Reviewerly/exHarmony huggingface dataset:
Efficiently Mitigating Face-Swap-Attacks: Compressed-PRNU Verification with Sub-Zones

Face-swap-attacks (FSAs) are a new threat to face recognition systems. FSAs are essentially imperceptible replay-attacks using an injection device and generative networks. By placing the device between the camera and computer device, attackers can present any face as desired. This is particularly potent as it also maintains liveliness features, as it is a sophisticated alternation of a real person, and as it can go undetected by traditional anti-spoofing methods. To address FSAs, this research proposes a noise-verification framework. Even the best generative networks today leave alteration traces in the photo-response noise profile; these are detected by doing a comparison of challenge images against the camera enrollment. This research also introduces compression and sub-zone analysis for efficiency. Benchmarking with open-source tampering-detection algorithms shows the proposed compressed-PRNU verification robustly verifies facial-image authenticity while being significantly faster. This demonstrates a novel efficiency for mitigating face-swap-attacks, including denial-of-service attacks.
Statistical Analysis on Random Matrices of Echo State Network in PEMFC System’s Lifetime Prediction

The data-driven method of echo state network (ESN) has been successfully used in the proton exchange membrane fuel cell (PEMFC) system’s lifetime prediction area. Nevertheless, the uncertainties of the randomly generated input and internal weight matrices in ESN have not been reported yet. In view of this, an ensemble ESN structure is proposed in this paper to analyze the effects of random matrices from a statistical point of view. For each ESN, the particle swarm optimization (PSO) method is utilized to optimize the hyperparameters of the leaking rate, spectral radius, and regularization coefficient. The statistical results of each ensemble ESN are analyzed from 100 repeated tests whose weight matrices are generated randomly. The mean value of the ensemble ESN and a confidence interval (CI) of 95% are given during the long-term lifetime prediction. The effects of two different distribution shapes, i.e., uniform distribution and Gaussian distribution, are fully compared. Finally, the effects of the ensemble structure and two different distribution shapes are tested by three experimental datasets under steady-state, quasi-dynamic, and full dynamic operating conditions.
A Robust Approach for Speaker Identification Using Dialect Information

The present research is an effort to enhance the performance of voice processing systems, in our case the speaker identification system (SIS) by addressing the variability caused by the dialectical variations of a language. We present an effective solution to reduce dialect-related variability from voice processing systems. The proposed method minimizes the system’s complexity by reducing search space during the testing process of speaker identification. The speaker is searched from the set of speakers of the identified dialect instead of all the speakers present in system training. The study is conducted on the Pashto language, and the voice data samples are collected from native Pashto speakers of specific regions of Pakistan and Afghanistan where Pashto is spoken with different dialectal variations. The task of speaker identification is achieved with the help of a novel hierarchical framework that works in two steps. In the first step, the speaker’s dialect is identified. For automated dialect identification, the spectral and prosodic features have been used in conjunction with Gaussian mixture model (GMM). In the second step, the speaker is identified using a multilayer perceptron (MLP)-based speaker identification system, which gets aggregated input from the first step, i.e., dialect identification along with prosodic and spectral features. The robustness of the proposed SIS is compared with traditional state-of-the-art methods in the literature. The results show that the proposed framework is better in terms of average speaker recognition accuracy (84.5% identification accuracy) and consumes 39% less time for the identification of speaker.
A Work-content Based Resource Availability Cost Problem: Mathematical Modeling and Solving Procedure

Resource availability cost problem (RACP) is a project scheduling problem in which the resource availability levels are decision variables and the goal is to minimize the project resource costs.To make the RACP more realistic, the concept of work-content may be incorporated into the problem in order to lead to a reduction of costs.In this paper, the mathematical model for the problem under study is first proposed and a self-adaptive genetic algorithm (GA) is applied to tackle the problem.The self-adaptive approach allows the GA to utilize both serial and parallel schedule generation schemes simultaneously.Moreover, a local search operator is designed to improve the performance of the GA and its crossover and mutation operators.Results of several numerical instances show that the proposed GA performs relatively well.In addition, it is shown that incorporating the concept of the work-content into the RACP leads to reduce the project costs.
Specific Emitter Identification with Limited Labelled Signals Based on Variational Autoencoder Embedded in Information-Maximising Generative Adversarial Network and Gradient Penalty

The lack of labelled signal datasets in noncooperative scenarios limits the performance of specific emitter identification (SEI). To address this limitation, a method for SEI with limited labelled signals is proposed. The bispectrum of the received signal is estimated to enhance individual discriminability. An information-maximising generative adversarial network (InfoGAN) is then developed to perform SEI with limited labelled signals. To prevent nonconvergence and mode collapse due to the complexity of the radiofrequency signals, we improve the InfoGAN, respectively, from the generator and discriminator perspective. For the former, an encoder is combined with the InfoGAN generator to form a variational autoencoder that reduces the difficulty of convergence during training. For the latter, a gradient penalty algorithm is applied during the training of the InfoGAN discriminator, which enables its training loss function to obey the 1-Lipschitz constraint, thereby avoiding gradient disappearance. The design of the objective function for the training of each subnetwork and the training procedure are provided. The proposed network is trained with limited labelled and abundant unlabelled data, and an auxiliary classifier categorizes the emitters after training. Numerical results indicate that our method outperforms state-of-the-art algorithms for SEI with limited labelled signal samples in terms of effectiveness, convergence, accuracy, and robustness against noise.
Efficient Text Classification with Deep Learning on Imbalanced Data Improved with Better Distribution

Technological developments and the widespread use of the internet cause the data produced on a daily basis to increase exponentially. An important part of this deluge of data is text data from applications such as social media, communication tools, customer service. The processing of this large amount of text data needs automation. Significant successes have been achieved in text processing recently. Especially with deep learning applications, text classification performance has become quite satisfactory. In this study, we proposed an innovative data distribution algorithm that reduces the data imbalance problem to further increase the text classification success. Experiment results show that there is an improvement of approximately 3.5% in classification accuracy and over 3 in F1 score with the algorithm that optimizes the data distribution.
Bayes in Wonderland! Predictive Supervised Classification Inference Hits Unpredictability

The marginal Bayesian predictive classifiers (mBpc), as opposed to the simultaneous Bayesian predictive classifiers (sBpc), handle each data separately and, hence, tacitly assume the independence of the observations. Due to saturation in learning of generative model parameters, the adverse effect of this false assumption on the accuracy of mBpc tends to wear out in the face of an increasing amount of training data, guaranteeing the convergence of these two classifiers under the de Finetti type of exchangeability. This result, however, is far from trivial for the sequences generated under Partition Exchangeability (PE), where even umpteen amount of training data does not rule out the possibility of an unobserved outcome (Wonderland!). We provide a computational scheme that allows the generation of the sequences under PE. Based on that, with controlled increase of the training data, we show the convergence of the sBpc and mBpc. This underlies the use of simpler yet computationally more efficient marginal classifiers instead of simultaneous. We also provide a parameter estimation of the generative model giving rise to the partition exchangeable sequence as well as a testing paradigm for the equality of this parameter across different samples. The package for Bayesian predictive supervised classifications, parameter estimation and hypothesis testing of the Ewens sampling formula generative model is deposited on CRAN as PEkit package.
Voice User Interface(VuI) Smart Office Door Application in the Context of Covid-19 Pandemic

Nowadays, all countries around the globe are trying their best to prevent the spread of Covid-19 from reaching their people.However, the virus spread is through the transmission of close contact with the infected person and touching a surface that already contains the virus.This project presents Voice User Interface (VUI) Smart Office Door System that interact with the Internet of Things (IoT) to execute the command given by the user.This project use Raspberry Pi as microcontroller which Thonny Python software used for running the coding script of the system.The electrical components such as dc motors and LEDs are connected to General-Purpose Input Output (GPIO) pin of Raspberry Pi and motor driver.One of the dc motors used for controlling the lock and another one used for controlling the door.Blue LED used as locked door notification while green LED used as unlocked notification.Microphone and speaker connect to the Raspberry Pi through usb port and Bluetooth.This system used Google Assistant as its VUI to control the smart door contactless.The user can give the command to the system through microphone to control the output of the system.The purpose of this project is to invent the smart door with IoT technology that help prevent the spread of Covid-19 at the workplaces.
[Geochemical Characteristics and Source Apportionment of Soil Elements in an Urban-rural Integration Area: A Case Study in the Qinglong Area of Tianfu New District].

The Qinglong Area of Tianfu New District in China is the connection area of Chengdu city, Sichuan Province, which dominantly supports the primary agricultural products for the metropolis. The soil quality, therefore, has been deteriorating in recent decades due to the intensive planting and industrial pollution. Thus, the evaluation of soil quality and the identification of the pollution sources are significant for sustainable soil planting and regulation. To this end, 395 soil samples were collected, and the parameters of pH, soil organic matter (SOM), total potassium (TK), available potassium (AK), total nitrogen (TN), alkali-hydrolyzable nitrogen (AN), total phosphorus (TP), available phosphorus (AP), Se, B, V, Zn, Mn, Mo, Ni, Co, As, Cd, Cr, Cu, Hg, and Pb were analyzed. The results revealed that TN, Se, Hg, and TP were obviously higher than the background value of the Chengdu Economic Zone, and the Cr, Mn, pH, and TK were depleted. The combination of correlation statistics, geostatistics, and positive matrix factorization (PMF) source analysis revealed that heavy metal enrichments were mainly affected by four sources, namely natural sources (32%)>industrial sources (30%)>agricultural sources (25%)>traffic sources (14%). Likewise, the source apportionments were verified regarding topography, industrial boundary, farmer density, and traffic flow. It was estimated that Mo, Co, Ni, and V were affected by slope; the enrichment of Hg and As was the result of agricultural activities; Cd, Mn, Cr, and Zn were affected by industrial waste diffusion; and Pb, Zn, Cu, Cd, and Hg were related to transportation. To a certain extent, agricultural fertilization and pesticide application have partially controlled soil fertility and pollution (Hg and As).
Measurement of Software Development Effort Estimation Bias: Avoiding Biased Measures of Estimation Bias

In this paper, we propose improvements in how estimation bias, e.g., the tendency towards under-estimating the effort, is measured. The proposed approach emphasizes the need to know what the estimates are meant to represent, i.e., the type of estimate we evaluate and the need for a match between the type of estimate given and the bias measure used. We show that even perfect estimates of the mean effort will not lead to an expectation of zero estimation bias when applying the frequently used bias measure: (actual effort – estimated effort)/actual effort. This measure will instead reward under-estimates of the mean effort. We also provide examples of bias measures that match estimates of the mean and the median effort, and argue that there are, in general, no practical bias measures for estimates of the most likely effort. The paper concludes with implications for the evaluation of bias of software development effort estimates.
Supervised Contrastive Learning-Based Modulation Classification of Underwater Acoustic Communication

Modulation parameters are very significant to underwater target recognition. But influenced by the severe and time-space varying channel, most currently proposed intelligent classification networks cannot work well under these large dynamic environments. Based on supervised contrastive learning, an underwater acoustic (UWA) communication modulation classifier named UMC-SCL is proposed. Firstly, the UMC-SCL uses a simply convolutional neural networks (CNN) to identify the presence of the UWA signals. Then, the UMC-SCL uses ResNet50 as an encoder and updates the network by supervised contrastive learning loss function, which can effectively use the category information and make the eigenvector distribution of the same category more concentrated. Then, the classifier uses the feature vector output by the encoder to distinguish the final modulation categories. Finally, extensive ocean, pool, and simulation experiments are done to verify the performance of the UMC-SCL. Without any prior information, the average classification accuracy for MPSK and MFSK can reach 98.6% at 0 dB and is increased by 6% compared to the benchmark algorithm under low SNR.
PEMANFAATAN MEDIA CANVA DAN APLIKASI QUIZIZZ PADA PEMBELAJARAN TEKS FABEL PESERTA DIDIK SMP

The use of technology in the learning process is an effort to encourage renewal so that the learning process becomes more effective. Utilization of technology in learning can be used in learning media. One of the uses of learning media that uses digital technology is the use of Canva and Quizizz applications. The purpose of this study is to describe the use of learning media assisted by the Canva and Quizizz applications in learning fable texts for junior high school students. The Canva application is a web-based platform that can be used to create various attractive designs. The material for the learning video will be using Canva's design, namely fable text. Meanwhile, Quizizz is a web tool to create interactive quiz games that are used as test assessment tools. The use of Quizizz is expected to make it easier for teachers to carry out assessment tests. This research use desciptive qualitative approach. Based on the research results, Canva and Quizizz applications can be used as online learning media. The use of these two applications makes the learning process not boring, because there are features, templates, designs that can be used to make digital learning media interesting. The use of Canva and the quizizz application is expected to be an alternative for teachers in improving the learning of fable texts for junior high school students.
A Modified Kalman Filter-Based Mobile Robot Position Measurement using an Accelerometer and Wheels Encoder

Position Measurement is a key process in the navigation system of a mobile robot.In this research paper, wheel encoders and accelerometer sensors were used with a Kalman filter to estimate the mobile robot position.• This research uses a modified Kalman filter to find the mobile robot position depending on the error between the predicted position produced by sensors measurement and the position produced by a reference path.• The modification of the Kalman filter consists of choosing a variable process covariance matrix to produce a better Kalman gain value that will reduce the estimation error.position measurement is an essential process of mobile robot navigation.In this research, a Kalman Filter is applied to locating a mobile robot furnisher with an encoder and accelerometer.The accelerometer updates its position off-hand.It has an acceptable short period of stability.However, this stability will be decreased over time.The odometry model is utilized to measure the mobile robot's position and heading angle using encoders equipped with the wheels of the mobile robot.Moreover, the odometry model's errors exist because of the wheel rotating speed's integrative nature and non-systematic errors.In this work, the mobile robot position estimation in closed environments was studied.In order to obtain the optimal estimation, a Kalman filter was used to estimate mobile robots' position and velocity, where the Kalman filter has been designed for better assessment of the mobile robot position.The suggested configuration collects accelerometer and odometry reading to assure more delicate position knowledge than stand-alone odometry or accelerometer.The proposed method's position error has an acceptable level that is less than (0.2 m) for both easy and difficult paths.
Monitoring and resource management taxonomy in interconnected cloud infrastructures: a survey

Cloud users have recently expanded dramatically. The cloud service providers (CSPs) have also increased and have therefore made their infrastructure more complex. The complex infrastructure needs to be distributed appropriately to various users. Also, the advances in cloud computing have led to the development of interconnected cloud computing environments (ICCEs). For instance, ICCEs include the cloud hybrid, intercloud, multi-cloud, and federated clouds. However, the sharing of resources is not facilitated by specific proprietary technologies and access interfaces used by CSPs. Several CSPs provide similar services but have different access patterns. Data from various CSPs must be obtained and processed by cloud users. To ensure that all ICCE tenants (users and CSPs) benefit from the best CSPs, efficient resource management was suggested. Besides, it is pertinent that cloud resources be monitored regularly. Cloud monitoring is a service that works as a third-party entity between customers and CSPs. This paper discusses a complete cloud monitoring survey in ICCE, focusing on cloud monitoring and its significance. Several current open-source monitoring solutions are discussed. A taxonomy is presented and analyzed for cloud resource management. This taxonomy includes resource pricing, assignment of resources, exploration of resources, collection of resources, and disaster management.
A Clustering-Anonymity Approach for Trajectory Data Publishing Considering both Distance and Direction

Trajectory data contains rich spatio-temporal information of moving objects. Directly publishing it for mining and analysis will result in severe privacy disclosure problems. Most existing clustering-anonymity methods cluster trajectories according to either distance- or direction-based similarities, leading to a high information loss. To bridge this gap, in this paper, we present a clustering-anonymity approach considering both these two types of similarities. As trajectories may not be synchronized, we first design a trajectory synchronization algorithm to synchronize them. Then, two similarity metrics between trajectories are quantitatively defined, followed by a comprehensive one. Furthermore, a clustering-anonymity algorithm for trajectory data publishing with privacy-preserving is proposed. It groups trajectories into clusters according to the comprehensive similarity metric. These clusters are finally anonymized. Experimental results show that our algorithm is effective in preserving privacy with low information loss.
Motion mode recognition in multi-storey buildings based on the naive Bayes method

When walking within a multi-storey building, pedestrians use a variety of modes of motion, such as going upstairs or downstairs, or walking on a plane. In each of these modes, the step size will be different, and this has a strong impact on the accuracy of pedestrian dead reckoning. In order to identify the patterns of movement in multi-storey buildings, we propose a new method of movement recognition based on smart phones. Firstly, the relationships between the patterns of indoor movement and the changes in the air pressure, acceleration and angular velocity data obtained from the built-in sensors of mobile phones are analysed. The naive Bayes method is then used to identify four different modes of motion: walking up and down the stairs, moving on a landing and moving at a constant speed along a corridor. Our experimental results show that the recognition accuracy of our scheme reaches 95.82%.
Video stabilization based on image registration

We address the problem of video stabilization via image registration. We propose a single convolutional neural network, which is simultaneously a dense feature descriptor and a keypoint detector, to find reliable keypoints and their features under each frames. To obtain accurate keypoint localization, the authors leverage the inherent feature hierarchy to restore spatial resolution and low-level details. Compared with traditional detectors, the obtained keypoints are more stable for the following image registration. Based on the correspondences collected from registration results, we propose a technique for video stabilization which is a spatial smooth sparse motion field with motion vectors only at the mesh vertexes. In practice, the authors assign each vertex an unique motion vector via their neighboring correspondences and a median filter. The video stabilization is performed on the vertex profiles, which are motion vectors collected at the same vertex location over time. The quantitative and qualitative evaluations show that the proposed registration-based video stabilization method can produce comparable results with the state-of-the-art methods and achieve more stable performance on challenging situations.
Designing an English learning website by using the ADDIE model

This paper describes the design of a website for the teaching and learning of English at Universidad del Cauca, Colombia, according to the ADDIE instructional design model to provide academic alternatives with the support of web-based technologies. The prototype of a website capable of hosting English courses managed through virtual or blended modality is the concrete result of this study. Although the low number of courses hosted and oriented on the website may be a limitation, the information collected allowed the tool sophistication throughout the process. This study contributed to apply the ADDIE model for designing a web tool to manage English courses at Universidad del Cauca.