Reviewerly/exHarmony huggingface dataset:
Optimization of Jacobi Moments Parameters using Artificial Bee Colony Algorithm for 3D Image Analysis

In this article, we propose a new algorithm for the optimization of the parameters of Jacobi moments by artificial bee colony (ABC) algorithm for the analysis of 3D images. The ABC algorithm is used to select the optimal parameters $\alpha$ and $\beta$ of Jacobi polynomials and the optimal orders of Jacobi moments to improve the quality of 3D image reconstruction. The performance of the proposed algorithm is evaluated on 3D images using quantitative criteria such as the mean square error and peak signal-to-noise ratio. The results of simulations have shown the efficiency of the proposed algorithm for the analysis of 3D images compared to conventional methods.
Wide-Area Crowd Counting: Multi-view Fusion Networks for Counting in Large Scenes

Crowd counting in single-view images has achieved outstanding performance on existing counting datasets. However, single-view counting is not applicable to large and wide scenes (e.g., public parks, long subway platforms, or event spaces) because a single camera cannot capture the whole scene in adequate detail for counting, e.g., when the scene is too large to fit into the field-of-view of the camera, too long so that the resolution is too low on faraway crowds, or when there are too many large objects that occlude large portions of the crowd. Therefore, to solve the wide-area counting task requires multiple cameras with overlapping fields-of-view. In this paper, we propose a deep neural network framework for multi-view crowd counting, which fuses information from multiple camera views to predict a scene-level density map on the ground-plane of the 3D world. We consider three versions of the fusion framework: the late fusion model fuses camera-view density map; the naïve early fusion model fuses camera-view feature maps; and the multi-view multi-scale early fusion model ensures that features aligned to the same ground-plane point have consistent scales. A rotation selection module further ensures consistent rotation alignment of the features. We test our 3 fusion models on 3 multi-view counting datasets, PETS2009, DukeMTMC, and a newly collected multi-view counting dataset containing a crowded street intersection. Our methods achieve state-of-the-art results compared to other multi-view counting baselines.
A Semantic Segmentation-based Method for Handwritten Japanese Text Recognition

Recently, the segmentation-free approach using Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for handwritten text recognition has been investigated by many research groups. Although good results are produced on some datasets, there remain some drawbacks. It is neither robust against the change of handwriting styles such as gaps between characters, stroke widths and shapes of characters nor stable for skewed, and curved text lines unless heavily trained by such patterns. In this paper, we propose a segmentation-based method for handwritten Japanese text recognition. The method employs a semantic segmentation model for precisely splitting text lines into single characters. The semantic segmentation model is based on an encoder-decoder architecture like U-Net, but we employ available techniques to improve the accuracy of pixel classification. They are a deeper encoder with ResNet 101, dilated convolutions and Spatial Pyramid Pooling. Subsequently, a CNN based OCR is used to recognize segmented character images. Finally, the recognized candidates are considered in a lattice diagram combined with a linguistic context. The result of experiments shows that the segmentation model increases the mean IoU significantly from 89.32% to 94.96% and the proposed approach is robust to the change of handwriting styles while a segmentation-free method is quite sensitive to them, resulting in the significant reduction of the recognition rate.
Simultaneous lead isotope ratio and gold-lead-bismuth concentration analysis of silver by laser ablation MC-ICP-MS

A new approach is presented for the simultaneous analysis of lead isotope ratios and gold, lead, and bismuth concentrations in metallic silver using nanosecond laser ablation multi-collector inductively-coupled plasma mass spectrometry (LA-MC-ICP-MS). Corrections for both isotope and concentration analyses are performed using an in-house matrix matched silver reference material RM3834. Accuracy and external reproducibility are demonstrated by repeat analyses of a further seven silver reference materials all characterised by solution (MC)-ICP-MS approaches. Typical internal precisions, expressed as two relative standard errors (S.E.) of the mean of the cycles comprising one analysis, are <0.5% for 206Pb/204Pb, 207Pb/204Pb and 208Pb/204Pb, and <0.03% for 207Pb/206Pb and 208Pb/206Pb. External reproducibilities, based on repeat analyses over a 10 month period and expressed as two relative standard deviations (S.D.) of the mean, are <0.4% for 206Pb/204Pb, 207Pb/204Pb and 208Pb/204Pb and <0.2% for 207Pb/206Pb and 208Pb/206Pb. Internal precisions and external reproducibilities of gold, lead and bismuth concentration analyses are typically <25%. Data are consistent with solution-based approaches. Methods are further demonstrated through analyses of a set of Islamic silver dirhams from the mint of al-Muhammadiyya, highlighting their applicability to geochemical studies of archaeological artefacts.
A New Digital Color Image Watermarking Algorithm with its FPGA and ASIC Implementation

In the world full of visual content, digital image authentication has become an important concern. Digital image watermarking can play a key role in this regard. Though several techniques and algorithms exist in literature but color image watermarking techniques with its hardware implementation are few. The objective of this paper is to introduce a new algorithm for watermarking a color cover image using color watermark. The basic technique is to alter the pixel values of the cover image, based on the similarity between cover image and watermark. The amount of alteration can be controlled by a parameter called modulation index, which also decides the quality of cover image as well as that of extracted watermark image. A pseudo-noise code is used for embedding and extraction of the watermark, hence only authorized users having exact pseudo-noise code can extract the watermark. This is an invisible watermarking technique, so it doesn't affect the appearance of the original image significantly. Furthermore, FPGA as well as ASIC based hardware implementation of the aforesaid algorithm is realized. For real-time application hardware realization is more efficient than software implementation. The proposed algorithm and its VLSI implementation have been compared with stat-of-art research work present in literature. Throughput of the proposed algorithm is high and it can also be used for digital video watermarking.
Pandemic Tech Utopias and Capitalist Surveillance Cultures: The Case of Privacy-Preserving Contact-Tracing Apps

In this article, we examine COVID-19 contact-tracing apps as a form of technological solutionism—a techno-social fix deployed at national scale in response to an urgent, supranational problem. A dystopian view sees contact-tracing apps as a vanguard technology for surveillance (French and Monahan, 2020), and threatens to transform a state of emergency into a state of exception, under which accepted or constitutional laws and norms might be suspended. However, our critique goes further. We argue the growing intervention of global technology corporations in digital governance and affairs of national sovereignty risks a new frontier of state–industrial surveillance that exploits people’s pre-investment in and dependence on technology corporations. We exemplify this with the “technofix” of the Google-Apple Exposure Notification (GAEN) framework and critically examine the notion of a decentralised and privacy-preserving Bluetooth-based contact-tracing framework proposed by technology market leaders that threatens state sovereignty in determining public health responses.
Sentiment Analysis Review Of Smartphones With Artificial Intelligent Camera Technology Using Naive Bayes and n-gram Character Selection

Abstract Mobile has become a basic necessity at this time. Everyone certainly has a cellphone according to their daily needs. To capture connections and carry out various activities with just one hand. The object of this research is a review of smartphones that have the best artificial intelligent cameras. Data processing methods used in research using the Naïve Bayes algorithm. Naïve Bayes is known as one of the methods with the best classification accuracy results for text mining. The research objective is to facilitate customers who will buy a smartphone with the best AI camera without having to read product reviews. So that it can see based on the classification of positive text and label negative text classification. In this study, n-gram is used as a character selector to provide better accuracy results. Based on the results of research conducted, the accuracy of Naïve Bayes results is 72.00%, then Naïve Bayes with n-gram selection accuracy is N-gram = 2, 72.00% accuracy results, n-gram = 3, 75.00% accuracy results, and n-gram = 4 accuracy results 74.50%. In this study, carried out 10 times the experiment to measure the increased accuracy of the addition of n grams. Thus concluding that the application of the n-gram character can increase the accuracy of the Naïve Bayes algorithm.
Immersive virtual reality as an action: measuring approach and learning status of learners after planning myVOR

This research investigates the learning progress and bottlenecks of students during learning via an immersive virtual reality environment. At the planning stage of this action research, an immersive virtual reality learning environment – myVOR- was designed and developed to teach concepts and procedures. myVOR was developed using the Unity game engine. Depth-camera integrated Head Mounted Display was used to support the skill training of learners via intuitive gesture interaction. In the action stage, myVOR was applied to fourteen 3rd year nursing students, once a week for a month. Qualitative and quantitative data were gathered to analyse learning status and, the fluctuation among sessions concerning behaviours, attitudes, reactions. Data were collected from video recordings, myVOR logs, interviews, and an information exam. The results indicated that myVOR was sufficient to teach concepts and complex procedures. However, the learners experienced problems before adapting the used technology, and this affected their behaviours and attitudes during training. The findings of this research support the requirement for a comprehensive needs analysis before designing immersive virtual reality learning environments.
3D mineral exploration targeting with multi-dimensional geoscience datasets, Tongling Cu(-Au) District, China

The Tongling Cu(-Au) district is the most important magma-skarn type Cu(-Au) polymetallic districts in the Middle-Lower Yangtze metallogenic belt of China. The previous studies in the Tongling Cu(Au) district compiled available digital geological, geochemical, geophysical, and remote sensing datasets. As the discovery of near-surface Cu(-Au) deposits becomes more and more difficult in the Tongling Cu(-Au) district, we face the challenge of exploring for blind mineral deposits in the subsurface. Three-dimensional (3D) mineral potential mapping can provide effective exploration targeting by integrating multi-source and multi-dimension geoscience datasets. In this paper, firstly, a 3D district-scale geological model was constructed from multiple geoscience datasets. Secondly, multi-level (0 m, −300 m, −600 m, −900 m, −1200 m) exploration criteria were extracted by spatial analyses. Thirdly, the weights-of-evidence method, discrete smooth interpolation, and ordinary kriging interpolation were used to integrate multi-level exploration criteria including two-dimensional (2D) surface geochemical and remote sensing data into a 3D posterior-probability model. Finally, the concentration-area and concentration-volume fractal methods were used to define posterior-probability thresholds to outline 2D and 3D targets, respectively. The results indicated that the integrating performance of ordinary kriging interpolation is better than that of discrete smooth interpolation in 3D posterior-probability modeling. The whole procedure yielded a series of high-potential Cu(-Au) targets. The methodology discussed here can be used in other magma-skarn districts in the world.
Adversarial Attacks on Deep Learning Systems for User Identification Based on Motion Sensors

For the time being, mobile devices employ implicit authentication mechanisms, namely, unlock patterns, PINs or biometric-based systems such as fingerprint or face recognition. While these systems are prone to well-known attacks, the introduction of an explicit and unobtrusive authentication layer can greatly enhance security. In this study, we focus on deep learning methods for explicit authentication based on motion sensor signals. In this scenario, attackers could craft adversarial examples with the aim of gaining unauthorized access and even restraining a legitimate user to access his mobile device. To our knowledge, this is the first study that aims at quantifying the impact of adversarial attacks on machine learning models used for user identification based on motion sensors. To accomplish our goal, we study multiple methods for generating adversarial examples. We propose three research questions regarding the impact and the universality of adversarial examples, conducting relevant experiments in order to answer our research questions. Our empirical results demonstrate that certain adversarial example generation methods are specific to the attacked classification model, while others tend to be generic. We thus conclude that deep neural networks trained for user identification tasks based on motion sensors are subject to a high percentage of misclassification when given adversarial input.
Temporally Consistent 3D Human Pose Estimation Using Dual 360° Cameras

This paper presents a 3D human pose estimation system that uses a stereo pair of 360°sensors to capture the complete scene from a single location.The approach combines the advantages of omnidirectional capture, the accuracy of multiple view 3D pose estimation and the portability of monocular acquisition.Joint monocular belief maps for joint locations are estimated from 360°images and are used to fit a 3D skeleton to each frame.Temporal data association and smoothing is performed to produce accurate 3D pose estimates throughout the sequence.We evaluate our system on the Panoptic Studio dataset, as well as real 360°v ideo for tracking multiple people, demonstrating an average Mean Per Joint Position Error of 12.47cm with 30cm baseline cameras.We also demonstrate improved capabilities over perspective and 360°multi-view systems when presented with limited camera views of the subject.
FrailWear: A Wearable IoT Device for Daily Activity Monitoring of Elderly Patients

This paper introduces an IoT wearable, specifically designed for the analysis of the physical activity of elderly people, in order to provide objective information to the healthcare staff to assess the frailty in these patients. The device, called FrailWear, is based on a STM32 low cost, low power and high-performance microcontroller. It is a multisensory system that includes an IMU and atmospheric pressure sensor for collecting data. In addition, it is possible to obtain the centimetric-accuracy position of the patient, if the infrastructure includes an external ultrasonic local positioning system. The wearable also includes a LoRaWAN based architecture, to communicate the system and the cloud, where the acquired data will be stored for a later analysis. Both physical activity and localization information are obtained, able to be analysed in real time or to be requested on demand by the carers. First test results of FrailWear are very promising and show its feasibility for this application.
SQL Injection Countermeasures Methods

Many data created very quickly every day and eventually this data is stored on databases. All processes and processors use the database management system because of the privacy of the stored data and the data must be secured in this system and do not fail in front of the potential database attacks, so it is necessary to use security models, although they differ between them. Database injection has become a common desirable attack targeting web applications, the attacker has access to the database to change SQL queries without authorization. In order to avoid losing data and making it vulnerable to the public, SQL injection must be studied and understood. In this paper, I explain SQL injection and its countermeasures.
Convolutional Network Model using Hierarchical Prediction and its Application in Clothing Image Classification

This article proposes a kind of new hierarchical classification model (HCNN) based on convolutional networks. The coarse category-fine category classification layer is set from the lower layer to the upper layer of the network. The experiment proves the necessity of hierarchical classification structure to classify clothing images. In previous studies, when using convolutional neural networks for image classification or other machine learning methods, most of them did not consider hierarchical. This paper attempts to classify apparel datasets using hierarchical CNN for the first time. The suggested model is a knowledge-embedded classifier which conveys some hierarchical information. We implemented HCNN using VGGNet as the underlying framework on the Fashion-MNIST dataset. The results show that the loss is reduced and the accuracy is improved when compared with the base model without hierarchy.
S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation

Interactive facial image manipulation attempts to edit single and multiple face attributes using a photo-realistic face and/or semantic mask as input. In the absence of the photo-realistic image (only sketch/mask available), previous methods only retrieve the original face but ignore the potential of aiding model controllability and diversity in the translation process. This paper proposes a sketch-to-image generation framework called S2FGAN, aiming to improve users’ ability to interpret and flexibility of face attribute editing from a simple sketch. First, to restore a vivid face from a sketch, we propose semantic level perceptual loss to increase the translation quality. Second, we dedicate the theoretic analysis of attribute editing and build attribute mapping networks with latent semantic loss to modify latent space semantics of Generative Adversarial Networks (GANs). The users can command the model to retouch the generated images by involving the semantic information in the generation process. In this way, our method can manipulate single or multiple face attributes by only specifying attributes to be changed. Extensive experimental results on the CelebAMask-HQ dataset empirically show our superior performance and effectiveness on this task. Our method successfully outperforms state-of-the-art sketch-to-image generation and attribute manipulation methods by exploiting greater control of attribute intensity.
Ontological Smart Contracts in OASIS: Ontology for Agents, Systems, and Integration of Services

In this contribution we extend an ontology for modelling agents and their interactions, called Ontology for Agents, Systems, and Integration of Services (in short, OASIS), with conditionals and ontological smart contracts (in short, OSCs). OSCs are ontological representations of smart contracts that permit to establish responsibilities and authorizations among agents and set agreements, whereas conditionals allow one to restrict and limit agent interactions, define activation mechanisms that trigger agent actions, and define constraints and contract terms on OSCs. Conditionals and OSCs, as defined in OASIS, are applied to extend with ontological capabilities digital public ledgers such as the blockchain and the smart contracts implemented on it. We will also sketch the architecture of a framework based on the OASIS definition of OSCs that exploits the Ethereum platform and the Interplanetary File System.
Marine accident learning with Fuzzy Cognitive Maps: a method to model and weight human-related contributing factors into maritime accidents

Previous statistical maritime accident studies are focused on identifying human factors. However, the previous studies were not capable of modelling the complex interrelations that exist between these factors. As accidents are complex processes, researchers fail to agree on the contribution of each human factor. Therefore, in this research study, a new Fuzzy Cognitive Map (FCM)-based technique known as MALFCMs has been introduced and applied. Its novelty is the application of FCM concepts to model the relationships of accident contributors by combining historic accident data with expert opinion. Our approach is capable of integrating information obtained from real occurrences, therefore, the results can be considered more objective. Thus, in this paper, MALFCMs was applied to grounding/stranding accidents in general-cargo vessels, revealing that ‘unprofessional behavior’, ‘lack of training’, and ‘inadequate leadership and supervision’ are the most critical factors, with a normalised importance weighting of 13.25%, 13.24%, and 13.24% respectively.
Virtual Reality System for Industrial Motor Maintenance Training

This study presents a virtual reality system for maintenance training of motors used in locomotives responsible for transportation of ore. Considering the cost and impact on the logistics chain of a mining company, preventive maintenance is required to reduce fault events and increase motor life. Technical staff training in a real locomotive has been difficult since the motor is long and heavy and hence not available for training. Thus, a virtual reality system offers a low-cost and efficient solution for maintenance training. This work consists in developing the 3D model for the virtual reality engine Unity 3D and proposing an XML module for motor part selection and the game design. We evaluated the simulator with 59 users, obtaining satisfactory results regarding comfort, immersion, usability, and user experience. Tests performed by technical staff validated the potential use in the training routine.
Eigenfunctions and minimum 1-perfect bitrades in the Hamming graph

The Hamming graph H(n,q) is the graph whose vertices are the words of length n over the alphabet {0,1,…,q−1}, where two vertices are adjacent if they differ in exactly one coordinate. The adjacency matrix of H(n,q) has n+1 distinct eigenvalues n(q−1)−q⋅i with corresponding eigenspaces Ui(n,q) for 0≤i≤n. In this work we study functions belonging to a direct sum Ui(n,q)⊕Ui+1(n,q)⊕⋯⊕Uj(n,q) for 0≤i≤j≤n. We find the minimum cardinality of the support of such functions for q=2 and for q=3, i+j>n. In particular, we find the minimum cardinality of the support of eigenfunctions from the eigenspace Ui(n,3) for i>n2. Using the correspondence between 1-perfect bitrades and eigenfunctions with eigenvalue −1, we find the minimum size of a 1-perfect bitrade in the Hamming graph H(n,3).