Reviewerly/exHarmony huggingface dataset:
Forgery Detection by Internal Positional Learning of Demosaicing Traces

We propose 4Point (Forensics with Positional Internal Training), an unsupervised neural network trained to assess the consistency of the image colour mosaic to find forgeries. Positional learning trains the model to learn the modulo-2 position of pixels, leveraging the translation-invariance of CNN to replicate the underlying mosaic and its potential inconsistencies. Internal learning on a single potentially forged image improves adaption and robustness to varied post-processing and counter-forensics measures. This solution beats existing mosaic detection methods, is more robust to various post-processing and counter-forensic artefacts such as JPEG compression, and can exploit traces to which state-of-the-art generic neural networks are blind. Check qbammey.github.io/4point for the code.
Artificial Intelligence Emergence in Disruptive Technology

Artificial intelligence (AI) is the most prominent technology driving digital transformation across the world. The digital transformation has made nimiety of data available for big data analytics. The area which effectively uses these data is called artificial intelligence. The world today is witnessing disruptive technology occasioned by AI. Disruptive technology transforms the way we do tasks such as business, health and travel. Disruptive technology changes the systems or habits it substitutes because it has recognizably superior attributes. Several types of AI, such as reactive machines, limited memory, theory of mind and self-awareness, can be employed to create the kind of disruptive technology for business transformation. Modern businesses require interactive artificial intelligence (IAI), functional artificial intelligence (FAI), analytic artificial intelligence, text artificial intelligence (TAI) and visual artificial intelligence (VAI) to succeed in this digital transformation era. Therefore, AI will continue to leverage machine learning algorithms, especially deep learning and reinforcement learning, to continue to create the kind of disruptive technologies required in every aspect of daily living that will significantly alter the way that consumers, industries or businesses operate.
Low resolution thermal imaging dataset of sign language digits

The dataset contains low resolution thermal images corresponding to various sign language digits represented by hand and captured using the Omron D6T thermal camera. The resolution of the camera is 32×32 pixels. Because of the low resolution of the images captured by this camera, machine learning models for detecting and classifying sign language digits face additional challenges. Furthermore, the sensor's position and quality have a significant impact on the quality of the captured images. In addition, it is affected by external factors such as the temperature of the surface in comparison to the temperature of the hand. The dataset consists of 3200 images corresponding to ten sign digits, 0-9. Thus, each sign language digit consists of 320 images collected from different persons. The hand is oriented in various ways to capture all of the variations in the dataset.
Markerless gait estimation and tracking for postural assessment

Abstract Postural assessment is crucial in the sports screening system to reduce the risk of severe injury. The capture of the athlete’s posture using computer vision attracts huge attention in the sports community due to its markerless motion capture and less interference in the physical training. In this paper, a novel markerless gait estimation and tracking algorithm is proposed to locate human key-points in spatial-temporal sequences for gait analysis. First, human pose estimation using OpenPose network to detect 14 core key-points from the human body. The ratio of body joints is normalized with neck-to-pelvis distance to obtain camera invariant key-points. These key-points are subsequently used to generate a spatial-temporal sequences and it is fed into Long-Short-Term-Memory network for gait recognition. An indexed person is tracked for quick local pose estimation and postural analysis. This proposed algorithm can automate the capture of human joints for postural assessment to analyze the human motion. The proposed system is implemented on Intel Up Squared Board and it can achieve up to 9 frames-per-second with 95% accuracy of gait recognition.
Hardware Acceleration of Video Edge Detection with Hight Level Synthesis on the Xilinx Zynq Platform

The study conducted in the current paper consists of validating an original design flow for the rapid prototyping of real-time image and video processing applications on FPGAs. A video application for edge detection with Simulink HDL coder and Vivado High-Level Synthesis (HLS) has been designed as if the code was going to be executed on a conventional processor. The developed tools will automatically translate the code into VHDL hardware language using an advanced compilation technique. This amounts to embedding processors on Xilinx Zynq-7000 System on-Chip (SoC) device in an optimal manner. This automated hardware design flow reduces the time to create a prototype since only the high-level description is required. The design of the video edge detection system is implemented on Xilinx Zynq-7000 platform. The result of the implementation gave effective resource utilization and a good frame rate (95 FPS) under 170MHz frequency.
A deep learning method and device for bone marrow imaging cell detection

Morphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency.In this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value.We verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system.Clinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.
Comparison of different ML methods concerning prediction quality, domain adaptation and robustness

Abstract Nowadays machine learning methods and data-driven models have been used widely in different fields including computer vision, biomedicine, and condition monitoring. However, these models show performance degradation when meeting real-life situations. Domain or dataset shift or out-of-distribution (OOD) prediction is mentioned as the reason for this problem. Especially in industrial condition monitoring, it is not clear when we should be concerned about domain shift and which methods are more robust against this problem. In this paper prediction results are compared for a conventional machine learning workflow based on feature extraction, selection, and classification/regression (FESC/R) and deep neural networks on two publicly available industrial datasets. We show that it is possible to visualize the possible shift in domain using feature extraction and principal component analysis. Also, experimental competition shows that the cross-domain validated results of FESC/R are comparable to the reported state-of-the-art methods. Finally, we show that the results for simple randomly selected validation sets do not correctly represent the model performance in real-world applications.
Regional model of teacher education as a research problem

Introduction. The authors investigate the problem of improving the quality of education with the main focus on initial teacher education. The purpose of this study is to identify the characteristic features of modern regional models of teacher education using the system modeling method. Materials and Methods. In order to investigate regional models of teacher education, the following methods were employed: a focus-group research technique, expert surveys and opinion polls. The sample included highly qualified practitioners and leaders of higher educational institutions, vocational educational settings and secondary educational institutions as well as recreation and health improvement organizations for children. The comparative analysis of the regional models was conducted using the profile of the regional model of teacher education developed by the authors. Results. The article presents an analytical review of modern regional teacher education models, carried out as a part of the research investigation conducted by Moscow Pedagogical State University on behalf of the Ministry of Education of the Russian Federation "Comparative analysis of models of regional teacher education systems with the main focus on 8 federal districts of the Russian Federation and the specifics of interaction between federal and regional stakeholders within the educational policy." The study has revealed, described and analyzed eight most common models of teacher education in regional education systems in each of the federal districts. The authors emphasize that it is significant to document unique practices of teacher education and present them to the professional community and experts using a unified model profile. Moreover, they highlight the need for expanding networking, involving both regional and federal stakeholders in the field of education. Conclusions. The research findings can be used by state authorities of the subjects of the Russian Federation, federal districts of the Russian Federation, and educational institutions of higher education to improve the efficiency of the regional teacher education models, integrating them into a unified environment of teacher education in Russia. The profile of the regional model of teacher education evaluated during the research investigation can be offered as a standard one.
FPGA implantations of TRNG architecture using ADPLL based on FIR filter as a loop filter

Abstract This article describes about the design, implementation, and analysis of a true random number generator (TRNG) employing an all-digital phase-locked loop (ADPLL) based on a finite impulse response (FIR) filter as the digital loop filter and implemented on the Artix 7(XC7A35T-CPG236-1) field programmable gate array (FPGA) board using the Xilinx Vivado v.2015.2 design suite. The coefficients of a 3rd-order broadcast low-pass digital FIR filter are computed using the Keiser window method. The MATLAB-FDA tool is used to calculate the filter coefficient. After reducing the bias in the sequence using the XOR-corrector post-processing approach, the suggested ADPLL-based TRNG designs created an unbiased stochastic random number with an overall throughput of 200 Mbps for both designs. The first proposed FIR-ADPLL-based TRNG design (FAT-1) consumes 0.072 W of power, whereas the second proposed FIR-ADPLL-based TRNG design (FAT-2) consumes 0.074 W. The resulting bitstream is verified for randomness using national institute of standards and technology (NIST) test following post-processing. Digital storage oscilloscope (DSO) is interfaced to the Artrix-7 FPGA board in order to capture the TRNG output waveforms. Both the proposed FIR-based ADPLL-TRNG designs passed the NIST SP 800-22 test, indicating that they are well-suited for a variety of industrial applications, including network security, cybersecurity, banking security, smart cards, radio-frequency identification tags, Internet of Things, and Industrial Internet of Things.
Optical neural network quantum state tomography

Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the “imaging” technique in quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extensively. We build and demonstrate an optical neural network (ONN) for photonic polarization qubit QST. The ONN is equipped with built-in optical nonlinear activation functions based on electromagnetically induced transparency. The experimental results show that our ONN can determine the phase parameter of the qubit state accurately. As optics are highly desired for quantum interconnections, our ONN-QST may contribute to the realization of optical quantum networks and inspire the ideas combining artificial optical intelligence with quantum information studies.
Utilization of Pollution Indices, Hyperspectral Reflectance Indices, and Data-Driven Multivariate Modelling to Assess the Bottom Sediment Quality of Lake Qaroun, Egypt

Assessing the environmental hazard of potentially toxic elements in bottom sediments has always been based entirely on ground samples and laboratory tests. This approach is remarkably accurate, but it is slow, expensive, damaging, and spatially constrained, making it unsuitable for monitoring these parameters effectively. The main goal of the present study was to assess the quality of sediment samples collected from Lake Qaroun by using different groups of spectral reflectance indices (SRIs), integrating data-driven (Artificial Neural Networks; ANN) and multivariate analysis such as multiple linear regression (MLR) and partial least square regression (PLSR). Jetty cruises were carried out to collect sediment samples at 22 distinct sites over the entire Lake Qaroun, and subsequently 21 metals were analysed. Potential ecological risk index (RI), organic matter (OM), and pollution load index (PLI) of lake’s bottom sediments were subjected to evaluation. The results demonstrated that PLI showed that roughly 59% of lake sediments are polluted (PLI &gt; 1), especially samples of eastern and southern sides of the lake’s central section, while 41% were unpolluted (PLI &lt; 1), which composed samples of the western and western northern regions. The RI’s findings were that all the examined sediments pose a very high ecological risk (RI &gt; 600). It is obvious that the three band spectral indices are more efficient in quantifying different investigated parameters. The results showed the efficiency of the three tested models to predict OM, PLI, and RI, revealing that the ANN is the best model to predict these parameters. For instance, the determination coefficient values of the ANN model of calibration datasets for predicting OM, PLI, and RI were 0.999, 0.999, and 0.999, while they were 0.960, 0.897, and 0.853, respectively, for the validation dataset. The validation dataset of the PLSR produced R2 values higher than with MLR for predicting PLI and RI. Finally, the study’s main conclusion is that combining ANN, PLSR, and MLR with proximal remote sensing could be a very effective tool for the detection of OM and pollution indices. Based on our findings, we suggest the created models are easy tools for forecasting these measured parameters.
Do Smart Glasses Dream of Sentimental Visions?

Emotion recognition in smart eyewear devices is highly valuable but challenging. One key limitation of previous works is that the expression-related information like facial or eye images is considered as the only emotional evidence. However, emotional status is not isolated; it is tightly associated with people's visual perceptions, especially those sentimental ones. However, little work has examined such associations to better illustrate the cause of different emotions. In this paper, we study the emotionship analysis problem in eyewear systems, an ambitious task that requires not only classifying the user's emotions but also semantically understanding the potential cause of such emotions. To this end, we devise EMOShip, a deep-learning-based eyewear system that can automatically detect the wearer's emotional status and simultaneously analyze its associations with semantic-level visual perceptions. Experimental studies with 20 participants demonstrate that, thanks to the emotionship awareness, EMOShip not only achieves superior emotion recognition accuracy over existing methods (80.2% vs. 69.4%), but also provides a valuable understanding of the cause of emotions. Pilot studies with 20 participants further motivate the potential use of EMOShip to empower emotion-aware applications, such as emotionship self-reflection and emotionship life-logging.
Green concerns in federated learning over 6G

As Information, Communications, and Data Technology (ICDT) are deeply integrated, the research of 6G gradually rises. Meanwhile, federated learning (FL) as a distributed artificial intelligence (AI) framework is generally believed to be the most promising solution to achieve "Native AI" in 6G. While the adoption of energy as a metric in AI and wireless networks is emerging, most studies still focused on obtaining high levels of accuracy, with little consideration on new features of future networks and their possible impact on energy consumption. To address this issue, this article focuses on green concerns in FL over 6G. We first analyze and summarize major energy consumption challenges caused by technical characteristics of FL and the dynamical heterogeneity of 6G networks, and model the energy consumption in FL over 6G from aspects of computation and communication. We classify and summarize the basic ways to reduce energy, and present several feasible green designs for FL-based 6G network architecture from three perspectives. According to the simulation results, we provide a useful guideline to researchers that different schemes should be used to achieve the minimum energy consumption at a reasonable cost of learning accuracy for different network scenarios and service requirements in FL-based 6G network.
EyeQoE

As virtual reality (VR) offers an unprecedented experience than any existing multimedia technologies, VR videos, or called 360-degree videos, have attracted considerable attention from academia and industry. How to quantify and model end users' perceived quality in watching 360-degree videos, or called QoE, resides the center for high-quality provisioning of these multimedia services. In this work, we present EyeQoE, a novel QoE assessment model for 360-degree videos using ocular behaviors. Unlike prior approaches, which mostly rely on objective factors, EyeQoE leverages the new ocular sensing modality to comprehensively capture both subjective and objective impact factors for QoE modeling. We propose a novel method that models eye-based cues into graphs and develop a GCN-based classifier to produce QoE assessment by extracting intrinsic features from graph-structured data. We further exploit the Siamese network to eliminate the impact from subjects and visual stimuli heterogeneity. A domain adaptation scheme named MADA is also devised to generalize our model to a vast range of unseen 360-degree videos. Extensive tests are carried out with our collected dataset. Results show that EyeQoE achieves the best prediction accuracy at 92.9%, which outperforms state-of-the-art approaches. As another contribution of this work, we have publicized our dataset on https://github.com/MobiSec-CSE-UTA/EyeQoE_Dataset.git.
An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction. A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum since the early 2000s, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields.
Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm

This paper constructs a sports action recognition model based on deep learning (DL) and clustering extraction algorithm. For the input detection image frame, athletes' movements are detected through DL network, and then athletes' sports movements are fused. Moreover, it expands new knowledge and improves learning ability through automatic learning training set. The neural network (NN) is applied to the sample set containing images of nonathletes, and the negative training sample set is iteratively enhanced according to the generated false positives, and the results are optimized by clustering method. Simulation experiments show that compared with other algorithms, the clustering extraction algorithm in this paper has achieved superior performance in recognition rate and false alarm rate, and the recognition speed is faster. The aim is to extract the athletes' training postures through the analysis of sports movements, so as to assist coaches to train athletes more professionally and provide some reference for sports movement recognition.