Reviewerly/exHarmony huggingface dataset:
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation

Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-the-art (SOTA) baselines. The demonstration video (https://www.youtube.com/watch?v=aqJCHcsj4w0) and the source code (https://github.com/OscarHuangWind/DRL-Transformer-SimtoReal-Navigation) are also provided.
Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation

Learning discriminative representations of unlabelled data is a challenging task. Contrastive self-supervised learning provides a framework to learn meaningful representations using learned notions of similarity measures from simple pretext tasks. In this work, we propose a simple and efficient framework for self-supervised image segmentation using contrastive learning on image patches, without using explicit pretext tasks or any further labeled fine-tuning. A fully convolutional neural network (FCNN) is trained in a self-supervised manner to discern features in the input images and obtain confidence maps which capture the network's belief about the objects belonging to the same class. Positive- and negative- patches are sampled based on the average entropy in the confidence maps for contrastive learning. Convergence is assumed when the information separation between the positive patches is small, and the positive-negative pairs is large. The proposed model only consists of a simple FCNN with 10.8k parameters and requires about 5 minutes to converge on the high resolution microscopy datasets, which is orders of magnitude smaller than the relevant self-supervised methods to attain similar performance. We evaluate the proposed method for the task of segmenting nuclei from two histopathology datasets, and show comparable performance with relevant self-supervised and supervised methods.
Generative Models for Inverse Imaging Problems: From mathematical foundations to physics-driven applications

Physics-informed generative modeling for inverse problems in computational imaging is a fast-growing field encompassing a variety of methods and applications. Here, we review a few generative modeling techniques, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), as well as more recent developments in score-based generative models. Through different imaging applications, we review how the generative modeling techniques are effectively combined with the physics of the imaging problem, e.g., the measurement forward model and physical properties of the target objects, to solve the inverse problems.
Deep Learning Based Parametrization of Diffeomorphic Image Registration for the Application of Cardiac Image Segmentation

Cardiac segmentation from magnetic resonance imaging (MRI) is one of the essential tasks to analyze the anatomy and function of the heart for the assessment and diagnosis of cardiac diseases. However, manual annotation is difficult and time consuming. This study proposes a novel end-to-end supervised cardiac MRI segmentation framework based on a diffeomorphic deformable registration that can segment the left ventricle from 2D and 3D images or volumes. In order to represent the actual cardiac deformation, the methodology parameterizes the transformation using radial and rotational components, computed using a deep learning approach The method was evaluated over three different data sets and showed significant improvements compared to exacting learning and non-learning based methods in terms of the Dice score and Hausdorff distance metrics.
FDE-net: Frequency-domain enhancement network using dynamic-scale dilated convolution for thyroid nodule segmentation

Thyroid nodules, a common disease of endocrine system, have a probability of nearly 10% to turn into malignant nodules and thus pose a serious threat to health. Automatic segmentation of thyroid nodules is of great importance for clinicopathological diagnosis. This work proposes FDE-Net, a combined segmental frequency domain enhancement and dynamic scale cavity convolutional network for thyroid nodule segmentation. In FDE-Net, traditional image omics method is introduced to enhance the feature image in the segmented frequency domain. Such an approach reduces the influence of noise and strengthens the detail and contour information of the image. The proposed method introduces a cascade cross-scale attention module, which addresses the insensitivity of the network to the change in target scale by fusing the features of different receptive fields and improves the ability of the network to identify multiscale target regions. It repeatedly uses the high-dimensional feature image to improve segmentation accuracy in accordance with the simple structure of thyroid nodules. In this study, 1355 ultrasound images are used for training and testing. Quantitative evaluation results showed that the Dice coefficient of FDE-Net in thyroid nodule segmentation was 83.54%, which is better than other methods. Therefore, FDE-Net can enable the accurate and rapid segmentation of thyroid nodules.
State-of-the-Art in Data Integrity and Privacy-Preserving in Cloud Computing

Cloud computing (CC) is a fast-growing technology that offers computers, networking, and storage services that can be accessed and used over the internet. Cloud services save users money because they are pay-per-use, and they save time because they are on-demand and elastic, a unique aspect of cloud computing. However, several security issues must be addressed before users store data in the cloud. Because the user will have no direct control over the data that has been outsourced to the cloud, particularly personal and sensitive data (health, finance, military, etc.), and will not know where the data is stored, the user must ensure that the cloud stores and maintains the outsourced data appropriately. The study's primary goals are to make the cloud and data security challenges more understandable, to briefly explain the techniques used to achieve privacy and data integrity, to compare various recent studies in both pre-quantum and post-quantum, and to focus on current gaps in solving privacy and data integrity issues.
Fall Detection, Wearable Sensors &amp; Artificial Intelligence: A Short Review

Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.
Global–Local Facial Fusion Based GAN Generated Fake Face Detection

Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which has a terrible impact on society. Existing GAN-generated face detection approaches rely on detecting image artifacts and the generated traces. However, these methods are model-specific, and the performance is deteriorated when faced with more complicated methods. What’s more, it is challenging to identify forgery images with perturbations such as JPEG compression, gamma correction, and other disturbances. In this paper, we propose a global–local facial fusion network, namely GLFNet, to fully exploit the local physiological and global receptive features. Specifically, GLFNet consists of two branches, i.e., the local region detection branch and the global detection branch. The former branch detects the forged traces from the facial parts, such as the iris and pupils. The latter branch adopts a residual connection to distinguish real images from fake ones. GLFNet obtains forged traces through various ways by combining physiological characteristics with deep learning. The method is stable with physiological properties when learning the deep learning features. As a result, it is more robust than the single-class detection methods. Experimental results on two benchmarks have demonstrated superiority and generalization compared with other methods.
Foreign Direct Investment and Inclusive Growth: The Role of the Financial Sector Development

This study examined the role of the domestic financial sector development in the relationship between foreign direct investment (FDI) inflows and inclusive growth in Nigeria over the period, 1981-2020 using annual time series. Analytically, the study employed the autoregressive distributed lag approach of cointegration. The bound test result shows that there is a long-run relationship between inclusive growth and financial sector development, as well as the other underlying variables. Empirically, the result reveals that the FDI exerted a significant positive effect on inclusive growth when the domestic financial sector has reached a certain minimum level of development. The result further shows that the FDI alone has a significant negative effect on inclusive growth. This means that FDI alone does not necessarily increase the well-being of the people, except when a certain minimum level of financial sector development is attained. This is evidence that the domestic financial sector development is a pre-condition for FDI to effectively promote inclusive growth in Nigeria. Therefore, the study recommends that the development of the domestic absorptive capacity-financial sector development should be extended by promoting reforms that will translate FDI inflow into inclusive growth.
Securing Data using Audio Steganography for the Internet of Things

The Internet of Things (IoT) is prevalent in today's world and is part of our everyday life. While the residential district gains in several respects, numerous problems are developed, such as data confidentiality and privacy. The community is worried, in reality, about what information might leak through IoT. Therefore, the need for a protected environment is necessary if data transmission from devices across the network is to be protected. As a consequence, this paper proposes a secure scheme for using audio steganography to secure data from Laptop, which is distributed as an IoT device to other devices, or on LAN or WAN networks, as an alternative protection strategy along with a home server. The outcome of the developed system shows that the amount of distortion exposed by the Signal to Noise Ratio (SNR) is low.
Photonic parallel channel estimation of MIMO-OFDM wireless communication systems

Channel estimation is a key technology in MIMO-OFDM wireless communication systems. Increasingly extensive application scenarios and exponentially growing data volumes of MIMO-OFDM systems have imposed greater challenges on the speed, latency, and parallelism of channel estimation based on electronic processors. Here, we propose a photonic parallel channel estimation (PPCE) architecture which features radio-frequency direct processing. Proof-of-concept experiment is carried out to demonstrate the general feasibility of the proposed architecture at different frequency bands (100 MHz, 4 GHz, and 10 GHz). The mean square errors (MSEs) between the experimental channel estimation results and the theoretically simulated ones lie on the order of 10 −3 . The bit error rates (BERs) are below the pre-forward error correction (pre-FEC) threshold. Besides, we analyze the performance of PPCE under different signal-to-noise ratios (SNRs), baseband symbol forms, and weight tuning precisions. The proposed PPCE architecture has the potential to achieve high-speed, highly parallel channel estimation in large-scale MIMO-OFDM systems after the photonic-electronic chip integration.
Envelope Functions for Sound Spectra of Pipe Organ Ranks and the Influence of Pitch on Tonal Timbre

Long-term average sound spectra (LTAS) of organ pipes from different flue and reed ranks have been studied with the intention to obtain pitch-dependent LTAS models for the constant part of the pipe sound. The measured LTAS of each rank of pipes cover up to nine octaves within the tonal range C0…C9. The envelope functions are piecewise linearly parametrized and serve as empirical approximations for the sound pressure levels of the harmonic partials. They allow for extrapolating the pitch range of most ranks and thus to explore these spectra beyond their physically existing compass. Two parameters for tonal timbre were calculated from the spectra: the first is a weighted average slope of the envelope function; the second is the spectral centroid of the sound spectrum. Each rank of pipes depicts as characteristic curve (from bass to treble) in a two-dimensional timbre chart of these two parameters, in which the different families of organ tone clearly separate from each other. The use of such timbre charts could assist in the work of pipe voicing by means of an electronic voicing device, which extracts the LTAS from the measured pipe sound and calculates the timbre chart from it.
The Impact of Internal Environment Factors in Achieving Strategic Agility During COVID- 19 Pandemic at Jordanian Commercial Banks: The Moderating Role of Information Technology

To remain competitive in today's uncertain business environment, banks must develop capabilities that enable them to adapt and respond quickly to market changes.Therefore, this study aims to examine the impact of internal environmental factors on achieving strategic agility through the moderating role of information technology at the Jordanian Commercial Banks.Out of the 13 banks, 10 took part in the survey.Internal environmental factors being investigated include agile human resources, organizational structure, and organizational culture.The 240 middle and first-line managers who worked at the headquarters of the 10 banks made up the sampling unit.To get the information and data needed, 240 questionnaires were sent out, and 203 of them could be used for statistical analysis.The results indicate a statistically significant impact of internal environmental factors in achieving strategic agility.The findings of the moderation hypothesis also reveal that information technology as a moderator has improved the impact of internal environmental factors in achieving banks' strategic agility by 0.04.The results show that the agility of human resources has the highest impact in achieving banks' strategic agility.Consequently, it was recommended to enhance the skills and competencies of the banks' staff and to equip them with the needed training courses to be able to adapt to change successfully.
The Use of Modern Digital Technologies for the Development of the Educational Environment in the System for Ensuring the Sustainable Development of the Region

The main purpose of the article is to determine the features of the use of modern digital technologies for the development of the educational environment in the system for ensuring the sustainable development of the region. As a result of the study, the main stages in the process of activating and intensifying the use of modern digital technologies for the development of the educational environment in the system for ensuring the sustainable development of the region were systematized and formed. To achieve the goals set in the research system, both general theoretical methods and specific modeling methods, such as the schematic modeling method, were used. This method made it possible to visually depict the algorithm for achieving the ultimate goal of the study. The study has its limitations; in the course of the study, the process of ensuring the sustainable development of the region was studied only in the context of the development of the educational environment and its digitalization and modernization. Given that sustainable development is a complex concept and includes many elements, the authors plan to study this topic in further research.
Absolute wavelength scanning interferometry for measuring the thickness of optical elements

A technique for measurement of the thickness of optical elements using absolute wavelength scanning interferometry is presented in this paper. To achieve high-grade optical components and systems, the thickness of both planar and non-planar optical components must be measured with an accuracy of a few micrometers. The proposed technique is based on the Fizeau interferometer and interconnects data from three different tunable laser diodes yielding a long effective wavelength range and thus low measurement uncertainty. The uncertainty of the central thickness measurement ranges from hundreds of nanometers to a few microns. The method allows to measure the thickness of both flat optical elements as well as lenses with curved surfaces. Moreover, the areal information provided by the interferometry and its high angle sensitivity help to quickly and precisely align the measured component and reduce misalignment errors. The results of thickness measurements have been validated and cross-tested with other techniques. In addition to the thickness, the technique provides some additional information (wedge, surface form error) in the case of flat samples and can be easily and quickly modified (mounting of a Fizeau transmission sphere) to measure other essential parameters of optical elements. Thus, this one approach can replace many single-purpose measuring devices while maintaining high accuracy.
Fetching Automatic Authority Data in ILS from Wikidata via OpenRefine

Authority data is vital for effective library and information services. It serves a major purpose in realizing the collocation function of library catalogues and indexes. Unfortunately, however, authority control has been neglected in library catalogues and other bibliographic databases in India. This paper seeks to demonstrate how authority data can be fetched automatically from Wikidata, a sibling project of Wikipedia. For this purpose, the query language SPARQL is required to formulate the names of persons of Indian origin along with their date of birth and place in Wikidata. The collected datasets are processed and implemented as MARC21-based authority data in KOHA, an open-source library management software. The ways in which the library and information science community can use these free, open-source platforms to gather, organize and share data and how they enhance the retrieval efficiency are shown.
Privacy and Efficiency of Communications in Federated Split Learning

Everyday, large amounts of sensitive data is distributed across mobile phones, wearable devices, and other sensors. Traditionally, these enormous datasets have been processed on a single system, with complex models being trained to make valuable predictions. Distributed machine learning techniques such as Federated and Split Learning have recently been developed to protect user data and privacy better while ensuring high performance. Both of these distributed learning architectures have advantages and disadvantages. In this paper, we examine these tradeoffs and suggest a new hybrid Federated Split Learning architecture that combines the efficiency and privacy benefits of both. Our evaluation demonstrates how our hybrid Federated Split Learning approach can lower the amount of processing power required by each client running a distributed learning system, reduce training and inference time while keeping a similar accuracy. We also discuss the resiliency of our approach to deep learning privacy inference attacks and compare our solution to other recently proposed benchmarks.
Measuring the effect of collaborative filtering on the diversity of users’ attention

Abstract While the ever-increasing emergence of online services has led to a growing interest in the development of recommender systems, the algorithms underpinning such systems have begun to be criticized for their role in limiting the variety of content exposed to users. In this context, the notion of diversity has been proposed as a way of mitigating the side effects resulting from the specialization of recommender systems. In this paper, using a well-known recommender system that makes use of collaborative filtering in the context of musical content, we analyze the diversity of recommendations generated through the lens of the recently proposed information network diversity measure . The results of our study offer significant insights into the effect of algorithmic recommendations. On the one hand, we show that the musical selections of a large proportion of users are diversified as a result of the recommendations. On the other hand, however, such improvements do not benefit all users. They are in fact mainly restricted to users with a low level of activity or whose past musical listening selections are very narrow. Through more in-depth investigations, we also discovered that while recommendations generally increase the variety of the songs recommended to users, they nonetheless fail to provide a balanced exposure to the different related categories.
From Starvation Freedom to All-Path Reachability Problems in Constrained Rewriting

An all-path reachability problem of a logically constrained term rewrite system is a pair of constrained terms representing state sets, and is demonically valid if every finite execution path from any state in the first set to a terminating state includes a state in the second set. We have proposed a framework to reduce the non-occurrence of specified error states in a transition system represented by a logically constrained term rewrite system to an all-path reachability problem of the system. In this paper, we extend the framework to verification of starvation freedom of asynchronous integer transition systems with shared variables such that some processes enter critical sections.