Miracle-dz/UniFAR-FTUs huggingface dataset:
Bi-component conformal electrode for radiofrequency sequential ablation and circumferential separation of large tumours in solid organs

— Objective : Complete destruction of large tumours by radiofrequency ablation (RFA) with surrounding tumour-free margin is difficult because of incomplete or non-uniform heating due to both heat sink effect of circulating blood and limitations of existing RF electrode design. A new RF electrode is described to overcome this limitation. Methods: A bi-component conformal (BCC) RFA probe providing sectorial sequential ablation followed by circumferential cutting is designed and evaluated. Three-dimensional finite-element analysis model was developed with temperature feedback-controlled simulation of RFA for electrode design and optimization. The prototype bipolar BCC probe with 3 embedded thermocouples was constructed and evaluated in tissue-mimicking phantoms. Results: Maximum tissue temperature was kept < 100 º C with power applied < 15 W. A 10 min ablation time was used for each sequence and after four sequential RFA, a large ablation zone of 55 cm 3 was achieved. Our experiment confirmed that lesions exceeding 3.7cm could be ablated and separated from the surrounded tissue. Conclusion: The new BCC probe is thus capable of controlled ablation followed by circumferential separation of the lesions, when required. Significance: The results of these experiments provide proof of concept validation that the BCC probe has the potential to ablate by sequential heating tumours in solid organs > 3.5cm then separate them by electrosurgical cutting from the surrounding normal parenchyma. The combined RF ablation and physical separation could completely destroy the cancer cells at the ablation site thus avoid any local recurrence of cancer. It requires further in-vivo validation studies in large animals.
Unsupervised Algorithms DDoS Detection on Internet

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.
On the Three P's of Parallel Programming for Heterogeneous Computing: Performance, Productivity, and Portability

As FPGAs and GPUs continue to make inroads into high-performance computing (HPC), the need for languages and frameworks that offer performance, productivity, and portability across heterogeneous platforms, such as FPGAs and GPUs, continues to grow. OpenCL and SYCL have emerged as frameworks that offer cross-platform functional portability between FPGAs and GPUs. While functional portability across a diverse set of platforms is an important feature of portable frameworks, achieving performance portability often requires vendor and platform-specific optimizations. Achieving performance portability, therefore, comes at the expense of productivity. This paper presents a quantification of the tradeoffs between performance, portability, and productivity of OpenCL and SYCL. It extends and complements our prior work on quantifying performance-productivity tradeoffs between Verilog and OpenCL for the FPGA. In addition to evaluating the performance-productivity tradeoffs between OpenCL and SYCL, this work quantifies the performance portability (PP) of OpenCL and SYCL as well as their code convergence (CC), i.e., a measure of productivity across different platforms (e.g., FPGA and GPU). Using two applications as case studies (i.e., edge detection using the Sobel filter, and graph link prediction using the Jaccard similarity index), we characterize the tradeoffs between performance, portability, and productivity. Our results show that OpenCL and SYCL offer complementary tradeoffs. While OpenCL delivers better performance portability than SYCL, SYCL offers better code convergence and a 1.6 x improvement in source lines of code over OpenCL.
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

What are the computational foundations of social grouping? Traditional approaches to this question have focused on verbal reasoning or simple (low-dimensional) quantitative models. In the real world, however, social preferences emerge when high-dimensional learning systems (brains and bodies) interact with high-dimensional sensory inputs during an animal’s embodied interactions with the world. A deep understanding of social grouping will therefore require embodied models that learn directly from sensory inputs using high-dimensional learning mechanisms. To this end, we built artificial neural networks (ANNs), embodied those ANNs in virtual fish bodies, and raised the artificial fish in virtual fish tanks that mimicked the rearing conditions of real fish. We then compared the social preferences that emerged in real fish versus artificial fish. We found that when artificial fish had two core learning mechanisms (reinforcement learning and curiosity-driven learning), artificial fish developed fish-like social preferences. Like real fish, the artificial fish spontaneously learned to prefer members of their own group over members of other groups. The artificial fish also spontaneously learned to self-segregate with their in-group, akin to self-segregation behavior seen in nature. Our results suggest that social grouping can emerge from three ingredients: (1) reinforcement learning, (2) intrinsic motivation, and (3) early social experiences with in-group members. This approach lays a foundation for reverse engineering animal-like social behavior with image-computable models, bridging the divide between high-dimensional sensory inputs and social preferences.
THEORETICAL STUDY OF THE PHOSPHATE UNITS STABILITY BY THE DFT B3LYP/6-311G QUANTUM METHOD

The phosphate units have been theoretically investigated through density functional theory (DFT) calculations. Ionization potential (I), electron affinity (A), electrophilicity index (ω), chemical potential (μ), hardness (ŋ), softness (S), and dipole moment (P) have been optimized. The obtained results revealed that the [PO 4 ] 3-(Q 0 ) units act as electron donors, while the [P 4 O 10 ] 0 (Q 3 ) unit acts as an electron acceptor. The passage from one unit to the other implies an increase in the number of bridging oxygens (BO) consistent with the variation of Mulliken charges. Moreover, the analysis of the optimized contours of the electrostatic potential has indicated that the electrophilic attack is more expected on the P-O-P bonds. The infrared and Raman spectra have been also predicted and the change of symmetric and asymmetric vibrational bands of the phosphate units with the number of bridging oxygens has been investigated.
OBJECT RECOGNITION IN CLUTTERED ENVIRONMENTS

On the way to autonomous robots able to perceive all the surrounding space, this paper focuses on visual object recognition in real scenarios where those objects can be occluded, modify their appearance due to a viewpoint changed or different illumination conditions, etc. In this paper, we aim at robustly detecting and recognizing different objects in real-life scenarios from a visual input. For that, we present an appoach to properly build a peripersonal background model such that the target objects are always detected and recognized without any a-priori knowledge about the robotic system. Several objects and different scene conditions have been used to evaluate the method’s performance by providing successful object detection and recognition in all the cases, as shown in the experimental section.
Mining Detailed Course Transaction Records for Semantic Information

In studies that generate course recommendations based on similarity, the typical enrollment data used for model training consists only of one record per student-course pair. In this study, we explore and quantify the additional signal present in course transaction data, which includes a more granular account of student administrative interactions with a course, such as wait-listing, enrolling, and dropping. We explore whether the additional non-enrollment records and the transaction data’s chronological order play a role in providing more signal. We train skip-gram, FastText, and RoBERTa models on transaction data from five years of course taking histories. We find that the models gain moderate improvements from the extra non-enrollment records, while the chronological order of the transaction data improves the performance of RoBERTa only. The generated embeddings can also predict course features (i.e. the department, its usefulness in satisfying requirements, and whether the course is STEM) with high accuracy. Lastly, we discuss future work on the use of transaction data to predict student characteristics and train course recommender models for degree requirements.
Language Teaching Research

While acknowledging the significant impact of Assessment for Learning (hereinafter referred to as AfL) on student learning, the specific factors that either support or impede its integration into daily classroom practices remain unclear in Asian EFL context. To address this gap, a comprehensive review was undertaken for the purpose of determining the AfL strategies applied in existing literature and the key factors that influence the effective implementation of AfL within particular contexts. This research synthesis summarized clarification and instruction of learning intentions and goals, teachers’ feedback, peer assessment and self-assessment as main AfL strategies applied in Asian EFL classrooms and detected several key elements concerning teachers, students, assessments, and the overall context that influence the effect of the implementation of AfL. By shedding light on these various aspects, this systematic research synthesis contributes to a more comprehensive understanding of the theoretical and practical considerations involved in the implementation of AfL.
Understanding the compound ﬂood risk along the coast of the contiguous United States

Compound ﬂooding is a type of ﬂood event caused by multiple ﬂood drivers. The associated risk has usually been assessed using statistics-based analyses or hydrodynamics-based numerical models. This study proposes a compound ﬂood (CF) risk assessment (CFRA) framework for coastal regions in the contiguous United States (CONUS). In this framework, a large-scale river model is coupled with a global ocean reanalysis dataset to (a) evaluate the CF exposure related to the coastal backwa-ter effects on river basins, and (b) generate spatially distributed data for analyzing the CF hazard using a bivariate statistical model of river discharge and storm surge. The two kinds of risk are also combined to achieve a holistic understanding of the continental-scale CF risk. The estimated CF risk shows remarkable inter-and intra-basin variabilities along the CONUS coast with more variabilities in the CF hazard over the US west and Gulf coastal basins. Different risk assessment methods present signiﬁcantly different patterns in a few key regions such as the San Francisco Bay area, the lower Mississippi River, and Puget Sound. Our re-sults highlight the need to weigh different CF risk measures and avoid using single statistics-based or hydrodynamics-based CFRAs. Uncertainty sources in these CFRAs include the use of gauge observations, which cannot account for the ﬂow physics
Determinants of breast cancer in Ethiopia: a systematic review and meta-analysis

Background Breast cancer (BC) is the first leading cancer sharing about 25% of the cancer burden among women globally. This study aimed to identify the determinants of BC in Ethiopia. Methods We comprehensively searched primary studies conducted in Ethiopia on associated factors of BC in PubMed, Cochrane Library, Hinari, Google, and Google Scholar) and available online until 2 June 2023. The necessary data were extracted from relevant studies and exported to STATA version 15 for analysis. The pooled odds ratio with its 95% confidence interval (CI) was estimated using a random effect model. The finding was reported following preferred reporting items for systematic reviews and meta-analyses guidelines. Results Five studies with 1,819 participants (792 cases and 1,027 controls) were included. The significant determinants of BC were age at menarche <12 years (adjusted odds ratio (AOR) = 3.36, 95% CI: 1.68–5.04), post-menopause (AOR = 2.37, 95% CI: 1.67–3.06), ever breastfeeding (AOR = 0.28, 95% CI: 0.15–0.42), and family history of cancer (AOR = 2.39, 95% CI: 1.29–3.44). Conclusion In Ethiopia, the significant determinants of BC among women were age at menarche <12 years, post-menopause, Ever breastfeeding, and family history of cancer. We recommend that the concerned organizations consider the aforementioned factors in addressing the problem of BC in Ethiopia by increasing community awareness, promoting breast self-examination, and developing programs to reduce the increasing burden of BC in the study setting.
Tracking the Evolution of the Knowledge Base on Problem-based Learning: A Bibliometric Review, 1972-2019

The current review employed bibliometric review methods to analyze 14,130 Scopus-indexed documents on problem-based learning published from 1972 through the end of 2019. The goals of the review were to describe the landscape and analyze the evolution of topical foci of interdisciplinary research on PBL over the past five decades. The review identified the accumulation of a substantial interdisciplinary corpus of research on PBL that is significantly larger than the literature on other approaches to active learning. The growth trajectory of the literature on PBL started out “low and flat” during the 1970s and 1980s but steadily gained momentum in subsequent decades, with 58% of the PBL literature published between 2010 and 2019. PBL has achieved an impressive scope of global research over the past 45 years, with a significant number of studies published from emerging regions of the world, especially during the past decade. While a long-term body of research has accumulated on the learning outcomes of PBL, the research front has begun to shift towards the study of self-directed learning, student satisfaction, self-efficacy, critical thinking, and cooperation. Research and practice challenges identified in the review focus on establishing the scope of use and efficacy of PBL across different cultural contexts and more systematically summarizing and synthesizing the use of PBL outside of the health professions.
Investigation into law of AE energy accumulation and damage evolution on coal and rock

In order to find out the relationship beween the energy accumulation and stress-strain during the fuilure process of coal and rock.the unaxial compression and AE experiments of the coal and mudstone were conducted,and the curves between the strain and AE energy accumulation and strain-stress were obtained.Based on the law of damage mechanics and thermodynamics,the damage evolution of coal and rock was deduced through analyzing the experimental results,subsequently,the curves of damage and AE accumulation were obtained.By analyzing and fitting the curves of damage and AE energy accumulation,the relation between the AE energy accumulation and stress-strain was obtained,and the relation was verified through the experimental data.
"Can we reach agreement?": A context- and semantic-based clustering approach with semi-supervised text-feature extraction for finding disagreement in peer-assessment formative feedback

In the process of review for assessing a piece of work, agreement or consensus among reviewers is vital to review quality. As classroom peer assessments are undertaken by na¨ıve peers, disagreement among peer assessors can confuse the assessees and lead them to question the review process. Although there are methods like inter-rater reliability (IRR) to measure disagreement in summative feedback, in the authors’ knowledge, there is no method for finding disagreements within formative feedback. It may take more time and effort for the instructor to review the feedback to find disagreements than it would to simply perform an expert review without involving peer assessors. An automated method can help locate disagreements among reviewers. In this work, we used a clustering algorithm and NLP techniques to find disagreement in formative feedback. As the review comments are related by context and semantics, we implemented a semi-supervised approach to fine-tune the SentenceTrans-former model to capture the context and semantics-based relation among the review texts, which in turn improved the comment clustering performance.
TARGET RECOGNITION BASED ON THE COMBINED WAVELET TRANSFORMATION

ASAR image target detection and recognition has been always a hot research. In this paper, on the foundation of the previous studies, a target recognition method based on combined wavelet transformation and cloud model is proposed. According to the good local characteristic, multi-resolution, effectiveness and the sensitivity to both direction and texture of the combined wavelet transformation, the detection method based on combined wavelet restrained the image noise and the errors, such as the object missing and false warning, caused by the use of the detection method based on the brightness alone. Then in order to solve the uncertainty of pixels, especially the object edge, an integrated method named soft segmentation for image segmentation based on the linguistic cloud model, which is a model for the conversion between qualitative and quantitative in the field of artificial intelligence is applied. Accurate target recognition is achieved after further processing eventually, such as mathematical morphology analysis. This integrated mechanism is applied to the ASAR data acquired from Zhuhai, China for carrying on the fish ponds recognition as well as the comparative experiments with classical methods. Results show that this approach can recognize the target more accurately and quick, which indicate that the synthetic scheme for target detection and recognition is flexible and robust and the advantages to the traditional detection operators or crisp segmentation methods are distinct.
Semantic Topic Chains for Modeling Temporality of Themes in Online Student Discussion Forums

Exploring students’ discourse in academic settings over time can provide valuable insight into the evolution of learner engagement and participation in online learning. In this study, we propose an analytical framework to capture topics and the temporal progression of learner discourse. We employed a Contextualized Topic Modeling technique on messages posted by undergraduates in online discussion forums from Fall 2019 to Spring 2020. We further evaluated if topics were originating from specific courses or more generally distributed across multiple courses. Our results suggested a significant increase in the number of general topics after the onset of the pandemic, suggesting emergent topics being discussed in a range of courses. In addition, using Word Mover’s Distance, we examined the semantic similarity of topics in adjacent months and constructed topic chains. Our findings indicated that previously course-centric topics such as public health developed into more general discussions that emphasize inequities and healthcare during the pandemic. Furthermore, emergent topics around students’ lived experiences underscored the role of discussion forums in capturing educational experiences temporally. Finally, we discuss the implications of current findings for post-pandemic higher education and the effectiveness of our framework in exploring unstructured large-scale educational data.
Knowledge Tracing Over Time: A Longitudinal Analysis

The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to ”detector rot. ” We compare the generalizability of Knowledge Training (KT) models by comparing model performance in predicting student knowledge within the academic year and across academic years. Models were trained on data from two popular open-source curricula available through Open Educational Resources. We observed that the models generally were highly performant in predicting student learning within an academic year, whereas certain academic years were more generalizable than other academic years. We posit that the Knowledge Tracing models are relatively stable in terms of performance across academic years yet can still be susceptible to systemic changes and underlying learner behavior. As indicated by the evidence in this paper, we posit that learning platforms leveraging KT models need to be mindful of systemic changes or drastic changes in certain user demographics.
Edinburgh Research Explorer The FADE mass-stat

The emergence of new applications of molecular dynamics (MD) simulation calls for the development of mass-statting procedures that insert or delete particles on-the-ﬂy. In this paper we present a new mass-stat which we term FADE, because it gradually “fades-in” (inserts) or “fades-out” (deletes) molecules over a short relaxation period within a MD simulation. FADE applies a time-weighted relaxation to the intermolecular pair forces between the inserting/deleting molecule and any neighbouring molecules. The weighting function we propose in this paper is a piece-wise polynomial that can be described entirely by two parameters: the relaxation time scale and the order of the polynomial. FADE inherently conserves overall system momentum independent of the form of the weighting function. We demonstrate various simulations of insertions of atomic argon, polyatomic TIP4P water, polymer strands, and C 60 Buckminsterfullerene molecules. We propose FADE parameters and a maximum density variation per insertion-instance that restricts spurious potential energy changes entering the system within desired tolerances. We also demonstrate in this paper that FADE compares very well to an existing insertion algorithm called USHER, in terms of accuracy, insertion rate (in dense ﬂuids), and computational efﬁciency. The USHER algorithm is applicable to monatomic and water molecules only, but we demonstrate that FADE can be generally applied to various forms and sizes of molecules, such as polymeric molecules of long aspect ratio, and spherical carbon fullerenes with hollow interiors. © 2014 AIP Publishing LLC . [http://dx.doi. org/10