Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Bus Tracking & Bus Recommendation System using IoT & Machine Learning

---------------------------------------------------------------------- Abstract – Currently, buses are an important part of the public transport system. Traveling by the bus is an option but in most cases the chance of a bus getting delayed is high, this can be of various reasons like heavy traffic, engine problems, etc. The objective of this project is to provide the real-time locations, routes of travel, also recommending bus to a particular destination and best travel options, that ensures available bus routes, available buses to a destination, provide bus timing to reach the bus stop and required time to reach the destination. IoT is used to provide real-time bus location and machine learning to build recommendation engine. The recommendation based on most scored, reviewed, budget, travel comments, similar travels. Cloud-based database is used for storing and manipulating the data. Database stores the location along with the update time. Updating time and location can be used to calculate the time taken to travel and average speed. If the bus is not running on that day, the proper indication is displayed along with the location and the timing of the next bus through the route will be given. Machine learning server uses data in the database to recommend the best route to travel and creates a model for future use. The user interface provided by using an android application. This proposed system saves time and increases the work efficiency of end-users because it reduces the user’s efforts to traveling for work and avoids the wastage of waiting time for the bus.
The Pathophysiological Role of Diet in Non-Communicable Disease Etiology

Background and aims. Adherence to healthful diet has been a challenge both in the developing and developed countries due to the changes in lifestyles, access to ready junk food, resource constraints, lack of adequate knowledge, poor regulatory approaches and culture, to mention but a few, making the world population more vulnerable to noncommunicable diseases (NCDs). By implication, an unhealthy diet is an important factor for disability and death relative to the increasing global NCD burden. This review updates knowledge on the role and dimension of diet and health outcomes such as; cardiovascular diseases, chronic obstructive pulmonary diseases, diabetes, and cancer. Methods. A Systematic review of reviews and meta-analyses on chronic health outcomes attributable to dietary patterns were studied. In total, 141 reviews and meta-analyses were searched in Google Scholar, PubMed, Medline but 41 reviews published from 2016 onwards were included in the study. Results. Consumption of a diet rich in animal fats, excessive energy, animal proteins, posed a higher risk or death due to NCDs. Alternatively, diets comprised of vegetables, fruits, nuts, low carbohydrates, low polyunsaturated fat, seafood, legumes, nuts, less meat, fish, and whole-grain were found to be protective for most NCDs. Conclusions. Research confirms diet to be both a risk factor (unhealthful diet) and at the same time protective (healthful diet) for NCDs. However, further epidemiological studies should emphasize the assessment of Health outcomes based on the quantity, frequency, concentration, synergistic interactions of various dietary nutrients.
Supervised Machine Learning For Sentiment Analysis

: Web has been becoming a very important part in people’s life. People express their opinions and reviews related to the products and services on the web. Therefore, product reviews are generated daily on large scale. By analyzing these products reviews, new customers find others opinion. The categorization of reviews is very important for any business to grow. Broadly reviews are classified as positive or negative. Sentiment analysis is broadly applied to voice of customer materials like opinions, reviews and responses. Manufacturers or organizations become aware of good and bad things about their products, service and their competitors by analyzing sentiments from reviews of users. In order to make and maintain impression in market, every organization is continuously watching user reviews. In this paper we proposed to classify the sentiments from product reviews using supervised machine learning. Performance of Support Vector Machines (SVM), K Nearest Neighbor (KNN) and Decision Tree algorithms are compared and analyzed.
Teacher perceptions of gifted cultural minorities: An Australian study

This paper presents findings from a qualitative case study set within an Australian boarding school, which explored the perceptions of teachers in relation to gifted cultural minorities. The study aimed to shed light on the persistent underrepresentation of some cultural minority groups in the extension and enrichment programs offered at the study site. Ten teacher participants at Boarding College [Pseudonym] were recruited and interviewed in relation to their views on the topic. Thematic analysis revealed that the teachers held diverse views of cultural minorities, and lacked the cultural competence to respond to the gifted needs of these learners. The teachers also viewed giftedness and gifted programs as performance based, and this may act to preclude some cultural minorities from being identified. Furthermore, it was found that the teachers often viewed the underrepresentation of Indigenous students in gifted education as stemming from issues relating to the students and their culture, and from systemic school-based factors. Despite these somewhat negative findings, teachers at the study site viewed it to be an educator’s responsibility to cater for the gifted cultural minority student, suggesting the participants would be willing to address the problems identified.
Citizens as Sensors Human Sensors as a Smart City Data Source

—This paper discusses how citizens can play the role of sensors, using their perceptions to discover and report problems using some kind of digital platform. The use of human sensors is connected to the concept of “smart cities”. After a literature review, the paper presents two platforms and their use in detail: “FixMyStreet” and “Sauberes Wiesbaden”. The two case examples are used to discuss the concept of human sensors in further detail. The paper concludes with proposals for further research on the topic presented.
Fla. 8153 Hybrid Tomato; Fla. 8059 and Fla. 7907 Breeding Lines

‘Fla. 8153’ is a fresh-market tomato ( Solanum lycopersicum L.) hybrid that is being released for the premium tomato mar- ket. It has high lycopene content and an at-tractive, deep red interior color as a result of the crimson ( og c ) gene (Thompson et al., 1965). Flavor is superior to most commer- cially available tomato cultivars based on the results of seven experienced sensory panels, a consumer panel, and numerous samplings from ﬁeld trials. Environmental conditions have a large impact on tomato ﬂavor (Scott, 2001), and the strength of this hybrid is its ability to produce fruit with good ﬂavor under a wide range of growing conditions. This feature, along with reliable yields of ﬁrm, large, marketable fruit, should allow for branding of vine ripe harvested fruit with con-sistent quality for the fresh market and will attract repeat purchasing in the supermarket. ‘Fla. 8153’ should also be popular in upscale restaurants. The parents, Fla. 8059 and Fla. 7907, are both crimson types that will be useful for tomato breeders interested in developing crimson and more ﬂavorfulcultivars.
Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNet

Ensemble learning is a general technique to improve accuracy in machine learning. However, the heavy computation of a ConvNets ensemble limits its usage in deep learning. In this paper, we present Group Ensemble Network (GENet), an architecture incorporating an ensemble of ConvNets in a single ConvNet. Through a shared-base and multi-head structure, GENet is divided into several groups to make explicit ensemble learning possible in a single ConvNet. Owing to group convolution and the shared-base, GENet can fully leverage the advantage of explicit ensemble learning while retaining the same computation as a single ConvNet. Additionally, we present Group Averaging, Group Wagging and Group Boosting as three different strategies to aggregate these ensemble members. Finally, GENet outperforms larger single networks, standard ensembles of smaller networks, and other recent state-of-the-art methods on CIFAR and ImageNet. Specifically, group ensemble reduces the top-1 error by 1.83% for ResNeXt-50 on ImageNet. We also demonstrate its effectiveness on action recognition and object detection tasks.
Comparative Modeling and Electronic Molecular Investigation for Designing Potential Inhibitor for Schizencephaly

Schizencephaly (SCH) is congenital brain malformation whose hallmark is the presence of one or more cleft spanning the pial surface and ependymal of one or both cerebral hemispheres associated with Homeobox protein EMX2. In current study, hybrid approach of comparative modeling and molecular docking were followed. An inhibitor (C12H15N3O2S) scrutinized from PubChem showed maximum binding affinity against EMX2. Docking studies revealed that Asn-66, Phe-71, Ala-72, Glu-73, Leu-108, Phe-109, Ala-110, Ser-111, Gln-112, Gln-113 and Tyr-127 are critical residues for receptor-ligand interaction. Comparative modeling approach coupled with docking energies and drug likeness rules illustrated that selected inhibitor protein kinase are potential inhibitor compound for targeting EMX2. This study suggests that selected inhibitor might be potent molecule based on the binding energy values and drug score. Further analysis of this inhibitor could be helpful for exploring the details of binding sites. Overall, findings of current effort may be helpful in designing the novel therapeutic targets to cure SCH.
Implications of $A_4$ modular symmetry on Neutrino mass, Mixing and Leptogenesis with Linear Seesaw

The present work is inspired to execute the $A_4$ modular symmetry in linear seesaw framework by limiting the use of multiple flavon fields. Linear seesaw is acknowledged by extending the Standard Model particle spectrum with six heavy fermions and a singlet scalar. The non-trivial transformation of Yukawa coupling under the $A_4$ modular symmetry helps to explore the neutrino phenomenology with a specific flavor structure of the mass matrix. We discuss the neutrino mixing and obtain the reactor mixing angle and CP violating phase compatible with the observed $3\sigma$ region of current oscillation data. Apart, we also collectively investigate the nonzero CP asymmetry from the decay of lightest heavy fermions to explain the preferred phenomena of baryogenesis through leptogenesis
The antitumor activities of Lentinula edodes C91-3 mycelia fermentation protein on S180 (Mouse sarcoma cell) in vivo and in vitro

Lentinula edodes is one of the most widely used medicinal mushrooms which exhibit significant antitumor activity. It is mostly studied and several studies have showed that its polysaccharides play critical roles in the antitumor effects. In this study, we determine the antitumor activity of Lentinula edodes C91-3 mycelia fermentation protein (LFP91-3) on S180tumor cells in vivo as well as in vitro. The results showed that LFP91-3 inhibited the growth of S180 tumor cells in vitro, and the mechanism may be related to apoptosis induced by LFP91-3. In vivo, LFP91-3 treatment significantly prolonged life span in S180 tumor-bearing mice. LFP91-3 can be used as an important antitumor element in Lentinula edodes C91-3 mycelia fermentative liquid. Key words: Lentinula edodes C91-3 fermentation protein, Lentinula edodes, antitumor activity.
Convex Regularization in Monte-Carlo Tree Search

Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making. The recent AlphaGo and AlphaZero algorithms have shown how to successfully combine these two paradigms in order to solve large scale sequential decision problems. These methodologies exploit a variant of the well-known UCT algorithm to trade off exploitation of good actions and exploration of unvisited states, but their empirical success comes at the cost of poor sample-efficiency and high computation time. In this paper, we overcome these limitations by considering convex regularization in Monte-Carlo Tree Search (MCTS), which has been successfully used in RL to efficiently drive exploration. First, we introduce a unifying theory on the use of generic convex regularizers in MCTS, deriving the regret analysis and providing guarantees of exponential convergence rate. Second, we exploit our theoretical framework to introduce novel regularized backup operators for MCTS, based on the relative entropy of the policy update, and on the Tsallis entropy of the policy. Finally, we empirically evaluate the proposed operators in AlphaGo and AlphaZero on problems of increasing dimensionality and branching factor, from a toy problem to several Atari games, showing their superiority w.r. t. representative baselines.
De-anonymization of authors through arXiv submissions during double-blind review

In this paper, we investigate the effects of releasing arXiv preprints of papers that are undergoing a double-blind review process. In particular, we ask the following research question: What is the relation between de-anonymization of authors through arXiv preprints and acceptance of a research paper at a (nominally) double-blind venue? Under two conditions: papers that are released on arXiv before the review phase and papers that are not, we examine the correlation between the reputation of their authors with the review scores and acceptance decisions. By analyzing a dataset of ICLR 2020 and ICLR 2019 submissions (n=5050), we find statistically significant evidence of positive correlation between percentage acceptance and papers with high reputation released on arXiv. In order to understand this observed association better, we perform additional analyses based on self-specified confidence scores of reviewers and observe that less confident reviewers are more likely to assign high review scores to papers with well known authors and low review scores to papers with less known authors, where reputation is quantified in terms of number of Google Scholar citations. We emphasize upfront that our results are purely correlational and we neither can nor intend to make any causal claims. A blog post accompanying the paper and our scraping code will be linked in the project website this https URL
Data Movement Is All You Need: A Case Study of Transformer Networks

—Transformer neural networks have become widely used for language modeling and sequence learning tasks, and are one of the most important machine learning workloads today. Training one is a very compute-intensive task, often taking days or weeks, and signiﬁcant attention has been given to optimizing transformers. Despite this, existing implementations do not efﬁciently utilize GPUs. We ﬁnd that data movement is the key bottleneck when training. Due to Amdahl’s Law and massive improvements in compute performance, training has now become memory-bound. Further, existing frameworks use suboptimal data layouts. Using these insights, we present a recipe for globally optimizing data movement in transformers. We reduce data movement by up to 22.91% and overall achieve a 1.30 × performance improvement over state-of-the-art frameworks when training BERT. Our approach is applicable more broadly to optimizing deep neural networks, and offers insight into how to tackle emerging performance bottlenecks.
FIRST J1419+3940 as the First Observed Radio Flare from a Neutron Star Merger

During their violent merger, two neutron stars can shed a few percent of their mass. As this ejecta expands, it collides with the surrounding interstellar gas, producing a slowly fading radio flare that lasts for years. Radio flares uniquely probe the neutron star merger populations as many events from past decades could still be detectable. Nonetheless, no radio flare observation has been reported to date. Here we show that the radio transient FIRST J1419+3940, first observed in 1993 and still detectable, could have originated from a neutron star merger. We carry out numerical simulations of neutron star merger ejecta to demonstrate that the observed radio light curve is well reproduced by a merger model with astrophysically expected parameters. We examine the observed radio data, as well as the host galaxy, to find clues that could differentiate the transient’s neutron star merger origin from the alternative explanation—the afterglow of an off-axis long gamma-ray burst. Near-future observations could find further evidence for the FIRST J1419+3940 radio transient’s origin. We show that existing radio surveys likely already recorded multiple radio flares, informing us of the origin and properties of neutron star mergers and their role in the nucleosynthesis of the heaviest elements in the universe.
Documentation of landoltia (Landoltia punctata) resistance to diquat

Abstract Landoltia was collected and cultured from a canal in Lake County, Florida, where diquat was used repeatedly during the past 20–30 yr for duckweed control. Recent applications of diquat failed to provide adequate control of duckweed, and a new commercial formulation of diquat was suspected. The new formulation was not the cause of reduced efficacy. Static exposures (48 h) to various concentrations of diquat were used to compare the susceptibility of the Lake County landoltia accession to one never exposed to diquat. These static tests indicated that landoltia, from a population with no prior history of herbicide treatment, was extremely susceptible to diquat. The accession from Lake County, FL had developed resistance to diquat, and was also cross resistant to paraquat. The resistance factor was 50 for diquat and 29 for paraquat. The Lake County accession also exhibited reduced ion leakage after diquat exposure under light and dark conditions. This suggests the resistance mechanism to the bipyridylium herbicides in landoltia is independent of photosynthetic electron transport. This research documents the first aquatic plant species that has developed resistance to the bipyridylium herbicides. Nomenclature: Diquat, paraquat; duckweed, Lemna spp. ; landoltia, Landoltia punctata (G. Meyer) D. H. Les and D.J. Crawford.
Sequential Unsupervised Domain Adaptation through Prototypical Distributions

We develop an algorithm for unsupervised domain adaptation (UDA) of a classifier from a labeled source domain to an unlabeled target domain in a sequential learning setting. UDA has been studied extensively recently but the vast majority of the existing methods consider a joint learning setting where the model is trained on the source domain and the target domain data simultaneously. We consider a more practical setting, where the model has been trained on the labeled source domain data and then needs to be adapted to the unlabeled source domain, without having access to the source domain training data. We tackle this problem by aligning the distributions of the source and the target domain in a discriminative embedding space. To overcome the challenges of learning in a sequential setting, we learn an intermediate prototypical distribution from the source labeled data and then use this distribution for knowledge transfer to the target domain. We provide theoretical justification for the proposed algorithm by showing that it optimizes an upper-bound for the expected risk in the target domain. We also conduct extensive experiments with several standard benchmarks and demonstrate the competitiveness of the proposed method compared to existing joint learning UDA algorithms.
Optimization of lipid accumulation by starchless mutant Chlorella sorokiniana for biodiesel production.

Improvement of oil accumulation in Chlorella sorokiniana was conducted by ultraviolet mutagenesis. In total, 63 starchless mutants from over 40,000 colonies were screened using an iodine fumigation technique. Eight starchless mutants had relatively high levels of oil accumulation compared to wild type strains. The lipid content of starchless C. sorokiniana DMKU5202-31 increased from 15% to 21.16%. Therefore, this strain was selected for further optimization using a statistical experimental design. Seven factors were screened using a Plackett-Burman design and further optimized using a central composite design (CCD). The Plackett-Burman design presented three significant factors—KNO3, pH and light intensity—that affected biomass and lipid accumulation significantly. CCD was used to optimize the significant factors and indicated that the optimal values for KNO3, pH and light intensity were 0.9 g.L-1, 6.2 and 4,000 lux, respectively. The response surface plots revealed that the maxima for biomass, lipid production and lipid content were 2.58 g.L-1, 1.40 g.L-1 and 54.49%, respectively. Thus, it could be concluded that ultraviolet mutation and the statistical experimental design can be used to improve oil accumulation in C. sorokiniana. After mutation and optimization, strain DMKU5202 had a lipid content increase from 15% to 54.49%. Therefore, these techniques were very efficient for the development of an upstream process for biodiesel production.
Robust navigation with tinyML for autonomous mini-vehicles

Autonomous navigation vehicles have rapidly improved thanks to the breakthroughs of Deep Learning. However, scaling autonomous driving to low-power and real-time systems deployed on dynamic environments poses several challenges that prevent their adoption. In this work, we show an end-to-end integration of data, algorithms, and deployment tools that enables the deployment of a family of tiny-CNNs on extra-low-power MCUs for autonomous driving mini-vehicles (image classification task). Our end-to-end environment enables a closed-loop learning system that allows the CNNs (learners) to learn through demonstration by imitating the original computer-vision algorithm (teacher) while doubling the throughput. Thereby, our CNNs gain robustness to lighting conditions and increase their accuracy up to 20% when deployed in the most challenging setup with a very fast-rate camera. Further, we leverage GAP8, a parallel ultra-low-power RISC-V SoC, to meet the real-time requirements. When running a family of CNN for an image classification task, GAP8 reduces their latency by over 20x compared to using an STM32L4 (Cortex-M4) or obtains +21.4% accuracy than an NXP k64f (Cortex-M4) solution with the same energy budget.