Reviewerly/exHarmony huggingface dataset:
SCCNet: Self-correction boundary preservation with a dynamic class prior filter for high-variability ultrasound image segmentation

The highly ambiguous nature of boundaries and similar objects is difficult to address in some ultrasound image segmentation tasks, such as neck muscle segmentation, leading to unsatisfactory performance. Thus, this paper proposes a two-stage network called SCCNet (self-correction context network) using a self-correction boundary preservation module and class-context filter to alleviate these problems. The proposed self-correction boundary preservation module uses a dynamic key boundary point (KBP) map to increase the capability of iteratively discriminating ambiguous boundary points segments, and the predicted segmentation map from one stage is used to obtain a dynamic class prior filter to improve the segmentation performance at Stage 2. Finally, three datasets, Neck Muscle, CAMUS and Thyroid, are used to demonstrate that our proposed SCCNet outperforms other state-of-the art methods, such as BPBnet, DSNnet, and RAGCnet. Our proposed network shows at least a 1.2-3.7% improvement on the three datasets, Neck Muscle, Thyroid, and CAMUS. The source code is available at https://github.com/lijixing0425/SCCNet.
Investigating and evaluating factors influencing undergraduate engineering students' choice of university

There is a need to have a better understanding of the quality of education at higher learning institutions (HEI). Good quality education strives to help students obtain the knowledge and skills so that they can be responsible and productive citizens. Quality is the most competitive weapon for HEI to attract and gain loyalty from students. Student enrolment is the most imperative outset for the success of any HEI. Thus, it is crucial to investigate the factors influencing students' choice of university to allow effective planning for student recruitment strategies. This study sought to critically analyse the success factors that influenced students' choice of university. The study adapted a descriptive survey design which employed a structured questionnaire to collect data. The targeted population of this study was the undergraduate students in the department of Mechanical and Industrial Engineering Technology (MIET) from various levels of study. A sample of 135 was selected through a convenience sampling from a population of 625. Data was analysed using a statistical software named Statistical Package for the Social Sciences (SPSS) with the assistance of a statistician from Statistical Consultation Services (STATKON). Findings of the study revealed that university reputation, variety of courses to choose from as well as safety and security were the top factors influencing students' choice of university. Factors such as the cost of the course and location and logistics did not have a high influence on students' choice of university.
AI for Immersive Metaverse Experience

Metaverse has received a huge attention in recent times with several Big Techs having invested in this concept. Accenture defines the metaverse as "an evolution of the Internet that enables a user to move beyond 'browsing' to 'inhabiting' in a persistent, shared experience that spans the spectrum of our real world to the fully virtual and in between". The evolution that Metaverse brings can be seen along three dimensions: 1) shift towards spatial experiences: which includes 2D, 3D, augmented, virtual, and mixed reality immersive experiences, 2) shared co-presence: where users experience a persistent shared space with a sense of co-presence with others, and 3) trusted identities and transactions to address challenges of fake identities, products, and transactions as present in today's internet.
Explainable Data Imputation using Constraints

Data values in a dataset can be missing or anomalous due to mishandling or human error. Analysing data with missing values can create bias and affect the inferences. Several analysis methods, such as principle components analysis or singular value decomposition, require complete data. Many approaches impute numeric data and some do not consider dependency of attributes on other attributes, while some require human intervention and domain knowledge. We present a new algorithm for data imputation based on different data type values and their association constraints in data, which are not handled currently by any system. We show experimental results using different metrics comparing our algorithm with state of the art imputation techniques. Our algorithm not only imputes the missing values but also generates explanations describing the significance of attributes used for every imputation.
Efficient Depth-First Search Approach for Mining Injective General Episodes

Frequent episode mining is useful for finding temporal patterns in sequential data. Episodes represent partially ordered sets of event-types and frequently occurring episodes can capture temporal dependencies in the data. There are many algorithms in the literature that find a subset of frequent episodes that best summarize the data using serial episodes (which are episodes with total order). But there are no such algorithms for the case of general episodes. An efficient Depth-First search (DFS) approach for mining general episodes would be a crucial tool and a necessary first step for generating a subset of general episodes that best represent the given temporal data. In this paper, we present an efficient algorithm to mine for general injective episodes in a DFS manner. Our algorithm uses both the apriori principle and the idea of bidirectional evidence to prune the search space and it returns closed frequent episodes. Through simulation studies, we show that the algorithm is quite effective and efficient when compared with the existing algorithms.
SIFT and RANSAC-Based Image Mosaic Algorithm for Image Compression

Intensive research has been done on image mosaic algorithms to improve the field of view through generated image mosaics. The current imagery software packages used are computationally intensive to be used in real-time applications and are not economically affordable. Therefore, we have proposed an improved SIFT algorithm, which was then evaluated with an open-source algorithm and commercial software using structural similarity index measure (SSIM) and mosaicing computational times for mosaic accuracy and processing efficiency, respectively. Our approach demonstrated a significant improvement of more than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> average on the mosaicing computational times for the five datasets used. Its mosaicing accuracy was found to be relatively within an acceptable range of above 90% averagely.
A novel DCNN-ELM hybrid framework for face mask detection

The Coronavirus disease (2019) has caused massive destruction of human lives and capital around the world. The latest variant Omicron is proved to be the most infectious of all its previous counterparts – Alpha, Beta and Delta. Various measures are identified, tested and implemented to minimize the attack on humans. Face masks are one of those measures that are shown to be very effective in containing the infection. However, it requires continuous monitoring for law enforcement. In the present manuscript, a detailed research investigation using different ablation studies is carried out to develop the framework for face mask recognition using pre-trained deep convolution neural networks (DCNN) models used in conjunction with a fast single layer feed-forward neural network (SLFNN) commonly known as Extreme Learning Machine (ELM) as classification technique. The ELM is well known for its real time data processing capabilities and has been successfully applied both for regression and classification problems of image processing and biomedical domain. It is for the first time that in this paper we have proposed the use of ELM as classifier for face mask detection. As a precursor to this, for feature selection, six pre-trained DCNNs such as Xception, Vgg16, Vgg19, ResNet50, ResNet 101 and ResNet152 are tested for this purpose. The best testing accuracy is obtained in case of ResNet152 transfer learning model used with ELM as the classifier. The performance evaluation through different ablation studies on testing accuracy explicitly proves that ResNet152 - ELM hybrid architecture is not only the best among the selected transfer learning models but also proves so when it is compared with several other classifiers used for the face mask detection operation. Through this investigation, novelty of the use of ResNet152 + ELM for face mask detection framework in real time domain is established.
Exploring wave–particle behaviors of entangled Bragg diffracted neutral atoms

In this paper, we theoretically study the concept of the wave–particle duality of two entangled neutral Bragg diffracted atoms. This is an extension of a recent study where the same idea was proposed in the photonic setup [Man et al., Sci. Rep. 7, 42539 (2017)] using two independent Mach–Zehnder interferometers. Now, we propose a similar scheme using the cavity-QED based setup, which comprises two independent atomic de Broglie Mach–Zehnder–Bragg interferometers, a source cavity that generates two external momenta state entangled atoms. Once the atoms pass through the source cavity initially prepared in the superposition of zero and one photon, they emerge out of the cavity in entangled momenta state such that if one atom is transmitted to the upper interferometer, then the second atom must traverse the lower interferometer, and vice versa. The final atomic de Broglie beam splitter at the top interferometer is prepared in the superposition of zero and one photon and facilitates observing the wave or particle aspect in a single setting. This entire setup functions in off-resonant Bragg diffraction and the proposed schematics are shown to be experimentally feasible under contemporary research scenarios.
MPJA-HAD: A Multi-Position Joint Angle Dataset for Human Activity Recognition Using Wearable Sensors

The Inertial Measurement Units (IMUs) have been widely used in human activity recognition (HAR) for data acquisition. However, most publicly available datasets based on IMUs only involve data from few body parts and are relatively homogeneous. Using data from few body parts can be limited in certain specific or complex activity recognition tasks. Hence, we created a new HAR dataset named Multi-Position Joint Angle Human Activity Dataset (MPJA-HAD). Other publicly available datasets only contain the raw inertial sensor readings. The MPJA-HAD dataset also provides joint angle changes from each of 15 body positions. Joint angles directly relate to human activities performing and experimental results show the competitiveness of the created dataset in HAR tasks.
Blockchain Technology for Secure Shared Medical Data

A blockchain is a shared database allowing trust to be created between individuals without using intermediaries. The architecture here is decentralized, the data is distributed among the users, and therefore the information can never be erased. Today, many fields are interested in the development of products and technical solutions based on Blockchain technology. In this paper, we investigate blockchain in developing a Secure Shared Medical Record system (SSMR) for health data management. The proposed system aims to collect, store and share electronic medical records securely. It also makes it possible to provide doctors, for example, with medical information from other healthcare professionals, by defining a medical profile for each patient.
Within-Project Defect Prediction Using Improved CNN Model via Extracting the Source Code Features

Errors in software systems are inevitable. However, fixing bugs requires cost and time. If they are detected early, this problem is solved with Artificial Intelligence in Software Engineering (AISE). Therefore, software error prediction is used to discover software errors in the source code and to take into account the testing effort in the development phase. The first of the three phases presented in this work is the re-implementation of the Congs model [12] on Colab. compared the result of the original Congs model to the newly implemented model result when we found that the Congs model under Colab approximates the original results using the Simplified PROMISE Source Code (SPSC) dataset. The second is to extract deep features from source codes or ASTs as model input and proposed deep models (CNN). Third, an improved CNN model for within-project defect prediction (WPDP) by hyperparameter tuning was proposed and our results are compared to current CNN results. Our improved CNN model outperforms the improved F -measure by 29% and achieves a 93% F1-measure in our CNN model.
Collaborative UGV/UAV Path Planning for Inventory Management in Warehouses

The use of autonomous mobile robots in different applications has increased significantly over the past years, which can be executed individually or collaboratively, considering the use of Multi-Robot Systems (MRS), that presents several advantages but also raises many challenges. Specifically, when considering heterogeneous robots, it is paramount to develop techniques that respect individual restrictions and benefit the use of their different capabilities. In this paper, we consider a ground/aerial collaboration for inventory inspection in warehouse environments. The goal is to determine a set of optimized combined paths that maximize the number of shelves inspected. The problem is formulated as a dual Orienteering Problem (OP), where the budget of both vehicles is considered, and the rewards, which are related to the shelves that can be visited, are estimated by a greedy strategy. Results in simulated scenarios considering different aspects provide a thorough evaluation and validation of the methodology.
CNN-Planner: A neural path planner based on sensor fusion in the bird's eye view representation space for mapless autonomous driving

Autonomous driving in urban environments without High Definition(HD) maps is probably one of the most difficult tasks for Autonomous Vehicles (AVs), Many solutions grouped in modular pipelines and end-to-end driving approaches have been proposed for solving this motion planning problem. However, safe mapless autonomous driving has not been fully resolved. In this paper we take the best of these two approaches and propose a motion planner powered by deep learning, sensor fusion, and a modular perception pipeline to perform confident mapless autonomous driving. For this, we divide motion planning into two tasks: lateral planning and longitudinal planning. We address lateral planning as a path-following problem and solve it by creating an end-to-end neural path planner that regresses a dense set of waypoints taking as input sensor data fusion in Bird's Eye View (BEV) space, then a low-level MPC controller follows generated waypoints. On the other hand, for longitudinal planning we use a modular pipeline that uses a Finite State Machine (FSM) to make decisions about the ego car speed, where a detection stack defines the FSM inputs and a low-level PID controller follows the desired speed. Putting lateral and longitudinal planners together we obtain a successful motion planner that outperforms several baselines and matches results of other more complex state-of-the-art motion planners in the CARLA Autonomous Driving Leaderboard.
Ecological and Confined Domain Ontology Construction Scheme Using Concept Clustering for Knowledge Management

Knowledge management in a structured system is a complicated task that requires common, standardized methods that are acceptable to all actors in a system. Ontology, in this regard, is a primary element and plays a central role in knowledge management, interoperability between various departments, and better decision making. The ontology construction for structured systems comprises logical and structural complications. Researchers have already proposed a variety of domain ontology construction schemes. However, these schemes do not involve some important phases of ontology construction that make ontologies more collaborative. Furthermore, these schemes do not provide details of the activities and methods involved in the construction of an ontology, which may cause difficulty in implementing the ontology. The major objectives of this research were to provide a comparison between some existing ontology construction schemes and to propose an enhanced ecological and confined domain ontology construction (EC-DOC) scheme for structured knowledge management. The proposed scheme introduces five important phases to construct an ontology, with a major focus on the conceptualizing and clustering of domain concepts. In the conceptualization phase, a glossary of domain-related concepts and their properties is maintained, and a Fuzzy C-Mean soft clustering mechanism is used to form the clusters of these concepts. In addition, the localization of concepts is instantly performed after the conceptualization phase, and a translation file of localized concepts is created. The EC-DOC scheme can provide accurate concepts regarding the terms for a specific domain, and these concepts can be made available in a preferred local language.
Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes, namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0% compared to three machine learning models, i.e., K-nearest neighbors, decision trees and traditional random forest, and two deep learning models, which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work, our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.
Geochemistry Process from Weathering Rocks to Soils: Perspective of an Ecological Geology Survey in China

Ecological characteristics are very important for the Earth’s environment and human lives. Recently, more attention has been paid to the ecological problems in the world. The soil and its parent materials/rocks serve as the supporting materials for the ecological system on the Earth’s surface. The ecological characteristics in one region are associated with or even dependent on the soil-forming process. Thus, the study of the weathering process of parent materials/rocks is important for our understanding of the geological genesis of ecological problems. In this study, three typical ecological problems in China are introduced: land salinization in the north, the desertification of land in the northwest, and Karst rocky desertification in the south. We chose 23 typical profiles for observation and sampling. The soil-forming processes in these areas were investigated by geochemical research, and eco-geology models were subsequently established to explain the profound relationship between vegetation cover and the soil-forming process. Our work first focuses on the geochemical methods used to explore these ecological issues, emphasizing the contribution of the geological genesis to the ecological characteristics. Using geochemical methods, such as the chemical index of alteration (CIA), major element and trace element distribution characteristics, the formation processes, and characteristics of bedrock-weathering soils under diverse epigenetic geological settings were determined. Eco-geology models were then developed by evaluating the vertical structure and material composition of soil, the characteristics of elemental migration during soil formation, and the enrichment and loss of elements in the supporting layers and their repercussions.
Few-Shot Learning Based on Double Pooling Squeeze and Excitation Attention

Training a generalized reliable model is a great challenge since sufficiently labeled data are unavailable in some open application scenarios. Few-shot learning (FSL) aims to learn new problems with only a few examples that can tackle this problem and attract extensive attention. This paper proposes a novel few-shot learning method based on double pooling squeeze and excitation attention (dSE) for the purpose of improving the discriminative ability of the model by proposing a novel feature expression. Specifically, the proposed dSE module adopts two types of pooling to emphasize features responding to foreground object channels. We employed both the pixel descriptor and channel descriptor to capture locally identifiable channel features and pixel features of an image (as opposed to traditional few-shot learning methods). Additionally, in order to improve the robustness of the model, we designed a new loss function. To verify the performance of the method, a large number of experiments were performed on multiple standard few-shot image benchmark datasets, showing that our framework can outperform several existing approaches. Moreover, we performed extensive experiments on three more challenging fine-grained few-shot datasets, the experimental results demonstrate that the proposed method achieves state-of-the-art performances. In particular, this work achieves 92.36% accuracy under the 5-way–5-shot classification setting of the Stanford Cars dataset.