Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of an Internet of Things (Iot) Based Smart Irrigation and Fertilization System Using Fuzzy Logic for Chili Plant

Proper monitoring and control of irrigation and fertilization system is crucial in agriculture field to ensure sufficient nutrients and moisture supplied to the plant is maintained all the time. There are two type of parameters being monitored which are soil moisture and its pH level. A systematic approach in monitoring and control of these parameters is thus necessary to uphold sustain smart farming solutions. Main objective of this project is to design smart irrigation and fertilization system for chili plant using Fuzzy Logic. In this project, the implementation of Internet of Things (IoT) also included where Wi-Fi node is used for the means of connection medium. Fuzzy Logic was implemented as a controller in this system to administer the flowrate of water, alkali and acid solutions into the soil in order to maintain its moisture and pH level. There are 2 memberships assigned as input fuzzy which are moisture and pH value while output have 3 memberships which are alkali, neutral and acid solution flowrate. The fuzzy rules were programmed into Arduino to control the water pump and valve opening. The system was able to store the data and display on mobile phone through Blynk mobile application. This system was tested on a chili plant where the growth of a plant under controlled environment have better performance compared to the traditional method.
Continuous EEG Decoding of Pilots’ Mental States Using Multiple Feature Block-Based Convolutional Neural Network

Non-invasive brain-computer interface (BCI) has been developed for recognizing and classifying human mental states with high performances. Specifically, classifying pilots’ mental states accurately is a critical issue because their cognitive states, which are induced by mental fatigue, workload, and distraction, may be fundamental in catastrophic accidents. In this study, we present an electroencephalogram (EEG) classification of four mental states (fatigue, workload, distraction, and the normal state) from EEG signals in both offline and pseudo-online analyses. To the best of our knowledge, this study is the first attempt to classify pilots’ mental states using only EEG signals during continuous decoding. We recorded EEG signals from seven pilots under various simulated flight conditions. We proposed a multiple feature block-based convolutional neural network (MFB-CNN) with temporal-spatio EEG filters to recognize the pilot’s current mental states. We validated the proposed method for two analyses across all subjects. In the offline analysis, we confirmed the classification accuracy of 0.75 (±0.04). Also, in the pseudo-online analysis, we obtained the detection accuracy of 0.72 (±0.20), 0.72 (±0.27), and 0.61 (±0.18) for fatigue, workload, and distraction, respectively. Hence, we demonstrate the feasibility of classifying various types of mental states for implementation in real-world environments.
Object Localization and Depth Estimation for Eye-in-Hand Manipulator Using Mono Camera

This paper proposes the object localization and depth estimation to select and set goals for robots via machine vision. An algorithm based on a deep region-based convolution neural network (R-CNN) will recognize targets and non-targets. After the targets are recognized, we employed both the k-nearest neighbors (kNN) and the fuzzy inference system (FIS) to localize two-dimension (2D) positions. Moreover, based on the field of view (FoV) and a disparity map, the depth is estimated by a mono camera mounted on the end-effector with an eye-in-hand manipulator structure. Although using a single mono camera, the system can easily find the camera baseline by only shifting the end-effector a few millimeters towards the x-axis. Thus, we can obtain and identify the depth of the layered environment in 3D points, which form a dataset to recognize the junction box covers on the table. Experimental tests confirmed that the algorithm could accurately distinguish junction box covers or non-targets and could estimate whether the targets are within the depth for grasping by three-finger grippers. Furthermore, the proposed optimized depth error of −0.0005%, and localization method could precisely position the junction box cover with recognizing and picking error rates 0.993 and 98.529% respectively.
Tailored Page Sizes

Main memory capacity continues to soar, resulting in TLB misses becoming an increasingly significant performance bottleneck. Current coarse grained page sizes, the solution from Intel, ARM, and others, have not helped enough. We propose Tailored Page Sizes (TPS), a mechanism that allows pages of size 2n, for all n greater than a default minimum. For x86, the default minimum page size is 212 (4KB). TPS means one page table entry (PTE) for each large contiguous virtual memory space mapped to an equivalent-sized large contiguous physical frame. To make this work in a clean, seamless way, we suggest small changes to the ISA, the microarchitecture, and the O/S allocation operation. The result: TPS can eliminate approximately 98% of page walk memory accesses and 97% of all L1 TLB misses across a variety of SPEC17 and big data memory intensive benchmarks.
Identification of COVID-19 Spreaders Using Multiplex Networks Approach

In this work, we present a methodology to identify COVID-19 spreaders using the analysis of the relationship between socio-cultural and economic characteristics with the number of infections and deaths caused by the COVID-19 virus in different countries. For this, we analyze the information of each country using the complex networks approach, specifically by analyzing the spreaders countries based on the separator set in 5-layer multiplex networks. The results show that, we obtain a classification of the countries based on their numerical values in socioeconomics, population, Gross Domestic Product (GDP), health and air connections; where, in the spreader set there are those countries that have high, medium or low values in the different characteristics; however, the aspect that all the countries belonging to the separator set share is a high value in air connections.
Intraday variation of metabolic key indicators in serum of dairy cows between week 2 antepartum and week 12 postpartum

Metabolic diseases during early lactation in dairy cows can be routinely diagnosed assessing key indicators in blood. The objectives of the present study were to characterize the impact of interindividual along with intraday variation on specific metabolites and to investigate the effect of the sampling time point relative to calving. Serum samples of four high-yielding, clinically healthy, multiparous dairy cows (body weight 589 ± 27 kg) were obtained in 3-h intervals during 24-h intervals throughout the transition period and early lactation (week –2 antepartum (ap), weeks 1, 2, 3, 5, 7, and 12 postpartum (pp)). The lowest intraday variation (less than 15%) as indicated by relative coefficients of variation (CV) was found for glucose, cholesterol, and aspartate aminotransferase (AST). Intraday variation characterized by a CV between 15 and 30% was typical of urea, β-hydroxybutyrate (BHB), total bilirubin, and non-esterified fatty acids (NEFA). The highest intraday variation (CV > 30%) was assessed for insulin. Week relative to calving had significant influence on interindividual means of BHB, NEFA, insulin, and cholesterol in blood, but did not affect the interindividual variation of all parameters investigated. No significant intraday variation patterns were found. It is concluded that the considerable intraday variation of especially BHB and NEFA has to be taken into account in herd health monitoring for estimating the proportional outcome in respect to animals exceeding thresholds for specific metabolic key parameters.
New Cuckoo Search Algorithm for the Resource Constrained Project Scheduling Problem

The aim of this paper is to suggest novel Cuckoo Search algorithm to solving Multi-Skill Resource Constrained Project Scheduling Problem (MS -RCPSP). Our proposed evolutionary algorithm, which called CSM, inspired by Cuckoo Search algorithm. CSM fast converge to global extremum, moreover our algorithm also avoid getting trapped in local extremum. Experimental results and comparisons with conventional optimization algorithms show that the proposed Cuckoo Search algorithm can effectively enhance the searching efficiently.
Improvement of feature set based on Apriori algorithm in Android malware classification using machine learning method

A well-constructed feature set plays an important role in accuracy improvement in malware detection. However, research and evaluation of the relations between features to acquire a good feature set have not been received much attention. In this work, a method based on Apriori algorithm was proposed to improve the feature set. The method studies association rules from the initial feature set to devise the highly correlated and informative features, which will be added to the initial set. The improved feature set will be evaluated via cross validation test using various machine learning algorithms, such as SVM, Random forest and CNN. The accuracy of the test reached is 96.49% with 96.71% improved compared with the test using initial set.
Exploiting the fine-grained similarity of a large-scale rice species using shape motif discovery

In this paper, we propose a method to exploit trait similarity of a large-scale of rice species. Particularly, fine-gained challenges are handled when we distinguish highly similar species/varieties of rice. To this end, we attempt to construct a large-scale dataset images of ninety rice species. The collected images are normalized in term of both the size and biological structures of a rice seed. We then convert 2-D boundary of a rice seed image as a normalized time series. To find structure and regularities of each species from the collected datasets, we consider the problem of discovering shape-based motifs, which are approximately repeated shapes within (or between) rice species. Distance of a two seeds in each species is calculated using Dynamic Time warping algorithms. The k-level motif is specified by ordering the DTW distances. Two applications of the motif discovery are presented. The first one specify and visually distinguish two similar rice species. This distinguishing suggests valuable features to boot performances of recognition techniques for the rice species classification. The second application presents a visual classification by constructing a similarity map of a large number of rice species. We produce a similar map of ninety species of rice seeds using shape-based motif discovery techniques.
Evidence for speciational change in the evolution of ratites (Aves: Palaeognathae)

To perform a comparative analysis of character associations framed in a phylogenetic context (e.g. independent contrasts), a model of character evolution must be assumed. According to phyletic gradualism, morphological change accumulates gradually over time within lineages, and speciation events do not have a major role. Under speciational models, morphological change is assumed to occur during or just after cladogenesis in both daughter species, and the resulting morphologies do not change over long periods of time (stasis), until the next cladogenetic event. A novel method is presented for comparing these models of character evolution that uses permutational multiple phylogenetic regressions. The addition of divergence times to well-corroborated phylogenetic trees and the utilization of the method developed in this paper allows the estimation of relative frequency of gradual change and speciational change from living organisms. This method is applied to a dataset from ratites with the conclusion that, for a range of morphological features, change tends to have been speciational rather than gradual.
Bacterial dinitrogen fixation in the leaf of the northern pitcher plant (Sarracenia purpurea)

Bacterial (Azotobacteriaceae) nitrogenase activity in the leaf cavity of the insectivorous northern pitcher plant (Sarracenia purpurea L.) was assessed by acetylene reduction assay. Results indicated that bacterial dinitrogen fixation occurs in the leaf cavity under normal physiological conditions. This may represent a previously unidentified source of nitrogen for the pitcher plant, as the level of nitrogenase activity measured was sufficient to provide the plant with nitrogen well in excess of that required. Key words: pitcher plant, Sarracenia purpurea, dinitrogen fixation, Azotobacteriaceae, nitrogenase.
Artificial Intelligence Applied to Software Testing: A Literature Review

In the last few years Artificial Intelligence (AI) algorithms and Machine Learning (ML) approaches have been successfully applied in real-world scenarios like commerce, industry and digital services, but they are not a widespread reality in Software Testing. Due to the complexity of software testing, most of the work of AI/ML applied to it is still academic. This paper briefly presents the state of the art in the field of software testing, applying ML approaches and AI algorithms. The progress analysis of the AI and ML methods used for this purpose during the last three years is based on the Scopus Elsevier, web of Science and Google Scholar databases. Algorithms used in software testing have been grouped by test types. The paper also tries to create relations between the main AI approaches and which type of tests they are applied to, in particular white-box, grey-box and black-box software testing types. We conclude that black-box testing is, by far, the preferred method of software testing, when AI is applied, and all three methods of ML (supervised, unsupervised and reinforcement) are commonly used in black-box testing being the “clustering” technique, Artificial Neural Networks and Genetic Algorithms applied to “fuzzing” and regression testing.
Hacking the RFID-based Authentication System of a University Campus on a Budget

Radio-Frequency IDentification (RFID) technologies have been widely used in physical ID cards in educational institutions due to its low-cost, simple integration and convenience. In a university campus, the RFID technology can be used for proximity-based authentication for services such as access control, student/employee attendance record, or in-campus payments. These RFID-based technologies present vulnerabilities that, if exploited, can compromise the university campus authentications systems. RFID skimming and tag killing are examples of attacks that are simple to execute and have a high impact on their victims. This paper exploits a tag-related vulnerability of an ID Card based on RFID technology for proximity-based authentication inside a university campus. The proof of concept presented shows that, by using low-cost commercial-off-the-shelf hardware and open-source software, it is simple to clone an ID card and perform RFID skimming, harming the real ID card users. Possible countermeasures are later introduced and discussed.
A Novel Synthesis for Bandwidth Switchable Bandpass Filters Using Semi-Conductor Distributed Doped Areas

This paper presents a novel synthesis for bandwidth switchable bandpass filters using Semi-conductor Distributed Doped Areas (ScDDAs) as active elements. A co-design method is proposed with a global and simultaneous conception for the active and the passive parts of the switchable filters. The ScDDAs, integrated in the silicon substrate, are able to commute from half-wavelength open-ended stubs to quarter-wavelength short-circuited ones. This co-design method offers a great flexibility and allows to integrate the active elements directly in the substrate, therefore avoiding any soldering of components. The synthesis is developed for the two-states of the active elements and applied, as a proof of concept, to a four-pole bandwidth switchable bandpass filter. This filter operates at 5 GHz with a 50 % bandwidth in the OFF-state (when the stubs are terminated by an open-circuit) and with a 70 % bandwidth in the ON-state (when the stubs are short-circuited). For this filter, the synthesis is detailed in the two-states allowing to choose the two desired bandwidths. A good fitting is obtained for these results proving the viability of such an approach.
Incorporating Variability of Resistive RAM in Circuit Simulations Using the Stanford–PKU Model

Intrinsic variability observed in resistive-switching devices (cycle-to-cycle and device-to-device) is widely recognised as a major hurdle for widespread adoption of Resistive RAM technology. While physics-based models have been developed to accurately reproduce the resistive-switching behaviour, reproducing the observed variability behavior of a specific RRAM has not been studied. Without a properly fitted variability in the model, the simulation error introduced at the device-level propagates through circuit-level to system-level simulations in an unpredictable manner. In this work, we propose an algorithm to fit a certain amount of variability to an existing physics-based analytical model (Stanford-PKU model). The extent of variability exhibited by the device is fitted to the model in a manner agnostic to the cause of variability. Further, the model is modified to better reproduce the variations observed in a device. The model, fitted with variability can well reproduce cycle-to-cycle, as well as device-to-device variations. The significance of integrating variability into RRAM models is underscored using a sensing example.
Molecular Dynamics Simulation-assisted Ionic Liquid Screening for Deep Coverage Proteome Analysis

Molecular dynamics simulation was established to elucidate the underlying effects of extractants on protein solubilization and bioactivity. In combination with experimental procedure, 1-dodecyl-3-methylimidazolium chloride was found to be the suitable candidate, and an ionic liquid-based filter-aided sample preparation (i-FASP) method was developed. Using this strategy, 3,300 proteins were identified from 1,000 HeLa cells in a single run, an improvement of 53% over FASP method. The i-FASP method further showed improved accuracy, reproducibility, and coverage than FASP method in label-free quantitative analysis. Graphical Abstract Highlights Mechanistic insights into ionic liquids and proteins at molecular level. Extractants prescreen for proteome analysis with MD simulation system. A loss-less sample preparation method developed for in-depth proteome profiling. Over 3,300 proteins were confidently identified from 1,000 HeLa cells in a 1 h run. Label-free quantitative proteome analysis of human liver cancer tissues. In-depth coverage of proteomic analysis could enhance our understanding to the mechanism of the protein functions. Unfortunately, many highly hydrophobic proteins and low-abundance proteins, which play critical roles in signaling networks, are easily lost during sample preparation, mainly attributed to the fact that very few extractants can simultaneously satisfy the requirements on strong solubilizing ability to membrane proteins and good enzyme compatibility. Thus, it is urgent to screen out ideal extractant from the huge compound libraries in a fast and effective way. Herein, by investigating the interior mechanism of extractants on the membrane proteins solubilization and trypsin compatibility, a molecular dynamics simulation system was established as complement to the experimental procedure to narrow down the scope of candidates for proteomics analysis. The simulation data shows that the van der Waals interaction between cation group of ionic liquid and membrane protein is the dominant factor in determining protein solubilization. In combination with the experimental data, 1-dodecyl-3-methylimidazolium chloride (C12Im-Cl) is on the shortlist for the suitable candidates from comprehensive aspects. Inspired by the advantages of C12Im-Cl, an ionic liquid-based filter-aided sample preparation (i-FASP) method was developed. Using this strategy, over 3,300 proteins were confidently identified from 103 HeLa cells (∼100 ng proteins) in a single run, an improvement of 53% over the conventional FASP method. Then the i-FASP method was further successfully applied to the label-free relative quantitation of human liver cancer and para-carcinoma tissues with obviously improved accuracy, reproducibility and coverage than the commonly used urea-based FASP method. The above results demonstrated that the i-FASP method could be performed as a versatile tool for the in-depth coverage proteomic analysis of biological samples.
A Green DDPG Reinforcement Learning-Based Framework for Content Caching

Caching popular files at the edge of the network near the users can effectively mitigate the pressure on the backhaul link and reduce the user’s transmission delay. The effectiveness of content caching highly depends on the hit rate of the request, so the research of content caching strategy is an important issue. Inspired by reinforcement learning to solve complex control problems, this paper studies the content caching problem that maximizing the use of the cache capacity of the base stations to minimize the average transmission delay of the user request and the energy loss of the network. Because the state of the network is unpredictable and its dimension is huge, the DDPG (Deep Deterministic Policy Gradient) algorithm is used to interact with the environment to learn the optimal cache strategy. By separating the action function network and the value function network, it is possible to optimize each other in the iterative process. Our results show that the proposed framework can improve cache hit ratio and reduce network energy loss.
Inference in Turbulent Molecular Information Channels Using Support Vector Machine

Inference of transmitter side information is essential to communication. In Molecular Communication (MC), whilst the Bayesian inference of mass diffusion channel parameters is well established, turbulent diffusion (TD) channels are not well-understood. Cascading vortices rapidly transform transmitted momentum (molecular information puffs) into heat, which raises the challenge of receiver inferring transmitter information. Our initial results found that in TD channels, inferring transmitted molecular concentration or timing is challenging. As such, we were motivated to infer transmitter velocity from a flexible receiver sample area. In this paper, we consider an unbounded scenario of Molecular Communication via Turbulent Diffusion (MCvTD) where a transmitter injects several molecular puffs with different velocities. We first developed a time difference concentration (TDC) method based on large-scale support vector machine (SVM) to distinguish the injection velocities. To trade-off the prediction accuracy and number of receiver spatial samples, we propose the stepwise maximum variance (SMV) algorithm to select the limited dominant receiver sampling locations. The overall performance can achieve 100% accuracy in transmitter velocity information recovery, with excellent error vs. receiver size trade-off (e.g., 5% error for 74% area reduction). The research results indicate that velocity modulation at transmitter and TDC with SVM receiver should be used in MCvTD channels.