Reviewerly/exHarmony huggingface dataset:
An Automated In-Depth Feature Learning Algorithm for Breast Abnormality Prognosis and Robust Characterization from Mammography Images Using Deep Transfer Learning

Diagnosing breast cancer masses and calcification clusters have paramount significance in mammography, which aids in mitigating the disease's complexities and curing it at early stages. However, a wrong mammogram interpretation may lead to an unnecessary biopsy of the false-positive findings, which reduces the patient's survival chances. Consequently, approaches that learn to discern breast masses can reduce the number of misconceptions and incorrect diagnoses. Conventionally used classification models focus on feature extraction techniques specific to a particular problem based on domain information. Deep learning strategies are becoming promising alternatives to solve the many challenges of feature-based approaches.This study introduces a convolutional neural network (ConvNet)-based deep learning method to extract features at varying densities and discern mammography's normal and suspected regions. Two different experiments were carried out to make an accurate diagnosis and classification. The first experiment consisted of five end-to-end pre-trained and fine-tuned deep convolution neural networks (DCNN). The in-depth features extracted from the ConvNet are also used to train the support vector machine algorithm to achieve excellent performance in the second experiment. Additionally, DCNN is the most frequently used image interpretation and classification method, including VGGNet, GoogLeNet, MobileNet, ResNet, and DenseNet. Moreover, this study pertains to data cleaning, preprocessing, and data augmentation, and improving mass recognition accuracy. The efficacy of all models is evaluated by training and testing three mammography datasets and has exhibited remarkable results.Our deep learning ConvNet+SVM model obtained a discriminative training accuracy of 97.7% and validating accuracy of 97.8%, contrary to this, VGGNet16 method yielded 90.2%, 93.5% for VGGNet19, 63.4% for GoogLeNet, 82.9% for MobileNetV2, 75.1% for ResNet50, and 72.9% for DenseNet121.The proposed model's improvement and validation are appropriated in conventional pathological practices that conceivably reduce the pathologist's strain in predicting clinical outcomes by analyzing patients' mammography images.
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies

Content Warning: This paper contains examples of stereotypes and associations, misgendering, erasure, and other harms that could be offensive and triggering to trans and nonbinary individuals.Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models.However, current discussions primarily treat gender as binary, which can perpetuate harms such as the cyclical erasure of non-binary gender identities.These harms are driven by model and dataset biases, which are consequences of the non-recognition and lack of understanding of non-binary genders in society.In this paper, we explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies.We also detail how current language representations (e.g., GloVe, BERT) capture and perpetuate these harms and related challenges that need to be acknowledged and addressed for representations to equitably encode gender information.
Gate-free state preparation for fast variational quantum eigensolver simulations

Abstract The variational quantum eigensolver is currently the flagship algorithm for solving electronic structure problems on near-term quantum computers. The algorithm involves implementing a sequence of parameterized gates on quantum hardware to generate a target quantum state, and then measuring the molecular energy. Due to finite coherence times and gate errors, the number of gates that can be implemented remains limited. In this work, we propose an alternative algorithm where device-level pulse shapes are variationally optimized for the state preparation rather than using an abstract-level quantum circuit. In doing so, the coherence time required for the state preparation is drastically reduced. We numerically demonstrate this by directly optimizing pulse shapes which accurately model the dissociation of H 2 and HeH + , and we compute the ground state energy for LiH with four transmons where we see reductions in state preparation times of roughly three orders of magnitude compared to gate-based strategies.
Universal Fidelity Reduction of Quantum Operations from Weak Dissipation

Quantum information processing is in real systems often limited by dissipation, stemming from remaining uncontrolled interaction with microscopic degrees of freedom. Given recent experimental progress, we consider weak dissipation, resulting in a small error probability per operation. Here, we find a simple formula for the fidelity reduction of any desired quantum operation, where the ideal evolution is confined to the computational subspace. Interestingly, this reduction is independent of the specific operation; it depends only on the operation time and the dissipation. Using our formula, we investigate the situation where dissipation in different parts of the system has correlations, which is detrimental for the successful application of quantum error correction. Surprisingly, we find that a large class of correlations gives the same fidelity reduction as uncorrelated dissipation of similar strength.
A Systematic Literature Review on Extended Reality: Virtual, Augmented and Mixed Reality in Working Life

Extended reality (XR), here jointly referring to virtual, augmented, and mixed (VR, AR, MR) reality, is becoming more common in everyday working life. This paper presents a systematic literature review of academic publications on XR indicating changes in practical organization of work. We analyse both application areas of XR and theoretical and methodological approaches of XR research. The review process followed the PRISMA statement. Design, remote collaboration, and training were the main application areas of XR. XR enabled overcoming of obstacles set by time and space, safety, and resources by mediating experience of space. Research on XR applications in actual working life settings is yet relatively rare and covers primarily three areas: collaboration, evaluation of knowledge transfer, and work practices. Virtual reality was the most common form of applied XR, although the hardware used varied case by case. We identified four research areas regarding XR: collaboration, work practices, and evaluation of knowledge transfer, which somewhat followed the application areas. We did not find XR-specific methodologies in the reviewed articles, only few recent studies used novel ways of collecting research material, such as recording the movement in virtual reality. For now, XR still holds significant potential rather than clearly confirmed general advantages in working life.
A Survey of Automatic Text Summarization: Progress, Process and Challenges

With the evolution of the Internet and multimedia technology, the amount of text data has increased exponentially. This text volume is a precious source of information and knowledge that needs to be efficiently summarized. Text summarization is the method to reduce the source text into a compact variant, preserving its knowledge and the actual meaning. Here we thoroughly investigate the automatic text summarization (ATS) and summarize the widely recognized ATS architectures. This paper outlines extractive and abstractive text summarization technologies and provides a deep taxonomy of the ATS domain. The taxonomy presents the classical ATS algorithms to modern deep learning ATS architectures. Every modern text summarization approach’s workflow and significance are reviewed with the limitations with potential recovery methods, including the feature extraction approaches, datasets, performance measurement techniques, and challenges of the ATS domain, etc. In addition, this paper concisely presents the past, present, and future research directions in the ATS domain.
Blockchain-Based Electronic Health Records Management: A Comprehensive Review and Future Research Direction

Electronic Health Records (EHRs) are electronically-stored health information in a digital format. EHRs are typically shared among healthcare stakeholders and face power failure, data misuse, lack of privacy, security, and audit trail. On the other hand, blockchain is the revolutionary invention of the twentieth century that offers a distributed and decentralized setting to communicate among nodes in a list of networks without a central authority. It can address the limitations of EHRs management and provide a safer, secured, and decentralized environment for exchanging EHRs data. Three categories of blockchain-based potential solutions have been proposed by researchers to handle EHRs: conceptual, prototype, and implemented. This study focused on a Systematic Literature Review (SLR) to find and analyze articles submitted either conceptual or implemented to manage EHRs using blockchain. The study examined 99 papers that were collected from various publication categories. The deep technical analysis focused on evaluating articles based on privacy, security, scalability, accessibility, cost, consensus algorithms, and the type of blockchain used. The SLR found that blockchain technology promises to provide decentralization, security, and privacy that traditional EHRs often lack. Moreover, results obtained from the detailed studies would provide potential researchers with the type of blockchain for future research. Finally, future research directions, in the end, would direct enthusiasm to combine new blockchain-based systems to manage EHRs properly.
Multi-Method Diagnosis of Blood Microscopic Sample for Early Detection of Acute Lymphoblastic Leukemia Based on Deep Learning and Hybrid Techniques

Leukemia is one of the most dangerous types of malignancies affecting the bone marrow or blood in all age groups, both in children and adults. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL). It is diagnosed by hematologists and experts in blood and bone marrow samples using a high-quality microscope with a magnifying lens. Manual diagnosis, however, is considered slow and is limited by the differing opinions of experts and other factors. Thus, this work aimed to develop diagnostic systems for two Acute Lymphoblastic Leukemia Image Databases (ALL_IDB1 and ALL_IDB2) for the early detection of leukemia. All images were optimized before being introduced to the systems by two overlapping filters: the average and Laplacian filters. This study consists of three proposed systems as follows: the first consists of the artificial neural network (ANN), feed forward neural network (FFNN), and support vector machine (SVM), all of which are based on hybrid features extracted using Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM) and Fuzzy Color Histogram (FCH) methods. Both ANN and FFNN reached an accuracy of 100%, while SVM reached an accuracy of 98.11%. The second proposed system consists of the convolutional neural network (CNN) models: AlexNet, GoogleNet, and ResNet-18, based on the transfer learning method, in which deep feature maps were extracted and classified with high accuracy. All the models obtained promising results for the early detection of leukemia in both datasets, with an accuracy of 100% for the AlexNet, GoogleNet, and ResNet-18 models. The third proposed system consists of hybrid CNN-SVM technologies, consisting of two blocks: CNN models for extracting feature maps and the SVM algorithm for classifying feature maps. All the hybrid systems achieved promising results, with AlexNet + SVM achieving 100% accuracy, Goog-LeNet + SVM achieving 98.1% accuracy, and ResNet-18 + SVM achieving 100% accuracy.
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer

Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning. However, these methods are difficult to be applied in real-world scenarios since they are inevitably accompanied by the problems of computational and memory costs caused by the complex operations. To solve this issue, we propose a Lightweight Bimodal Network (LBNet) for SISR. Specifically, an effective Symmetric CNN is designed for local feature extraction and coarse image reconstruction. Meanwhile, we propose a Recursive Transformer to fully learn the long-term dependence of images thus the global information can be fully used to further refine texture details. Studies show that the hybrid of CNN and Transformer can build a more efficient model. Extensive experiments have proved that our LBNet achieves more prominent performance than other state-of-the-art methods with a relatively low computational cost and memory consumption. The code is available at https://github.com/IVIPLab/LBNet.
The effect of digital marketing on purchase intention: Moderating effect of brand equity

This study aims to investigate the effect of digital marketing, social media marketing and electronic word-of-mouth EWOM, on the purchase intention with moderating effect of brand equity. A quantitative research approach was used to achieve the research objectives. The data was collected from a sample consisting of 254 online shoppers of IKEA Jordan. By using a random sampling technique, the data was collected through an electronic questionnaire. Statistical analyses were conducted such as data normality and scale reliability by using IBM SPSS 21 software, followed by measurement model and hypothesis testing by using Smart PLS3 software. The results assessed the validity of the measurement model, structural model as well moderation analysis that was conducted based on the study objectives. The findings confirmed the assumptions which stated the digital marketing had a positive significant effect on purchase intention, and the moderating effect of brand equity revealed a significant effect. The study has contributed to the existing literature by providing future research suggestions and directions linked to this topic in the context of Jordan social media marketing and shopping.
Exploratory Data Analysis using Python

Data need to be analyzed so as to produce good result. Using the result decision can be taken. For example recommendation system, ranking of the page, demand fore casting, prediction of purchase of the product. There are some leading companies where the review of the customer plays a great role to analyze the factor which influences the review rating. We have used exploratory data analysis (EDA) where data interpretations can be done in row and column format. We have used python for data analysis. it is object oriented ,interpreted and interactive programming language. it is open source with rich sets of libraries like pandas, MATplotlib, seaborn etc. We have used different types of charts and various types of parameter to analyze Amazon review data sets which contains the reviews of electronic data items. We have used python programming for the data analysis.
Casting Light on Central Bank Digital Currency

Several central banks have begun actively investigating the possibility of issuing central bank digital currency (CBDC). This new central bank liability would be a widely accessible digital form of fiat money, intended as legal tender. This chapter aims to answer a simple question: Does CBDC offer benefits? On the demand side, would it satisfy end user needs better than other forms of money? And on the supply side, would issuing CBDC allow central banks to more effectively satisfy public policy goals, including financial inclusion, operational efficiency, financial stability, monetary policy effectiveness, and financial integrity? In short, is CBDC a desirable form of money given existing and rapidly evolving alternatives? The chapter includes a summary of pilot projects and studies from central banks exploring the possibility of issuing CBDC. The analysis is based on publicly issued materials and discussions with staff members at central banks and technology providers around the world.
ObjectFormer for Image Manipulation Detection and Localization

Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection. In this paper, we propose ObjectFormer to detect and localize image manipulations. To capture subtle manipulation traces that are no longer visible in the RGB domain, we extract high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. Additionally, we use a set of learnable object prototypes as mid-level representations to model the object-level consistencies among different regions, which are further used to refine patch embeddings to capture the patch-level consistencies. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method, outperforming state-of-the-art tampering detection and localization methods.
The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions

ABSTRACTIn a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).KEYWORDS: Generative artificial intelligencemachine learninglarge language modelChatGPTBard Disclosure statementNo potential conflict of interest was reported by the author(s).
Feasibility Study: Using Highways for Bounded-Suboptimal Multi-Agent Path Finding

Multi-agent path-finding (MAPF) is important for applications such as the kind of warehousing done by Kiva systems. Solving the problem optimally is NP-hard, yet finding low-cost solutions is important. Bounded-suboptimal MAPF algorithms, such as enhanced conflict-based search (ECBS), often do not perform well in warehousing domains with many agents. We therefore develop bounded-suboptimal MAPF algorithms, called CBS+HWY and ECBS+HWY, that exploit the problem structure of a given MAPF instance by finding paths for the agents that include edges from user-provided highways, which encourages a global behavior of the agents that avoids collisions. On the theoretical side, we develop a simple approach that uses highways for MAPF and provides suboptimality guarantees. On the experimental side, we demonstrate that ECBS+HWY can decrease the runtimes and solution costs of ECBS in Kiva-like domains with many agents if the highways capture the problem structures well.
Hybrid Planning Heuristics Based on Task Decomposition Graphs

Hybrid Planning combines Hierarchical Task Network (HTN) planning with concepts known from Partial-Order Causal-Link (POCL) planning. We introduce novel heuristics for Hybrid Planning that estimate the number of necessary modifications to turn a partial plan into a solution. These estimates are based on the task decomposition graph that contains all decompositions of the abstract tasks in the planning domain. Our empirical evaluation shows that the proposed heuristics can significantly improve planning performance.
Intelligent Agents for Online Learning

This research investigated the effects of applying intelligent agent techniques to an online learning environment. The knowbots (or Knowledge Robots) created for the research were intelligent software agents that automated the repetitive tasks of human facilitators in a series of online workshops. The study specifically captured experimental results of using knowbots in multiple sessions of an ALN (Asynchronous Learning Network) online workshop, Getting Started Creating Online Courses. The study used experimental groups and comparison groups to examine the association between the use of knowbots and workshop completion rates. Also examined were the effects of knowbots on other factors such as facilitation time and learner satisfaction. The findings indicated that the use of knowbots was positively associated with higher learner completion rates in the workshops. In addition, knowbots implemented a learning-support tool that reminded learners about deadlines. The support knowbots were found to be effective autonomous motivators. In sum, the results of this research suggest that the application of agent technology to online learning holds promise for improving completion rates, learner satisfaction, and motivation.
Compressive Binary Patterns: Designing a Robust Binary Face Descriptor with Random-Field Eigenfilters

A binary descriptor typically consists of three stages: image filtering, binarization, and spatial histogram. This paper first demonstrates that the binary code of the maximum-variance filtering responses leads to the lowest bit error rate under Gaussian noise. Then, an optimal eigenfilter bank is derived from a universal assumption on the local stationary random field. Finally, compressive binary patterns (CBP) is designed by replacing the local derivative filters of local binary patterns (LBP) with these novel random-field eigenfilters, which leads to a compact and robust binary descriptor that characterizes the most stable local structures that are resistant to image noise and degradation. A scattering-like operator is subsequently applied to enhance the distinctiveness of the descriptor. Surprisingly, the results obtained from experiments on the FERET, LFW, and PaSC databases show that the scattering CBP (SCBP) descriptor, which is handcrafted by only 6 optimal eigenfilters under restrictive assumptions, outperforms the state-of-the-art learning-based face descriptors in terms of both matching accuracy and robustness. In particular, on probe images degraded with noise, blur, JPEG compression, and reduced resolution, SCBP outperforms other descriptors by a greater than 10 percent accuracy margin.
SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol

Machine learning applications are intensively utilized in various science fields, and increasingly the biomedical and healthcare sector. Applying predictive modeling to biomedical data introduces privacy and security concerns requiring additional protection to prevent accidental disclosure or leakage of sensitive patient information. Significant advancements in secure computing methods have emerged in recent years, however, many of which require substantial computational and/or communication overheads, which might hinder their adoption in biomedical applications. In this work, we propose SecureLR, a novel framework allowing researchers to leverage both the computational and storage capacity of Public Cloud Servers to conduct learning and predictions on biomedical data without compromising data security or efficiency. Our model builds upon homomorphic encryption methodologies with hardware-based security reinforcement through Software Guard Extensions (SGX), and our implementation demonstrates a practical hybrid cryptographic solution to address important concerns in conducting machine learning with public clouds.