Miracle-dz/UniFAR-FTUs huggingface dataset:
A Swing-Up and Stable Control Strategy for Pendubot Based on Nonlinear Model Predictive Method

The control objective of Pendubot is to stabilise the system in a vertically upward position by swinging up from a vertically downward position. Facing various physical constraints in practical applications, this paper proposes a swing-up stabil-isation control strategy based on a nonlinear model prediction method to accomplish the above control objective. On the basis of the system dynamics model, the forward Euler method is used to construct a nonlinear prediction model of the system. According to the control objectives and physical constraints of the system, the performance index function is designed, and then the control inputs of each sampling cycle are obtained through rolling optimisation and feedback correction, so that the system state gradually converges to the target trajectory, thus achieving the motion control objective. Based on the simulation results, it can be seen that the strategy proposed in this paper is efficient and robust under external disturbances.
Mutations in coat protein binding sites of alfalfa mosaic virus RNA 3 affect subgenomic RNA 4 accumulation and encapsidation of viral RNAs

The 3'-untranslated regions (3'-UTRs) of the three RNAs of alfalfa mosaic virus (AMV) contain a specific binding site for coat protein (CP) and act as a promoter for minus-strand RNA synthesis by the purified AMV RNA-dependent RNA polymerase (RdRp) in an in vitro assay. Binding of CP to the viral RNAs is required to initiate infection. The sequence of the 3'-terminal 39 nucleotides of AMV RNA 3 can be folded into two stem-loop structures flanked by three single-stranded AUGC sequences and represents a CP binding site. Mutations in this sequence that are known to interfere with CP binding in vitro were introduced into an infectious clone of RNA 3, and mutant RNA transcripts were used as templates in the in vitro RdRp assay and to infect protoplasts and plants. Mutation of AUGC motif 2 or disruption of the stem of the 3'-proximal hairpin 1 interfered with CP binding in vitro but not with minus-strand promoter activity in vitro or replication of RNA 3 in vivo. However, hairpin 1 appeared to be essential for encapsidation of RNA 3. Reversion of three G-C base pairs in hairpin 1 had no effect on CP binding but interfered with minus-strand promoter activity in vitro and with RNA 3 replication in vivo. It is concluded that the viral RdRp and CP recognize different elements in the 3'-UTRs of AMV RNAs. Moreover, several mutations that interfered with CP binding in vitro interfered with the accumulation in vivo of RNA 4, the subgenomic messenger for CP, but not with the accumulation of RNA 3.
Antigen-specific inhibition of experimental autoimmune uveoretinitis by bone marrow-derived immature dendritic cells.

PURPOSE To investigate the effect of maturation status of bone marrow-derived dendritic cells (BMDCs) on the in vivo immune response to interphotoreceptor retinoid-binding protein (IRBP) 161-180 peptide in experimental autoimmune uveoretinitis (EAU). METHODS Immature and mature BMDCs were generated without or with the stimulation by lipopolysaccharide (LPS), and their mRNA cytokine profile and phenotype were analyzed by RNase protection assay and flow cytometry. The effect of immature and mature DCs in inducing antigen-specific T-cell proliferation and cytokine profile was further investigated in an IRBP peptide-induced model of EAU. RESULTS BMDCs generated in granulocyte-macrophage-colony-stimulating factor (GM-CSF) were relatively immature (i)DCs, as determined by flow cytometry and cytokine profile. However, stimulation with LPS induced these cells to become mature (m)DCs with higher levels of surface major histocompatibility complex (MHC)-II and costimulatory molecules and higher mRNA expression of IL-1alpha, -1beta, -6, and -12. Subcutaneous administration of iDCs induced a state of relative tolerance to the peptide induced-EAU, and the effect was lost after the DCs underwent maturation induced by in vitro exposure to LPS. In vitro, both iDCs and mDCs induced typical peptide-specific T-cell proliferation, but IFN-gamma production by uveitogenic T cells was markedly inhibited by iDCs. In vivo, peptide-loaded iDCs induced draining lymph node (DLN) cells to secrete a distinct pattern of cytokine: namely, increased IL-10 and IL-5 and decreased IFN-gamma and IL-2, indicating an altered immune responses to a low T-helper (Th) cell type 1 profile and a high Th2 profile after uveitogenic challenge. CONCLUSIONS The data suggest that induction of tolerance to an autoantigen by peptide-loaded DCs requires presentation of antigen by iDCs and involves the generation of a high-level IL-10 and IL-5 immune response in DLN cells.
Personal Goal Management Intervention and Mood States in Soccer Academies

The aim of this study was to evaluate the effects of a personal goal-based intervention on positive and negative moods among young athletes at a soccer academy. Study participants (N =22) were randomized into either a treatment group, which participated in a personal goal-management program (Bouffard, Labelle, Dube, & Lapierre, 1999), or a neutral-task control group. Participants’ mood states were measured every 3 weeks. Results indicated significant postintervention group differences in positive and negative moods states, with the treatment group reporting higher levels of positive moods and lower levels of negative moods. A significant within-group difference over time was also found for the treatment group, indicating an increase in positive mood states and decrease in negative mood states as the program progressed. Findings from this study are used to inform recommendations for sport psychology interventions that use specific goal management procedures to facilitate positive emotional states among you.
Using Artificial Intelligence to Track and Predict Student Performance in Degree Programmes

Predicting learners' prospective performance depending on their current educational data is critical for implementing appropriate educational measures for guaranteeing learners' timely and acceptable graduation. Though there is a large body of research on predicting students’ performance when resolving issues or preparing for classes employing statistical methods, predicting students’ performance when finishing degrees is a new area of research. Techniques enable machines to arrive at judgements for humans in artificial intelligence (AI). This technology improves the consumer encounter in a variety of manners. Numerous research in the realm of education were undertaken to address the issue of learner focus and performance employing different Machine Learning (ML) methods. We demonstrate that the suggested technique outperforms standard methods in extended simulations on an undergraduate learner database gathered over a 3-year period at UCLA.
Self-Attention Based Image Feature Representation for Medical Visual Question Answering

Medical Visual Question Answering is an emerging field which uses artificial intelligence to effectively combine the multimodal data and generate output. Users interact with the visual data through text to generate answers, which may not always be directly accessible from the image. In this paper, a network has been proposed that aims to generate a comprehensive image feature representation, capturing the most relevant visual information present in the image, which is essential for effectively answering questions. The image features are extracted using Uniform Local Binary Pattern Histogram and Gray Level Co-occurrence Matrix, which are then combined using the multihead self-attention mechanism obtaining a compact and meaningful representation. This combined representation is further fused with deep features to get the desired image feature embedding. Multimodal fusion of the image and text feature embeddings generated the final representation required for the downstream task of Visual Question Answering.
The Metagenomic Binning Problem: Clustering Markov Sequences

The goal of metagenomics is to study the composition of microbial communities, typically using high-throughput shotgun sequencing. In the metagenomic binning problem, we observe random substrings (called contigs) from a mixture of genomes and aim to cluster them according to their genome of origin. Based on the empirical observation that genomes of different bacterial species can be distinguished based on their tetranucleotide frequencies, we model this task as the problem of clustering <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> sequences generated by <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> distinct Markov processes, where <inline-formula> <tex-math notation="LaTeX">$M \ll N$ </tex-math></inline-formula>. Utilizing the large-deviation principle for Markov processes, we establish the information-theoretic limit for perfect binning. Specifically, we show that the length of the contigs must scale with the inverse of the Chernoff divergence rate between the two most similar species. Furthermore, our result implies that contigs should be binned using the KL divergence rate as a measure of distance, as opposed to the Euclidean distance often used in practice.
Experimental Design of Machine Learning based Enhanced Communication Efficiency and Noise Reduction over Mobile Cellular Networks

Modern communication relies heavily on mobile cellular networks, which can meet the increasing demands of users for higher data speeds, lower latency, and better communication quality. In order to improve communication efficiency and reduce noise in mobile cellular networks, this research presents a novel experimental design that uses machine learning. Essentially, this study makes use of the Learning Assisted Communication Evaluator Protocol (LACEP), a clever method that blends machine learning algorithms into the network architecture with ease. Notable gains result from LACEP’s intelligent optimization of network settings, adaptive response to changing environmental circumstances, and efficient mitigation of communication noise. Prominently, the suggested model attained a remarkable throughput of 97.24%, surpassing the throughput of 3.05% of current protocols such as Internet Protocol, Link Layer Protocol, and TDD Protocol. The experimental findings offer strong proof of the ability of machine learning-based techniques to deal with the dynamic and frequently noisy characteristics of mobile cellular networks. It’s also critical to emphasize that Python software is used to achieve this creative approach, guaranteeing scalability and adaptability. This experimental design, which adopts machine learning via LACEP, represents a positive step towards more effective and resilient mobile communication systems. It also adds to the continuous endeavors to enhance the performance of mobile cellular networks and provide high-quality communication services even in difficult settings.
SmartTask: A Neural Network Powered Block-chain based Intelligent Task Scheduling for Project Management

This research pioneers SmartTask, an innovative paradigm for intelligent task scheduling in project management, leveraging the novel technique of Reinforcement Learning with Proximal Policy Optimization (PPO). Departing from traditional methods, SmartTask utilizes PPO to dynamically adapt to evolving project requirements, resulting in a significant 30% improvement in task scheduling accuracy. Through meticulous analysis of historical project data using PPO, the system achieves an impressive 25% increase in overall project efficiency, surpassing existing techniques. The implementation of SmartTask with PPO leads to a substantial reduction in scheduling errors, marking a distinct departure from conventional methods. The technology’s exceptional proficiency in resource utilization minimizes idle time and enhances task completion rates. In extensive testing, SmartTask demonstrates unprecedented precision in task scheduling with an outstanding 92% accuracy, establishing a new benchmark for efficiency in project management. This research positions SmartTask as a technological breakthrough and underscores its novelty with the introduction of PPO, reshaping intelligent task scheduling in project management.
Enhancing Video Retrieval: A User Behavior-Based Query Expansion Approach

In this study, we propose a user behavior-based query optimization strategy to address the commonly observed suboptimal query performance and user experience in the field of video retrieval. This strategy primarily involves analyzing user behavioral data during the video retrieval process to expand query keywords, enhancing their relevance and diversity, thus optimizing search results. We achieved this by augmenting the original dataset to generate simulated user behavioral data and Ground Truth data. Subsequently, an automated query expansion algorithm effectively applied this data to video retrieval. Through experimental assessment on the video retrieval dataset (MSR- VTT), we validate the effectiveness of this method. The results demonstrate that this user behavior- driven query expansion approach significantly improves the accuracy of video retrieval and user satisfaction.
Comparison of Out-of-Distribution Detection Performance of CLIP-based Fine-Tuning Methods

In recent years, large-scale vision-language models such as CLIP have shown remarkable performance on various zero-shot classification tasks. Inspired by these pretrained models, many studies have proposed effective fine-tuning methods to exploit the models’ pre-trained knowledge. One common approach is to fine-tune the entire model to transfer the pre-trained knowledge to target tasks. On the other hand, given the high cost of fine-tuning large-scale models, parameter-efficient fine-tuning methods are also being explored. While there have been performance comparisons of existing fine-tuning methods, they often focus solely on classification performance. Such a singular focus is not sufficient to assess the quality of the transferred pre-trained knowledge comprehensively. For a more rigorous evaluation, other metrics, such as the detection of out-of-distribution samples, should be considered, given their importance for model reliability. However, the comparison of fine-tuning methods concerning model reliability has been less explored. Therefore, we aim to fill this gap by offering a comprehensive comparative analysis on the out-of-distribution detection performance of CLIP-based fine-tuning methods along with their in-distribution classification performance. Our experimental results on the OpenOOD v1.5 benchmark dataset suggest that fine-tuning the entire model provides superior performance in both classification and out-of-distribution detection in a few-shot setting.
Demo: Automatic personal identification system for security in critical services: a case study

The demonstration proposal moves from the capabilities of a wireless biometric badge [4], which integrates a localization and tracking service along with an automatic personal identification mechanism, to show how a full system architecture is devised to enable the control of physical accesses to restricted areas. The system leverages on the availability of a novel IEEE 802.15. 4/Zigbee Cluster Tree network model, on enhanced security levels and on the respect of all the users' privacy issues.
Performance Evaluation of Xception Networks and Short-Time Fourier Transform Spectrograms for Motor Imagery Classification

This study seeks to explore the success of the Xception convolutional neural network and short-time Fourier transform in classifying EEG signals with limited training samples using pre-trained deep transfer learning. So far, most deep learning research has concentrated on one-dimensional time series inputs. Two-dimensional inputs are another excellent way of giving EEG signals to deep learning. This study used a two-dimensional STFT-based method to transform EEG signals into images and Xception for classification. BCI Competition IV dataset 2b, which includes nine participants, was used to evaluate the performance. This study is the first to report the STFT + Xception approach results for classifying motor imagery signals. It achieved good results from a small number of MIEEG data, averaging over 80% for nine subjects with a very low standard deviation of 2.9% between subjects. Applying data augmentation and training from scratch with more data can show more successful results in the future.
Effect of Different Lighting Conditions on Camera-based Non-Contact Vital Sign Monitoring

Wearables have been used to collect physiological signals for long-term pulse rate (PR) monitoring. Most of the wearables require skin contact for continuous monitoring. Recently, the potential of the contactless reflective photoplethysmogram (crPPG) has been extensively explored for non-contact sensing, particularly in geriatric patients and mobility tracking. Despite numerous studies, it has been recognized that the outcomes can be affected by ambient light conditions. In this work, an attempt has been made to explore the effects of varied light conditions for non-contact pulse rate monitoring using a laptop camera. The facial videos and ECG signals as Ground Truths are recorded from normal subjects (N=10) under three lighting conditions: white, shadow, and sunlight. The videos are applied to the face detection approach, and the regions of interest (forehead and cheek) are extracted. The mean pixel intensity of green channel frames and the corresponding Hue-channels signals are calculated. The hue signals are subjected to the fast Fourier transform (FFT) for the frequency spectrum and the peaks of the frequencies are used to compute the pulse rate in beats per minute (bpm). The results show that the proposed approach is able to extract bpm from extracted crPPG signals. The bpm exhibits high variation in sunlight conditions, whereas it is stable in shadow lighting conditions. The proposed algorithm shows neutrality in controlled and shadow conditions. Thus, the proposed approach could be useful for non-contact pulse rate monitoring in real-time conditions.
Learning abilities and disabilities: Generalist genes in early adolescence

Introduction. The new view of cognitive neuropsychology that considers not just case studies of rare severe disorders but also common disorders, as well as normal variation and quantitative traits, is more amenable to recent advances in molecular genetics, such as genome-wide association studies, and advances in quantitative genetics, such as multivariate genetic analysis. A surprising finding emerging from multivariate quantitative genetic studies across diverse learning abilities is that most genetic influences are shared: they are “generalist”, rather than “specialist”. Methods We exploited widespread access to inexpensive and fast Internet connections in the United Kingdom to assess over 5000 pairs of 12-year-old twins from the Twins Early Development Study (TEDS) on four distinct batteries: reading, mathematics, general cognitive ability (g) and, for the first time, language. Results. Genetic correlations remain high among all of the measured abilities, with language as highly correlated genetically with g as reading and mathematics. Conclusions. Despite developmental upheaval, generalist genes remain important into early adolescence, suggesting optimal strategies for molecular genetic studies seeking to identify the genes of small effect that influence learning abilities and disabilities.
Tractable Query Answering over Ontologies with Datalog+/-

We present a family of expressive extensions of Datalog, called Datalog, as a new paradigm for query answering over ontologies. The Datalog family admits existentially quantiﬁed variables in rule heads, and has suitable restrictions to ensure highly efﬁcient ontology querying. In particular, we show that query answering under so-called guarded Datalog is PTIME-complete in data complexity, and that query answering under so-called linear Datalog is in AC0 in data complexity. We also show how negative constraints and a general class of key constraints can be added to Datalog while keeping ontology querying tractable. We then show that linear Datalog, enriched with a special class of key constraints, generalizes the well-known DL-Lite family of tractable description logics. Furthermore, the Datalog family is of interest in its own right and can, moreover, be used in various contexts such as data integration and data exchange.
Implementation of Sobel Edge Detection on DRRA and DiMArch Architectures *

Edge detection is a fundamental operation in image processing, serving as a crucial step in various applications such as object recognition, image segmentation, and scene understanding. The Sobel edge detection algorithm has emerged as a widely used method for detecting vertical and horizontal edges in digital images. However, performing edge detection on high-resolution images with large dimensions can be computationally intensive and time-consuming. Specialized hardware solutions such as Field Programmable Gate Arrays (FPGAs) and Coarse-Grained Reconfigurable Arrays (CGRAs) offer significant advantages over general-purpose processors for implementing edge detection algorithms. This paper proposes algorithms for implementing the Sobel edge detection algorithm using two CGRA fabrics: dynamically reconfigurable resource array and distributed memory architecture. Furthermore, we discuss the implementation of Sobel edge detection on the target architecture for an input matrix of arbitrary size. Finally, the proposed approaches were compared with other CGRA-based implementations in terms of latency. The experimental results show that the proposed approaches exhibit significantly lower latency compared to other CGRA - based implempntations.
Error Compensation of Inkjet-printed Electronics using Incremental Learning and Knowledge Distillation

The paper targets the development of an error compensation step inline a fabrication process of inkjet-printed electronics. In the process, a microchip is placed in a circuit board cavity pre-filled with adhesive material which is cured afterwards. This pick and place process is very precise, yet difficult due to the miniaturization of the chip and the board. The paper targets the development of a robust algorithm that can detect a typical defect that results from the process and calculate the corrective data. The main challenge is that inspection data are generated in batches over time based on resources availability and process development which make each batch of data unique. Therefore, an incremental learning methodology is sought so that the algorithm can be trained on streams of data during process development and provide feedback for defect repair.