Reviewerly/exHarmony huggingface dataset:
A new image steganography method with optimum pixel similarity for data hiding in medical images

Steganography is one of the approaches used in data hiding. Image steganography, is a type of steganography that the image is used as a covering object. Data hiding capacity and image quality of the cover object are important factors in image steganography. Because the deterioration of image quality can be noticed by the human vision system, it attracts the attention of attackers. Therefore, the purpose of this study is increasing the amount of data to be hidden and stego image is to ensure high image quality. In the study, a new optimization-based method has been proposed by making use of the similarities of the pixels. In order to test the performance of the proposed method has been used visual quality analysis metrics such as MSE, RMSE, PSNR, SSIM and UQI. As a cover object; different sizes medical images have been used that obtained from the open access Dicom library database. Doctor comments in different capacities have been hidden to the medical images. Experimental results show that the average PSNR value is 66.5374, 59.4420 and 56.3936, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 512 × 512 images. In addition, the average PSNR value is 60.4308, 53.3529 and 47.4113, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 256 × 256 images. 10,000 characters of data have not been hidden in 256 × 256 images without using data compression techniques with classical similarity based LSB method. In the proposed method, 10,000 characters of data have been hidden in 256 × 256 size images without using data compression techniques.
Feature selection based on regularization of sparsity based regression models by hesitant fuzzy correlation

In this paper, the Ridge, LASSO and Elastic Net regression methods are adapted for the task of selecting feature. In order to enhance the feature selection performance via these methods, a Hesitant Fuzzy Correlation Matrix (HFCM) is added to the objective functions of these models for addressing the minimum redundancy of features. To this end, the fuzzy C-means clustering is utilized, and the obtained fuzzy clusters are projected on the features in a way that the number of fuzzy Membership Functions (MF) for each feature is equal to the number of clusters. Then, the projected MFs on each feature are considered as a Hesitant Fuzzy Set (HFS), and thereby the hesitant fuzzy correlation between features is calculated. Afterward, the obtained HFCM is employed in the regression methods for securing the minimum redundancy of features. Eventually, the accuracies of the selected features, achieved by these methods, are determined by three different classification models such as Naive Bayes, SVM and Decision Tree. A large number of experiments are conducted over twenty-four classification datasets to demonstrate the efficiency and applicability of using HFCM in some classical regression methods.
Machine learning based estimation of land productivity in the contiguous US using biophysical predictors

Estimation of land productivity and availability is necessary to predict land production potential, especially for the emerging bioenergy crop production, which may compete land with food crop production. This study provides land productivity estimates in the contiguous United States (CONUS) through a machine learning approach. Land productivity is defined as the potential in producing agricultural outputs given biophysical properties including climate, soil, and land slope. The land productivity is approximated by the potential yields of six major crops in the CONUS, i.e. corn, soybean, winter wheat, spring wheat, cotton, and alfalfa. This quantitative relationship is then applied to estimating the availability of marginal land for bioenergy crop production in the CONUS. Furthermore, the levels of uncertainties associated with land productivity and marginal land estimates are quantified and discussed. Based on the modeling results, the total marginal land of the CONUS ranges 55.0–172.8 mha, but the 95% inter-percentile distance of the estimated productivity index reaches up to 60% of its expected value in data-scarce regions. Finally, in a cross-check analysis, marginal lands estimated based on biophysical criteria are found to be comparable to those based on an economic criterion.
Vinyl won’t save us: reframing disconnection as engagement

Disconnection has recently come to the forefront of public discussions as an antidote to an increasing saturation with digital technologies. Yet, experiences with disconnection are often reduced to a form of disengagement that diminishes their political impact. Disconnective practices focused on health and well-being are easily appropriated by big tech corporations, defusing their transformative potential into the very dynamics of digital capitalism. In contrast, a long tradition of critical thought, from Joseph Weizenbaum to Jaron Lanier passing through hacktivism, demonstrates that engagement with digital technologies is instrumental to develop critique and resistance against the paradoxes of digital societies. Drawing from this tradition, this article proposes the concept of ‘Disconnection-through-Engagement’ to illuminate situated practices that mobilize disconnection in order to improve critical engagement with digital technologies and platforms. Hybridity, anonymity and hacking are examined as three forms of Disconnection-through-Engagement, and a call to decommodify disconnection and recast it as a source of collective critique to digital capitalism is put forward.
Evaluating strategies for environmental sustainability in a supply chain of an emerging economy

Due to an increased pressure to be environmentally sustainable, many manufacturing organizations, especially from developing countries like Bangladesh, are attempting to make necessary changes in practices and supply chains. However, those attempts need to be applied strategically with the objective to be both environmentally sustainable and economically viable. This paper offers a decision-making methodology by integrating a fuzzy cognitive map (FCM) and data envelopment analysis (DEA) for evaluating strategies for environmental sustainability based on their impact on the overall supply chain network of an organization. This paper first identifies 18 generic strategies for environmental sustainability and three supply chain performance measurement (PM) factors. Afterwards, the cause-effect relationships among these strategies and PM factors are utilized to capture the complicated relationships by FCM. The extended delta rule (EDR) learning algorithm was used in association with FCM to quantify the impact of those strategies on supply chain PM factors. Finally, DEA is used to prioritize strategies using these impact values. A real-life case using a fast-moving consumer goods (FMCG) manufacturer from Bangladesh is presented to justify the applicability of the proposed methodology. The results reflect the usefulness of this methodology for evaluating strategies for environmental sustainability in a supply chain (SC), specifically in the FMCG sector of an emerging economy. Thus, other manufacturing organizations from any industry can use this methodology to evaluate strategies for environmental sustainability.
Realization of a quantum autoencoder for lossless compression of quantum data

As a ubiquitous aspect of modern information technology, data compression has a wide range of applications. Therefore, a quantum autoencoder which can compress quantum information into a low-dimensional space is fundamentally important to achieve automatic data compression in the field of quantum information. Such a quantum autoencoder can be implemented through training the parameters of a quantum device using classical optimization algorithms. In this article, we analyze the condition of achieving a perfect quantum autoencoder and theoretically prove that a quantum autoencoder can losslessly compress high-dimensional quantum information into a low-dimensional space (also called latent space) if the number of maximum linearly independent vectors from input states is no more than the dimension of the latent space. Also, we experimentally realize a universal two-qubit unitary gate and design a quantum autoencoder device by applying machine learning method. Experimental results demonstrate that our quantum autoencoder is able to compress two two-qubit states into two one-qubit states. Besides compressing quantum information, the quantum autoencoder is used to experimentally discriminate two groups of nonorthogonal states.
Within-Sample Variability-Invariant Loss for Robust Speaker Recognition Under Noisy Environments

Despite the significant improvements in speaker recognition enabled by deep neural networks, unsatisfactory performance persists under noisy environments. In this paper, we train the speaker embedding network to learn the "clean" embedding of the noisy utterance. Specifically, the network is trained with the original speaker identification loss with an auxiliary within-sample variability-invariant loss. This auxiliary variability-invariant loss is used to learn the same embedding among the clean utterance and its noisy copies and prevents the network from encoding the undesired noises or variabilities into the speaker representation. Furthermore, we investigate the data preparation strategy for generating clean and noisy utterance pairs on-the-fly. The strategy generates different noisy copies for the same clean utterance at each training step, helping the speaker embedding network generalize better under noisy environments. Experiments on VoxCeleb1 indicate that the proposed training framework improves the performance of the speaker verification system in both clean and noisy conditions.
Multimodal Learning for Classroom Activity Detection

Classroom activity detection (CAD) focuses on accurately classifying whether the teacher or student is speaking and recording both the length of individual utterances during a class. A CAD solution helps teachers get instant feedback on their pedagogical instructions. This greatly improves educators’ teaching skills and hence leads to students’ achievement. However, CAD is very challenging because (1) the CAD model needs to be generalized well enough for different teachers and students; (2) data from both vocal and language modalities has to be wisely fused so that they can be complementary; and (3) the solution shouldn’t heavily rely on additional recording device. In this paper, we address the above challenges by using a novel attention based neural framework. Our framework not only extracts both speech and language information, but utilizes attention mechanism to capture long-term semantic dependence. Our framework is device-free and is able to take any classroom recording as input. The proposed CAD learning framework is evaluated in two real-world education applications. The experimental results demonstrate the benefits of our approach on learning attention based neural network from classroom data with different modalities, and show our approach is able to outperform state-of-the-art baselines in terms of various evaluation metrics.
SHealth: A Blockchain-Based Health System With Smart Contracts Capabilities

Governments rely on collected data to analyze their citizens' health requirements and needs.Such data is usually of scattered manner gathered by several agencies and entities that fall under different business models and goals.Hence, the scattered nature of this data creates a burden on governments in precisely planning adequate health bills that guarantee the wellbeing of their citizens.Furthermore, the issue of synchronizing and having a real-time access to such data exacerbates the problem even more.Therefore, a system that guarantees the integrity, accessibility, correctness and security of medical data in a highly synchronized environment is crucial for governments to accurately project their future needs.In this paper, we present Smart-Health (SHealth), a blockchain-based health management system.In its crux, SHealth is a private multi-layered blockchain integrated with a multi-tiered addressing scheme that defines the privileges and permissions of entities in the system.Blockchain-based systems assure security, reliability, availability, resistance against tampering and malicious attacks, seamless integration, and easy data management.SHealth proposes a complete autonomy to its users.Through a user-friendly graphical interface it capitalizes on smart contracts to initiate various requests and inquiries pertaining to patients such as appointments, medical tests, medications, medical procedures, or history.SHealth is simple, robust, efficient, secured, and completely automated.All the stakeholders in the system can access the health related data stored in a distributed database without compromising its authenticity.SHealth covers all the possible scenarios in health systems from which some of them are explained in this work.
MoGA: Searching Beyond Mobilenetv3

The evolution of MobileNets has laid a solid foundation for neural network applications on mobile end. With the latest MobileNetV3, neural architecture search again claimed its supremacy in network design. Unfortunately, till today all mobile methods mainly focus on CPU latencies instead of GPU, the latter, however, is much preferred in practice because it has faster speed, lower overhead and less interference. Bearing the target hardware in mind, we propose the first Mobile GPU-Aware (MoGA) neural architecture search in order to be precisely tailored for real-world applications. Further, the ultimate objective to devise a mobile network lies in achieving better performance by maximizing the utilization of bounded resources. Urging higher capability while restraining time consumption is not reconcilable. We alleviate this tension by weighted evolution techniques. Moreover, we encourage increasing the number of parameters for higher representational power. With 200× fewer GPU days than MnasNet, we obtain a series of models that outperform MobileNetV3 under the similar latency constraints <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Efficient and Secure Multiparty Computation from Fixed-Key Block Ciphers

Many implementations of secure computation use fixed-key AES (modeled as a random permutation); this results in substantial performance benefits due to existing hardware support for AES and the ability to avoid recomputing the AES key schedule.Surveying these implementations, however, we find that most utilize AES in a heuristic fashion; in the best case this leaves a gap in the security proof, but in many cases we show it allows for explicit attacks.Motivated by this unsatisfactory state of affairs, we initiate a comprehensive study of how to use fixed-key block ciphers for secure computation-in particular for OT extension and circuit garbling-efficiently and securely.Specifically:• We consider several notions of pseudorandomness for hash functions (e.g., correlation robustness), and show provably secure schemes for OT extension, garbling, and other applications based on hash functions satisfying these notions.• We provide provably secure constructions, in the (nonprogrammable) random-permutation model, of hash functions satisfying the different notions of pseudorandomness we consider.Taken together, our results provide end-to-end security proofs for implementations of secure-computation protocols based on fixedkey block ciphers (modeled as random permutations).Perhaps surprisingly, at the same time our work also results in noticeable performance improvements over the state-of-the-art.
Effectiveness of 360° virtual reality and match broadcast video to improve decision-making skill

Video-based training is a commonly used method to develop decision-making in athletes and officials. This method typically uses match broadcast footage, yet technological advancements have made 360° Virtual Reality (360°VR) a possible effective tool to investigate. This study aimed to investigate the effectiveness of both 360°VR and match broadcast footage on improving decision-making. Amateur Australian football umpires (N= 32) participated in a randomised control trial. Participants completed a 5-week training intervention in either a 360°VR, match broadcast or control group. Decision-making was assessed at pre-training, 1-week post-training and 4-week retention points using reliable and valid 360°VR and match broadcast decision-making tests. Participants completed a short questionnaire detailing their perceptions of psychological fidelity, enjoyment, relevance, concentration and effort for each video mode. The 360°VR performed significantly better (p < 0.05) than the control group in the 360°VR retention test. No groups statistically improved over the intervention. Remaining pairwise comparisons for this test and the match broadcast test were not significantly different. 360°VR was rated significantly higher (p < 0.05) than match broadcast footage for psychological fidelity, enjoyment and relevance. 360°VR appears to be a beneficial training tool compared to a control, with stronger engagement from the participants than previously used match broadcast footage.
Detection of Intestinal Protozoa in Trichrome-Stained Stool Specimens by Use of a Deep Convolutional Neural Network

Intestinal protozoa are responsible for relatively few infections in the developed world, but the testing volume is disproportionately high. Manual light microscopy of stool remains the gold standard but can be insensitive, time-consuming, and difficult to maintain competency. Artificial intelligence and digital slide scanning show promise for revolutionizing the clinical parasitology laboratory by augmenting the detection of parasites and slide interpretation using a convolutional neural network (CNN) model.
V-REP-based navigation of automated wheeled robot between obstacles using PSO-tuned feedforward neural network

Abstract This paper describes the navigation of an automated Pioneer P3-DX wheeled robot between obstacles using particle swarm optimization (PSO) algorithm tuned feedforward neural network (FNN). This PSO algorithm minimizes the mean square error between the actual and predicted values of the FNN. In this work, 2 separate DC motors and 16 ultrasonic sensors have been used for making differential drive steering angle and for collecting the distance from obstacles, respectively. The proposed without tuned FNN and PSO-tuned FNN receives obstacle's distance as inputs form ultrasonic sensors and control the steering angle of a differential drive of automated Pioneer P3-DX wheeled robot as output. We have compared the results between without tuned FNN and PSO-tuned FNN, and it has been found that PSO-tuned FNN gives a better trajectory and takes less distance to reach the target. Virtual Robot Experimentation Platform software has been used to design the real-time simulation results. A comparative study between without tuned FNN and PSO-tuned FNN verifies the effectiveness of PSO-tuned FNN for automated Pioneer P3-DX wheeled robot navigation. Also, we have compared this winner PSO-tuned FNN to the previously developed PSO-optimized Fuzzy Logic Controller navigational technique to show the authenticity and real-time implementation of PSO-tuned FNN.
A three-factor anonymous user authentication scheme for Internet of Things environments

To accelerate the deployment of fifth-generation (5G) cellular networks, millions of devices are being connected to massive Internet of Things (IoT) networks. However, advances in the scale of connectivity on 5G networks may increase the attack surface of these devices, thereby increasing the number of attack opportunities. To address the potential security risks in IoT systems, one feasible security practice involves the development of secure and efficient user authentication schemes. In 2017, Dhillon and Kalra proposed a three-factor user authentication scheme for IoT. We noted that their scheme suffers from several security weaknesses. In this study, we specifically demonstrate that the scheme proposed by Dhillon and Kalra (1) is not secured from a stolen mobile device attack; (2) does not prevent a user impersonation attack; (3) does not provide a session key agreement; (4) does not have a contingency plan (e.g., a revocation phase) for situations where a user’s private key is compromised, or a mobile device is stolen or lost. We propose an improved three-factor user authentication scheme to resolve these security issues. Furthermore, we demonstrate that the proposed scheme provides desirable attributes for IoT environments and that its computation and communication costs are suitable for extremely low-cost IoT devices.
Domain-Independent Extraction of Scientific Concepts from Research Articles

We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. First, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process. This set of concepts is utilised to annotate a corpus of scientific abstracts from 10 domains of Science, Technology and Medicine at the phrasal level in a joint effort with domain experts. The resulting dataset is used in a set of benchmark experiments to (a) provide baseline performance for this task, (b) examine the transferability of concepts between domains. Second, we present a state-of-the-art deep learning baseline. Further, we propose the active learning strategy for an optimal selection of instances from among the various domains in our data. The experimental results show that (1) a substantial agreement is achievable by non-experts after consultation with domain experts, (2) the baseline system achieves a fairly high F1 score, (3) active learning enables us to nearly halve the amount of required training data.
Automated detection of kidney abnormalities using multi-feature fusion convolutional neural networks

Kidney abnormalities have a high incidence in people of all ages. The requisite manual examinations to detect these abnormalities are costly and time consuming. The rapid and accurate detection of kidney abnormalities has emerged as a focus of computer-aided medical research. Traditional methods work on images of kidneys and rely on hand-crafted features to identify abnormal symptoms as independent classes that lack generalization to different kidney diseases. In this study, an automated architecture to detect various kidney abnormalities is proposed that works on abdominal ultrasound images using convolutional neural networks. Our model consists of three components: the first selects appropriate ultrasound images of kidneys, the second is a detection model used to locate the area occupied by the kidney in the given image, and the third component using the multi-feature fusion neural network (Mf-Net) discriminates normal and abnormal kidneys, by determining whether there are abnormal symptoms in images. The detection model is combined with a weighted ensemble method to improve performance. A multi-feature fusion layer is also designed in the Mf-Net to extract distinctive features for multiple views of images. The three components work together to automatically recognize abnormalities associated with kidneys. A large dataset containing 3,722 abdominal ultrasound images with classification and localization annotations is established to train and evaluate the model. Experimental results show that the proposed ensemble detection model performs best with an average TPF of 98.0%, and the Mf-Net achieves an average classification accuracy of 94.67%. The obtained high classification and detection accuracy demonstrate the effectiveness of the proposed method for recognizing kidney abnormalities.