Miracle-dz/UniFAR-FTUs huggingface dataset:
The Best Location for You to Live

The Best Location for You to Live is a tool that allows anyone to find their ideal residence place in a given city by specifying their personal level of importance for the location’s visual appeal and environmental parameters. The underlying model is based on multiple layers of machine learning techniques applied to city streets images obtained with Google Street View’s API. While environmental characteristics are collected from multiple sources, images’ appeals were derived from the results of an interactive survey. Each image was then associated to its vector of features by means of object recognition and image processing algorithms. Finally, regression and classification models were developed to find correlations between these features and the appeal, which allowed to make predictions on new images from other cities.
Cascade: Fast AI/ML for Delay-Sensitive IoT Pipelines

Efforts to deploy AI/ML in IoT settings encounter technology gaps. IoT applications (often implemented as distributed programs on a cluster or federated into pipelines) may need to track rapidly-changing data, yet existing platforms are designed mostly for ofﬂine batched use and exhibit problematic behavior under time-pressure. Cascade enables use of AI and ML in high-bandwidth, delay-sensitive environments. Our focus is on the critical path from an IoT sensor through a pipeline of developer-provided machine intelligence, and on the puzzle of minimizing latency while providing strong guarantees. Innovations include a new stored-procedure feature that hosts application lambdas directly in the Cascade address space, object and task collocation controlled using afﬁnity group keys , leveraging of RDMA and GPU accelerators, and a separation of updates from queries that minimizes locking and copying. Microbenchmarks conﬁrm that Cascade achieves exceptionally low latency and high throughput.
Relationship between Medical Students' Perceived Stress and Gaming Behavior at King Saud University

Gaming addiction has gradually developed among medical students and has been a contentious topic for nearly a decade. This study is aimed at estimating the prevalence of gaming addiction among medical students at King Saud University and examining the relationship between perceived stress levels and gaming addiction. We conducted a cross-sectional study among 370 students from 2019 to 2020 using a self-reporting questionnaire consisting of two validated test scales: the Gaming Disorder Test and the Perceived Stress Scale. The questionnaire was sent to all students through an email. Descriptive analyses and t-test statistical tests were used in this study. The prevalence rate of gaming addiction was found to be 4.6%, while the prevalence of perceived stress was confirmed at 95.9%. Students with excellent GPAs and high family income were associated (p < 0.001). Younger groups, females, and students with high family incomes showed higher levels of perceived stress than others. According to Pearson's correlation, gaming addiction was not significantly correlated with stress among medical students (p > 0.05). According to chi-square test also, no significant association was found between gaming disorder and perceived stress (χ2 = 4.412; p = 0.353). In conclusion, gaming addiction among medical students has scored low prevalence, indicating gaming is not a factor contributing to stress among this group. The high level of perceived stress among medical students should draw attention to the provision of regular psychological care.
Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes’ energy consumption data. From the literature, it has been identified that the data imputation with machine learning (ML)-based single-classifier approaches are used to address data quality issues. However, these approaches are not effective to address the hidden issues of smart home energy consumption data due to the presence of a variety of anomalies. Hence, this paper proposes ML-based ensemble classifiers using random forest (RF), support vector machine (SVM), decision tree (DT), naive Bayes, K-nearest neighbor, and neural networks to handle all the possible anomalies in smart home energy consumption data. The proposed approach initially identifies all anomalies and removes them, and then imputes this removed/missing information. The entire implementation consists of four parts. Part 1 presents anomaly detection and removal, part 2 presents data imputation, part 3 presents single-classifier approaches, and part 4 presents ensemble classifiers approaches. To assess the classifiers’ performance, various metrics, namely, accuracy, precision, recall/sensitivity, specificity, and F1 score are computed. From these metrics, it is identified that the ensemble classifier “RF+SVM+DT” has shown superior performance over the conventional single classifiers as well the other ensemble classifiers for anomaly handling.
Metabolic Engineering of Nicotiana benthamiana to Produce Cannabinoid Precursors and Their Analogues

In recent years, the perspective towards the use of cannabis has slowly shifted from being an illicit drug to a medicinal plant. The pathway and enzymes involved in the production of cannabinoids are known; however, studies evaluating the production of cannabinoids in heterologous plants and cell cultures are still limited. In this study, we assessed the potential use of N. benthamiana (Nicotiana benthamiana) plants as a heterologous host for producing natural and novel cannabinoids. Transgenic N. benthamiana plants expressing genes encoding cannabis acyl-activating enzyme and olivetol synthase were generated, which were then used for transiently expressing other downstream pathway genes. Production of olivetolic acid and divarinic acid, the universal precursors for major and minor cannabinoids, respectively, was observed in transgenic N. benthamiana plants. To produce novel cannabinoid precursors with different side chains, various fatty acids were infiltrated into the transgenic N. benthamiana plants and the production of novel derivatives was observed. Although we were not able to derive the core intermediate, cannabigerolic acid, from our transgenic plants, possibly due to the low production levels of the precursors, our transgenics plants still serve as a high-potential platform for further development and exploring the N. benthamiana chemical space for generating novel cannabinoids.
Structural Characterization of Alzheimer DNA Promoter Sequences from the Amyloid Precursor Gene in the Presence of Thioflavin T and Analogs

Understanding DNA–ligand binding interactions requires ligand screening, crystallization, and structure determination. In order to obtain insights into the amyloid peptide precursor (APP) gene–Thioflavin T (ThT) interaction, single crystals of two DNA sequences 5′-GCCCACCACGGC-3′ (PDB 8ASK) and d(CCGGGGTACCCCGG)2 (PDB 8ASH) were grown in the presence of ThT or its analogue 2-((4-(dimethylamino)benzylidene)amino)-3,6-dimethylbenzo[d]thiazol-3-ium iodide (XRB). Both structures were solved by molecular replacement. In the case of 8ASK, the space group was H3 with unit cell dimensions of a = b = 64.49 Å, c = 46.19 Å. Phases were obtained using a model generated by X3DNA. The novel 12-base-pair B-DNA structure did not have extra density for the ThT ligand. The 14-base-pair A-DNA structure with bound ThT analog XRB was isomorphous with previously the obtained apo-DNA structure 5WV7 (space group was P41212 with unit cell dimensions a = b = 41.76 Å, c = 88.96 Å). Binding of XRB to DNA slightly changes the DNA’s buckle parameters at the CpG regions. Comparison of the two conformations of the XRB molecule: alone and bound to DNA indicates that the binding results from the freedom of rotation of the two aromatic rings.
Corpus Creation and Evaluation for Speech-to-Text and Speech Translation

The National Virtual Translation Center (NVTC) seeks to acquire human language technology (HLT) tools that will facilitate its mission to provide verbatim English translations of foreign language audio and video files. In the text domain, NVTC has been using translation memory (TM) for some time and has reported on the incorporation of machine translation (MT) into that workflow (Miller et al., 2020). While we have explored the use of speech-to-text (STT) and speech translation (ST) in the past (Tzoukermann and Miller, 2018), we have now invested in the creation of a substantial human-made corpus to thoroughly evaluate alternatives. Results from our analysis of this corpus and the performance of HLT tools point the way to the most promising ones to deploy in our workflow.
Molecular Phylogeny and Taxonomy of the Butterfly Subtribe Scolitantidina with Special Focus on the Genera Pseudophilotes, Glaucopsyche and Iolana (Lepidoptera, Lycaenidae)

Simple Summary The Palearctic butterfly genera Pseudophilotes, Glaucopsyche and Iolana have attracted the attention of many entomologists because their species are used as model objects for studying ecology and evolution. The genera have previously been the subjects of several taxonomic studies based on the analysis of their morphological and molecular characteristics, but none of these studies are based on complete species sampling. In our work, we used a set of mitochondrial and nuclear genes to reveal the phylogeny of these genera as well as the phylogeny of the subtribe Scolitantidina, to which these genera belong. In the genus Pseudophilotes, we identified 10 species including among them, the enigmatic Central Asian taxon P. panope, which has often been assigned to other genera. We clarified the taxonomic structure of the genus Glaucopsyche, which was found to consist of four subgenera. We confirm that the genus Iolana includes nine species distributed across the southwestern part of the Palearctic. The results obtained here will be important for the conservation of the Scolitantidina species, some of which are local and protected by national and international laws. Abstract The Palearctic blue butterfly genus Pseudophilotes Beuret, 1958 is not homogenous regarding the morphology of its genital structures. For this reason, some of its species have been considered to be representatives of other genera of the subtribe Scolitantidina (subfamily Polyommatinae). Here, we address these taxonomic problems by analyzing the phylogenetic relationships between the genera, subgenera, and species of this subtribe inferred via the analysis of five nuclear and two mitochondrial DNA sequences. We demonstrate that the enigmatic Asian species P. panope (Eversmann, 1851) belongs to the genus Pseudophilotes but not to Praephilotes Forster, 1938 or Palaeophilotes Forster, 1938 and does not represent the independent genus Inderskia Korshunov, 2000, as hypothesized previously. We synonymize P. svetlana Yakovlev, 2003 (syn. nov.) and P. marina Zhdanko, 2004 (syn. nov.) with P. panope. We demonstrate a deep genetic divergence between lineages that were previously considered as subspecies of the single species Iolana iolas (Ochsenheimer, 1816). As a result, we confirm the multispecies concept of the genus Iolana Bethune-Baker, 1914. We show that the Holarctic genus Glaucopsyche can be divided into four subgenera: Glaucopsyche Scudder, 1872 (=Shijimiaeoides Beuret, 1958), Apelles Hemming, 1931, Bajluana Korshunov and Ivonin, 1990, and Phaedrotes Scudder, 1876.
Automated Decision Technique for the Crowd Estimation Method Using Thermal Videos

Counting and detecting the pedestrians is an important and critical aspect for several applications such as estimation of crowd density, organization of events, individual’s flow control, and surveillance systems to prevent the difficulties and overcrowding in a huge gathering of pedestrians such as the Hajj occasion, which is the annual event for Muslims with the growing number of pilgrims every year. This paper is based on applying some enhancements to two different techniques for automatically estimating the crowd density. These two approaches are based on individual motion and the body’s thermal features. Theessential characteristic of crowd counting techniques is that they do not require a previously stored and trained data; instead they use a live video stream as input. Also, it does not require any intervention from individuals. So, this feature makes it easy to automatically estimate the crowd density. What makes this work special than other approaches in literature is the use of thermal videos, and not just relying on a way or combining several ways to get the crowd size but also analyzing the results to decide which approach is better considering different cases of scenes. This work aims at estimating the crowd density using two methods and decide which method is better and more accurate depending on the case of the scene; i.e., this work measures the crowd size from videos using the heat signature and motion analysis of the human body, plus using the results analysis of both approaches to decide which approach is better. The better approach can vary from video-to-video according to many factors such as the motion state of humans in this video, the occlusion amount, etc. Both approaches are discussed in this paper. The first one is based on capturing the thermal features of an individual and the second one is based on detecting the features of an individual motion. The result of these approaches has been discussed, and different experiments were conducted to prove and identify the most accurate approach. The experimental results prove the advancement of the approach proposed in this paper over the literature as indicated in the result section.
Characterizing the Status of Energetic Metabolism of Dinoflagellate Resting Cysts under Mock Conditions of Marine Sediments via Physiological and Transcriptional Measurements

Similar to the seeds of higher plants, resting cysts, a non-motile, benthic, and dormant stage in the life history of many dinoflagellate species, play vital roles via germination in the seasonal dynamics and particularly the initiation of harmful algal blooms (HABs) of dinoflagellates. It is thus crucial for resting cysts to balance between the energetic catabolism for viability maintenance and the energy preservation for germination during their dormancy. Despite this importance, studies on how resting cysts of dinoflagellates accomplish energetic metabolism in marine sediment have been virtually absent. In this study, using the cosmopolitan HABs-causing species Scrippsiella acuminata as a representative, we measured the transcriptional activity of the most efficient pathway of the energy catabolism tricarboxylic acid (TCA) cycle, cell viability (via neutral red staining), and the cellular ATP content of resting cysts under a set of mock conditions in marine sediments (e.g., 4 °C, darkness, and anoxia) for a maximum period of one year. Based on the correlation analyses among the expression levels of genes, cyst viability, and ATP content, we revealed that the TCA cycle was still a crucial pathway of energetic catabolism for resting cysts under aerobic conditions, and its expression was elevated at higher temperatures, light irradiation, and the early stage of dormancy. Under anaerobic conditions, however, the TCA cycle pathway ceased expression in resting cysts, as also supported by ATP measurements. Our results have laid a cornerstone for the comprehensive revelation of the energetic metabolism and biochemical processes of dormancy of resting cysts in marine sediments.
Pharmacodynamics and Clinical Implications of the Main Bioactive Peptides: A Review

Bioactive peptides (BPs) are a heterogeneous class of molecules found in a wide range of plant and animal sources. BPs have a number of different industrial applications including pharmacology (nutraceuticals), food, cosmetology, and pet food. Though BPs were initially used mainly as food additives, today the estimated peptide-based product market is around US $40 billion per year, highlighting consumer demand. The nutraceutical field is one of the most interesting applications for BPs, however there are some limitations to the efficacy of BPs in nutraceutical treatments, including low bioaccessibility and bioavailability. Thus, new extraction and isolation techniques have been developed, using both vegetable and animal sources, to obtain BPs with specific activities and improve the bioactivity and the bioavailability. Randomized clinical trials show a possible relationship between the administration of BPs and the reduction of several cardiovascular risk factors, including hypertension, hypercholesterolemia, hypertriglyceridemia and hyperglycaemia. In addition, BPs exhibit antioxidant, anti-inflammatory, antimicrobial, and anticancer potential, but long-term clinical studies are still needed. The aim of this review is to give a general introduction of BPs, describe their production and application methods, present data regarding bioactivity and bioavailability, and finally highlight the future prospects of this class of molecules in clinical practice.
Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

Efficient and sustainable bike-sharing service (BSS) operations require accurate demand forecasting for bike inventory management and rebalancing. Probabilistic forecasting provides a set of information on uncertainties in demand forecasting, and thus it is suitable for use in stochastic inventory management. Our research objective is to develop probabilistic time-series forecasting for BSS demand. We use an RNN–LSTM-based model, called DeepAR, for the station-wise bike-demand forecasting problem. The deep-learning structure of DeepAR captures complex demand patterns and correlations between the stations in one trained model; therefore, it is not necessary to develop demand-forecasting models for each individual station. DeepAR makes parameter forecast estimates for the probabilistic distribution of target values in the prediction range. We apply DeepAR to estimate the parameters of normal, truncated normal, and negative binomial distributions. We use the BSS dataset from Seoul Metropolitan City to evaluate the model’s performance. We create district- and station-level forecasts, comparing several statistical time-series forecasting methods; as a result, we show that DeepAR outperforms the other models. Furthermore, our district-level evaluation results show that all three distributions are acceptable for demand forecasting; however, the truncated normal distribution tends to overestimate the demand. At the station level, the truncated normal distribution performs the best, with the least forecasting errors out of the three tested distributions.
Wavelength-Independent Correlation Detection of Aberrations Based on a Single Spatial Light Modulator

The cumulative achievements in the fields of science and technology have allowed us to substantially approach the solution of the phase problem in optics. Among all phasometric methods, single-beam methods are the most promising, since they are more variable and versatile. Single-beam methods are based either on the analysis of the intensity distribution, as is conducted by interferometers and wavefront sensors, or on the transformation of the phase into an intensity distribution due to spatial filtering, as is conducted by holographic methods. However, all these methods have the problem of working with polychromatic radiation and require spectral filters to process such radiation. This paper presents a new approach to the synthesis of Fourier holograms used in holographic wavefront sensors that make it possible to create achromatic elements and work with white light without the use of additional filters. The approach was numerically and experimentally verified.
An exploration of course and cohort communication spaces in Discord, Teams, and Moodle

This research examined the impact of supplementing a learning management system, Moodle, with communication tools, Discord and Teams, to support communication in blended and distance undergraduate courses in computer science, information technology, mathematics and statistics at a New Zealand university with well-established use of Moodle. Nineteen students participated in semi-structured interviews. Findings show that adding Discord or Teams increases information and knowledge exchange and helps students to connect with peers and teachers in their courses. Teams was beneficial particularly in settings with formal group work. Discord, which enables both students and staff to set up additional communication channels that are not restricted to course enrolments, was instrumental in connecting students across year levels with peers and alumni. This enabled discussions on course selection, career options and disciplinary topics beyond the course curriculum. Importantly, these beyond-course communication spaces nurtured belonging to wider discipline and study communities. The research establishes the importance of the increased levels of communication by highlighting the effects on student learning and connections to others. Looking beyond the specific tools, the level of formality and the degree of student co-ownership are identified as key factors in supporting the within- and beyond-course communication spaces. Implications for practice or policy: Learning management systems provide valuable course support but do not meet all learning and teaching communication needs, partly due to their formal and university-controlled nature. Educators and students benefit from using strong chat tools with improved information and knowledge exchange. Both educators and students experience enhanced belonging when using collaboration and chat tools. Students value Discord’s informality and student-led characteristics. Educators who use Teams to support formal group work must carefully manage integration with Moodle.
The Invisible Foundations of Collaboration in the Workplace: a Multiplex Network Approach to Advice-Seeking and Knowledge Sharing

A revolutionary advancement of technology in the past decade brought the attention of academics and management practitioners on how to improve the innovative capabilities of organizations. Advice-seeking relationships have an essential role in the knowledge production of modern-day organizations as they enable actors to acquire information, professional support, and knowledge elements they can recombine to form new knowledge. This paper conceptualizes advice-seeking behaviour as part of an inherently complex social world that can best be captured by a multiplex approach to organizational network research. It investigates how different layers of interpersonal relationships in the workplace may contribute to the appearance of advice-seeking interactions. This study examines the cases of three knowledge-intensive organizations and applies binary logistic regression to shed light on the yet invisible relational foundations of workplace collaboration. Implications for Central European audience : Central European countries attempt to improve their economic competitiveness by attracting knowledge-intensive companies as well as incentivizing innovation and digital transformation. Knowledge-intensive firms, such as business services centres or information and communication technology companies, are significant contributors to the economic output of countries like the Czech Republic, Hungary, and Poland. Recommendations derived from the results of this paper provide insights to the leadership of knowledge-intensive companies on how to create organizational environments that foster knowledge sharing and innovation. Measures that promote interpersonal trust, the visibility of expertise, and boundary-spanning behaviour are advised.