Miracle-dz/UniFAR-FTUs huggingface dataset:
Fostering Lifelong Learning through Professional Development and Recognition at the Open University UK

Professional development has long been recognised as key to effective organisational change and to improve student learning. This is even more the case today as the need to upskill, build capacity and recognise expertise in online learning across the higher education sector has increased due to the pandemic. // This paper reports on the findings of the evaluation of the Open University’s Advance HE accredited fellowship scheme, called Applaud. Applaud is an online scheme and offers OU colleagues the opportunity to gain recognition for their excellence in teaching and support their learning. Since Applaud started in 2016, the scheme has recognised over 700 fellows. As an attempt to continue improving the scheme and understand participants’ experiences, an evaluation of the scheme was conducted through an online survey and then follow up online interviews. // In this paper, the research, including the impact of a reflective process that underpins HEA fellowship on teaching practices and on students’ experiences, will be shared. By providing teachers the opportunity to reflect on their practice, and to be recognised for their efforts in enhancing students’ experiences, Applaud helps to promote lifelong learning and quality education, which is aligned with the UN’s Sustainable Development Goal 4.
looking at the effect of tree uncertainty on measuring signal in interaction networks

I have read with interest the submitted preprint Do closely related species interact with similar partners? Testing for phylogenetic signal in bipartite interaction networks. I think the manuscript is well written and clearly presented in general. I think it could make for a valuable contribution to the field given the meticulous analyses of the performance of different metrics to test for phylogenetic signal in interaction networks. The two expert reviewers also agree that the work is well written and could be of interest to evolutionary biologists and evolutionary ecologists. The two reviewers have made a number of excellent suggestions on how to improve the work which should be addressed prior to recommendation. I have very little to add to the thorough reviews by both expert reviewers.
Faster and Cheaper Energy Demand Forecasting at Scale

Energy demand forecasting is one of the most challenging tasks for grids operators. 1 Many approaches have been suggested over the years to tackle it. Yet, those still 2 remain too expensive to train in terms of both time and computational resources, 3 hindering their adoption as customers behaviors are continuously evolving. 4 We introduce Transplit, a new lightweight transformer-based model, which sig- 5 nificantly decreases this cost by exploiting the seasonality property and learning 6 typical days of power demand. We show that Transplit can be run efficiently on 7 CPU and is several hundred times faster than state-of-the-art predictive models, 8 while performing as well. 9
Uncovering the Relationship between Tissue-Specific TF-DNA Binding and Chromatin Features through a Transformer-Based Model

Chromatin features can reveal tissue-specific TF-DNA binding, which leads to a better understanding of many critical physiological processes. Accurately identifying TF-DNA bindings and constructing their relationships with chromatin features is a long-standing goal in the bioinformatic field. However, this has remained elusive due to the complex binding mechanisms and heterogeneity among inputs. Here, we have developed the GHTNet (General Hybrid Transformer Network), a transformer-based model to predict TF-DNA binding specificity. The GHTNet decodes the relationship between tissue-specific TF-DNA binding and chromatin features via a specific input scheme of alternative inputs and reveals important gene regions and tissue-specific motifs. Our experiments show that the GHTNet has excellent performance, achieving about a 5% absolute improvement over existing methods. The TF-DNA binding mechanism analysis shows that the importance of TF-DNA binding features varies across tissues. The best predictor is based on the DNA sequence, followed by epigenomics and shape. In addition, cross-species studies address the limited data, thus providing new ideas in this case. Moreover, the GHTNet is applied to interpret the relationship among TFs, chromatin features, and diseases associated with AD46 tissue. This paper demonstrates that the GHTNet is an accurate and robust framework for deciphering tissue-specific TF-DNA binding and interpreting non-coding regions.
ScriptWorld : A Scripts-based RL Environment

Text-based games provide a framework for developing natural language under-1 standing and commonsense knowledge about the world in reinforcement learning 2 algorithms. Existing text-based environments often rely on fictional situations and 3 characters to create a gaming framework and are far from real-world scenarios. In 4 this paper, we introduce ScriptWorld : A text-based environment for teaching 5 agents about real-world daily chores, imparting commonsense knowledge. To the 6 best of our knowledge, it is the first interactive text-based gaming framework that 7 considers data written by humans (scripts datasets) to create procedural games for 8 daily real-world human activities. We provide gaming environments for 10 daily 9 activities and perform a detailed analysis to capture the richness of the proposed 10 environment. We also test the developed environment using human gameplay 11 experiments and reinforcement learning algorithms as baselines. Our experiments 12 show that the flexibility of the proposed environment makes it a suitable testbed for 13 reinforcement learning algorithms to learn the underlying procedural knowledge in 14 daily human chores. 15
Expos´e Study Project: Evaluating Dilation in Time Series Classiﬁcation

A time series is a sequence of values ordered by time. Time series classification is a form of supervised learning to learn from time series with labels and then predict the class label of time series with an unknown label. The main difference to general classification algorithms is the ordering of the values. There are different approaches used in time series classification but many state-of-the-art classifiers are using (sliding) windows (subsequences) to process the time series. The goal is to extract specific local patterns in the time series which then helps to classify the time series. ROCKET [DPW20] is a relatively new classifier that promises lower computational complexity with the same accuracy when compared to many stateof-the-art classifiers. ROCKET uses a sliding window combined with dilation and gets results faster with the same accuracy [DPW20]. Dilation describes the spacing between values inside the sliding window so the effect of dilation is smoothening a time series by deliberately ignoring some values. With increasing dilation, it is possible to capture the same pattern at different scales and look at a wider range of data without increasing the number of values inside the window, hence keep the memory and computation usage low.
Internet Video Delivery using Neural Video Codecs

Source compression plays a crucial role in reducing the band-width needs of Internet video applications. Traditional video compression algorithms have served well in meeting the bandwidth requirements of today’s applications. However, these compression algorithms are reaching a saturation point in compression efficiency while hitting the complexity wall in terms of computation and power consumption, creating a major bottleneck to stream next generation 4K/8K/360-degree video streaming and AR/VR applications. In recent years, computer vision and deep learning community has started exploring data-driven neural video coding and shown significant promise for compression opportunities. This work is the first attempt in understanding the implications of re-placing the traditional coding algorithms with neural video codecs in the Internet video ecosystem and illustrating its opportunities and challenges.
Identifying impactful devices on disaggregation performance

One of the key barriers to widespread deployment of disaggregation algorithms is the difficulty that these algorithms have in real-world environments containing many devices. While a greater number of devices inevitably results in "noisier" aggregate consumption, different devices will have varying impacts on the overall difficulty of disaggregation. To investigate the extent of this effect, we conduct an empirical study in which we disaggregate fixed appliance loads from real-world data while systematically varying the set and complexity of other loads present. Our study highlights the outsized impact of certain device types and suggests paths towards scaling disaggregation algorithms to more complex environments.
Wound-Healing Effects of Curcumin and Its Nanoformulations: A Comprehensive Review

Wound healing is an intricate process of tissue repair or remodeling that occurs in response to injury. Plants and plant-derived bioactive constituents are well explored in the treatment of various types of wounds. Curcumin is a natural polyphenolic substance that has been used since ancient times in Ayurveda for its healing properties, as it reduces inflammation and acts on several healing stages. Several research studies for curcumin delivery at the wound site reported the effectiveness of curcumin in eradicating reactive oxygen species and its ability to enhance the deposition of collagen, granulation tissue formation, and finally, expedite wound contraction. Curcumin has been widely investigated for its wound healing potential but its lower solubility and rapid metabolism, in addition to its shorter plasma half-life, have limited its applications in wound healing. As nanotechnology has proven to be an effective technique to accelerate wound healing by stimulating appropriate mobility through various healing phases, curcumin-loaded nanocarriers are used for targeted delivery at the wound sites. This review highlights the potential of curcumin and its nanoformulations, such as liposomes, nanoparticles, and nano-emulsions, etc. in wound healing. This paper emphasizes the numerous biomedical applications of curcumin which collectively prepare a base for its antibiofilm and wound-healing action.
Attribute Prediction as Multiple Instance Learning

Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reﬂect the content of an image is not always straightforward, as per-image ground truth attributes are often not available. We propose applying the Multiple Instance Learning (MIL) paradigm to attribute learning (AMIL) while only using class-level labels. We allow the model to under-predict the positive attributes, which may be missing in a particular image due to occlusions or unfavorable pose, but not to over-predict the negative ones, which are almost certainly not present. We evaluate it in the zero-shot learning (ZSL) setting, where training and test classes are disjoint, and show that this also allows to proﬁt from knowledge about the semantic relatedness of attributes. In addition, we apply the MIL assumption to ZSL classiﬁcation and propose MIL-DAP, an attribute-based zero-shot classiﬁcation method, based on Direct Attribute Prediction (DAP), to evaluate attribute prediction methods when no image-level data is available for evaluation. Experiments on CUB-200-2011, SUN Attributes and AwA2 show improvements on attribute detection, attribute-based zero-shot classiﬁcation and weakly supervised part localization.
Phishing awareness and education – When to best remind?

—The use of security awareness and education programmes is very common in organisations. But how effective are they over time? Some initial research on this question is, among others, the extensive study of Reinheimer et al. [74] that measured effectiveness at several time intervals. Their research found still significantly better results than before the awareness program after four months, but no longer after six months. This left open a two months interval for the reminder. The contribution of our paper is to study whether the reminder should be closer to four or six months. Thus, we measured effectiveness after five months. With still significant better results than before the programme after five months, we conclude that it is recommended to remind users more towards six months rather than already after five. However, we kindly invite the community to conduct more long-term studies, in different contexts, to confirm these findings.
Subaru Telescope limits on cosmological variations in the fine-structure constant

Previous, large samples of quasar absorption spectra have indicated some evidence for relative variations in the fine-structure constant ($\Delta\alpha/\alpha$) across the sky. However, they were likely affected by long-range distortions of the wavelength calibration, so it is important to establish a statistical sample of more reliable results, from multiple telescopes. Here we triple the sample of $\Delta\alpha/\alpha$ measurements from the Subaru Telescope which have been `supercalibrated' to correct for long-range distortions. A blinded analysis of the metallic ions in 6 intervening absorption systems in two Subaru quasar spectra provides no evidence for $\alpha$ variation, with a weighted mean of $\Delta\alpha/\alpha=3.0\pm2. 8_{\rm stat}\pm2. 0_{\rm sys}$ parts per million (1$\sigma$ statistical and systematic uncertainties). The main remaining systematic effects are uncertainties in the long-range distortion corrections, absorption profile models, and errors from redispersing multiple quasar exposures onto a common wavelength grid. The results also assume that terrestrial isotopic abundances prevail in the absorbers; assuming only the dominant terrestrial isotope is present significantly lowers $\Delta\alpha/\alpha$, though it is still consistent with zero. Given the location of the two quasars on the sky, our results do not support the evidence for spatial $\alpha$ variation, especially when combined with the 21 other recent measurements which were corrected for, or resistant to, long-range distortions. Our spectra and absorption profile fits are publicly available.
Fabrication of TiO2 based Dye-Sensitized Solar Cell using Nerium oleander as a sensitizer

The globe has already started using renewable energy sources, mainly solar energy, to meet the rising need for energy. The Dye-Sensitized Solar Cell (DSSC), one of many varieties of solar cells, is now popular because of its low manufacturing cost and ability to function in low light. In this work, TiO2 and erbium-doped TiO2 are chosen as photoanode and Nerium oleander as a sensitizer. The photoanode materials are prepared by one-step microwave assisted hydrothermal method. These nanomaterials and sensitizer are characterized by various characterization techniques that show significant results. The designed DSSC is evaluated by the current-voltage properties study. The efficiency of the fabricated TiO2 and Er-doped TiO2 based DSSCs is found to be 0.95 % and 2.95 % respectively.
A network security situational awareness system for text classification

The evaluation function of the existing network security situational awareness system is not perfect and often causes large average absolute errors. Therefore, a network security situational awareness system for text classification is designed. The hardware part selects the sensor main control chip and connects the circuit power supply to ensure the safe and stable operation of the whole system. In the software part, the data packet information is collected and processed to remove the data noise, and the information extraction technology is used to construct the classification model of this paper. The network abnormal behavior is identified according to the host log, the data characteristics of security behavior are extracted, and the system module evaluation function is set according to the extraction results. The experimental results show that the average absolute error of the designed system is less than that of the other three existing systems, and the performance of the security situational awareness system is better.
Empirical Phase Diagram of Congested Traffic Flow

We present an empirical phase diagram of the congested traffic flow measured on a highway section with one effective on-ramp. Through the analysis of local density-flow relations and global spatial structure of the congested region, four distinct congested traffic states are identified. These states appear at different levels of the upstream flux and the on-ramp flux, thereby generating a phase digram of the congested traffic flow. Observed traffic states are discussed in connection with recent theoretical analyses.
NON-POISSON DONATION BEHAVIORS IN VIRTUAL WORLDS

Similar to charitable giving in real world, donation behaviors play an important role in the complex interactions among individuals in virtual worlds. However, it is not clear if the donation process is random or not. We investigate this problem using detailed data from parallel virtual worlds adhered to a massively multiplayer online role-playing game. We find that the inter-donation durations follow power-law-tailed distributions distributed with an average tail exponent close to 1.91, have strong long-range correlations, and possess multifractal features. These findings indicate that the donation process is non-Poissonian, which has potential worth in modeling the complicated individual behaviors in virtual worlds.
Effective models of WIMP Direct Detection in DAMA/LIBRA–phase2

Since the DAMA/LIBRA collaboration released updated results from their search for the annual modulation signal expected from Dark Matter (DM) scattering in their NaI detectors, we have fitted the updated DAMA result for the modulation amplitudes for a Weakly Interacting Massive Particle (WIMP) signal, parameterizing the interaction with nuclei in terms of the most general effective Lagrangian for a WIMP particle spin 1/2, systematically assuming dominance of one of the 14 possible interaction terms and a standard Maxwellian for the WIMP velocity distribution. We find that most of the couplings of the non-relativistic effective Hamiltonian can provide a better fit compared to the standard Spin Independent interaction case, and with a reduced fine-tuning of the three parameters (WIMP mass, WIMP-nucleon effective cross-section and ratio between the WIMP-neutron and the WIMP-proton couplings). In addition, effective models for which the cross section depends explicitly on the WIMP incoming velocity can provide a better fit of the DAMA data at large values of $m_{\chi}$ compared to the standard velocity-independent cross-section due to a different phase of the modulation amplitudes. All the best fit solutions are in tension with exclusion plots of both XENON1T and PICO60.
Sex-specific genetic loci linked to early and late onset type 2 diabetes

Purpose To investigate the effect of sex and age on the timing of a type 2 diabetes (T2D) diagnosis and the influence T2D-related genes, parental history of T2D, and obesity on T2D development. Methods In this case-control study, 1012 T2D cases and 1008 healthy subjects were selected from the Diabetes in Mexico Study database. Participants were stratified by sex and age at T2D diagnosis (early, [≤]45 years; late, [≥]46 years). Seventy T2D-associated SNPs were explored and the percentage contribution (R2) of T2D-related genes, parental history of T2D, and obesity (body mass index [BMI] and waist-hip ratio [WHR]) on T2D development was calculated using univariate and multivariate logistic regression models. Results T2D-related genes influenced T2D development most in males who were diagnosed early (R2 = 23.5%; females diagnosed early, R2 = 13.5%; males and females diagnosed late, R2 = 11.9% and R2 = 7.3%, respectively). With an early diagnosis, insulin production genes were more influential in males (76.0% of R2) whilst peripheral insulin resistance genes were more influential in females (52.3% of R2). With a late diagnosis, insulin production genes from chromosome region 11p15.5 notably influenced males while peripheral insulin resistance and inflammation genes notably influenced females. Influence of parental history was higher among those diagnosed early (males, 19.9%; females, 17.5%) versus late (males, 6.4%; females, 5,3%). Unilateral maternal T2D history was more influential than paternal T2D history. BMI influenced T2D development for all, while WHR exclusively influenced males. Conclusions The influence of T2D-related genes, maternal T2D history, and fat distribution on T2D development was greater in males than females.
DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing

In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students' mastery of knowledge concepts to provide high-quality education. In KT, there are natural graph structures among questions and knowledge concepts so some studies explored the application of graph neural networks (GNNs) to improve the performance of the KT models which have not used graph structure. However, most of them ignored both the questions' difficulties and students' attempts at questions. Actually, questions with the same knowledge concepts have different difficulties, and students' different attempts also represent different knowledge mastery. In this paper, we propose a difficulty and attempts boosted graph-based KT (DAGKT), using rich information from students' records. Moreover, a novel method is designed to establish the question similarity relationship inspired by the F1 score. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed DAGKT.
Ranking Edges by their Impact on the Spectral Complexity of Information Diffusion over Networks

Despite the numerous ways now available to quantify which parts or subsystems of a network are most important, there remains a lack of centrality measures that are related to the complexity of information flows and are derived directly from entropy measures. Here, we introduce a ranking of edges based on how each edge's removal would change a system's von Neumann entropy (VNE), which is a spectral-entropy measure that has been adapted from quantum information theory to quantify the complexity of information dynamics over networks. We show that a direct calculation of such rankings is computationally inefficient (or unfeasible) for large networks: e.g.\ the scaling is $\mathcal{O}(N^3)$ per edge for networks with $N$ nodes. To overcome this limitation, we employ spectral perturbation theory to estimate VNE perturbations and derive an approximate edge-ranking algorithm that is accurate and fast to compute, scaling as $\mathcal{O}(N)$ per edge. Focusing on a form of VNE that is associated with a transport operator $e^{-\beta{ L}}$, where ${ L}$ is a graph Laplacian matrix and $\beta>0$ is a diffusion timescale parameter, we apply this approach to diverse applications including a network encoding polarized voting patterns of the 117th U.S. Senate, a multimodal transportation system including roads and metro lines in London, and a multiplex brain network encoding correlated human brain activity. Our experiments highlight situations where the edges that are considered to be most important for information diffusion complexity can dramatically change as one considers short, intermediate and long timescales $\beta$ for diffusion.