The computer assisted speech recognition system enabling voice recognition for understanding the spoken words using sound digitization is extensively being used in the field of education, scientific research, industry, etc. Objective: This article unveils the technological perspective of automated speech recognition system in order to realize the spoken English speech recognition system based on MATLAB. Methods: A speech recognition technology has been designed and implemented in this work which can collect the speech signals of the spoken English learning system and then filter those speech signals. This paper mainly adopts the preprocessing module for the processing of the raw speech data collected utilizing the MATLAB commands. The method of feature extraction is based on HMM model, codebook generation and template training. Results: The research results show that the recognition accuracy of 98% is achieved by the spoken English speech recognition system studied in this paper. It can be seen that the spoken English speech recognition system based on MATLAB has high recognition accuracy and fast speed. Conclusion: This work addresses the current research issued needed to be tackled in the speech recognition field. This approach is able to provide the technical support and interface for the spoken English learning system.
Spammer group detection and diversification of customers’ reviews

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profit or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, Spam Review Detection problem (SRD) has gained much attention from researchers, but still there is a need to identify review spammers who often work collaboratively to promote or demote targeted products. It can severely harm the review system. This work presents the Spammer Group Detection (SGD) method which identifies suspicious spammer groups based on the similarity of all reviewer’s activities considering their review time and review ratings. After removing these identified spammer groups and spam reviews, the resulting non-spam reviews are displayed using diversification technique. For the diversification, this study proposed Diversified Set of Reviews (DSR) method which selects diversified set of top-k reviews having positive, negative, and neutral reviews/feedback covering all possible product features. Experimental evaluations are conducted on Roman Urdu and English real-world review datasets. The results show that the proposed methods outperformed the existing approaches when compared in terms of accuracy.
Targeted adversarial discriminative domain adaptation

Domain adaptation is a technology enabling aided target recognition and other algorithms for environments and targets with data or labeled data that is scarce. Recent advances in unsupervised domain adaptation have demonstrated excellent performance but only when the domain shift is relatively small. We proposed targeted adversarial discriminative domain adaptation (T-ADDA), a semi-supervised domain adaptation method that extends the ADDA framework. By providing at least one labeled target image per class, used as a cue to guide the adaption, T-ADDA significantly boosts the performance of ADDA and is applicable to the challenging scenario in which the sets of targets in the source and target domains are not the same. The efficacy of T-ADDA is demonstrated by cross-domain, cross-sensor, and cross-target experiments using the common digits datasets and several aerial image datasets. Results demonstrate an average increase of 15% improvement with T-ADDA over ADDA using just a few labeled images when adapting to a small domain shift and afforded a 60% improvement when adapting to large domain shifts.
Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

In this paper, two optimization methods are used to adjust the gain values for the cascade PID controller. These algorithms are the butterfly optimization algorithm (BOA), which is a modern method based on tracking the movement of butterflies to the scent of a fragrance to reach the best position and the second method is particle swarm optimization (PSO). The PID controllers in this system are used to control the position, velocity, and current of a permanent magnet DC motor (PMDC) with an accurate tracking trajectory to reach the desired position. The simulation results using the Matlab environment showed that the butterfly optimization algorithm is better than the particle swarming optimization (PSO) in terms of performance and overshoot or any deviation in tracking the path to reach the desired position. While an overshoot of 2.557% was observed when using the PSO algorithm, and a position deviation of 7.82 degrees was observed from the reference position.
BeeRank

There is an incredible change in the world wide web, and the users face difficulty in accessing the needed information as per their need. Different algorithms are devised at each step of the information retrieval process, and it is observed that ranking is one of the core ingredients of any search engine that plays a major role in arranging the information. In this regard, different measures are adopted for ranking the web pages by using content, structure, or log data. The BeeRank algorithm is proposed that provides quality results, which is inspired by the artificial bee colony algorithm for web page ranking and uses both the structural and content approach for calculating the rank value and provides better results. It also helps the users in finding the relevant web pages by minimizing the computational complexity of the process and achieves the result in minimum time duration. The working is illustrated and is compared with the traditional PageRank algorithm that incorporates only structural links, and the result shows an improvement in ranking and provides user-specific results.
Scientific Discourse Tagging for Evidence Extraction

Evidence plays a crucial role in any biomedical research narrative, providing justification for some claims and refutation for others.We seek to build models of scientific argument using information extraction methods from fulltext papers.We present the capability of automatically extracting text fragments from primary research papers that describe the evidence presented in that paper's figures, which arguably provides the raw material of any scientific argument made within the paper.We apply richly contextualized deep representation learning pre-trained on biomedical domain corpus to the analysis of scientific discourse structures and the extraction of "evidence fragments" (i.e., the text in the results section describing data presented in a specified subfigure) from a set of biomedical experimental research articles.We first demonstrate our state-of-the-art scientific discourse tagger on two scientific discourse tagging datasets and its transferability to new datasets.We then show the benefit of leveraging scientific discourse tags for downstream tasks such as claim-extraction and evidence fragment detection.Our work demonstrates the potential of using evidence fragments derived from figure spans for improving the quality of scientific claims by cataloging, indexing and reusing evidence fragments as independent documents.
Novel Secure Hybrid Image Steganography Technique Based on Pattern Matching

The secure communication of information is a major concern over the internet.The information must be protected before transmitting over a communication channel to avoid security violations.In this paper, a new hybrid method called compressed encrypted data embedding (CEDE) is proposed.In CEDE, the secret information is first compressed with Lempel Ziv Welch (LZW) compression algorithm.Then, the compressed secret information is encrypted using the Advanced Encryption Standard (AES) symmetric block cipher.In the last step, the encrypted information is embedded into an image of size 512 × 512 pixels by using image steganography.In the steganographic technique, the compressed and encrypted secret data bits are divided into pairs of two bits and pixels of the cover image are also arranged in four pairs.The four pairs of secret data are compared with the respective four pairs of each cover pixel which leads to sixteen possibilities of matching in between secret data pairs and pairs of cover pixels.The least significant bits (LSBs) of current and imminent pixels are modified according to the matching case number.The proposed technique provides doublefolded security and the results show that stego image carries a high capacity of secret data with adequate peak signal to noise ratio (PSNR) and lower mean square error (MSE) when compared with existing methods in the literature.
RETRACTED: Applied research of VR technology in physical education

VR is a new type of teaching media, which is more and more important to PE teaching. The paper discussed the definition, viability and application, concluded the application of PE teaching in detail. The purpose of this article is to study the application of VR technology in college physical education and to explore the effect of VR technology on improving the quality of physical education. This article uses literature, PEST analysis and case empirical analysis to summarize the specific application directions and principles of virtual reality technology in the field of sports. Secondly, it analyzes the external macro environment through the feasibility of VR+ sports in my country. This article first introduces the advantages and research status of VR technology and its application in traditional education and teaching, and then puts forward the application strategy of VR technology. The research results show that the application of VR technology in college physical education can greatly improve the quality and level of physical education.
Distance Learning Experience in the Context of Globalization of Education

Today, the digitalization of education is one of the main tasks of the state social policy and an urgent sphere of life in modern society. At the beginning of 2020, the spread of COVID-19 forced educational institutions to introduce urgently all levels of distance learning technologies focused on the possibility to learn remotely and in real-time. The research focuses on the need to create an effective mechanism for organizing distance learning in the context of the globalization of education. The purpose of the research is to reveal the experience of distance learning in the context of globalization of education (based on the analysis of using the online learning platform Moodle). The results of the research reveal the prerequisites for the popularization of distance learning technologies in the practice of educational institutions, present the experience of using Moodle in the university educational process and determine the prospects of distance learning in educational practice.
Digital Image Watermarking Processor Based on Deep Learning

Much research and development have been made to implement deep neural networks for various purposes with hardware. We implement the deep learning algorithm with a dedicated processor. Watermarking technology for ultra-high resolution digital images and videos needs to be implemented in hardware for real-time or high-speed operation. We propose an optimization methodology to implement a deep learning-based watermarking algorithm in hardware. The proposed optimization methodology includes algorithm and memory optimization. Next, we analyze a fixed-point number system suitable for implementing neural networks as hardware for watermarking. Using these, a hardware structure of a dedicated processor for watermarking based on deep learning technology is proposed and implemented as an application-specific integrated circuit (ASIC).
Blockchain Based E-Voting System

Increasingly digital technology in the present helped many people lives. Unlike the electoral system, there are many conventional uses of paper in its implementation. The aspect of security and transparency is a threat from still widespread election with the conventional system (offline). General elections still use a centralized system, there is one organization that manages it. Some of the problems that can occur in traditional electoral systems is with an organization that has full control over the database and system, it is possible to tamper with the database of considerable opportunities. Blockchain technology is one of solutions, because it embraces a decentralized system and the entire database are owned by many users. Blockchain itself has been used in the Bitcoin system known as the decentralized Bank system. By adopting blockchain in the distribution of databases on e-voting systems can reduce one of the cheating sources of database manipulation. This research discusses the recording of voting result using blockchain algorithm from every place of election. Unlike Bitcoin with its Proof of Work, this thesis proposed a method based on a predetermined turn on the system for each node in the built of blockchain
Effect of stemming on text similarity for Arabic language at sentence level

Semantic Text Similarity (STS) has several and important applications in the field of Natural Language Processing (NLP). The Aim of this study is to investigate the effect of stemming on text similarity for Arabic language at sentence level. Several Arabic light and heavy stemmers as well as lemmatization algorithms are used in this study, with a total of 10 algorithms. Standard training and testing data sets are used from SemEval-2017 international workshop for Task 1, Track 1 Arabic (ar–ar). Different features are selected to study the effect of stemming on text similarity based on different similarity measures. Traditional machine learning algorithms are used such as Support Vector Machines (SVM), Stochastic Gradient Descent (SGD) and Naïve Bayesian (NB). Compared to the original text, using the stemmed and lemmatized documents in experiments achieve enhanced Pearson correlation results. The best results attained when using Arabic light Stemmer (ARLSTem) and Farasa light stemmers, Farasa and Qalsadi Lemmatizers and Tashaphyne heavy stemmer. The best enhancement was about 7.34% in Pearson correlation. In general, stemming considerably improves the performance of sentence text similarly for Arabic language. However, some stemmers make results worse than those for original text; they are Khoja heavy stemmer and AlKhalil light stemmer.
Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?Ryoma SatoRyoma Satopp.549 - 557Chapter DOI:https://doi.org/10.1137/1.9781611977172.62PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Fairness is a crucial property in recommender systems. Although some online services have adopted fairness aware systems recently, many other services have not adopted them yet. In this work, we propose methods to enable the users to build their own fair recommender systems. Our methods can generate fair recommendations even when the service does not (or cannot) provide fair recommender systems. The key challenge is that a user does not have access to the log data of other users or the latent representations of items. This restriction prohibits us from adopting existing methods designed for service providers. The main idea is that a user has access to unfair recommendations shown by the service provider. Our methods leverage the outputs of an unfair recommender system to construct a new fair recommender system. We empirically validate that our proposed method improves fairness substantially without harming much performance of the original unfair system. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
Optimal Complexity in Decentralized Training

Decentralization is a promising method of scaling up parallel machine learning systems. In this paper, we provide a tight lower bound on the iteration complexity for such methods in a stochastic non-convex setting. Our lower bound reveals a theoretical gap in known convergence rates of many existing decentralized training algorithms, such as D-PSGD. We prove by construction this lower bound is tight and achievable. Motivated by our insights, we further propose DeTAG, a practical gossip-style decentralized algorithm that achieves the lower bound with only a logarithm gap. Empirically, we compare DeTAG with other decentralized algorithms on image classification tasks, and we show DeTAG enjoys faster convergence compared to baselines, especially on unshuffled data and in sparse networks.
Detection of GAN-Synthesized Image Based on Discrete Wavelet Transform

Recently, generative adversarial networks (GANs) and its variants have shown impressive ability in image synthesis. The synthesized fake images spread widely on the Internet, and it is challenging for Internet users to identify the authenticity, which poses huge security risk to the society. However, compared with the powerful image synthesis technology, the detection of GAN-synthesized images is still in its infancy and face a variety of challenges. In this study, a method named fake images discriminator (FID) is proposed, which detects that GAN-synthesized fake images use the strong spectral correlation in the imaging process of natural color images. The proposed method first converts the color image into three color components of R, G, and B. Discrete wavelet transform (DWT) is then applied to RGB components separately. Finally, the correlation coefficient between the subband images is used as a feature vector for authenticity classification. Experimental results show that the proposed FID method achieves impressive effectiveness on the StyleGAN2-synthesized faces and multitype fake images synthesized with the state-of-the-art GANs. Also, the FID method exhibits good robustness against the four common perturbation attacks.
Example-Driven Intent Prediction with Observers

A key challenge of dialog systems research is to effectively and efficiently adapt to new domains.A scalable paradigm for adaptation necessitates the development of generalizable models that perform well in few-shot settings.In this paper, we focus on the intent classification problem which aims to identify user intents given utterances addressed to the dialog system.We propose two approaches for improving the generalizability of utterance classification models: (1) observers and (2) example-driven training.Prior work has shown that BERT-like models tend to attribute a significant amount of attention to the [CLS] token, which we hypothesize results in diluted representations.Observers are tokens that are not attended to, and are an alternative to the [CLS] token as a semantic representation of utterances.Example-driven training learns to classify utterances by comparing to examples, thereby using the underlying encoder as a sentence similarity model.These methods are complementary; improving the representation through observers allows the example-driven model to better measure sentence similarities.When combined, the proposed methods attain state-of-the-art results on three intent prediction datasets (BANKING77, CLINC150, HWU64) in both the full data and few-shot (10 examples per intent) settings.Furthermore, we demonstrate that the proposed approach can transfer to new intents and across datasets without any additional training.