Reviewerly/exHarmony huggingface dataset:
Improved least significant bit algorithm based on RS-code

As a widely applied data hiding method, least significant bit (LSB) algorithm is a spatial steganography which uses the lowest bits of each pixel’s grayscale value to hide messages. In spite of its high capacitance, high load rate and imperceptibility is remarkable, the lack of robustness is the fatal problem which prevents the LSB algorithm’s further application. This paper proposes a new high-robustness LSB method based on the Reed-Solomon (RS) code and provides higher immunity of attack with slight cost of imperceptibility and capacity. This algorithm exploits the considerable error correction ability of RS-code to encode the watermark message before embedding them to the carrier image. Despite the size of the encoded message increases times to the original, this method makes self-correction of encrypted images feasible, which would provide more superiority among the algorithms that require carrier image as reference to extract watermark message. This paper provides the testing results of the LSB algorithm based on RS-code in Matlab and verifies the immunity of both the proposed method and original method against noise and rotation attack, and finally determines that this algorithm is able to substantially enhance the robustness of encrypted image under a certain range of noise density.
Leveraging spatio-temporal features to forecast time-to-accident

Globally, traffic accidents account for over 3,700 daily deaths, equating to 1.35 million deaths annually. Studies show that collision avoidance systems can significantly reduce the probability and intensity of accidents. Time-to-accident (TTA) is considered the principal parameter for collision avoidance systems allowing for decision-making in traffic, dynamic path planning, and accident mitigation. Despite the importance of TTA, the literature has insufficient research on TTA estimation for traffic scenarios. The majority of recent work focuses on accident anticipation by providing a probabilistic measure of an immediate or future collision. We propose to forecast TTA based on Spatio-temporal features extracted from accident videos obtained via dashboard cameras. Our model can also recognize accident and non-accident scenes with 100% accuracy. Additionally, the impact of spatial resolution and temporal depth on prediction error is analyzed in this work. We implement state-of-the-art video learning architectures and compare the results against static image architectures. Our comprehensive experiments suggest that leveraging Spatio-temporal features is an effective method to estimate TTA. Our best model can estimate the TTA with an average prediction error of 0.30 seconds with a mean prediction horizon of 3.4 seconds.
A shortcut weighted fusion pyramid network for microcalcification detection in breast mammograms

High-precision detection for individual and clustered microcalcifications in mammograms is important for the early diagnosis of breast cancer. Large-scale differences between the two types and low-contrast images are major difficulties faced by radiologists when performing diagnoses.Deep learning-based methods can provide end-to-end solutions for efficient detection. However, multicenter data bias, the low resolution of network inputs, and scale differences between microcalcifications lead to low detection rates. Aiming to overcome the aforementioned limitations, we propose a pyramid feature network for microcalcification detection in mammograms, MicroDMa, with adaptive image adjustment and shortcut connections.First, mammograms from multiple centers are represented as histograms and cropped by adaptive image adjustment, which mitigates the impact of dataset bias. Second, the proposed shortcut connection pyramid network ensures that the feature map contains more information for multiscale objects, while a shortcut path that jumps over layers enhances the efficiency of feature propagation from bottom to top. Third, the weights of each feature map at different scales in the fusion are trainable; thus, the network can automatically learn the contributions of all feature maps in the fusion stage.Experiments were conducted on our in-house dataset and the public dataset INbreast. When the average number of positives per image is one on the in-house dataset, the recall rates of MicroDMa are the 96.8% for individual microcalcification and 98.9% for clustered microcalcification, which are higher than 69.1% and 91.2% achieved by recent deep learning model. Free-response receiver operating characteristic curve of MicroDMa is also higher than other methods when models are performed on INbreast.MicroDMa network is better than other methods and it can effectively help radiologists detect and identify two types of microcalcifications in clinical applications.
An Energy Efficient Specializing DAG Federated Learning Based on Event-Triggered Communication

Specializing Directed Acyclic Graph Federated Learning (SDAGFL) is a new federated learning framework with the advantages of decentralization, personalization, resisting a single point of failure, and poisoning attack. Instead of training a single global model, the clients in SDAGFL update their models asynchronously from the devices with similar data distribution through Directed Acyclic Graph Distributed Ledger Technology (DAG-DLT), which is designed for IoT scenarios. Because of many the features inherited from DAG-DLT, SDAGFL is suitable for IoT scenarios in many aspects. However, the training process of SDAGFL is quite energy consuming, in which each client needs to compute the confidence and rating of the nodes selected by multiple random walks by traveling the ledger with 15–25 depth to obtain the “reference model” to judge whether or not to broadcast the newly trained model. As we know, the energy consumption is an important issue for IoT scenarios, as most devices are battery-powered with strict energy restrictions. To optimize SDAGFL for IoT, an energy-efficient SDAGFL based on an event-triggered communication mechanism, i.e., ESDAGFL, is proposed in this paper. In ESDAGFL, the new model is broadcasted only in the event that the new model is significantly different from the previous one, instead of traveling the ledger to search for the “reference model”. We evaluate the ESDAGFL on the FMNIST-clustered and Poets dataset. The simulation is performed on a platform with Intel®CoreTM i7-10700 CPU (CA, USA). The simulation results demonstrate that ESDAGFL can reach a balance between training accuracy and specialization as good as SDAGFL. What is more, ESDAGFL can reduce the energy consumption by 42.5% and 51.7% for the FMNIST-clustered and Poets datasets, respectively.
Dynamically attentive viewport sequence for no-reference quality assessment of omnidirectional images

Omnidirectional images (ODIs) have drawn great attention in virtual reality (VR) due to the capability of providing an immersive experience to users. However, ODIs are usually subject to various quality degradations during different processing stages. Thus, the quality assessment of ODIs is of critical importance to the community of VR. The quality assessment of ODIs is quite different from that of traditional 2D images. Existing IQA methods focus on extracting features from spherical scenes while ignoring the characteristics of actual viewing behavior of humans in continuously browsing an ODI through HMD and failing to characterize the temporal dynamics of the browsing process in terms of the temporal order of viewports. In this article, we resort to the law of gravity to detect the dynamically attentive regions of humans when viewing ODIs. In this article, we propose a novel no-reference (NR) ODI quality evaluation method by making efforts on two aspects including the construction of Dynamically Attentive Viewport Sequence (DAVS) from ODIs and the extraction of Quality-Aware Features (QAFs) from DAVS. The construction of DAVS aims to build a sequence of viewports that are likely to be explored by viewers based on the prediction of visual scanpath when viewers are freely exploring the ODI within the exploration time via HMD. A DAVS that contains only global motion can then be obtained by sampling a series of viewports from the ODI along the predicted visual scanpath. The subsequent quality evaluation of ODIs is performed merely based on the DAVS. The extraction of QAFs aims to obtain effective feature representations that are highly discriminative in terms of perceived distortion and visual quality. Finally, we can adopt a regression model to map the extracted QAFs to a single predicted quality score. Experimental results on two datasets demonstrate that the proposed method is able to deliver state-of-the-art performance.
RGBD Salient Object Detection, Based on Specific Object Imaging

RGBD salient object detection, based on the convolutional neural network, has achieved rapid development in recent years. However, existing models often focus on detecting salient object edges, instead of objects. Importantly, detecting objects can more intuitively display the complete information of the detection target. To take care of this issue, we propose a RGBD salient object detection method, based on specific object imaging, which can quickly capture and process important information on object features, and effectively screen out the salient objects in the scene. The screened target objects include not only the edge of the object, but also the complete feature information of the object, which realizes the detection and imaging of the salient objects. We conduct experiments on benchmark datasets and validate with two common metrics, and the results show that our method reduces the error by 0.003 and 0.201 (MAE) on D3Net and JLDCF, respectively. In addition, our method can still achieve a very good detection and imaging performance in the case of the greatly reduced training data.
Heterogeneously Integrated III/V-on-Si Injection Seeding Laser Neuron

We implement the first on-chip all-optical neuron on silicon by injection seeding a III/V-on-Si microring laser. The output of the laser at a selected wavelength resembles a sigmoid function and can be integrated as the non-linear activation function within a silicon photonic neural network.
Transfer Learning to Decode Brain States Reflecting the Relationship Between Cognitive Tasks

Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance improvement by transfer learning. In neuroscience, the relationship between cognitive tasks is usually represented by similarity of activated brain regions or neural representation. However, no study has linked transfer learning and neuroscience to reveal the relationship between cognitive tasks. In this study, we propose a transfer learning framework to reflect the relationship between cognitive tasks, and compare the task relations reflected by transfer learning and by the overlaps of brain regions (e.g., neurosynth). Our results of transfer learning create cognitive taskonomy to reflect the relationship between cognitive tasks which is well in line with the task relations derived from neurosynth. Transfer learning performs better in task decoding with fMRI data if the source and target cognitive tasks activate similar brain regions. Our study uncovers the relationship of multiple cognitive tasks and provides guidance for source task selection in transfer learning for neural decoding based on small-sample data.
Semi-supervised adversarial discriminative domain adaptation

Domain adaptation is a potential method to train a powerful deep neural network across various datasets. More precisely, domain adaptation methods train the model on training data and test that model on a completely separate dataset. The adversarial-based adaptation method became popular among other domain adaptation methods. Relying on the idea of GAN, the adversarial-based domain adaptation tries to minimize the distribution between the training and testing dataset based on the adversarial learning process. We observe that the semi-supervised learning approach can combine with the adversarial-based method to solve the domain adaptation problem. In this paper, we propose an improved adversarial domain adaptation method called Semi-Supervised Adversarial Discriminative Domain Adaptation (SADDA), which can outperform other prior domain adaptation methods. We also show that SADDA has a wide range of applications and illustrate the promise of our method for image classification and sentiment classification problems.
Red deer optimization for automatic modulation classification using hybrid extreme learning machine with bagging classifier

Automatic modulation classification (AMC) became an important process in various communication systems including commercial, telecommunication and military applications. Further, the accuracy of AMC impacts the performance of these applications. Various machine learning approaches were developed to improve the performance of AMC. However, they failed to classify the different modulation schemes, which needs to satisfy all the spectrum requirements under multipath fading environment. Further, the conventional methods suffer with computational complexity in training to satisfy the real-time operational requirements. So, this paper focuses on implementation of extreme learning machine (ELM) for reduction of training complexities and improves the classification performance. Initially, deep leaning convolutional neural network (DLCNN) model is introduced for extracting the interdependent modulation features based on different modulation types. Then, red deer optimization algorithm (RDOA) is introduced for selecting the best features from DLCNN extracted features. Further, the hybrid ELM with bagging classifier (HELM-BC) is used to classify the various modulation types, i.e., families. The simulation results show that the performance of the proposed AMC system using RDOA-based HELM-BC approach is superior to the conventional AMC systems.
Porosity, strength, and alteration – Towards a new volcano stability assessment tool using VNIR-SWIR reflectance spectroscopy

Volcano slope stability analysis is a critical component of volcanic hazard assessments and monitoring. However, traditional methods for assessing rock strength require physical samples of rock which may be difficult to obtain or characterize in bulk. Here, visible to shortwave infrared (350–2500 nm; VNIR–SWIR) reflected light spectroscopy on laboratory-tested rock samples from Ruapehu, Ohakuri, Whakaari, and Banks Peninsula (New Zealand), Merapi (Indonesia), Chaos Crags (USA), Styrian Basin (Austria) and La Soufrière de Guadeloupe (Eastern Caribbean) volcanoes was used to design a novel rapid chemometric-based method to estimate uniaxial compressive strength (UCS) and porosity. Our Partial Least Squares Regression models return moderate accuracies for both UCS and porosity, with R2 of 0.43–0.49 and Mean Absolute Percentage Error (MAPE) of 0.2–0.4. When laboratory-measured porosity is included with spectral data, UCS prediction reaches an R2 of 0.82 and MAPE of 0.11. Our models highlight that the observed changes in the UCS are coupled with subtle mineralogical changes due to hydrothermal alteration at wavelengths of 360–438, 532–597, 1405–1455, 2179–2272, 2332–2386, and 2460–2490 nm. These mineralogical changes include mineral replacement, precipitation hydrothermal alteration processes which impact the strength of volcanic rocks, such as mineral replacement, precipitation, and/or silicification. Our approach highlights that spectroscopy can provide a first order assessment of rock strength and/or porosity or be used to complement laboratory porosity-based predictive models. VNIR-SWIR spectroscopy therefore provides an accurate non-destructive way of assessing rock strength and alteration mineralogy, even from remote sensing platforms.
Improving Neural Machine Translation for Low-resource English-Myanmar-Thai Language Pairs with SwitchOut Data Augmentation Algorithm

To improve the data resource of low-resource English- Myanmar- Thai language pairs, we build the first parallel medical corpus, named as En-My- Th medical corpus which is composed of total 14,592 parallel sentences. In our paper, we make experiments on the English-Myanmar language pair of new En-My-Th medical corpus and in addition, English-Thai and Thai-Myanmar language pairs from the existing ASEAN- MT corpus. The experiments of SwitchOut data augmentation algorithm and the baseline attention-based sequence to sequence model are trained on the aforementioned language pairs in both directions. Experimental results show that combination of Switch Out algorithm with the baseline model outperforms the baseline only model in the translation of most language pairs for both corpora. Furthermore, we investigate the performance of the baseline model and baseline+SwitchOut model by adding or removing word dropout at the recurrent layers, at which baseline+SwitchOut model with the dropout increases around (+1.0) BLEU4 and GLEU scores in some of language nairs.
An Attempt of Linking Maxwell with Newton to Study Gravitational Phenomena

All high energy particles are primarily made of charges and all neutral bodies around us contain charges too inside.Now, electric charges and light possess momenta and energies that we experience with our sense organs.Therefore, these are real physical entities (objects).All objects are subject to the universal law of gravitation of Newton.Therefore, charges and light should similarly be subject to gravitation.In this article we have extended the universal law of gravitation of Newton to charges and light and applied that law to high energy particle, high energy bodies containing charges and light when they move in a high gravitating field.Thus we attempt to connecting Maxwell with Newton and find that the results of such a connection are promising.
Blockchain for the asset management industry

The financial asset management industry is constantly looking for ways to reduce the cost of running funds by simplifying their processes and making it easier to comply with regulatory requirements. Blockchain provides a feasible solution to these requirements. This paper explores the operational and implementation aspects of the business-grade version of the enterprise blockchain technology that benefits all stakeholders in the asset management industry using a theory-driven approach. Implementation technology, procedure, regulation, and social dimensions for practical implementation are discussed. Enterprise Ethereum Alliance's (EEA) Ethereum Architecture Stack describes the architectural framework in the technical implementation. This paper explains how EEA features give the asset management ecosystem a value proposition from a practical implementation perspective. Hence, stakeholders (regulators, asset management companies, registrars) and financial intermediaries get directions on migrating to the blockchain network.
Study of a Weather Prediction System Based on Fuzzy Logic Using Mamdani and Sugeno Methods

Weather is a very important factor in supporting various human activities. However, weather is a natural event that keeps on changing due to various air conditions that affect it. One way to anticipate weather changes that may occur early is to create a system that can predict weather changes. Fuzzy logic is one of the methods that can be used in system prediction to find out the cause at a certain time and place. In this system, two fuzzy logic methods were used, they are the Mamdani and Sugeno methods, with three supporting criteria, including air temperature, humidity, and air pressure. In this research, data levers were carried out in June 2022 and resulted in a percentage accuracy of 73.34% for the Mamdani method and 70% for the Sugeno method.
Incorporating Ensembled Stacked Approach into Automated Machine Learning in an Attempt to Predict Acute Ischemic Heart Disease in Patients with Atypical Chest Pain: Secondary Analysis of a Single-Center Retrospective Cohort Study

Considerable research has been carried out to explore the adoption of novel methodologies in a bid to optimize the management protocols for acute ischemic heart disease (AIHD) that is prevalent worldwide and has significant morbidity and mortality associated with it. With Artificial intelligence in general and machine learning in particular exhibiting superior predictive and prognositicative capabilities, when compared with conventional statistical techniques, we explored automated Machine Learning to predict AIHD in such patients presenting with atypical chest pain. The study population comprised 3,833 patients presenting with chest pain who visited the ED of a tertiary-care hospital from January 2014 to December 2018. Such patients who exhibited typical chest pain were excluded. (Mean age = 60.3; Female subjects = 45.6%; Diagnosed with AIHD = 11.8%). Variables included a plethora of variables obtained on a routine ED evaluation including vital signs and laboratory data. The current state of the art for automated Machine Learning was adopted to develop classification models using algorithms including Extreme Gradiant Boosting, Random Forest and Neural Network. The models were stacked after implementation of hyperparameter tuning. Ensemble approach, which is the amalgamation of two or more than two algorithmic models to develop such a model which is better than either of its computive components, was superimposed on stacked models. The macro-weighted average Area under the Response-operating Curve (mwA-AUROC) gauged the discriminating ability of models with the highest value of 1 indicating perfect discriminative classification ability. An ensemble of stacked Extra Trees, Neural Network, CatBoost, Light Gradient Boosted Machine, Random Forest algorithmic models achieved an mwA-AUROC of 0.78 and an accuracy of 87.9%. (Figure 1) Our model outperformed those developed by Kim KH et al. The ensemble model exhibited the least log loss among all of the developed algorithmic models. Such predictive models, when deployed on cloud, have the capability of significantly influencing the prognosticating protocols for AIHD by providing instantaneous predictions. Such an optimization of the risk stratification protocols achieved via incorporation of aML would serve to improve the management of not only the respective patient but also that of the resource allocation. This would ultimately translate into significant reduction of morbidity and mortality associated with AIHD. These authors contributed equally to data curation, development of methodology, statistical analyses and writing with subsequent reviewing and editing of the abstract draft. These authors, indicated alphabetically, contributed equally to statistical analyses and validation.
Chatbots in language learning: AI systems on the rise

The use of chatbots in language learning has been on the rise. In recent Computer-Assisted Language Learning (CALL) research, there is a consensus that rule-based, scripted voice systems are optimal for language learning. Such systems integrate well into instructed language learning in that interactions with the user are predictable and controlled. Open, AI-based voice systems (such as in personal assistants like Siri) do not provide that degree of task-oriented learning. However, the argument is made here that they have the potential to provide open-ended conversational practice and language development which aligns with an ecological, usage-based perspective on language development.