Reviewerly/exHarmony huggingface dataset:
Transformer Based Models for Unsupervised Anomaly Segmentation in Brain MR Images

The quality of patient care associated with diagnostic radiology is proportionate to a physician’s workload. Segmentation is a fundamental limiting precursor to both diagnostic and therapeutic procedures. Advances in machine learning (ML) aim to increase diagnostic efficiency by replacing a single application with generalized algorithms. The goal of unsupervised anomaly detection (UAD) is to identify potential anomalous regions unseen during training, where convolutional neural network (CNN) based autoencoders (AEs), and variational autoencoders (VAEs) are considered a de facto approach for reconstruction based-anomaly segmentation. The restricted receptive field in CNNs limits the CNN to model the global context. Hence, if the anomalous regions cover large parts of the image, the CNN-based AEs are not capable of bringing a semantic understanding of the image. Meanwhile, vision transformers (ViTs) have emerged as a competitive alternative to CNNs. It relies on the self-attention mechanism that can relate image patches to each other. We investigate in this paper Transformer’s capabilities in building AEs for the reconstruction-based UAD task to reconstruct a coherent and more realistic image. We focus on anomaly segmentation for brain magnetic resonance imaging (MRI) and present five Transformer-based models while enabling segmentation performance comparable to or superior to state-of-the-art (SOTA) models. The source code is made publicly available on GitHub .
Postdigital Nature(s)

Nature is not only the fabric and crucible out of which all human life comes into being, it is also the source of an infinite variety of inspirational images, ideas, and narratives for people to think and feel with, both as individuals and as sociocultural groups. Nature is a very large concept that is difficult to succinctly yet adequately define, but can be collapsed into three large, intersecting domains, covering: the discursive realm of ideas and symbolism; the empirical realm in which humans interact with/in the natural world; and the theoretical world of causality – the laws of nature. Postdigital nature adds a fourth dimension: nature as a resource for the generation of digital data. This entry considers the status of nature in the postdigital era and links between postdigital nature and related terms, including virtual nature, network nature, and postnature.
Temporally Adjustable Longitudinal Fluid-Attenuated Inversion Recovery MRI Estimation / Synthesis for Multiple Sclerosis

Multiple Sclerosis (MS) is a chronic progressive neurological disease characterized by the development of lesions in the white matter of the brain. T2-fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other MRI modalities. Longitudinal brain FLAIR MRI in MS, involving repetitively imaging a patient over time, provides helpful information for clinicians towards monitoring disease progression. Predicting future whole brain MRI examinations with variable time lag has only been attempted in limited applications, such as healthy aging and structural degeneration in Alzheimer’s Disease. In this article, we present novel modifications to deep learning architectures for MS FLAIR image synthesis / estimation, in order to support prediction of longitudinal images in a flexible continuous way. This is achieved with learned transposed convolutions, which support modelling time as a spatially distributed array with variable temporal properties at different spatial locations. Thus, this approach can theoretically model spatially-specific time-dependent brain development, supporting the modelling of more rapid growth at appropriate physical locations, such as the site of an MS brain lesion. This approach also supports the clinician user to define how far into the future a predicted examination should target. Accurate prediction of future rounds of imaging can inform clinicians of potentially poor patient outcomes, which may be able to contribute to earlier treatment and better prognoses. Four distinct deep learning architectures have been developed. The ISBI2015 longitudinal MS dataset was used to validate and compare our proposed approaches. Results demonstrate that a modified ACGAN achieves the best performance and reduces variability in model accuracy. Public domain code is made available at https://github.com/stfxecutables/Temporally-Adjustable-Longitudinal-MRI-Synthesis .
Tuning U-Net for Brain Tumor Segmentation

We propose a solution for BraTS22 challenge that builds on top of our previous submission—Optimized U-Net method. This year we focused on improving the model architecture and training schedule. The proposed method further improves scores on both our internal cross validation and challenge validation data. The validation mean dice scores are: ET 0.8381, TC 0.8802, WT 0.9292, and mean Hausdorff95: ET 14.460, TC 5.840, WT 3.594.
Probabilistic Tissue Mapping for Tumor Segmentation and Infiltration Detection of Glioma

Segmentation of glioma structures is vital for therapy planning. Although state of the art algorithms achieve impressive results when compared to ground-truth manual delineations, one could argue that the binary nature of these labels does not properly reflect the underlying biology, nor does it account for uncertainties in the predicted segmentations. Moreover, the tumor infiltration beyond the contrast-enhanced lesion – visually imperceptible on imaging – is often ignored despite its potential role in tumor recurrence. We propose an intensity-based probabilistic model for brain tissue mapping based on conventional MRI sequences. We evaluated its value in the binary segmentation of the tumor and its subregions, and in the visualisation of possible infiltration. The model achieves a median Dice of 0.82 in the detection of the whole tumor, but suffers from confusion between different subregions. Preliminary results for the tumor probability maps encourage further investigation of the model regarding infiltration detection.
Employing ConvexAdam for BraTS-Reg

Multiparametric MRI registration between pre- and postoperative brain scans, which is the subject of the BraTS-Reg Challenge, poses a complex problem to solve due to large deformations and missing correspondences. We make use of hand-crafted geometric features, coupled convex optimisation, and an Adam-based instance optimisation. Our method only requires few trainable parameters and is able to perform accurate image alignment while using smooth and plausible deformation fields. For our challenge submission, we achieved a mean median target registration error of 1.77 mm on the validation dataset. ConvexAdam ranks 2nd both in the ISBI-2022 and MICCAI-2022 BraTS-Reg Challenge.
Governance in the Exploration of Global and Regional Determinants of ICT Development

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Regulating Transformative Technologies

Transformative technologies like generative artificial intelligence promise to accelerate productivity growth across many sectors, but they also present new risks from potential misuse. We develop a multi-sector technology adoption model to study the optimal regulation of transformative technologies when society can learn about these risks over time. Socially optimal adoption is gradual and convex. If social damages are proportional to the productivity gains from the new technology, a higher growth rate leads to slower optimal adoption. Equilibrium adoption is inefficient when firms do not internalize all social damages, and sector-independent regulation is helpful but generally not sufficient to restore optimality.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Embracing Finance 4.0: the Opportunity for Armenian Banks to Transform and Thrive

This article explores the potential for Armenian banks to embrace new technologies and digital advancements to improve their operations, better serve customers, and stay competitive in the industry. The author argues that the adoption of Finance 4.0, which includes technologies such as artificial intelligence, blockchain, the internet of things (IoT) and machine learning, can provide Armenian banks with significant opportunities to improve their efficiency, reduce costs, and enhance customer experiences. The article highlights some of the key challenges and benefits of adopting these technologies and provides insights for Armenian banks looking to transform and thrive in the new digital era. The article emphasizes the vast opportunities that Finance 4.0 presents to Armenian banks, including the ability to streamline operations, improve risk management, and create personalized customer experiences through data analytics and AI. By leveraging blockchain technology, banks can also enhance security and transparency in transactions, while the IoT can enable new channels for customer engagement and provide realtime insights into consumer behavior. Ultimately, the adoption of these technologies can position Armenian banks for growth and success in the increasingly competitive global financial landscape.
Exploring Explainable Artificial Intelligence for Transparent Decision Making

Artificial intelligence (AI) has become a potent tool in many fields, allowing complicated tasks to be completed with astounding effectiveness. However, as AI systems get more complex, worries about their interpretability and transparency have become increasingly prominent. It is now more important than ever to use Explainable Artificial Intelligence (XAI) methodologies in decision-making processes, where the capacity to comprehend and trust AI-based judgments is crucial. This abstract explores the idea of XAI and how important it is for promoting transparent decision-making. Finally, the development of Explainable Artificial Intelligence (XAI) has shown to be crucial for promoting clear decision-making in AI systems. XAI approaches close the cognitive gap between complicated algorithms and human comprehension by empowering users to comprehend and analyze the inner workings of AI models. XAI equips stakeholders to evaluate and trust AI systems, assuring fairness, accountability, and ethical standards in fields like healthcare and finance where AI-based choices have substantial ramifications. The development of XAI is essential for attaining AI's full potential while retaining transparency and human-centric decision making, despite ongoing hurdles.
Solutions of Density Operator Master Equations

Quantum decoherence is a common essential problem in quantum information and quantum computing and has brought great trouble to the development and application of information technology. So, the decoherence effect of interaction system is of great concern. In the interaction representation, the quantum decoherence effect of an open system can be described by the quantum master equation of the reduced density operator of the system. In Refs. [1–5], the decoherence evolution law of the system is usually analyzed by solving the P-representation of the density operator or the Fock-Planck equation satisfied by the Wigner distribution function.
MEM Based Hand Gesture Controlled Wireless Robot

The robustness of MEMS based Gesture Controlled Robot is a kind of robot that can be by our hand gestures rather than an ordinary old switches or keypad. In Future there is a chance of making robots that can interact with humans in a natural manner. Hence our target interest is with hand motion-based gesture interfaces. An innovative Formula for gesture recognition is developed for identifying the distinct action signs made through hand movement. A MEMS Sensor was used to carry out this and also an Ultrasonic sensor for convinced operation. In order to full-fill our requirement a program has been written and executed using amicrocontroller system. Upon noticing the results of experimentation proves that our gesture formula is very competent and it’s also enhanced the natural way of intelligence and also assembled in a simple hardware circuit.
A Survey on Feature Extraction Techniques, Classification Methods and Applications of Sentiment Analysis

Rapid developments in the era of IoT technologies, coupled with the espousal of social media tools and applications, have promoted the use of data analytics as a means to gain significant insights from unstructured data.Sentiment analysis is an approach that identifies data polarity to classify a text as positive, neutral, or negative.Also referred to as opinion mining or subjective mining, sentiment analysis has applications that range from marketing and customer service to clinical medicine.The application of sentiment analysis in the epoch of big data has proved invaluable in classifying sentiment and, in general, determining opinions from the average person's frame of mind Several sentiment analysis techniques have been developed over the years.In this regard, this article presents a brief survey on the sentiment analysis applications, as well as feature extraction and sentiment classification techniques.This article surveys various feature extractions techniques and concludes that each technique has its own pros and cons, and can be combined for better results.The survey on classification methods suggests that hybrid methods provide finer results than individual ones.The survey of applications surmises that sentiment analysis as applied to different sectors, helps expand business opportunities.Also, the paper presents a few open challenges in carrying out sentiment analysis. HIGHLIGHTS• Surveyed various feature extraction and Classification Techniques that can be used for sentiment analysis.• Surveyed various applications of sentiment analysis.• Surveyed various issues and challenges.
Environmental and Economical Impact of Blockchain

Traditional food supply chains are centralised and have problems with variable product quality, missing data, and irregular product production. As seen by the countless cases of food fraud and adulteration that are reported on a daily basis from various parts of India, an improved decentralised supply chain model is urgently needed. India is a developing nation whose demographic dividend is its greatest advantage; it cannot afford to eat contaminated dairy products. We suggest a blockchain-enabled dairy supply chain platform for the sector as a result of this predicament. Blockchain is a technology that is starting to be widely employed in a variety of commercial areas. Traditional supply chains can be turned into decentralised, robust, transparent, impermeable networks with the use of blockchain technology. The suggested supply chain platform intends to increase food safety, maintain the nutritional value of dairy products, identify adulteration and contamination in dairy products, and stop the creation of fake dairy products. This platform offers more than just food traceability. The suggested platform for the dairy supply chain integrates smart contracts, and blockchain technology and has a positive effect on the environment.
Machine Learning for Predictive Analytics in Social Media Data

Machine Learning (ML) has become a potent predictive analytics tool in several fields, including the study of social media data. Social media sites have developed into massive repositories of user-generated information, providing insightful data about user trends, interests, and behavior. This abstract emphasizes the use of machine learning methods for predictive analytics in social media data and examines the potential and problems unique to this field. Utilizing the capabilities of machine learning algorithms to identify significant trends and forecast user behavior from social media data is the goal of this study. The study makes use of a sizable dataset made up of user profiles, blog posts, comments, and engagement metrics gathered from well-known social networking sites. Predictive models are created using a variety of machine learning algorithms, such as ensemble methods, neural networks, decision trees, and support vector machines. As a result, this study emphasizes how important machine learning is for doing predictive analytics on social media data. The employment of diverse algorithms and preprocessing methods yields insightful information about user behavior and enables precise prediction of user behaviors. To improve the prediction powers of machine learning in this area, future research should concentrate on tackling the obstacles related to social media data, such as privacy concerns and data quality issues.
Cryptography: Advances in Secure Communication and Data Protection

In the innovative work secure communication and data protection are being main field, which are emerged by cryptography as a fundamental pillar. Strong cryptographic methods are now essential given the rising reliance on digital technologies and the threats posed by bad actors. This abstract examines the evolution of secure communication protocols and data protection techniques as it relates to the advancements in cryptography. The development of post-quantum cryptography is the most notable development in cryptography discussed in this study. As quantum computers become more powerful, they pose a serious threat to traditional cryptographic algorithms, such as RSA and ECC. Designing algorithms that are immune to attacks from quantum computers is the goal of post-quantum cryptography. Lattice-based, code-based, and multivariate-based cryptography are only a few of the methods that have been investigated in this context.
A Novel approach for Load Balancing distribution and storage by using Cloud Computing

Today’s Cloud computing is the popular technology to complete the work in an efficient way, this provides an important platform to storing data in terms of pay as per use and accessible for everyone by the help of the internet. As we know the use of this technology invites many issues, some are security, failure rate and most critical load balancing. These research papers focus on load balancing and also provide an algorithm to overcome this issue. This paper proposed an idea to minimize the extra burden on the nodes through load shift or load transfer according to the availability or requirement while nodes are overloaded. Here we are discussing many algorithms and their features and find out the proposed joint algorithm which works accordingly as demanded and also offer an idea to overcome the problem of over burdens on nodes by load balancing.
Implications &amp; Impact of Artificial Intelligence in Digital Media: With Special Focus on Social Media Marketing

Purpose: This study will investigate the implications and impact of artificial intelligence in digital media, focusing on social media marketing. Theoretical Framework: The authors started their research based on technology adoption based on the Unified Theory of Acceptance and Use of Technology (UTOUT). This theory was given by Venkatesh et al. (2003). Design/Methodology/Approach: The authors used statistical analysis of technology acceptance and use based on quantitative research data. The authors examine the relationships between experience in “social media marketing” (SMM), knowledge application of “machine learning (ML) in SMM,” and rate of applying “ML algorithms in SMM campaigns,” with potential users’ comprehension of “AI-based software on three pillars (audience analysis, sentiment analysis, and image analysis)” in a sample of one hundred respondents from around the world. Findings: As a result, online marketing may focus more on strategy. A professional in social media marketing may teach a computer using algorithms for machine learning to identify trends in postings that correspond to targeted posts on social networks, hence providing doors for specialized analytical categories. This training can be done on a computer. The results show that prospective users of AI Media software have a high degree of curiosity and trust in its significant proposal. Research Practical and Social Implications: In this digital transformation era, businesses have access to a greater customer of data than ever on their customer’s behaviors. One of the most significant difficulties associated with social media marketing is anticipating people’s interests to provide them with relevant content. Fortunately, machine learning (ML) techniques have been developed that enable natural language replies to online comments. Originality/Value: This study is just the first step in figuring out how the people who will use the AI media software made as part of the Future Web research project plan to use the programmer’s features to their advantage. Due to the quantitative nature of the study and the fact that interviewees came from different countries all over the world at random, the results of the current research should be taken with a grain of salt.
Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracy

The main aim is to compare Novel Recurrent Neural Network over Artificial Neural Network in predicting Email spammers with improved accuracy. Material and Methods : This research study contains two groups namely Novel Recurrent Neural Network and Artificial Neural Network. Each group consists of a sample size of 10 and the study parameters are calculated using clincalc with preset parameters as alpha 0.8, beta 0.2 and CI as 90%. Results and Discussion : The Novel Recurrent Neural Network has the highest accuracy 97.96% when compared to Artificial Neural Network it has 93.79% accuracy in Electronic Mail spam prediction with significance value p=0.000(p&lt;0.05) that is significantly better. The G-power value is 80%. When used as a spam predictor for electronic mail, the Novel Recurrent Neural Network performance analysis outperforms the best results than the Artificial Neural Network performance.
Quantum Computing: Algorithms,Architectures, and Applications

Cryptography, optimization, simulation, and machine learning are just a few of the industries that might be completely transformed by quantum computing. This abstract gives a thorough introduction to quantum computing with an emphasis on its algorithms, architectures, and applications. In conclusion, this abstract offers an in-depth analysis of quantum computing, including its algorithms, structures, and applications. It highlights the revolutionary potential of quantum computing in tackling difficult issues that are beyond the scope of conventional computers, laying the groundwork for further research and understanding of this quickly developing topic.