Miracle-dz/UniFAR-FTUs huggingface dataset:
Lying, computers and self-awareness

Abstract From the initial analysis of John Morris in 1976 about if computers can lie, I have presented my own treatment of the problem using what can be called a computational lying procedure. One that uses two Turing Machines. From there, I have argued that such a procedure cannot be implemented in a Turing Machine alone. A fundamental difficulty arises, concerning the computational representation of the self-knowledge a machine should have about the fact that it is lying. Contrary to Morris’ claim, I have thus suggested that computers – as far as they are Turing Machines – cannot lie. Consequently, I have claimed that moral agency attribution to a robot or any other automated AI system, cannot be made, strictly grounded on imitating behaviors. Self-awareness as an ontological grounding for moral attribution must be evoked. This can pose a recognition problem from our part, should the sentient system be the only agent capable of acknowledging its own sentience.
A Study on the Antioxidant Properties of Some Selected Medicinal Plants from Kandhamal District of Odisha, India

Antioxidant properties of medicinal plants of Kandhamal district of Odisha have not reported, while these plants such as Nyctanthes arbor-tristis L. (NA), Tinospora cordifolia (Thunb.) Miers (TC), Phyllanthus niruri L. (PN), Andrographis paniculata (Burm.f.) Wall. ex Nees (AP), Lantana camara L. (LC), Mimosa pudica L. (MP), Justicia adhatoda L. (JA), Cuscuta reflexa Roxb. (CR), Cyperus rotundus L. (CyR), Piper nigrum L. (PiN), Ocimum sanctum Linn. (OS), Catharanthus roseus (L.) G.Don (CaR) and Clitoria ternatea L. (CT) are used to treat different ailments like malaria, rheumatoid, cold and cough, piles, diarrhoea and tooth ache etc. The present work aimed to evaluate the total phenolic and flavonoid contents as well as in vitro antioxidant properties of methanolic extract of medicinal plants. The total phenolic and flavonoid contents were determined by FolinCiocalteu and, aluminium chloride method with standard gallic acid and rutin respectively. Antioxidant activity was determined by 1, 1diphenyl2picrylhydrazyl (DPPH), hydroxyl radical, superoxide radical, and hydrogen peroxide assay. The total phenolic content of medicinal plants varied from 44.53 to 297.07 mg/g while the flavonoid content was varied from 14.03 to 60.49 mg/g. In conclusion, these plants are rich sources of phenolic compounds and antioxidants and they could Original Research Article Behera et al.; AJRIB, 1(2): 1-9, 2018; Article no.AJRIB. 46636 2 be used as a natural antioxidant.
Development of a New Negative Obstacle Sensor for Augmented Electric Wheelchair

Due to pathologies or age-related problems, in some disabled people, motor impairment is associated with cognitive and/or visual impairments. This combination of limitations unfortunately leads to an inability to move around independently. Indeed, their situation does not allow them to use a conventional electric wheelchair, for safety reasons, and for the moment there is no other technological solution providing safe movement capacity. This lack of access to an autonomous travel solution has the consequence of weakening the intellectual, personal, social, cultural and moral development, as well as the life expectancy, of the people concerned. In this context, our team is working on the development of an optoelectronic system that secures the displacement of electric wheelchairs. This is a large project that requires the development of several functionalities such as: the anti-collision of the wheelchair with its environment, the prevention of falls from the wheelchair on uneven levels, and the adaptation of the system mechanically and electronically to the majority of commercially available electric wheelchair models, among others. In this article, we introduce our solution for detecting dangerous height differences, also called “negative obstacles”, through the creation of a dedicated sensor. This sensor works by optical triangulation and can embed several laser beams in order to extend its detection zone. It has the particularity of being robust in direct sunlight and rain and has a sufficiently high measurement rate to be suitable for the displacement of electric wheelchairs. We develop an adapted algorithm, and point out compromises, in particular between the orientation of the laser beams and the maximal speed of the wheelchair.
A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-Enabled IoT

The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.
A Framework for Managing Device Association and Offloading the Transport Layer’s Security Overhead of WiFi Device to Access Points

Considering the resource constraints of Internet of Things (IoT) stations, establishing secure communication between stations and remote servers imposes a significant overhead on these stations in terms of energy cost and processing load. This overhead, in particular, is considerable in networks providing high communication rates and frequent data exchange, such as those relying on the IEEE 802.11 (WiFi) standard. This paper proposes a framework for offloading the processing overhead of secure communication protocols to WiFi access points (APs) in deployments where multiple APs exist. Within this framework, the main problem is finding the AP with sufficient computation and communication capacities to ensure secure and efficient transmissions for the stations associated with that AP. Based on the data-driven profiles obtained from empirical measurements, the proposed framework offloads most heavy security computations from the stations to the APs. We model the association problem as an optimization process with a multi-objective function. The goal is to achieve maximum network throughput via the minimum number of APs while satisfying the security requirements and the APs’ computation and communication capacities. The optimization problem is solved using genetic algorithms (GAs) with constraints extracted from a physical testbed. Experimental results demonstrate the practicality and feasibility of our comprehensive framework in terms of task and energy efficiency as well as security.
IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).
Averting BER Floor with Iterative Source and Channel Decoding for Layered Steered Space-Time Codes

The widespread development in wireless technologies and the advancements in multimedia communication have brought about a positive impact on the performance of wireless transceivers. We investigate the performance of our three-stage turbo detected system using state-of-the-art high efficiency video coding (HEVC), also known as the H.265 video standard. The system makes use of sphere packing (SP) modulation with the combinational gain technique of layered steered space-time code (LSSTC). The proposed three-stage system is simulated for the correlated Rayleigh fading channel and the bit-error rate (BER) curve obtained after simulation is free of any floor formation. The system employs low complexity source-bit coding (SBC) for protecting the H.265 coded stream. An intermediate recursive unity-rate code (URC) with an infinite impulse response is employed as an inner precoder. More specifically, the URC assists in the prevention of the BER floor by distributing the information across the decoders. There is an observable gain in the BER and peak signal-to-noise ratio (PSNR) performances with the increasing value of minimum Hamming distance (dH,min) using the three-stage system. Convergence analysis of the proposed system is investigated through an extrinsic information transfer (EXIT) chart. Our proposed system demonstrates better performance of about 22 dB than the benchmarker utilizing LSSTC-SP for iterative source-channel detection, but without exploiting the optimized SBC schemes.
Seasonal and diurnal variability in O3, black carbon, and CO measured at the Rwanda Climate Observatory

Abstract. Air pollution is understudied in sub-Saharan Africa, resulting in a gap in the scientific understanding of emissions, atmospheric processes, and impacts of air pollutants in this region. The Rwanda Climate Observatory, a joint partnership between MIT and the government of Rwanda, has been measuring ambient concentrations of key long-lived greenhouse gases and the short-lived climate-forcing pollutants CO2, CO, CH4, black carbon (BC), and O3 with state-of-the-art instruments on the summit of Mt. Mugogo (1.586∘ S, 29.566∘ E; 2590 m above sea level) since May 2015. Rwanda is a small, mountainous, and densely populated country in equatorial East Africa, currently undergoing rapid development but still at less than 20 % urbanization. Black carbon concentrations during Rwanda's two dry seasons (December–January–February, DJF, and June–July–August, JJA), which coincide with the two regional biomass burning seasons, are higher at Mt. Mugogo than in major European cities with daily values (24 h) during the dry season of around 5 µg m−3 (daily average concentrations ranging from less than 0.1 to over 17 µg m−3 for the entire measurement period). BC baseline concentrations during biomass burning seasons are loosely correlated with fire radiative power data for the region acquired with a MODIS satellite instrument. The position and meteorology of Rwanda is such that the emissions transported from both the northern and southern African biomass burning seasons affect BC, CO, and O3 concentrations in Rwanda. Spectral aerosol absorption measured with a dual-spot Aethalometer varies seasonally due to changes in types of fuel burned and the direction of pollution transport to the site. Ozone concentrations peaked during Rwanda's dry seasons (daily measured maximum of 70 ppbv). The understanding and quantification of the percent contributions of regional and local (beyond large-scale biomass) emissions is essential to guide policy in the region. During the rainy seasons, local emitting activities (e.g., cooking, transportation, trash burning) remain steady, regional biomass burning is low, and transport distances are shorter as rainout of pollution occurs regularly. Thus, local pollution at Mugogo can be estimated during this time period and was found to account for up to 35 % of annual average BC measured. Our measurements indicate that air pollution is a current and growing problem in equatorial East Africa.
Co-Density Distribution Maps for Advanced Molecule Colocalization and Co-Distribution Analysis

Cellular and subcellular spatial colocalization of structures and molecules in biological specimens is an important indicator of their co-compartmentalization and interaction. Presently, colocalization in biomedical images is addressed with visual inspection and quantified by co-occurrence and correlation coefficients. However, such measures alone cannot capture the complexity of the interactions, which does not limit itself to signal intensity. On top of the previously developed density distribution maps (DDMs), here, we present a method for advancing current colocalization analysis by introducing co-density distribution maps (cDDMs), which, uniquely, provide information about molecules absolute and relative position and local abundance. We exemplify the benefits of our method by developing cDDMs-integrated pipelines for the analysis of molecules pairs co-distribution in three different real-case image datasets. First, cDDMs are shown to be indicators of colocalization and degree, able to increase the reliability of correlation coefficients currently used to detect the presence of colocalization. In addition, they provide a simultaneously visual and quantitative support, which opens for new investigation paths and biomedical considerations. Finally, thanks to the coDDMaker software we developed, cDDMs become an enabling tool for the quasi real time monitoring of experiments and a potential improvement for a large number of biomedical studies.
Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers’ future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.
Conductive Polymer Composites for Hydrogen Sulphide Sensors Working at Sub-PPM Level and Room Temperature

Hybrid composites based on tin chloride and the conductive polymers, polyaniline (PAni) and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), were integrated into high-performance hydrogen sulphide (H2S) gas sensors working at room temperature. The morphology and chemical properties were studied by scanning and transmission electron microscopy (SEM, TEM), energy dispersive spectroscopy (EDS) and Fourier-transform infrared (FTIR). The composites demonstrated a slightly porous nanostructure and strong interactions between the polymers and the metal salt, which slightly dopes PAni. The hybrid sensors exhibited a very low detection limit (<85 ppb), fast response, repeatability, reproducibility and stability over one month. Moreover, this work presents how calibration based on the derivative of the signal can give hybrid sensors the ability to quantify the concentration of targeted gas, even during continuous variation of the analyte concentration. Finally, the effect of interfering species, such as water and ammonia, is discussed.
How Does Job Insecurity Affect Workplace Harassment? The Interaction Effect of Hypercompetitive Attitude, Coworker Impression Management, and Leader Narcissism

With concerns that artificial intelligence may replace existing jobs, job insecurity is becoming more prevalent. In-depth study of how job insecurity affects our society has become an important research topic. This study investigates the internal mechanisms through which such job insecurity influences workplace harassment. Based on the theories of psychological contract breach and the conservation of resources, this study proposes an indirect effect of job insecurity and a three-way moderation effect of hypercompetitive attitude, perceived coworker impression management, and leader narcissism on aggression intention. Using survey data from 286 employees in South Korea, bootstrapping analysis, hierarchical regression analysis, and a slope-difference test were performed to confirm the mediation and moderation effects. The results showed that hypercompetitive attitude mediates the association between job insecurity and aggression intention. The three-way interaction effect was also confirmed, such that the interaction effect of hypercompetitive attitude and coworker impression management is only effective when leader narcissism is high. This study contributes to the literature and business practices by offering significant suggestions to aid a more in-depth understanding of the workplace harassment occurrence process.
A New Opinion Polarization Index Developed by Integrating Expert Judgments

Opinion polarization is increasingly becoming an issue in today’s society, producing both unrest at the societal level, and conflict within small scale communications between people of opposite opinion. Often, opinion polarization is conceptualized as the direct opposite of agreement and consequently operationalized as an index of dispersion. However, in doing so, researchers fail to account for the bimodality that is characteristic of a polarized opinion distribution. A valid measurement of opinion polarization would enable us to predict when, and on what issues conflict may arise. The current study is aimed at developing and validating a new index of opinion polarization. The weights of this index were derived from utilizing the knowledge of 58 international experts on polarization through an expert survey. The resulting Opinion Polarization Index predicted expert polarization scores in opinion distributions better than common measures of polarization, such as the standard deviation, Van der Eijk’s polarization measure and Esteban and Ray’s polarization index. We reflect on the use of expert ratings for the development of measurements in this case, and more in general.
Differences in the Anthropometric and Physiological Profiles of Hungarian Male Rowers of Various Age Categories, Rankings and Career Lengths: Selection Problems

Background: Little is known about the anthropometric and physiological profiles of lower-ranking athletes who aspire to rise to the pinnacle of their profession. Aim: The aim of this study was to create anthropometric and physiological profiles of Hungarian male rowers of different age categories (15–16, 17–18, and over 18 years), sports rankings and career lengths. Materials and Methods: Anthropometric and physiological profiles were created for 55 juniors, 52 older juniors and 23 seniors representing seven of the largest Hungarian rowing clubs. One-way independent analysis of variance (ANOVA) was used to compare arithmetic means. Results: Rowers in older age categories were significantly taller (185.0 ± 5.0 cm vs. 183.0 ± 7.3 cm vs. 178.7 ± 7.2 cm) and heavier (81.1 ± 8.8 kg vs. 73.7 ± 8.4 kg vs. 66.8 ± 12.3 kg) than their younger peers, with significantly higher BMI values and larger body dimensions. Compared to younger athletes, rowers in older age categories also covered 2,000 m significantly faster (6.6 ± 0.3 min vs. 6.9 ± 0.4 min vs. 7.5 ± 0.5 min) while developing significantly more power (372.2 ± 53.0 W vs. 326.8 ± 54.5 W vs. 250.6 ± 44.6 W). Similarly, seniors and older juniors had higher values of maximal oxygen uptake and force max (by 6.2 and 7.0 ml/kg/min, and by 263.4 and 169.8 N). Within the older juniors, internationally ranked rowers had significantly greater body height (+ 5.9 cm), body mass (+ 6.1 kg), sitting height (+ 2.7 cm), arm span (+ 7.9 cm), limb length (+ 3.73 cm) and body surface area (+ 0.21 m2). They also rowed 2,000 m significantly faster (–0.43 min, p < 0.001) and had significantly higher values of power (+ 58.3 W), relative power (+ 0.41 W/kg), jump height (+ 4.5 cm), speed max (+ 0.18 m/s) and force max (+ 163.22 N). Conclusion: The study demonstrated that potential differences in anthropometric and physiological profiles are more difficult to capture in non-elite rowers, and that the final outcome may be determined by external factors. Therefore, athletes with superior aptitude for rowing are more difficult to select from among lower-ranking rowers, and further research is needed to determine specific training requirements to achieve the maximum rowing performance.
Nonlinear model of liquid chromatography considering finite rates of adsorption-desorption kinetics and core-shell adsorbents

Abstract A nonlinear general rate model is numerically approximated to simulate fixed-bed chromatographic columns packed with core-shell particles. The model incorporates explicitly the effect of finite rates of adsorption and desorption at the adsorption sites, typically assumed to be very fast compared to the rates of the various transport processes. Using core-shell particles as a stationary phase can have advantages over applying a fully-porous stationary phase, such as higher efficiencies and better resolution of the sample components. A high resolution finite volume scheme is extended and applied to approximate the model equations. Ranges of the kinetic parameters in which limited rates of the intrinsic adsorption and desorption steps needs to be taken into account are estimated. A few case studies of predicting the elution of single-component and two-component mixtures are considered to evaluate the effects of adsorption and desorption rate constants, core-radius fraction, axial-dispersion coefficient, film mass transfer, and intraparticle diffusion on the elution profiles. Furthermore, it is demonstrated that optimum values of the inert core radius can be obtained by evaluating a typical criterion for process performance. Graphical Abstract