Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Control and Optimisation of Multi-Agent Systems and Complex Networks for Systems Engineering

Systems Engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address Systems Engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in Industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks; along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.
Comparative Analysis of the Feature Extraction Performance of Augmented Reality Algorithms

The algorithms that extract keypoints and descriptors in augmented reality applications are getting more and more important in terms of performance. Criterions like time and correct matching of points gain more impact according to the type of application. In this paper, the performance of the algorithms used to identify an image using keypoint and descriptor extraction is studied. In the context of this research, main criterion like the number of keypoints and descriptors that the algorithms extract, algorithm execution time, and the quality of keypoints and descriptors extracted are considered as the performance metrics. Same data stacks were used for obtaining comparison results. In addition to comparisons for a group of well-known augmented reality applications, the best performing algorithms for varying applications were also suggested. C++ language and OpenCV library were used for the implementation of the augmented reality algorithms compared.
Great Britain Specific Inhibition of Pyruvate Transport in Rat Liver Mitochondria and Human Erythrocytes by a-Cyano-4-hydroxycinnamate

Previous studies have been unable to establish with any certainty whether or not there is a specific carrier system involved in the transport of pyruvate across either the plasma membrane of cells or the inner membrane of mitochondria (Watts & Randle, 1967; Watts, 1969; Papa et al., 1971; Zahlten et al., 1972). The principal reason for this uncertainty has been the absence of a specific inhibitor of pyruvate transport (Klingenberg, 1970a,b). In this*communication, we report studies which demonstrate the specific inhibition of pyruvate transport across both the plasma membrane of erythrocytes and also the inner membrane of rat liver mitochondria by a-cyano-4-hydroxycinnamate. This is taken to imply that there is a specific carrier involved in pyruvate transport across both membranes. Evidence is presented that lactate may also be transported across the plasmamembrane of erythrocytes by the same carrier. The inhibitory properties of a-cyano-4-hydroxycinnamate became apparent during a survey of analogues of the enol form of pyruvate aimed at identifying specific inhibitors ofenzymes involved in pyruvate metabolism. The possibility that this compound inhibited the mitochondrial transport of pyruvate was suspected when it was observed that rates of pyruvate oxidation by mitochondria from blowfly flight muscle and rat heart, pyruvate-dependent CO2 fixation in liver mitochondria and pyruvate inhibition of pyruvate dehydrogenase kinase in fat-cell mitochondria were all greatly diminished on addition of low concentrations of a-cyano-4-hydroxycinnamate (less than 200pUM) to the mitochondrial incubation media. However, at concentrations of up to 2mM, the compound had little or no inhibitory effects on the appropriate enzymes prepared from extracts of mitochondria (A. P. Halestrap & R. M. Denton, unpublished work).
Current Perspectives on Medicinal and Aromatic Plants

Recently, excessive and unconscious uses of chemical fertilizer cause significant hazards on natural resources and subsequently cause serious environmental problems. As an alternative instead of chemical fertilizer, microbial fertilizers are used for healthier soil and plant. The aim of this study was to determine the effect of different doses of microbial fertilizers on yield and essential oil ratio in Mint (Mentha piperita L.) cultivars. The experiment was carried out in a pot according to the randomized parcel design. The study was carried out with three cultivars namely, Mentha piperita swiss, M. piperita chocolate, M. piperita multimentha, four doses (control, 1, 1.5 and 2 doses) and three replicates. In the study; plant height (cm), fresh herb yield (g/m2), drog herb yield (g/m2), drog leaf yield (g/m2), essential oil ratio (%) and essential oil yield (L/m2) were examined. Along with the study, Microbial fertilizers exhibited significant effects on the parameters examined herein. The yield of fresh herb yield and drog leaf yield were found between 1342.5-2001.1 g/m2, 160.9-228.0 g/m2 respectively and the essential oil ratio varied between 1.55-1.93% according to microbial fertilizer doses.
Effects of Household Income on Graduation Level Across US Counties

This paper analyzes the effect of household income on the graduation level on a county scale across the United States, following the hypothesis that household income should have a positive impact on graduation rates, measured in the percentage of people ages 18 to 24 with at least a high school degree as the dependent variable. Data from the 2010 US Census is used to estimate simple and multiple regression models. Other independent variables used are teen birth rate, average household size, poverty level, percent of people 25 and older with at least a high school degree, urban/rural location type, logarithm of household income, and percent of people ages 45 to 64 with at least a high school degree to take into account family history and regional differences and reduce omitted variable bias. The simple regression shows a positive relation between graduation rate and household income. The multiple regressions show that most independent variables have the hypothesized relation with the dependent variable, but fail to provide conclusive evidence about the hypothesis.
PRBM_A_231792 67..78

Haini Liu Jingjie Zhao Yi Cao Hualin Jiang Shaoru Zhang Zhongqiu Hua Jing Ren Dan Ren 1Department of Nursing, Health Science Center, Xi’an Jiaotong University, Xi’an, People’s Republic of China; 2College of Media, Xijing University, Xian, People’s Republic of China; 3Department of Epidemiology and Health Statistics, Health Science Center, Xi’an Jiaotong University, Xi’an City, People’s Republic of China Purpose: Drug-resistant tuberculosis (DR-TB) remains a major global public health issue. For DR-TB patients, effective adaptation is crucial to prevent disease progression, improve health outcomes and decrease mortality. To date, there is no appropriate tool for evaluating the adaptation status of DR-TB patients. In this work, we aim to develop an adjustment scale for DR-TB patients (AS-DRTBP) and to evaluate its psychometric properties. Patients and Methods: The development of the AS-DRTBP was based on the theory of the Roy adaptation model (RAM). The scale was designed through a literature review, indepth individual interviews, a Delphi survey, and pilot testing. In total, 433 patients with DRTB were recruited to validate the instrument. The split-half reliability coefficient, Cronbach’s alpha coefficient, and test-retest reliability coefficient were calculated to assess the reliability of the instrument. Content validity, construct validity and concurrent validity tests were applied to calculate the validity of the instrument. Results: The final AS-DRTBP consisted of four dimensions and 26 items. The Cronbach’s alpha coefficient, split-half reliability coefficient and test-retest reliability coefficient were 0.893, 0.954, and 0.853, respectively. The content validity index was 0.92. Four factors that explained 64.605% of the total variance were also further determined by confirmatory factor analysis (CFA). The CFA results showed that the fitting effect of the model was appropriate (CMIN/DF = 1.681, GFI = 0.832, AGFI = 0.799, RMSEA = 0.055, SRMR = 0.0684). The AS-DRTBP and adjustment scale had correlation in the total score, and the correlation coefficient was 0.355 (p<0.05). Conclusion: The findings of this study demonstrate that the AS-DRTBP is a reliable and valid instrument formeasuring the adaptation status of patients withDR-TB, allowing health providers to comprehend the adaptive level of DR-TB patients and thus laying the foundation for interventions to help these patients achieve a physiologically, psychologically and socially optimal outcome.
Multilingual NLP methods for Multilingual Dialogue Systems

Multilinguality have mostly been of interest in machine translation research but multilinguality is a system feature that will grow in importance in other areas as well, since natural language technologies are entering the commercial market and people want to have access to the latest technology in their own language. Multilinguality is also a desirable feature for developers as their products may reach a much broader market. In this paper, I will focus on multilinguality as a feature in dialogue systems. Many multilingual dialogue systems are still systems that have been developed separately for each of the languages that they manage. In this paper, I will take a glance at cross-linguistic differences and look at how they affect the choice of NLP techniques when building a multilingual dialogue system i.e. a single system that can handle dialogues in different
First Aid and Training of Middle Finger Injury Diagnosis Based on Biomechanics

Shot put is a competitive project that combines speed of movement and coordination. The blind force exerts the finger damage to a large extent, which affects the scientific and systematic nature of sports training, and should be taken seriously and solved. We further compare and analyze the technical parameters. In the final stage of pushing the shot, the strength of the left side support will have an impact on the speed of the shot and the height of the shot. The shot speed is the combined speed of the horizontal speed and the vertical speed. With good left side support, the necessary vertical speed can be obtained with as little instantaneous horizontal speed as possible. The technical requirement of final exertion is that the working distance of the throwing arm, i.e. the working distance of the shot, should be extended as far as possible from the beginning of the final exertion to the moment of the shot put, and the action should be completed in the shortest time. In this paper, the mechanism of finger injury is analyzed and diagnosed based on the principle of biomechanics, and the methods of improving training are put forward to reduce finger injury in shot putting as much as possible.
Reducing Noise from Competing Neighbours: Word Retrieval with Lateral Inhibition in Multilink

Multilink is a computational model for word retrieval in monolingual and multilingual individuals under different task circumstances (Dijkstra et al., 2018). In the present study, we added lateral inhibition to Multilink's lexical network. Parameters were fit on the basis of reaction times from the English, British, and Dutch Lexicon Projects. We found a maximum correlation of 0.643 (N=1,205) on these data sets as a whole. Furthermore, the simulations themselves became faster as a result of adding lateral inhibition. We tested the fitted model to stimuli from a neighbourhood study (Mulder et al., 2018). Lateral inhibition was found to improve Multilink's correlations for this study, yielding an overall correlation of 0.67. Next, we explored the role of lateral inhibition as part of the model's task/decision system by running simulations on data from two studies concerning interlingual homographs (Vanlangendonck et al., in press; Goertz, 2018). We found that, while lateral inhibition plays a substantial part in the word selection process, this alone is not enough to result in a correct response selection. To solve this problem, we added a new task component to Multilink, especially designed to account for the translation process of interlingual homographs, cognates, and language-specific control words. The subsequent simulation results showed patterns remarkably similar to those in the Goertz study. The isomorphicity of the simulated data to the empirical data was further attested by an overall correlation of 0.538 (N=254) between reaction times and simulated model cycle times, as well as a condition pattern correlation of 0.853 (N=8). We conclude that Multilink yields an excellent fit to empirical data, particularly when a task-specific setting of the inhibition parameters is allowed.
Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional Network

This paper presents a neural network based method Multi-Task Affect Net(MTANet) submitted to the Affective Behavior Analysis in-the-Wild Challenge in FG2020. This method is a multi-task network and based on SE-ResNet modules. By utilizing multi-task learning, this network can estimate and recognize three quantified affective models: valence and arousal, action units, and seven basic emotions simultaneously. MTANet achieve Concordance Correlation Coefficient(CCC) rates of 0.28 and 0.34 for valence and arousal, F1-score of 0.427 and 0.32 for AUs detection and categorical emotion classification.
Machine Ethics: The Creation of a Virtuous Machine

Artificial intelligence (AI) was initially developed as an implicit moral agent to solve simple and clearly defined tasks where all options are predictable. However, it is now part of our daily life powering cell phones, cameras, watches, thermostats, vacuums, cars, and much more. This has raised numerous concerns and some scholars and practitioners stress the dangers of AI and argue against its development as moral agents that can reason about ethics (e.g., Bryson 2008; Johnson and Miller 2008; Sharkey 2017; Tonkens 2009; van Wynsberghe and Robbins 2019). Even though we acknowledge the potential threat, in line with most other scholars (e.g., Anderson and Anderson 2010; Moor 2006; Scheutz 2016; Wallach 2010), we argue that AI advancements cannot be stopped and developers need to prepare AI to sustain explicit moral agents and face ethical dilemmas in complex and morally salient environments.
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving

Automated driving in urban settings is challenging. Human participant behavior is difficult to model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when they face unmodeled dynamics. On the other hand, the more recent, end-to-end Deep Reinforcement Learning (DRL) based model-free ADSs have shown promising results. However, pure learning-based approaches lack the hard-coded safety measures of model-based controllers. Here we propose a hybrid approach for integrating a path planning pipe into a vision based DRL framework to alleviate the shortcomings of both worlds. In summary, the DRL agent is trained to follow the path planner's waypoints as close as possible. The agent learns this policy by interacting with the environment. The reward function contains two major terms: the penalty of straying away from the path planner and the penalty of having a collision. The latter has precedence in the form of having a significantly greater numerical value. Experimental results show that the proposed method can plan its path and navigate between randomly chosen origin-destination points in CARLA, a dynamic urban simulation environment. Our code is open-source and available online 11https://github.com/Ekim-Yurtsever/Hybrid-DeepRL-Automated-Driving.
Global Weak Solutions to a Cahn-Hilliard-Navier-Stokes System with Chemotaxis and Singular Potential

We analyze a diffuse interface model that describes the dynamics of incompressible two-phase flows with chemotaxis effect. The PDE system couples a Navier-Stokes equation for the fluid velocity, a convective Cahn-Hilliard equation for the phase field variable with an advection-diffusion-reaction equation for the nutrient density. For the system with a singular potential, we prove the existence of global weak solutions in both two and three dimensions. Besides, in the two dimensional case, we establish a continuous dependence result that implies the uniqueness of global weak solutions. The singular potential guarantees that the phase field variable always stays in the physically relevant interval [-1,1] during time evolution. This property enables us to obtain the well-posedness result without any extra assumption on the coefficients that has been made in the previous literature.
Using Inaudible Audio to Improve Indoor-Localization- and Proximity-Aware Intelligent Applications

While it is often critical for indoor-location- and proximity-aware applications to know whether a user is in a space or not (e.g., a specific room or office), a key challenge is that the difference between standing on one side or another of a doorway or wall is well within the error range of most RF-based approaches. In this work, we address this challenge by augmenting RF-based localization and proximity detection with active ultrasonic sensing, taking advantage of the limited propagation of sound waves. This simple and cost-effective approach can allow, for example, a Bluetooth smart-lock to discern whether a user is inside or outside their home in order to lock or unlock doors automatically. We describe a configurable architecture for our solution and present experiments that validate this approach but also demonstrate that different user behavior and application needs can impact system configuration decisions. Finally, we describe applications that could benefit from our solution and address privacy concerns.
Fairness-Aware Hybrid Precoding for mmWave NOMA Unicast/Multicast Transmissions in Industrial IoT

This paper investigates dual-layer non-orthogonally superimposed transmissions for industrial internet of things (IoT) millimeter-wave communications. Essentially, the overlayer is a ubiquitous multicast signal devised to serve all the devices in coverage with a common message, i.e., critical control packet. The underlayer is a composite signal that consists of private unicast messages. Due to safety implications, it is critical that all devices can decode the multicast information. To ensure this requirement, we jointly optimize the hybrid precoder, analog combiners, power allocation, and fairness. Specifically, we incorporate a power splitting constraint between the two overlaid signals and enforce supplementary per-device constraints to guarantee multicast fairness. Performance is evaluated in terms of the spectral efficiency, multicast fairness, and bit error rate, thus corroborating the feasibility of our proposed scheme.
A Corrective View of Neural Networks: Representation, Memorization and Learning

We develop a corrective mechanism for neural network approximation: the total available non-linear units are divided into multiple groups and the first group approximates the function under consideration, the second group approximates the error in approximation produced by the first group and corrects it, the third group approximates the error produced by the first and second groups together and so on. This technique yields several new representation and learning results for neural networks. First, we show that two-layer neural networks in the random features regime (RF) can memorize arbitrary labels for arbitrary points under under Euclidean distance separation condition using $\tilde{O}(n)$ ReLUs which is optimal in $n$ up to logarithmic factors. Next, we give a powerful representation result for two-layer neural networks with ReLUs and smoothed ReLUs which can achieve a squared error of at most $\epsilon$ with $O(C(a,d)\epsilon^{-1/(a+1)})$ for $a \in \mathbb{N}\cup\{0\}$ when the function is smooth enough (roughly when it has $\Theta(ad)$ bounded derivatives). In certain cases $d$ can be replaced with effective dimension $q \ll d$. Previous results of this type implement Taylor series approximation using deep architectures. We also consider three-layer neural networks and show that the corrective mechanism yields faster representation rates for smooth radial functions. Lastly, we obtain the first $O(\mathrm{subpoly}(1/\epsilon))$ upper bound on the number of neurons required for a two layer network to learn low degree polynomials up to squared error $\epsilon$ via gradient descent. Even though deep networks can express these polynomials with $O(\mathrm{polylog}(1/\epsilon))$ neurons, the best learning bounds on this problem require $\mathrm{poly}(1/\epsilon)$ neurons.
Optimizing Correlated Graspability Score and Grasp Regression for Better Grasp Prediction

Grasping objects is one of the most important abilities to master for a robot in order to interact with its environment. Current state-of-the-art methods rely on deep neural networks trained to predict a graspability score jointly but separately from regression of an offset of grasp reference parameters, although the predicted offset could decrease the graspability score. In this paper, we extend a state-of-the-art neural network with a scorer which evaluates the graspability of a given position and introduce a novel loss function which correlates regression of grasp parameters with graspability score. We show that this novel architecture improves the performance from 81.95% for a state-of-the-art grasp detection network to 85.74% on Jacquard dataset. Because real-life applications generally feature scenes of multiple objects laid on a variable decor, we also introduce Jacquard+, a test-only extension of Jacquard dataset. Its role is to complete the traditional real robot evaluation by benchmarking the adaptability of a learned grasp prediction model on a different data distribution than the training one while remaining in totally reproducible conditions. Using this novel benchmark and evaluated through the Simulated Grasp Trial criterion, our proposed model outperforms a state-of-the-art one by 7 points.
Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks

In recent decades, neural network based methods have significantly improved the performance of speech enhancement. Most of them estimate time-frequency (T-F) representation of target speech directly or indirectly, then resynthesize waveform using the estimated T-F representation. In this work, we proposed the temporal convolutional recurrent network (TCRN), an end-to-end model that directly map noisy waveform to clean waveform. The TCRN, which is combined convolution and recurrent neural network, is able to efficiently and effectively leverage short-term ang long-term information. Furthermore, we present the architecture that iterately downsample and upsample speech during forward propagation. We show that our model is able to improve the performance of model, compared with existing convolutional recurrent networks. Furthermore, We present several key techniques to stabilize the training process. The experimental results show that our model consistently outperforms existing speech enhancement approaches, in terms of speech intelligibility and quality.