Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Agronomic Practices on Volatile Composition of Hyssopus officinalis L. Essential Oils

The chemical composition of Hyssopus officinalis (Lamiaceae) essential oil grown in southeastern Spain was analyzed by GC-MS. Due to the high relevance of this species in the world market, the study is focused on chemical heterogeneity of different oil batches and their extraction yield, cultivated under irrigation and non-irrigation conditions and with different harvesting dates. All essential oil samples have two main terpene compounds which are pinocamphone and iso-pinocamphone, accounting for approximately 35–40% of the total oil content. Other relevant compounds were identified, with β-pinene, which accounted for 10–17% contribution to the total composition, standing out. Significant differences between their volatile composition have been observed between treatments, being limonene, (E)-β-ocimene, pinocarveol, α-pinene and β-phellandrene the compounds that contributed most to the discrimination. It was also observed that the irrigation period is the most favourable for the cultivation of hyssop in this region, specially for batch 7 which gives the highest extraction yield and the best EO quality.
Architectural Exploration to Address the Reliability Challenges for ReRAM-Based Buffer in SSD

Hybrid solid state drive based on ReRAM and NAND flash technologies has shown promising performance and energy efficiency. In this application, ReRAM is mainly used as a non-volatile buffer to hold recently accessed data pages or address mapping information. The previous studies on the ReRAM-based buffer mainly focus on performance and efficiency improvement, while reliability issues are not taken into consideration. Nevertheless, according to our quantitative evaluation, the limited endurance and random bit errors pose challenges to ReRAM-based buffer design. For example, without wear leveling, the raw lifetime of ReRAM-based SSD buffer may be as low as 0.02 year for some specific I/O traces. Therefore, we propose two efficient architecture techniques, i.e., multi-bloom-filter-based wear leveling and hybrid error protection, to improve the lifetime and reduce the error protection cost of ReRAM-based buffer, respectively. Simulation results demonstrate that the proposed wear leveling technique can extend the lifetime of ReRAM-based buffer by $34.7\times $ on average. The proposed hybrid error protection scheme can improve the response time by at least 4.2% compared with other error protection schemes.
Direct Geometrical Map to Low-Level Grid Map Registration for Robust Online Localization

In autonomous driving, robustness and precision can be increased through additional information sources in the form of prior maps. To benefit from these maps, precise localization is necessary. Localization enables the vehicle to drive along planned trajectories using map information like lane geometry and static environment information. State-of-the-art localization methods often use feature based maps or rely on dense appearance based maps for direct, association free matching. In this paper we combine both ideas. We solve the localization problem by matching dense measurement grids from LiDAR sensors directly against a geometrical, feature based map. Reflectivity and occupancy grids are obtained from multiple Li-DAR sensors. In order to avoid information reduction through feature extraction, the low-level grids are registered directly against a high-level geometrical map (Fig. 1). This new concept is solved using an exhaustive search in combination with a multi-level-resolution search. Real-time capable registration is achieved by exploiting the computational power of modern GPUs. To realize an online localizer, a Kalman Filter (KF) fuses the registration results with odometry information, obtained from serial production sensors. To ensure a fixed runtime, the registration resolution is adapted based on the state uncertainty. The robustness and accuracy of the developed methods is evaluated in extensive experiments. The localization proved to be accurate and robust against challenging situations like difficult maneuvers or missing localization features in the map. Thus, our new approach is well suited for autonomous driving applications.
Chaos-Based Bitwise Dynamical Pseudorandom Number Generator On FPGA

In this paper, a new pseudorandom number generator (PRNG) based on the logistic map has been proposed. To prevent the system to fall into short period orbits as well as increasing the randomness of the generated sequences, the proposed algorithm dynamically changes the parameters of the chaotic system. This PRNG has been implemented in a Virtex 7 field-programmable gate array (FPGA) with a 32-bit fixed point precision, using a total of 510 lookup tables (LUTs) and 120 registers. The sequences generated by the proposed algorithm have been subjected to the National Institute of Standards and Technology (NIST) randomness tests, passing all of them. By comparing the randomness with the sequences generated by a raw 32-bit logistic map, it is shown that, by using only an additional 16% of LUTs, the proposed PRNG obtains a much better performance in terms of randomness, increasing the NIST passing rate from 0.252 to 0.989. Finally, the proposed bitwise dynamical PRNG is compared with other chaos-based realizations previously proposed, showing great improvement in terms of resources and randomness.
A Web Second-Order Vulnerabilities Detection Method

Second-order vulnerabilities are more subtle and more destructive than the first-order vulnerabilities. After researching and analyzing the principles of web penetration testing and second-order attack principles, this paper proposes a method to detect web second-order security vulnerabilities. The method detects web second-order security vulnerabilities through two crawl scans. It crawls the website URL for the first time, sends anchor points, crawls URLs of the storage anchor point for the second time, and detects second-order web security vulnerabilities specifically for these suspicious URLs. The approach greatly reduces the time complexity of detecting second-order web security vulnerabilities and makes up for the lack of methods to detect web security second-order vulnerabilities.
Word Embedding in Small Corpora: A Case Study in Quran

Text is a complex set of words to carry the meaning and representations of words is the first step to perform linguistic processing and text comprehension. So far, many researches have been done on the semantic representations of words using neural networks in various areas of natural language processing using large text corpus from general domain. In the meantime, some efforts have been done to apply deep learning methods to represent the words of small corpus supporting the hypothesis that the bigger corpora doesn't necessarily provide better results in words representation. In this research the capability of word2vec for learning semantic representation of words in small corpus is investigated. Here, we consider Skip-gram and CBOW learning models with different values of hyper parameters. Two new data sets have been created to evaluate the model's performance on the small domain-specific Quranic corpus. First and second datasets are used to test the words categorization and word pairwise similarity respectively. Our results demonstrate that the best performance for skip-gram occurs with 30 numbers of iterations when the dimension is set to 7.
OP-CAS: Collision Avoidance with Overtaking Maneuvers

This paper presents a novel collision avoidance system for autonomous vehicles based on overtaking procedures. The proposed Overtaking Procedure for Collision Avoidance Systems (OP-CAS) takes a behavioral cloning-based approach which uses images obtained out of a low cost monocular camera. The algorithm selectively records the expert's corrective driving behavior during data collection. This is performed recording oscillatory driving behavior when the vehicle is returning to the center of the lane. This data augmentation method addresses the issue of covariate shift commonly found in behavioral cloning methods. This approach is computationally inexpensive, making it a viable option for real time embedded deployment. A feasibility study was performed with two remotely controlled scaled vehicles as a proof of concept. Results showed that when two expert drivers demonstrated overtaking behaviors for data collection, even a small dataset was sufficient to model the overtaking sequence. The overtaking maneuvers were deployed in real time on 1/8th scale RC platforms, validating OP-CAS for civilian vehicle safety applications.
Intracellular pHluorin as Sensor for Easy Assessment of Bacteriocin-Induced Membrane-Damage in Listeria monocytogenes

Bacteriocins are antimicrobial peptides naturally produced by many bacteria and were shown to be effective against various pathogens including Listeria monocytogenes. L. monocytogenes is a food-borne pathogen that frequently causes disease outbreaks around the world with fatal outcomes in at-risk individuals. Thus, bacteriocins are a promising solution to prevent contaminations with L. monocytogenes and other microorganisms during food production and preservation. In the present study, we constructed L. monocytogenes EGD-e/pNZ-Phelp-pHluorin, a strain that constitutively expresses the pH-sensitive fluorescent protein pHluorin, as a sensor strain to detect disruption of the pH gradient by the membrane-damaging activity of bacteriocins. The ratiometric fluorescence properties of pHluorin were validated both in crude extracts and permeabilized cells of this sensor strain. L. monocytogenes EGD-e/pNZ-Phelp-pHluorin was used to assess membrane damaging activity of the bacteriocins nisin A and pediocin PA-1 and to determine the minimal concentrations required for full disruption of the pH gradient across the membrane. Moreover, the sensor strain proved useful to analyze the presence of compounds affecting membrane integrity in supernatants of a nisin Z-producing Lactococcus lactis strain at different timepoints during growth. Supernatants of this strain that were active in disrupting the pH gradient across the membrane were also shown to inhibit growth of L. monocytogenes. In summary, the presented results suggest that the generated sensor strain is a convenient, fast and reliable tool to identify and characterize novel bacteriocins and other compounds that target membrane integrity.
Memory-Augmented Attention Model for Scene Text Recognition

Natural scene text recognition is a very challenging task. Attention-based encoder-decoder framework has achieved the state-of-the-art performance. However, for some complex and/or low-quality images, the alignments estimated by the content-based attention network are not accurate enough, and so, the generated glimpse vector is also not powerful enough to represent the predicted character at current time step. To solve this problem, in the paper we propose a memory-augmented attention model for scene text recognition. The proposed memory-augmented attention network (MAAN) feeds the part of character sequence already generated and all attended alignment history to the attention model when predicting the character at current time step. The whole network can be trained end-to-end. Experimental results on several challenging benchmark datasets demonstrate that the proposed memory-augmented attention model for scene text recognition can achieve a comparable or better performance compared with state-of-the-art methods.
A Bayesian Multiagent Trust Model for Social Networks

In this paper, we introduce a framework for modeling the trustworthiness of peers in the setting of online social networks. In these contexts, it may be important to be filtering the wealth of messages that have been sent, which form the ongoing communication within a large community of users. This is achieved by constructing an intelligent agent that reasons about the message and each peer rater of the message, learning over time to properly gage whether a message is good or bad to show a user, based on message ratings, rater similarity, and rater credibility. Our approach employs a partially observable Markov decision process for trust modeling, moving beyond the more traditional adoption of probabilistic reasoning using beta reputation functions. In addition to outlining the technique in full, we present empirical results to demonstrate the effectiveness of our methods, both in simulations featuring head to head comparisons with competitors, and in the context of some existing online social networks where ground truth data are available.
Incremental Frequent Sub-trajectory Mining Based on Dual Division

Frequent pattern mining has been one of the most important data mining tasks, which is widely used in traffic management, animal migration and path prediction. In the view of the large scale, high dimension and real-time updating characteristics of trajectory data in free space, this paper presents an efficient framework for frequent sub-trajectory mining. Firstly, we divide the trajectory twice in order to smooth and reduce the trajectory effectively. Secondly, we expand the distance measure in two dimensions to three to improve the reliability of frequent sub-trajectory mining. Finally, we introduce an incremental clustering algorithm based on density, which greatly reduces the space-time consumption of frequent sub-trajectory mining in real-time updating database. Experimental results demonstrate that our framework improved the efficiency greatly and obtained more reliable results.
Precompensation, BIST and Analogue Berger Codes for Self-Healing of Neuromorphic RRAM

Neuromorphic RRAM has become the most promising candidate for AI applications. But it suffers three issues including degradation, defects and errors. To overcome the three issues we proposed a precompensation technique for compensating resistive degradation. A linear-system-based BIST architecture is developed with proposed diagonal sliding march test can effectively and efficiently screen out the uncompensated degradation and permanent defects. Analog Berger codes is proposed for detecting transient errors for variation learning and self-checking for asymmetric errors. From evaluations, the precompensation takes only 5/B time for batch operations of B cycles. Proposed BIST approach and method can reduced 2LN march tests to 6N for L-level RRAMs. The self-healing ability is verified by analog-Berger-code error detection. From experiments using a typical neural network for MNIST handwritten digit dataset the network can be healed with only 2% of accuracy and about 35% of training steps.
The Relative Risk and Incidence of Immune Checkpoint Inhibitors Related Pneumonitis in Patients With Advanced Cancer: A Meta-Analysis

Background: Recently, immune checkpoint inhibitors (ICIs) have been proved one of the most promising anti-cancer therapy, series clinical trials have confirmed their efficacy. But they are also associated with distinctive set of toxic effects, which are recognized as immune-related adverse events. Among those immune-related adverse events, pneumonitis is rare, but it is often clinically serious and potentially life-threatening. Although many clinical trial results of PD-1/PD-L1 inhibitors had been reported incidence of pneumonitis, the knowledge based on the individual cohort data from each clinical trial is limited. So we conducted a meta-analysis of trials of PD-1/PD-L1 inhibitors in patients with advanced cancer and compared relative risk and incidence among different tumor types and therapeutic regimens. Such an analysis may provide important knowledge of this rare but clinically significant and potentially serious immune-related adverse event. Methods: Electronic databases were used to search eligible literatures, include randomized controlled trials (RCTs) comparing immune checkpoint inhibitors vs. standard therapies. All-grade (1–4) or high-grade (3–4) pneumonitis events were extracted. The summary relative risk, summary incidence, and 95% confidence intervals were calculated. Results: The incidence of all-grade and high-grade pneumonitis in non-small cell lung cancer (NSCLC) was significantly higher compared with other tumor types, such as Melanoma, urothelial carcinoma (UC), head and neck squamous cell carcinoma (HNSCC) (3.1% vs. 2.0%; p = 0.02, 1.4% vs. 0.6%; p = 0.03). The risk of all-grade pneumonitis was obtained from all patients in both experimental arm and control arm. Treatment with immune checkpoint inhibitors targeting PD-1/PD-L1 did significantly increase the risk of all-grade and high-grade pneumonitis compared with controls (fixed effects, RR: 4.70; 95% CI: 2.81–7.85; p < 0.00001, RR: 3.33; 95% CI: 1.68–6.59; p = 0.0006). Conclusion: The incidence of immune checkpoint inhibitors related pneumonitis was higher in NSCLC than other tumor types. Patients treated with immune checkpoint inhibitor in experiment arms are more likely to experience any grade pneumonitis than control arms. These findings suggest that clinician need to draw more attention on this rare but serious adverse event.
Military Scenario Named Entity Recognition Method Based on Deep Neural Network

For the military scenario named entity, the article proposes a supervised named entity recognition method based on deep neural network, which aims to identify and extract the troops, geographical location, weapons and equipment, organization, facilities, battlefield environment, time, etc. in the military scenario. The method avoids the complexity of artificially constructed features and the inaccuracy of military text segmentation. Bi-directional Long Short-Term Memory neural network based on character vector and the conditional random field model are used to automatically extract text features, and then identify the military scenario named entities. Experiments show that the method is higher in recognition accuracy than the traditional method and close to the level of named entity recognition in the 2eneral field.
Robust Dynamic Output Feedback Fault-Tolerant Control for T-S Fuzzy Systems Via Adaptive Sliding Mode Observers

This paper studies the problem of robust fault estimation (FE) and fault-tolerant control (FTC) for a class of Takagi-Sugeno (T-S) fuzzy systems where both uncertainties and output disturbances are considered. Firstly, A fuzzy adaptive sliding-mode observer (ASMO) is designed to estimate the system state and actuator faults. Secondly, based on the estimation information, a fuzzy dynamic output feedback fault-tolerant controller is designed to compensate for the impact of actuator faults and to stabilize the closed-loop system with the prescribed $H_{\infty}$ performance. Finally, the simulation results of nonlinear inverted pendulum with cart system validate the efficiency of the proposed method.
New non-reference image quality evaluation method for underwater turbulence blurred images

Turbulence is an important cause of image distortion in an underwater ocean environment, along with light scattering and color reduction. Turbulence blur is a universal phenomenon of underwater image degradation, and causes a loss of high frequency portion and image detail. We need a metric to evaluate underwater image quality and provide a reference for the restoration of turbulence-blurred images. It is difficult to obtain clear original images, and a subjective quality metric is time-consuming and impractical for real-time implementation, so we propose a new no-reference, objective quality evaluation algorithm, by employing CIELab color space features and mean subtracted contrast normalized (MSCN) statistical features to assess the quality of blurred images. The experimental results illustrate that our metric outperforms the other advanced image-quality metrics in the underwater turbulent-image dataset, and has a comparable performance in other datasets. Importantly, besides being highly in line with human perception, the proposed metric can effectively predict image quality with low computational complexity and meet the requirement of a real-time system.
Effects of different types of surfactants on AC breakdown voltage of refined, bleached and deodorized palm oil based CuO nanofluids

In this paper, the effects of different types of surfactants on AC breakdown voltage of Refined, Bleached and Deodorized Palm Oil (RBDPO) olein in the presence of Copper (II) Oxide (CuO) nanoparticle were investigated. The AC breakdown voltage measurement was carried out based on ASTM D 1816 at gap distances of 1 mm. Sodium Dodecyl Sulfate (SDS) as anionic surfactant, oleic acid (OA) as non-ionic surfactant and Cetyl Trimethyl Ammonium Bromide (CTAB) as cationic surfactants were used to investigate the influence of different concentrations surfactants on the AC breakdown performance of RBDPO. The concentrations were varied at 25%, 40%, 50%, 60% and 75% of volume percentage based concentration of 0.05% CuO. The results showed an enhancement of AC breakdown voltage under presence of SDS, OA and CTAB until 50% volume of concentration.
q-means: A quantum algorithm for unsupervised machine learning

Quantum machine learning is one of the most promising applications of a full-scale quantum computer. Over the past few years, many quantum machine learning algorithms have been proposed that can potentially offer considerable speedups over the corresponding classical algorithms. In this paper, we introduce q-means, a new quantum algorithm for clustering which is a canonical problem in unsupervised machine learning. The $q$-means algorithm has convergence and precision guarantees similar to $k$-means, and it outputs with high probability a good approximation of the $k$ cluster centroids like the classical algorithm. Given a dataset of $N$ $d$-dimensional vectors $v_i$ (seen as a matrix $V \in \mathbb{R}^{N \times d})$ stored in QRAM, the running time of q-means is $\widetilde{O}\left( k d \frac{\eta}{\delta^2}\kappa(V)(\mu(V) + k \frac{\eta}{\delta}) + k^2 \frac{\eta^{1.5}}{\delta^2} \kappa(V)\mu(V) \right)$ per iteration, where $\kappa(V)$ is the condition number, $\mu(V)$ is a parameter that appears in quantum linear algebra procedures and $\eta = \max_{i} ||v_{i}||^{2}$. For a natural notion of well-clusterable datasets, the running time becomes $\widetilde{O}\left( k^2 d \frac{\eta^{2.5}}{\delta^3} + k^{2.5} \frac{\eta^2}{\delta^3} \right)$ per iteration, which is linear in the number of features $d$, and polynomial in the rank $k$, the maximum square norm $\eta$ and the error parameter $\delta$. Both running times are only polylogarithmic in the number of datapoints $N$. Our algorithm provides substantial savings compared to the classical $k$-means algorithm that runs in time $O(kdN)$ per iteration, particularly for the case of large datasets.
Accelerating the Computation of Multi-Objectives Scheduling Solutions for Cloud Computing

This paper presents two practical Large Scale Multi-Objectives Scheduling (LSMOS) strategies, proposed for Cloud Computing environments. The goal is to address the problems of companies that manage a large cloud infrastructure with thousands of nodes, and would like to optimize the scheduling of several requests submitted online by users. In our context, requests submitted by users are configured according to multi-objectives criteria, as the number of used CPUs and the used memory size, to take an example. The novelty of our strategies is to select effectively, from a large set of nodes forming the Cloud Computing platform, a node that execute the user request such that this node has a good compromise among a large set of multi-objectives criteria. In this paper, first we show the limit, in terms of performance, of exact solutions. Second, we introduce approximate algorithms in order to deal with high dimensional problems in terms of nodes number and criteria number. The proposed two scheduling strategies are based on exact Kung multi-objectives decision algorithm and k-means clustering algorithm or LSH hashing (random projection based) algorithm. The experiments of our new strategies demonstrate the potential of our approach under different scenarios.