Reviewerly/exHarmony huggingface dataset:
Multi-Modal Tumor Segmentation With Deformable Aggregation and Uncertain Region Inpainting

Multi-modal tumor segmentation exploits complementary information from different modalities to help recognize tumor regions. Known multi-modal segmentation methods mainly have deficiencies in two aspects: First, the adopted multi-modal fusion strategies are built upon well-aligned input images, which are vulnerable to spatial misalignment between modalities (caused by respiratory motions, different scanning parameters, registration errors, etc). Second, the performance of known methods remains subject to the uncertainty of segmentation, which is particularly acute in tumor boundary regions. To tackle these issues, in this paper, we propose a novel multi-modal tumor segmentation method with deformable feature fusion and uncertain region refinement. Concretely, we introduce a deformable aggregation module, which integrates feature alignment and feature aggregation in an ensemble, to reduce inter-modality misalignment and make full use of cross-modal information. Moreover, we devise an uncertain region inpainting module to refine uncertain pixels using neighboring discriminative features. Experiments on two clinical multi-modal tumor datasets demonstrate that our method achieves promising tumor segmentation results and outperforms state-of-the-art methods.
Developing ‘Learningverse’-a 3D Metaverse Platform to Support Teaching, Social and Cognitive Presences

Despite the metaverse having been increasingly designed, developed and applied to education, critical issues regarding the lack of truly immersive learning environments, custom tools, clear instructional design and inconvenience of using the platform and ethics and privacy concerns exist. This study aimed to design and develop a 3D metaverse platform- ‘Learningverse’-to support teaching, social and cognitive presences. Learningverse combines the key features of an immersive learning environment with (1) avatars that mirror the real users, (2) rich social interactions among avatars, (3) custom tools that allow users to construct or create their own artefacts and (4) a low threshold for users to join the metaverse on an ordinary computer with a webcam and without having to wear a VR headset. A usability testing was conducted to understand students' perceived teaching, social and cognitive presences in a scenario called ‘Assembling the Solar System’, which had an immersive learning instructional design embedded in Learningverse. The study involved 36 postgraduate students in a course on the innovative design of interactive learning environments. The results showed that students perceived the three presences positively. Discussions were made, followed by design principles for the educational metaverse based on this study. Finally, future work was explored.
Performance Evaluation and Analysis of Deep Learning Frameworks

The rapid development of deep learning has contributed to the increasing number of open-source deep learning frameworks, and in practice, benchmarking deep learning frameworks to effectively understand the performance characteristics of these frameworks and make choices becomes a challenge. Based on this, this paper uses three types of neural networks (convolutional neural networks, recurrent neural networks, and vision transformer models) to conduct extensive experimental evaluation and analysis of three popular deep learning frameworks, TensorFlow, PyTorch, and PaddlePaddle. Experiments are mainly conducted in CPU and GPU environments using different datasets, and performance parameters such as accuracy, training time, inference time, hardware utilization and other non-performance factors are considered. Finally, the performance characteristics, advantages and disadvantages of different frameworks are analyzed based on the above indexes, which provides theoretical guidance for users to choose.
New trends on mathematical modeling and simulation of biological systems

Coupled chaotic oscillators usually display rich dynamics and undergo complicated bifurcations when the coupling strength changes. Dynamics reconstruction of the coupled system without relying on a model is a difficult problem in the field of nonlinear dynamics, especially in the presence of bistability. Following previous works (PRE 104 (2021) 014205 and 024205), we design a reservoir computer with parameter input channel consisting of the coupling strength and an indicator parameter. The indicator parameter is used to distinguish the possible coexisting dynamical states. Using a ring of coupled Rössler oscillators, we demonstrate the power of the reservoir computer in dynamics reconstruction and predicting the transitions between different dynamical states. We find that a single one reservoir computer is enough to reconstruct the complete bifurcation diagrams of the original coupled system.
A Disaster Relief UAV Path Planning Based on APF-IRRT* Fusion Algorithm

Unmanned Aerial Vehicle (UAV) path planning has increasingly become the key research point for civilian drones to expand their use and enhance their work efficiency. Focusing on offline derivative algorithms, represented by Rapidly-exploring Random Trees (RRT), are widely utilized due to their high computational efficiency. However, deploying these offline algorithms in complex and changing disaster environments presents its own drawbacks, such as slow convergence speed, poor real-time performance, and uneven generation paths. In this paper, the Artificial Potential Field -Improved Rapidly-exploring Random Trees (APF-IRRT*) path-planning algorithm is proposed, which is applicable to disaster relief UAV cruises. The RRT* algorithm is adapted with adaptive step size and adaptive search range coupled with the APF algorithm for final path-cutting optimization. This algorithm guarantees computational efficiency while giving the target directivity of the extended nodes. Furthermore, this algorithm achieves remarkable progress in solving problems of slow convergence speed and unsmooth path in the UAV path planning and achieves good performance in both offline static and online dynamic environment path planning.
Teaching english using mobile applications to improve academic performance and language proficiency of college students

The purpose of the study is to determine the effectiveness of mobile applications in conjunction with the students' online collaboration in the educational environment of Chinese colleges (the context of learning English). The students were selected from all those who study English in their educational programs. At the first stage, they passed a test on the level of language knowledge and among 423 students 140 were selected who had a level of B2 and below. Then they were divided into control and experimental groups. Each had 70 people. The experimental group was trained using the following mobile applications: Busuu, Lingoda, LinguaLeo, BBC Learning English. The results showed that the participants in the experimental group scored higher on the final test (74.71) than the control group participants (65.9). It suggests that mobile learning technologies can improve student achievement. The preliminary test determined the level of knowledge of the experimental group students in this way: 85% of students know English at the B2 level, 14% - B1, and 1% - A2. These figures improved significantly in the second test: the number of students who reached the C2 level was 7%, C1–79%, and 14% of all students remained with the B2 level. For the control group students, these indicators remained unchanged. Most of the students found this format of education suitable and interesting for online collaboration. These results may be useful for teaching practice, because they provide evidence base with the experimental research for the introduction of mobile technologies in the modern educational process. It solves the problem of using the certain mobile applications (Busuu, Lingoda, LinguaLeo, BBC Learning English) that have not been explored before.
Artificial Intelligence Application in Membrane Processes and Prediction of Fouling for Better Resource Recovery

Water contamination is a global issue due to the emergence of new contaminants from solvents, personal care products, and pharmaceutical compounds. Membrane processes appear to be effective and promising in water treatment. While membrane processes can significantly reduce the levels of contaminants, problems continue to arise, such as fouling. The utilization of artificial intelligence (AI) to predict fouling and enhance the characteristics of membranes is currently receiving attention. Various artificial intelligence (AI) models can be employed to optimize the input parameters based on the output, which helps in predicting membrane performance and assessing its ability to reject contaminants effectively. The possibilities for improvement in membrane technologies and filtration processes using AI techniques are discussed in this paper. Membrane fouling causes significant issues during the operation due to the accumulation of impurities onto the membrane, which reduces the membrane’s ability to function properly. AI algorithms can be used to predict permeate flux and fouling growth properties. The paper concludes that AI utilization for the prediction of membrane fouling can enhance the membrane selection for the processes, reduce costs with better fouling control system development and make the process more scalable on an industrial scale. The literature showed that there are models, such as the Neural-fuzzy interference system, that can predict forward osmosis membranes’ performance with a high correlation of 0.997 and a root mean square error of 0.04. The paper also concludes that the exploration of more novel deep learning architectures like GANs would facilitate better resource recovery from wastewater and improved prediction of fouling in membrane processes.
VLAD: Task-agnostic VAE-based lifelong anomaly detection

Lifelong learning represents an emerging machine learning paradigm that aims at designing new methods providing accurate analyses in complex and dynamic real-world environments. Although a significant amount of research has been conducted in image classification and reinforcement learning, very limited work has been done to solve lifelong anomaly detection problems. In this context, a successful method has to detect anomalies while adapting to changing environments and preserving knowledge to avoid catastrophic forgetting. While state-of-the-art online anomaly detection methods are able to detect anomalies and adapt to a changing environment, they are not designed to preserve past knowledge. On the other hand, while lifelong learning methods are focused on adapting to changing environments and preserving knowledge, they are not tailored for detecting anomalies, and often require task labels or task boundaries which are not available in task-agnostic lifelong anomaly detection scenarios. This paper proposes VLAD, a novel VAE-based Lifelong Anomaly Detection method addressing all these challenges simultaneously in complex task-agnostic scenarios. VLAD leverages the combination of lifelong change point detection and an effective model update strategy supported by experience replay with a hierarchical memory maintained by means of consolidation and summarization. An extensive quantitative evaluation showcases the merit of the proposed method in a variety of applied settings. VLAD outperforms state-of-the-art methods for anomaly detection, presenting increased robustness and performance in complex lifelong settings.
GeT a CAKE: Generic Transformations from Key Encaspulation Mechanisms to Password Authenticated Key Exchanges

Password Authenticated Key Exchange (PAKE) have become a key building block in many security products as they provide interesting efficiency/security trade-offs. Indeed, a PAKE allows to dispense with the heavy public key infrastructures and its efficiency and portability make it well suited for applications such as Internet of Things or e-passports. With the emerging quantum threat and the effervescent development of post-quantum public key algorithms in the last five years, one would wonder how to modify existing password authenticated key exchange protocols that currently rely on Diffie-Hellman problems in order to include newly introduced and soon-to-be-standardized post-quantum key encapsulation mechanisms ( $$\textsf{KEM} $$ ). A generic solution is desirable for maintaining modularity and adaptability with the many post-quantum $$\textsf{KEM} $$ that have been introduced. In this paper, we propose two new generic and natural constructions proven in the Universal Composability (UC) model to transform, in a black-box manner, a $$\textsf{KEM} $$ into a PAKE with very limited performance overhead: one or two extra symmetric encryptions. Behind the simplicity of the designs, establishing security proofs in the UC model is actually non-trivial and requires some additional properties on the underlying KEM like fuzziness and anonymity. Luckily, post-quantum $$\textsf{KEM} $$ protocols often enjoy these two extra properties. As a demonstration, we prove that it is possible to apply our transformations to Crystals-Kyber, a lattice-based post-quantum KEM that will soon be standardized by the National Institute of Standards and Technology (NIST). In a nutshell, this work opens up the possibility to securely include post-quantum cryptography in PAKE-based real-world protocols.
Mathematical model and artificial intelligence for diagnosis of Alzheimer’s disease

Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer’s disease. Alzheimer’s disease research conducted later in life focuses on describing ways for early detection of dementia, a kind of mental disorder. To tailor our care to each patient, we utilized visual cues to determine how they were feeling. We did this by outlining two approaches to diagnosing a person’s mental health. Support vector machine is the first technique. Image characteristics are extracted using a fractal model for classification in this method. With this technique, the histogram of a picture is modeled after a Gaussian distribution. Classification was performed with several support vector machines kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network architecture to identify Alzheimer’s-related mental disorders. According to the findings, the support vector machines approach accurately recognized over 93% of the photos tested. The deep convolutional neural network approach was one hundred percent accurate during model training, whereas the support vector machines approach achieved just 93 percent accuracy. In contrast to support vector machines accuracy of 89.3%, the deep convolutional neural network model test findings were accurate 98.8% of the time. Based on the findings reported here, the proposed deep convolutional neural network architecture may be used for diagnostic purposes involving the patient’s mental state.
Using Hybrid HMM/DNN Embedding Extractor Models in Computational Paralinguistic Tasks

The field of computational paralinguistics emerged from automatic speech processing, and it covers a wide range of tasks involving different phenomena present in human speech. It focuses on the non-verbal content of human speech, including tasks such as spoken emotion recognition, conflict intensity estimation and sleepiness detection from speech, showing straightforward application possibilities for remote monitoring with acoustic sensors. The two main technical issues present in computational paralinguistics are (1) handling varying-length utterances with traditional classifiers and (2) training models on relatively small corpora. In this study, we present a method that combines automatic speech recognition and paralinguistic approaches, which is able to handle both of these technical issues. That is, we trained a HMM/DNN hybrid acoustic model on a general ASR corpus, which was then used as a source of embeddings employed as features for several paralinguistic tasks. To convert the local embeddings into utterance-level features, we experimented with five different aggregation methods, namely mean, standard deviation, skewness, kurtosis and the ratio of non-zero activations. Our results show that the proposed feature extraction technique consistently outperforms the widely used x-vector method used as the baseline, independently of the actual paralinguistic task investigated. Furthermore, the aggregation techniques could be combined effectively as well, leading to further improvements depending on the task and the layer of the neural network serving as the source of the local embeddings. Overall, based on our experimental results, the proposed method can be considered as a competitive and resource-efficient approach for a wide range of computational paralinguistic tasks.
Influence of advertising on Abuja women’s choices of e-commerce platforms

Evolution and dynamic penetration of the Internet provided radical paradigm shifts in different aspects of life. Such sectors include, but are not limited to education, health, governance, marketing among others. E-Commerce platforms or online marketing are among significant beneficiaries of the exponential growth of users of the Internet. Taking advantage of the various features of the Internet, e-marketers use strategies, including advertising to attract and persuade their target customers. This study sought to know the influence advertising exerts on women who live in Abuja to choose specific e-Commerce platforms for their online purchases. Theories used to underpin the study are Technological Determinism theory and AIDA Formula. Using Dusick Online Sampling Calculator, an approximate of 384 females were stratified among women in Wuse, Gwarimpa and Garki areas of AMAC, to harvest data out of an estimated population of 1.75m women, resident in Abuja. A multi-stage sampling approach was adopted for the study, and tools used to gather data were the Questionnaire and FGD. ANOVA was used to measure the extent advertising influences patronage of E-platforms by respondents. It was revealed that advertising played a significant role in determining respondents’ choice of online malls, while other variables contributed to pull customers to specific E-platforms. It is recommended that e-marketers adopt a holistic marketing communications approach, and use proper audience and positioning strategies to attract right customers to their e-platforms.
A covert channel over blockchain based on label tree without long waiting times

Blockchain, as a decentralized database, provides natural anonymity and tamper-resistance, safeguarding the identity of both parties in covert communication and preventing malicious third parties from tampering with secret messages. Compared to traditional covert communication over a network, blockchain offers a more reliable communication carrier. However, the strict order between special transactions in blockchain covert communication increases the time consumption of communication. This paper proposes a label-tree-based covert communication model, named LTCC, which uses dynamic labels with tree-like relationships to maintain the connections between special transactions. LTCC allows senders to upload transactions containing secret messages in a single pass, free from the high latency of blockchains. Even in the event of a failed transaction submission, it is possible to regenerate the transaction containing a specific label to be submitted to the blockchain, avoiding the reuse of addresses. The experimental results indicate that LTCC possesses excellent qualities such as concealment, reliability, robustness, and communication efficiency. Therefore, it can be feasibly implemented in the present-day, mainstream blockchain systems.
Permutation Tests Using Arbitrary Permutation Distributions

Abstract Permutation tests date back nearly a century to Fisher’s randomized experiments, and remain an immensely popular statistical tool, used for testing hypotheses of independence between variables and other common inferential questions. Much of the existing literature has emphasized that, for the permutation p-value to be valid, one must first pick a subgroup G of permutations (which could equal the full group) and then recalculate the test statistic on permuted data using either an exhaustive enumeration of G , or a sample from G drawn uniformly at random. In this work, we demonstrate that the focus on subgroups and uniform sampling are both unnecessary for validity—in fact, a simple random modification of the permutation p-value remains valid even when using an arbitrary distribution (not necessarily uniform) over any subset of permutations (not necessarily a subgroup). We provide a unified theoretical treatment of such generalized permutation tests, recovering all known results from the literature as special cases. Thus, this work expands the flexibility of the permutation test toolkit available to the practitioner.
Recurrence Network Analysis of Histopathological Images for the Detection of Invasive Ductal Carcinoma in Breast Cancer

The histopathological image analysis is one of the most crucial diagnostic procedures to identify Invasive ductal carcinoma (IDC) in breast cancers. However, this diagnosis process is currently time-consuming and heavily dependent on human expertise. Prior research has shown that different degrees of tumors present various microstructures in the histopathological images. However, very little has been done to utilize spatial recurrence features of microstructures for identifying IDC. This paper presents a novel recurrence analysis methodology for automatic image-guided IDC detection. We first utilize wavelet decomposition to delineate the subtle information in the images. Then, we model the patches with a weighted recurrence network approach to characterize the recurrence patterns of the histopathological images. Finally, we develop automated IDC detection models leveraging machine learning methods with spatial recurrence features extracted. The developed recurrence analysis models successfully characterize the complex microstructures of histopathological images and achieve the IDC detection performances of at least AUC = 0.96. This research developed a spatial recurrence analysis methodology to effectively identify IDC regions in histopathological images for BC. It shows a high potential to assist physicians in the decision-making process. The proposed methodology can further be applicable to image processing for other medical or biological applications.
Graded fuzzy edge detection for imperceptibility optimization of image steganography

The edge area of an image is more resilient to distortion caused by embedding in steganography, driving the advancement of edge detection-based methods. State-of-the-art techniques, including hybrid, dilation, and Fuzzy-based approaches, have been developed to enhance steganography performance. Typically, these methods categorize image pixels into two regions: edge and non-edge areas. This research introduces graded Fuzzy edge detection, classifying pixels into main, supporting, and non-edge categories. Consequently, the embedding priority and message bit determination become more diverse and adaptive. Experimental results demonstrate that graded Fuzzy edge detection optimizes imperceptibility while preserving security and capacity. This technique is combined with various steganographic and reversible data hiding (RDH) methods, exhibiting improvements in stego image quality with over 1dB enhancement based on PSNR, as well as maintaining payload capacity and security aspects according to RS and PDH steganalysis.