Miracle-dz/UniFAR-FTUs huggingface dataset:
Sugar profiles and conductivity of stingless bee honeys from Venezuela

Les profils glucidiques obtenues par HPLC et la conductibilite de 42 echantillons de miels d'abeilles sans dard du Venezuela ont ete determines. Les miels provenaient principalement de trois Melipona (n = 24), les autres etant produits par cinq especes de Trigone (non-Melipona), principalement Frieseomellita (n = 8). Les resultats sont presentes dans le tableau I, les profils glucidiques typiques sur la figure 1. La composition glucidique des miels de Melipona est uniforme : le fructose et le glucose representent 96 % des sucres. La teneur moyenne en fructose varie entre 34,8 et 36,8 g/100g, celle du glucose entre 36,3 et 36,9 g/100g. Le maltose n'est present qu'en petites quantites et les oligosaccharides a l'etat de traces. Le spectre glucidique des autres miels (Frieseomelitta et autres non-Melipona) est totalement different. Le sucre principal est un dissacharide qui a le meme temps de retention que le maltose. Sa teneur moyenne est de 32,3 g (Frieseomelitta) et de 19,7 g (autres non-Melipona). Le fructose et le glucose ont des teneurs moyennes plus faibles, respectivement de 24,4 et 18,1 g/100 g (Frieseometlitta), 30,4 et 26,4 g/100 g (autres non-Melipona). Les miels de Melipona ne renferment que de petites quantites de maltose et des traces d'oligosaccharides ; les miels des non-Melipona renferment de petites quantites des oligosaccharides suivants ; turanose, trehalose et erlose. La conductibilite des differents miels de Melipona n'est pas significativement differente ; les valeurs moyennes sont comprises entre 0,32 et 0,44 mS/cm. Elles est en revanche significativement plus basse que celle des miels de Frieseomelitta (1,04 en moyenne) et des autres non-Melipona (1,07).
Poster Abstract: Combating Transceiver Layout Variation in Device-Free WiFi Sensing using Convolutional Autoencoder

Sensitivity of WiFi channel measurements to the transceiver placement is a major limitation for on-demand deployment of device-free WiFi sensing in environments where the transmitting/receiving devices may move. Using publicly available datasets, we show that even slight deviations of transmitter/receiver placements from the reference values can degrade device-free gesture recognition accuracy significantly. We design a convolutional autoencoder to translate WiFi spectrograms from arbitrary receiver placements to a reference placement configuration in a given area of interest with minimal human effort. Our experiments with the public datasets reveal that the proposed autoencoder can successfully reduce WiFi measurement variability caused by transmitter/receiver movement, which ultimately increases gesture recognition accuracy by up to 58%.
Investigation of Electrical Parameters on a DSSC Solar Cell in Function of the Cell Temperature

On the basis of cell temperature it has been experimentally examined that the shortcut currents, which flow through the dye sensitized solar cells and the open circuit voltages, fall between the outputs of the cell. A special sandwich target with its temperature modification possibility has been designed and dedicated experiments have been made to fulfil the aim. After running the measurements, all the data have been analyzed. The steps of the data analyzing process will be described and the most important results will be pointed out in this publication together with possible conclusions and a summary.
Smart Power Socket using Internet of Things

The growing population brings with it a problem of scarce natural resources. As a consequence, man-made resources such as electricity are not able to cope up and face the uphill task of catering to the rapidly increasing demand. Over the last decades various measures have been taken to improve the equipment and system design to improve efficiency of production, transmission and reduce power consumption. With the ushering of the digital era, microcontrollers and the internet through the Internet of Things (IoT) are the next level of tools in tackling the power crunch. By providing real-time information on the various parameters of the system and equipment, it allows for more radical and precise techniques for improving power system utilization thereby easing the demand on natural resources. Focusing on the endpoint domestic consumers, the Smart Power Socket aims at not only providing ease of control of household devices in a simple plug-and-play configuration, but also provides useful consumption/utilization data which familys can use for reducing electricity costs. With further digitization and implementation of IoT systems in the power domain, these systems can then be bolted onto the overall system, giving the power producers more in-depth insight into consumption patterns and aid in the design of better production systems and distribution systems.
Sexual reproduction in the phyllodioicous bryophyte Homalothecium lutescens (Hedw.) H.Rob. in relation to habitat age, growth conditions and genetic variation

Abstract Sporophyte production and female fertility were investigated in seventeen calcareous grassland demes of the moss Homalothecium lutescens (Hedw.) H.Rob. on the Baltic island of Öland, with the aim of understanding the relationships between sexual reproduction, habitat age, genetic variation and factors related to growth conditions. The overall proportion of fertile female shoots (with perichaetia) was 35%. Fertility status at the level of individual shoots was positively associated with shoot length and density, while within deme fertility was positively associated with bush cover. There was no association between female fertility and habitat age, genetic diversity (HS) or allelic richness. Out of 1344 investigated shoots, only two were normal-sized fertile males. Dwarf males were also extremely rare, and found almost exclusively on shoots with sporophytes. Few sporophytes were observed (in the two demes with highest fertility and bush cover). No relationship between genetic variation and the frequency of sporophytes and males was found. The lack of a relationship between sexual reproduction and genetic variation suggests that sexual reproduction may not occur in the same grassland fragments as the recruitment of new clones (from spores or vegetative fragments). The majority of the dry, open grassland habitats, where H. lutescens is typically found in the study area, appear to be suboptimal for both dwarf males and fertilization. Sexual reproduction is more likely to occur in shaded (although grazed) grassland patches, where moisture levels are likely to be higher and the moss colonies are generally more vigorous.
Lightweight models for fruits recognition

Image classification is a well known example of a complex problem that can be tackled using deep learning. In the latest period, convolutional neural networks were successfully applied in order to solve the problem. Moreover, recently, the research directions have been focusing more on developing lightweight models that can still achieve good classification performance. In this paper we propose a lightweight solution for solving an image classification problem. The solution is tested on an open dataset for fruits recognition. We achieve state of the art performance in terms of both classification accuracy and execution speed.
Real-Time Scheduling upon a Host-Centric Acceleration Architecture with Data Offloading

Challenging scheduling problems arise in the implementation of cyber-physical systems upon heterogeneous platforms with (serial) data offloading and (parallel) computation. In this paper, we adapt techniques from scheduling theory to model, analyze, and derive scheduling algorithms for real-time workloads on such platforms. We characterize the performance of the proposed algorithms, both analytically via the approximation ratio metric and experimentally through simulation experiments upon synthetic workloads that are justified via a case study on a CPU-GPU platform. The evaluation exposes some divergence between the analytical characterization and experimental one; recommendations that seek to balance such divergent characterizations are made regarding the choice of algorithmic approaches.
Single Image Haze Removal Using Deep Cellular Automata Learning

Deep learning is one of the most popular approaches to machine learning, which has been widely used for classification. In this paper, we propose a novel learning method based on a combination of an idea of the deep learning approach and the cellular automata model, called DeepCA for single image haze removal. DeepCA’s learning is divided into two main parts. The first part is a cellular automata-based deep feature extraction: multi-layer cellular automata with the rules are used to extract the data feature matrices of the image, in which the matrices can be divided into several layers. Then, the score matrices were generated as the model in which was trained by the cellular automata rules. The second part is a decision stage: we used the score matrices to the mapping between the proper data. For demonstration, we take the single image haze removal task as an example to confirm the capability of the proposed method. In this regard, the dichromatic model is chosen as the major model to remove the haze of the image. The multi-layer cellular automata with the rules work as a mechanical extractor of the light source feature of the hazy image. The decision stage of DeepCA performs as the recognizer for properly predicting the global light source for dehazing. This aims to improve the light source and the transmission map that they are important compositions for haze-free image restoration. For performance evaluation, we perform quantitative and qualitative measures. For the qualitative performance of the haze removal, DeepCA did not even cause the halo artifact effect that occurred in other haze removal algorithms. The empirical results in quantitative measures show that DeepCA improved intensity, color saturation quality, and halo artifact when compared with the state-of-the-art methods.
Smart learning environment, measure online student satisfaction: a case study in the context of higher education in Morocco

Nowadays, new technologies have been widely adopted in schools and in particular in universities. E-learning platforms are designed with learning content, self-assessments and support services, thus improving the quality of learning by facilitating the use of services and resources, on the one hand, and distance exchange and collaboration on the other. Aside from the benefits of e-learning, it is also important to check whether the learners are sufficiently satisfied with their experience. This study focused on the learners’ satisfaction toward e-learning systems in higher education and also proposed a theoretical model combining several factors to better understand learners’ satisfaction in Morocco e-learning systems. A structural equation modelling approach was used to test the correlation between the components of our model. The results indicated that, the diversity in assessments, course flexibility, social interactions, system quality and perceived usefulness has positive effects on learner satisfaction. Findings of this research will be useful for both institutions and practitioners of e-learning systems.
Sharing-aware Data Acquisition Scheduling for Multiple Rules in the IoT

In the Internet-of-Things (IoT) environments, users define event-condition-action (ECA) rules, and expect IoT frameworks to evaluate conditions and take appropriate actions within a certain time limit after an event occurs. To evaluate the conditions with fresh data items, the frameworks acquire required data from IoT sensors. Since the data acquisition causes battery consumption of sensors, the frameworks should minimize the number of the data acquisition while keeping the sensor data fresh until finishing the condition evaluation. However, existing data acquisition schedulers inefficiently acquire sensor data because the schedulers assume each ECA rule in a program is independent of each other although different rules may share some sensing data from the same sensors. This work proposes an efficient sharing-aware data acquisition scheduling algorithm that reduces unnecessary data acquisition by sharing sensor data commonly used in different rules while satisfying time constraints. To evaluate the proposed scheduling algorithm, this work deploys 19 devices in an office, collects values of 26 different sensors for 144 hours, and simulates the proposed algorithm and a baseline algorithm. Compared to the baseline algorithm, the proposed algorithm reduces communication count and deadline miss ratio by 31.9% and 50.2% respectively.
A Holistic Memory Contention Analysis for Parallel Real-Time Tasks under Partitioned Scheduling

When adopting multi-core systems for safety-critical applications, certification requirements mandate bounding the delays incurred in accessing shared resources. This is the case of global memories, whose access is often regulated by memory controllers optimized for average-case performance and not designed to be predictable. As a consequence, worst-case bounds on memory access delays often result to be too pessimistic, drastically reducing the advantage of having multiple cores. This paper proposes a fine-grained analysis of the memory contention experienced by parallel tasks running on a multi-core platform. To this end, an optimization problem is formulated to bound the memory interference by leveraging a three-phase execution model and holistically considering multiple memory transactions issued during each phase. Experimental results show the advantage in adopting the proposed approach on both synthetic task sets and benchmarks.
A Multi-Objective Approach for the Calibration of Microscopic Traffic Flow Simulation Models

The calibration of traffic-flow simulation models continues to be a significant problem without a generalized, comprehensive, and low-cost solution. Existing calibration approaches either have not explicitly addressed the multi-objective characteristics of the problem or determining their hyperparameters requires significant effort. In addition, statistical evaluation of alternative solution algorithms is not performed to ensure dominance and stability. This study proposes an adaptation and advanced implementation of the Multi-Objective Global-Best Harmony Search (MOGBHS) algorithm for calibrating microscopic traffic-flow simulation models. The adapted MOGBHS provides five key capabilities for solving the proposed problem including 1) consideration of multiple objectives, 2) easily extendable to memetic versions, 3) simultaneous consideration of continuous and discrete variables, 4) efficient ordering of no dominated solutions, 5) relatively easy tuning of hyperparameters, and 6) easily parallelization to maximize exploration and exploitation without increasing computing time. Three traffic flow models of different dimensionality and complexity were used to test the performance of seventeen metaheuristics for solving the calibration problem. The efficiency and effectiveness of the algorithms were tested based on convergence, minimization of errors, calibration criterion, and two statistical nonparametric tests. The proposed approach dominated all alternative algorithms in all cases and provided the most stable and diverse solutions.
Granivory in the Chihuahuan Desert : interactions within and between trophic levels

We investigated the effects of Chihuahuan Desert granivores on three seasonal classes of plant resource species, the effects of these resource classes on one another, and the way in which interactions through plant resources affect the abundances of seed consumers. At our study site, three seasonally distinct classes of annual plants produce the seeds used by ants and rodents, and two major taxa of resident granivores. Winter annuals and summer annuals have temporally nonoverlapping growth periods. However, one numerically prominent annual, Eriogonum abertianum, germinates with winter annuals, reproduces with summer annuals, and may link the population dynamics of plants in the two groups. Experiments showed that high densities of winter annuals inhibit populations of E. abertianum and that populations of this species can strongly suppress those of summer annuals. Densities of all three classes of annuals are regulated by the combined effects of competition and seed predation. In E. abertianum, interspecific and intraspecific competition appeared to predominate in alternate years and to produce 2—yr abundance cycles, accentuated in magnitude where ants, the major seed predators, had been removed. The period of these cycles appeared to lengthen where rodent removal intensified the effects of interspecific competition. Granivore removal and seed addition experiments showed that competition also helps to regulate some granivore populations. Although rodents reduced ant resources through their effects on seeds of both summer annuals and winter annuals, ants were competitors of rodents only for seeds produced during the weaker winter resource peak. Seeds of E. abertianum were used almost exclusively by ants. Rodents facilitated this plant species indirectly by reducing densities of other winter annuals. Ants and rodents were affected differently by one another's removal. After an initial time delay, workers of Pheidole xerophila increased in numbers and/or activity on rodent removal plots, but colony densities of a second ant species, Pogonomyrmex desertorum, simultaneously declined. Rodents did not compensate measurably in abundance, biomass, or reproductive activity where ants had been eliminated. The explanation for these responses is complex and includes such factors as: (1) seasonality in the production of seed resources and in their use by the two taxa, (2) specialization by ants and rodents on different density distributions of seeds; (3) diffuse compensation, or compensation spread over many species populations; and (4) indirect interaction pathways, mediated through competing resource classes. In general, our experiments show that, despite the climatic variability and unpredictability of desert environments, populations respond to the steady deterministic processes of competition and predation. Nevertheless, comparison of the results of similar experimental studies in the Sonoran and Chihuahuan Deserts demonstrates how climate and seasonality can alter the structure and intensity of interactions in ecosystems.
Indications of density-dependent effects from comparisons of sperm whale populations

Standardized studies of living sperm whales Physeter macrocephalus were carried out in 3 areas with very different recent whaling intensities. These were off the Galapagos Islands, where there seems to have been little recent whaling, off the Seychelles and Amirantes Islands, where animals were regularly caught by passing Soviet whalers, and off mainland Ecuador, adjacent to the area whaled very heavily from Paita, Peru. Sperm whale densities off the Galapagos were about 4 . 2 times higher than off the Seychelles and 2.4 times higher than off mainland Ecuador. Feeding success, as indicated by defecation rates, was significantly higher off the Seychelles than in the other 2 areas. Calving rates showed the same trend, but differences between areas were not significant. Length distributions of females and immatures were similar off the Galapagos and Seychelles but rather larger animals were found off mainland Ecuador. There were very low densities of large, mature males in all areas. Our results, with the exception of the low feeding success and calving rate found off mainland Ecuador, are consistent with general models of density-dependent processes. The discrepancies may be due to migration of whales to mainland Ecuador from less exploited regions.
Impact of Big Data and Machine Learning on Digital Transformation in Marketing: A Literature Review

This paper describes the impact of big data and machine learning (ML) on digital transformation of the marketing industry and the challenges it faces from a data and information management perspective. To do this, the study identified areas of digital transformation in marketing that have not yet been sufficiently covered by existing peer-reviewed academic research. Research papers were retrieved from five databases, namely Web of Science, Scopus, ScienceDirect, Emerald, and ProQuest. Screening of 214 articles resulted in 69 articles being selected for this literature review. A gap in the existing research was identified, with five areas directly related to digital transformation in marketing. Another five potential opportunities for future research in the area of big data utilisation and application of ML-driven technologies in the marketing field were also identified. An investigation of the prospects of digital transformation in marketing can benefit both academic researchers and business practitioners working in the domain of information technologies (IT), information systems (IS), business, and marketing, by identifying areas needing primary research, systemisation, follow-up research, validation or gathering of empirical data.
Nutritional evaluation of Ficus thonningii Blume leaves as ruminant livestock feed in the Ahferom district of Tigray, Ethiopia

We evaluated the fodder quality of Ficus thonningii Blume in the Ahferom district of Tigray, Ethiopia. Determination of foliar macronutrients and proximate chemical compositions was undertaken using five diameter classes (11–20, 21–30, 31–40, 41–50 and 51–60 cm at breast height). In both cases, four replications in randomised complete blocks were considered. Data on foliar nitrogen (N), phosphorus (P), potassium (K), dry matter (DM), crude protein (CP), digestible crude protein (DCP), ether extract (EE), crude fibre (CF), ash content and nitrogen free extract (NFE) were collected. Results revealed that the tree diameter size had an effect on foliar N, P, K, DM, CP, DCP, EE and CF, whereas it had no effect on ash content and NFE. The results also showed that F. thonningii leaves are a good source of nutrients (proteins, fats, carbohydrates and minerals) as well as fibre and are within the recommended range for ruminant livestock growth and development. Hence, use of F. thonningii as a supplement/substitute livestock feed to low-quality grasses during the periods of feed scarcity should be widely considered.
Evaluation of the antioxidant activity of the fruiting body of Phellinus linteus using the on-line HPLC-DPPH method

Phellinus linteus has traditionally been used as a medicinal mushroom in Oriental countries. Bioactive phytochemicals from the fruiting body of P. Linteus were investigated using the on-line HPLC-DPPH system. The system was used to separate 10 antioxidants: protocatechuic acid (1) protocatechualdehyde, (2) caffeic acid (3), ellagic acid (4), hispidin (5), davalliallactone (6), interfungins A (7), hypholomine B (8), inoscavin A (9), and methyldavallialactone (10). Of these, compounds 6 and 7 showed the strongest inhibitory effect against the DPPH radical and superoxide anion radical-scavenging capacity. On comparison with quercetin (IC50, 44.0 μM), the IC50 value of compounds 6 and 7 for DPPH radical-scavenging capacity were 19.6 μM and 18.5 μM. Moreover, the antioxidative capacity of compound 6 at 40mM was 2.95 nmol Trolox equivalents per mg dry wt. These results indicated that hispidin dimers such as hispidin with hispidin or hispolon had a stronger effect on radical-scavenging capacity than hispidin itself.