Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Machine Learning Techniques for Intrusion Detection System

It is very important to protect organizations assets and resources over the network from attackers. Much of the work is already been done in the past by different researchers from different corners of the universe. Many of them have been proved successful over the years. The work is related to detection of such intruders prior any damage being done by them to important assets. The systems used for intruder detection are called as Intrusion Detection Systems (IDS). Now a days, solving problems with data acquisition is very effective, which is nothing but the machine learning approach. Same approach can be used for implementation of IDS. In fact, many researchers have done the lot of work in IDS implementations by using different machine learning techniques. There are number of machine learning techniques which are proving and producing better results for in different areas for problem solutions. Here, we will try to find and compare the different results obtained by researchers with different machine learning techniques for IDS implementation.
Matlab Mobile as a Support Tool for The Performance of Students in Engineering

Based on a quasi-experimental study, this paper analyzes MATLAB MOBILE software as a support tool for academic performance in the subject of teaching numerical methods, algorithms and programming languages for engineering students. The decision was made to address this problem with a sample of 84 students among the groups for different semesters studying the aforementioned subject. From work as a teacher, based on observations and information recorded in the records of the school services department, it is necessary to look for new strategies for academic improvement; because the results obtained from the average of this subject show ups and downs during the years 2015 to 2018, because the average obtained is 16 in relation to the maximum score of 20 for the achievement of the expected competition, which means that they do not bring bases for the development of skills and abilities to learn to program and interpret algorithms. The work done in front of the group of students, allows observing that when asking the student to solve a problem using a programming language, they present difficulties, which causes that they do not wake up in them, the interest to solve them through the programming, seen this way The programming tool that combines the features to solve the problem is MATLAB MOBILE. Reason why, the purpose of this work is to demonstrate, how the MATLAB MOBILE integrating software in the aforementioned subject can contribute to increase comprehension and academic performance.
WiHAR: From Wi-Fi Channel State Information to Unobtrusive Human Activity Recognition

A robust and unobtrusive human activity recognition system is essential to a multitude of applications, such as health care, active assisted living, robotics, sports, and tele-immersion. Existing well-performing activity recognition methods are either vision- or wearable sensor-based. However, they are not fully passive. In this paper, we develop WiHAR—an unobtrusive Wi-Fi-based activity recognition system. WiHAR uses the Wi-Fi network interface card to capture the channel state information (CSI) data. These CSI data are effectively processed, and then amplitude and phase information is used to obtain the spectrogram. In the subsequent step, the time-variant mean Doppler shift (MDS) caused by the human body movements in the radio signals before their arrival at the receiver is estimated. The MDS is used to extract time and frequency domain features that are needed to train the supervised learning algorithms (i.e., decision tree, linear discriminant analysis, and support vector machines (SVM)) to assess the performance of the WiHAR. Our results show that WiHAR combined with SVM achieves 96.2% recognition accuracy on the data set consisting of 9 participants where each participant performed four activities including: walking, falling, picking up an object from the ground, and sitting on a chair.
Measurement and Characterization of Nano-Electro-Mechanical Systems Using Laser Interferometry

In this work, we describe a method to measure and characterize the mechanical properties of nano-electro-mechanical systems (NEMS) based on laser interferometry. The resonant mechanical modes of doubly-clamped nanomechanical beam resonators were first characterized using their thermal fluctuations in air. Afterwards, the NEMS devices were electro-thermally actuated, and their frequency responses were measured, also in air. The main novelty in this work is the simultaneous use of two electro-thermal actuators integrated on opposite ends of the NEMS, which could selectively actuate higher order modes based on the relative phase difference (of 0°, 45° or 90°) applied between the two actuators. All the measurements were carried out in a homodyne Michelson interferometer, allowing for ultrasensitive non-contact, non-invasive, remote, and non-destructive analysis.
Authors’ Reply: Robust and Efficient Estimation of the Tail Index of a Single-Parameter Pareto Distribution - Discussion by Vytaras Brazauskas; Robert Serfling

does not depend on UBP. It grows with n as O(n) compared to O(n) for the generalized median. Exact evaluation of an M-estimator for e 5 . 07 and n 5 500 requires only five seconds with the S-PLUS 2000 software on a Pentium III 500 MHz computer. In conclusion, M, CM, and GM estimators are globally robust; they can all reach a high breakdown point. For a given UBP; M and, to a larger extent, CM estimators are more efficient than the GM estimator. However, the GM estimator is more locally robust.
The Vertebrate Fauna of the Upper Permian of Niger—VII. Cranial Anatomy and Relationships of Bunostegos akokanensis (Pareiasauria)

ABSTRACT We describe newly recovered cranial material of Bunostegos akokanensis, a pareiasaurian reptile known from the Upper Permian Moradi Formation of northern Niger. Bunostegos is highly autapomorphic, with diagnostic cranial features including two or three hemispherical bosses located above and between the external nares; laterally projecting supraorbital ‘horn’ formed by an enlarged postfrontal; large foramen present on ventral surface of postfrontal; and hemispherical supratemporal boss located at posterolateral corner of skull roof. We addressed the phylogenetic position of Bunostegos by incorporating it into a cladistic analysis of 29 parareptilian taxa (including all 21 currently valid pareiasaurs) and 127 cranial and postcranial characters. The results of this analysis place Bunostegos as more derived than middle Permian forms such as Bradysaurus and as the sister taxon to the clade including Deltavjatia plus Velosauria. Certain characters, such as the pattern of cranial ornamentation and the size and placement of the tabulars, appear to be more similar to more derived pareiasaurs such as Elginia from Scotland and Arganaceras from Morocco, but the most parsimonious tree topology indicates that these features were evolved independently in the Nigerien form. The lack of both dicynodont herbivores and Glossopteris, combined with the presence of a giant herbivorous captorhinid, indicates a markedly different community structure in the Permian of Niger compared with those for contemporaneous southern Pangean basins (i.e., Karoo, Luangwa, Ruhuhu). The endemic tetrapod fauna of Niger supports the theory that central Pangea was biogeographically isolated from the rest of the supercontinent by desert-like conditions during Late Permian times. SUPPLEMENTAL DATA—Supplemental materials are available for this article for free at www.tandfonline. com/UJVP
High Bit-Depth Seismic Data Compression: A Novel Codec Under the Framework of HEVC

Motivated by the superior performance of High Efficiency Video Coding (HEVC), and driven by the rapid growth in data volume produced by seismic surveys, in this work we explore a 32 bits per pixel (b/p) extension of the HEVC codec for compression of seismic data. We propose to reassemble seismic slices in a format that corresponds to video signal and benefit from the coding gain achieved by HEVC inter mode, besides the possible advantages of the (still image) HEVC intra mode. To this end, we modify almost all components of the original HEVC codec to cater for high bit-depth coding of seismic data: Lagrange multiplier used in optimization of the coding parameters has been adapted to the new data statistics, core transform and quantization have been reimplemented to handle the increased bit-depth range, and modified adaptive binary arithmetic coder has been employed for efficient entropy coding. Even though the new codec after implementation of the proposed modifications goes beyond the standardized HEVC, it still maintains a generic HEVC structure, and it is developed under the general HEVC framework. Thus, we tailored a specific codec design which, when compared to the JPEG-XR and commercial wavelet-based codec, significantly improves the peak-signal-to-noise-ratio (PSNR) vs. compression ratio performance for 32 b/p seismic data. Depending on a configuration, PSNR gain goes from 3.39 dB up to 9.48 dB. Also, relying on the specific characteristics of seismic data, we proposed an optimized encoder that reduces encoding time by 67.17% for All-I configuration on trace image dataset, and 67.39% for All-I, 97.96% for P2-configuration and 98.64% for B-configuration on 3D wavefield dataset, with negligible coding performance losses.
Framework for the Evaluation of Internet Development. Case Study: Application of Internet Universality Indicators in Ecuador

Digital technologies offer unprecedented opportunities in order to have: information access, freedom of expression, human connectivity, technological innovations and participation of multiple actors. Also, these technologies formulate significant challenges, particularly in relation to freedom of expression, privacy, online misinformation, journalists' safety, transparency, gender and other kind of inequality gaps. In this context, Internet universality indicators (IUI) emerge as a quantification and management technologies for helping states and governments in the assessment of their Internet environments, the reduction of digital gaps and the improvement of internet access. Moreover, these indicators relate to the core of UNESCO's mandate to “promote the free movement of ideas through word and image” and to build inclusive knowledge societies. This tool is aimed at strengthening the Internet's role in achieving the Sustainable Development Goals (SDGs). The IUI framework serves to operationalize the concept of “Internet universality”, adopted by UNESCO in 2015, through a practical evaluation of the Internet based on the ROAM-X principles, which promote a human rights-based Internet, which have to be open and accessible to all, and include the participation of multiple stakeholders. The results of implementation of these indicators around of world will serve to highlight possible gaps and serve as a basis for developing recommendations to achieve targeted improvements. One of the first implementations of this framework was done in Ecuador during 2019. This study uses the SWOT method in order to analyse this implementation and the results achieved. Findings show that a clear definition of latent concepts as included in each category of IUI in order to identify the best factors-variable for estimating the corresponding aspect in a specific implementation is needed, however, this is a topic that reveals an important research interest from academia in special for quantification and management technologies field.
Adaptive NN Backstepping With Considering Integral of Tracking Error

In order to simplify the design procedure of traditional neural network backstepping and improve the robustness and precision of control, an improved scheme is studied for a class of nonlinear systems. To avoid reconstructing virtual control inputs in each recursive step, RBF neural networks are utilized as approximators to estimate the desired feedback control of the whole system only. Meanwhile, the integral action of tracking error is introduced into the backstepping design procedure, which not only participates in updating the neural network weight, but also serves as a component part of the control input. This design may benefit the parameter tuning and make controller perform better sometimes. Based on the Lyapunov synthesis approach, theoretical analysis and simulation results are provided to show the feasibility of the improved scheme.
On the Application of Machine Learning for Cut-in Maneuver Recognition in Platooning Scenarios

Cut-in into vehicle platoons is a dangerous driving maneuver that affects the safety and efficiency of platooning vehicles. An accurate prediction of such maneuver enables the platooning system to take safety measures that ensure the platoon safety and integrity. The contribution of this paper consists of an evaluation of a set of supervised machine learning algorithms for cut-in maneuver recognition, for eventual use in platooning systems. The models were trained and tested on a large-scale publicly available driving dataset, from which cut-in events were extracted. The results show that tree-based classifiers such as Gradient Booting Machine can recognize the cut-in maneuvers with an Fl-score of 98%. An experiment to investigate the model performance with advanced prediction times shows that up to 80.5% of the cut-ins were correctly predicted 1 second before the lane crossing time.
Substrate Bias Effect on Dynamic Characteristics of a Monolithically Integrated GaN Half-Bridge

his paper investigates the substrate bias effects on a monolithically integrated half-bridge fabricated using lateral GaN-on-Si technology. The dynamic characteristics, including dynamic Ron degradation and gate charge (Qg) shift, are presented for both high- and low-side GaN power devices. Compared to the grounded substrate, significant dynamic Ron degradations and decreased Qg are observed in high-side GaN power devices under negative DC substrate biases. Pulse-mode substrate biasing has also been studied with suppressed degradation by eliminating the cross-talk effect. The trade-off between dynamic Ron degradation and Qg shift has been explored under different switching frequencies for a monolithically integrated GaN half-bridge.
Lifetime Improvement of Digital Microfluidic Biochips based on the Improved Whale Optimization Algorithm for Protein Analysis Instrument System

Digital micro-fluidic biochips (DMFBs) are revolutionizing laboratory procedures for point-of-care clinical diagnostics, environmental monitoring, and protein analysis. Those procedures require high precision of the output for every operation so it need to ensure the chip reliability and the chip lifetime. Because the electrodes on the chip may be reused at different experimental stages, a degraded electrode may be reused for many times. So the lifetime of the chips is closely related to the total actuating time of an electrode. Thus, the electrode total actuating time needs to be considered carefully in an efficient DMFBs design process. This paper proposed an improved whale optimization algorithm (IWOA), which can reduce excessive use of an electrode and reuse electrodes in average manner to optimal the longest lifetime of DMFBs during the design process. Firstly, the position mass of individual whales was improved to solve the problem that the data, calculated by the WOA, cannot be directly used to represent the sequence of operations. Secondly, the inertial weight was added to enhance the local search ability of WOA. The simulation experimental results showed that this algorithm was able to solve lifetime optimization problems. The maximum electrode time used was reduced by 10% to 20%. The performance of efficiency and convergence of algorithm were very good.
Polysaccharide breakdown by mixed populations of human faecal bacteria

Measurements of polysaccharide-degrading activity in different fractions of human faeces showed that bacterial polysaccharidases and glycosidases were primarily associated with the washed bacterial fractions. Amylase, pectinase and xylanase were the major polysaccharide-hydrolysing enzymes detected, whilst α-L-arabinofuranosidase, β-D-xylosidase, β-D-galactosidase and β-D-glucosidase were the most active glycosidases. Starch and 3 non-starch polysaccharides (NSP; pectin, xylan and arabinogalactan) were fermented by mixed populations of human faecal bacteria in batch culture. Detailed carbohydrate analysis demonstrated that starch and pectin were the most rapidly degraded substrates and that arabinogalactan and the relatively insoluble polysaccharide xylan were broken down more slowly. Free sugars and oligosaccharides did not accumulate in culture media with any polysaccharide tested. Time-course measurements of polysaccharide remaining in the batch culture fermentations showed that the arabinose side chains of pectin, xylan and arabinogalactan were co-utilised with the backbone sugars. In these cultures, polysaccharide-degrading activity was mainly cell-associated, but extracellular polysaccharidase activity increased as the fermentations progressed. Molar ratios of acetate, propionate and butyrate produced in these experiments were dependent upon the polysaccharide substrate tested. Molar ratios of acetate, propionate and butyrate in the starch, arabinogalactan, xylan and pectin fermentations were 50:22:29, 50:42:8, 82:15:3, and 84:14:2, respectively. The presence of starch did not inhibit the breakdown of arabinogalactan, xylan or pectin by faecal bacterial, providing evidence that multicomponent substrate utilisation occurs when complex populations of faecal bacteria are provided with mixed polysaccharide substrates.
Application Of Uncertainty Analysis To Experimental Reliability Of Sensors By Thermal Shock Test

In this paper an example is discussed concerning the possibility of using metrological concepts, like uncertainty of measurement, systematic effects, statistical distribution of measurements, in order to improve the interpretation capability of a specific failure mechanism in a controlled field of influences. This approach has been also used to set an interpolation in a certain range of condition when the temperature of tests is changed. The case study refers to the degradation assessment of ultrasonic sensors for distance measurement, which is ascribed to the quality decay of solder joints due to cracks or remarkable shrink holes. The components are subjected to ALT cyclic thermal stresses. The results of tests allowed to gaining information about the validation of a model of fault, useful to design accelerated tests at higher temperatures.
AdBandit: A New Algorithm For Multi-Armed Bandits

In this paper we present a new algorithm for the ﬁnite-horizon stochastic multi-armed bandit problem with Bernoulli rewards called AdBan-dits. It is based upon the Thompson Sampling combined with a frequentist exploitation strategy. We report the results of this new algorithm comparing it with classical solutions to the multi-armed bandit such as: UCB, UCB-Bayes and Thompson Sampling.
Vehicle Trajectory Estimation based on Dynamic Bayesian Networks

In this paper we propose a method to estimate the most likely region a vehicle will transverse when the current position and a priori spatial-temporal trajectory data of multiple vehicles are known. The proposed solution is based on a hidden Markov chain that models the trajectory of each vehicle. The estimation of the vehicle trajectory relies on Viterbi algorithm, which identifies the most likely trajectory as a new vehicle’s location is known. The proposed modeling and estimation methodology is evaluated using real mobility traces sampled from multiple taxis traveling in the city of Porto in Portugal. The estimation performance of the most likely region a vehicle will cross is between 32% and 89%, depending on several factors that include the probability of trajectory’s occurrence and the number of previous locations considered in the estimation methodology (Markov order). Finally, we discuss the advantages and limitations of the proposed method.
Lightweight Online Profiling-Based Configuration Adaptation for Video Analytics System in Edge Computing

Video Analytics System has emerged as a promising technology to realize deep neural network based intelligent applications for video streams. Its objective is to maximize the video analytics performance of video streams, such as accuracy, while utilizing the limited computing resource capacity efficiently. Existing video analytics systems attempt to adapt configurations to optimize resource-accuracy tradeoffs under limited resource capacity. Especially, several works recently propose a configuration adaptation algorithm based on online profiling to overcome the inefficiency of one-time offline profiling caused by the dynamics of the configuration’s impact on video analytics accuracy. However, their systems are inefficient or limited to process the video analytics of multiple video streams in a GPU-enabled edge server. Furthermore, their online profiling methods still leads to a high profiling cost. In this paper, we design a video analytics system to adapt configurations for optimizing the resource-accuracy tradeoffs of multiple video streams with respect to frame rate and resolution fully under limited resource capacity of a GPU-enable edge server. In addition, we propose a lightweight online profiling method, utilizing the underlying characteristics of the video objects. Then, based on it, we propose a configuration adaptation algorithm to find the best configuration of each video stream and minimize accuracy degradation of multiple video streams under limited resource capacity of a GPU-enable edge server. To evaluate the proposed algorithm, we use a subset of surveillance videos and annotations from VIRAT 2.0 Ground dataset. The experimental results show that, in a GPU-enabled edge server, our video analytics system achieves the optimal configuration adaptation on resource-accuracy tradeoffs and our algorithm reduce the profiling cost of existing systems significantly while achieving the video analytics performance comparable to them.
Morphological and Genetic Variation between Two Sympatric Forms of Oxymycterus (Rodentia: Sigmodontinae): An Evaluation of Hypotheses of Differentiation within the Genus

Abstract Species limits in the genus Oxymycterus have been controversial because of difficulties in detecting discrete morphological and cytogenetic variation. Several authors have considered many of the species described from Brazil to be conspecific with O. rufus, including O. dasytrichus described from Bahia. This tendency represents a null hypothesis for the species-level diversity within the genus. A recently proposed alternative hypothesis based on analyses of allopatric samples recognizes O. rufus and O. dasytrichus as valid species within rufus and dasytrichus species complexes, respectively. Two sympatric forms of Oxymycterus from Viçosa, Minas Gerais State, southeastern Brazil, are phenotypically similar to members of these 2 species-groups. Investigation on the status of these populations and their relationships to the rufus and dasytrichus complexes provided a test of the alternative hypothesis regarding diversity within the genus. Morphometric, morphological, and molecular (cytochrome-b DNA sequences) comparisons of samples representing the rufus, dasytrichus, and judex species-groups provided evidence that the 2 sympatric forms from Viçosa are distinct evolutionary units, one related to the dasytrichus species-group and the other to the rufus species-group. The structures of morphological and molecular variation within each species-group supported an isolation-by-distance model of phenotypic differentiation in the rufus group, and a smooth pattern of phenotypic differentiation along a north–south axis in the dasytrichus group. The relevance of these patterns for species delimitation within these groups and the validity of O. dasytrichus and O. rufus are discussed.