Reviewerly/exHarmony huggingface dataset:
Visualization of Real-Time Heterogeneous Smart City Data Using Virtual Reality

As the size and dimensionality of big heterogeneous data increases, finding patterns and anomalies with existing visualization methods and tools poses a significant challenge. The majority of open data platforms that offer smart city data visualizations use browser-based two-dimensional (2D) visualizations as 2D displays are widely adopted. These displays are however ineffective in depicting multi-dimensional heterogeneous data. The recent growth in the virtual reality (VR) consumer market resulted in an affordable alternative for 3D visualizations. In this paper, we propose a VR system capable of visualizing real-time smart city data concerning the city of Brussels. A subset of external data sources that is already visualized in existing web platforms is incorporated in the VR application. A user study is conducted to assess perceived workloads and data immersion parameters for a set of data exploration tasks in three existing web platforms and in the proposed VR system. Results indicate significantly lower levels of perceived frustration and significantly higher levels of data intuitivity, immersion, overview in data, and intuitive interaction. However, no significant difference in total perceived workload is observed.
Blockchain and the Identity based Encryption Scheme for High Data Security

Using the blockchain technology to store the privatedocuments of individuals will help make data more reliable and secure, preventing the loss of data and unauthorized access. The Consensus algorithm along with the hash algorithms maintains the integrity of data simultaneously providing authentication and authorization. The paper incorporates the block chain and the Identity Based Encryption management concept. The Identity based Management system allows the encryption of the user's data as well as their identity and thus preventing them from Identity theft and fraud. These two technologies combined will result in a more secure way of storing the data and protecting the privacy of the user.
ADGAN: A Scalable GAN-based Architecture for Image Anomaly Detection

With deep learning booming, related technologies have been applied in various fields. However, it remains an open question in terms of how to perform well on anomaly detection of images with diverse content and complexity. To address such problem, we propose ADGAN (Anomaly Detection Generative Adversarial Network), a scalable encoder-decoder-encoder architecture for image anomaly detection. Through extracting and utilizing multi-scale features of normal samples, we obtain fine-grained reconstructed images of normal class. Combined with adversarial training, the proposed model learns the distribution of normality thus large reconstruction errors occur when it processes anomalous samples during inference. We verify the effectiveness of ADGAN on two benchmark datasets: CIFAR-10 and CIFAR-100. The experimental results demonstrate that our method outperforms current anomaly detection work. We improve the top performing baseline AUCs by 9% and 6% on the CIFAR-10 dataset and the CIFAR-100 dataset respectively.
Hand Movement Activity-Based Character Input System on a Virtual Keyboard

Nowadays, gesture-based technology is revolutionizing the world and lifestyles, and the users are comfortable and care about their needs, for example, in communication, information security, the convenience of day-to-day operations and so forth. In this case, hand movement information provides an alternative way for users to interact with people, machines or robots. Therefore, this paper presents a character input system using a virtual keyboard based on the analysis of hand movements. We analyzed the signals of the accelerometer, gyroscope, and electromyography (EMG) for movement activity. We explored potential features of removing noise from input signals through the wavelet denoising technique. The envelope spectrum is used for the analysis of the accelerometer and gyroscope and cepstrum for the EMG signal. Furthermore, the support vector machine (SVM) is used to train and detect the signal to perform character input. In order to validate the proposed model, signal information is obtained from predefined gestures, that is, “double-tap”, “hold-fist”, “wave-left”, “wave-right” and “spread-finger” of different respondents for different input actions such as “input a character”, “change character”, “delete a character”, “line break”, “space character”. The experimental results show the superiority of hand gesture recognition and accuracy of character input compared to state-of-the-art systems.
Toward Fast Convergence and Calibration-Free Visual Servoing Control: A New Image Based Uncalibrated Finite Time Control Scheme

In this paper, a visual servoing robotic control scheme of fast convergence and calibration-free is proposed. The objective of this paper is to achieve a better convergence around the equilibrium for uncalibrated visual servoing systems. Moreover, there are threefold unknown parameters, the visual kinematic parameters, the dynamic parameters and the parameters of the feature points, considered in the design of the proposed control scheme. In order to achieve the above control objective, the finite-time tracking controller and three adaptive laws are proposed. The adaptability to the unknown parameters is guaranteed by the online adaptive laws. The finite-time convergence is achieved by the continuously non-smooth fractional order function in the controller. The rigorously mathematic proof of stability is given by homogeneous theory and the Lyapunov function formalism. Three real-time experiments are conducted to demonstrate the practical performance of the proposed scheme.
Handwritten Telugu Compound Character Prediction using Convolutional Neural Network

Handwritten recognition is a challenging task for a long time either English or other language. Especially, Indian language handwritten letter is having many curves. It has been an open challenge for long time. In the advent of Machine Learning, the handwritten recognition becomes easier. However, many challenges still persist, because feature extraction is a difficult task since the characters are more in Indian languages. In this paper, we have chosen handwritten Telugu Compound Character called Guninthalu (Character formed with combination of Telugu Vowels and Consonants) for recognition. Since each letter in Telugu Guninthalu is almost similar to other. The classification is a challenging task. There are numerous machine learning techniques, however, the accuracy is the key challenge to achieve. Therefore, we deploy deep learning technique to enhance the accuracy which is observed test accuracy and training accuracy as high as 79.61% and 96.13% respectively. We have built a machine learning model with Convolutional Neural Network for Telugu Handwritten Gunithalu. We have created our own dataset and it is available in IEEE Dataport.(http://dx.doi.org/10.21227/phg6-m127).
An Attack Path Generation Methods Based on Graph Database

With the popularity of network technology and the expansion of network scale, the network security risks are increasingly serious. Network vulnerability assessment methods, a technology of active network security defense, have attracted many researchers. Most existing network vulnerability assessment methods store different types of data in different ways, which makes querying and analyzing inefficient, especially in the complex large-scale network environment. In order to solve this problem, this paper proposes a method of network vulnerability assessment based on graph database. The network host information, association relationship between hosts and vulnerability information of the target network are stored in the graph database, the query and analysis are carried out by using the graph database query language. Graph database stores the information of the network hosts, association relationship among hosts and vulnerabilities of the target network. The graph database query language supports querying and analysis. Visualizing the network topology, vulnerability information and all possible attack paths provides a reference to develop the network security protection strategy. Experiments' results illustrate that the method runs efficiently and helps with querying and analysis, which is applicable to large-scale complex network environment.
Individual vs. Group Violent Threats Classification in Online Discussions

Violent threat is a serious crime affecting the targeted individuals or groups. It is essential for media providers to block the users that post such threats. In this paper, we focused on detection of violent threat language in YouTube comments. We categorized the threatening comments into those targeting an individual or a group. We started from an existing dataset with violent threat language identified, but without any categorization into comments targeting individuals or groups. We adopted a binary classification approach for the prediction of individual- vs. group-targeting threats. We compared two text representations: bag of words (BOW) and pre-trained word embedding such as GloVe and fastText. We used deep-learning classifiers such as 1D-CNN, LSTM, and bidirectional LSTM (BiLSTM). GloVe embedding showed the worst results, fastText performed much better, and BiLSTM on BOW with term frequency-inverse document frequency (TF-IDF) weighting scheme gave the best results, achieving 0.94% ROC-AUC and Macro-F1 score of 0.85%.
Analyzing and Detecting Collusive Users Involved in Blackmarket Retweeting Activities

With the rise in popularity of social media platforms like Twitter, having higher influence on these platforms has a greater value attached to it, since it has the power to influence many decisions in the form of brand promotions and shaping opinions. However, blackmarket services that allow users to inorganically gain influence are a threat to the credibility of these social networking platforms. Twitter users can gain inorganic appraisals in the form of likes, retweets, and follows through these blackmarket services either by paying for them or by joining syndicates wherein they gain such appraisals by providing similar appraisals to other users. These customers tend to exhibit a mix of organic and inorganic retweeting behavior, making it tougher to detect them. In this article, we investigate these blackmarket customers engaged in collusive retweeting activities. We collect and annotate a novel dataset containing various types of information about blackmarket customers and use these sources of information to construct multiple user representations. We adopt Weighted Generalized Canonical Correlation Analysis (WGCCA) to combine these individual representations to derive user embeddings that allow us to effectively classify users as: genuine users, bots, promotional customers, and normal customers. Our method significantly outperforms state-of-the-art approaches (32.95% better macro F1-score than the best baseline).
Lovextortion: Persuasion strategies in romance cybercrime

Online romance scams represent a growing global phenomenon. This study investigates a corpus of authentic messages of this type written in English, focusing on their structural, discursive, and linguistic features. The analysis shows that errors of judgment are exploited by scammers in order to lead victims to an inability to recognize an attempted fraud, despite the signs present in the message. The data obtained may contribute to the development and production of guidelines to be circulated widely, especially on sensitive sites online (e.g. dating sites), to help end-users to become more knowledgeable about adversarial fraudulent strategies.
Using Generative Adversarial Networks to Break and Protect Text Captchas

Text-based CAPTCHAs remains a popular scheme for distinguishing between a legitimate human user and an automated program. This article presents a novel genetic text captcha solver based on the generative adversarial network. As a departure from prior text captcha solvers that require a labor-intensive and time-consuming process to construct, our scheme needs significantly fewer real captchas but yields better performance in solving captchas. Our approach works by first learning a synthesizer to automatically generate synthetic captchas to construct a base solver. It then improves and fine-tunes the base solver using a small number of labeled real captchas. As a result, our attack requires only a small set of manually labeled captchas, which reduces the cost of launching an attack on a captcha scheme. We evaluate our scheme by applying it to 33 captcha schemes, of which 11 are currently used by 32 of the top-50 popular websites. Experimental results demonstrate that our scheme significantly outperforms four prior captcha solvers and can solve captcha schemes where others fail. As a countermeasure, we propose to add imperceptible perturbations onto a captcha image. We demonstrate that our countermeasure can greatly reduce the success rate of the attack.
Protecting Electoral Integrity in the Digital Age: Developing E-Voting Regulations in Canada

As elections around the world become digital, governments have begun adopting regulations to govern the use of voting technologies and protect electoral integrity. Canada, however, is an exception. Despite the prevalence of voting technologies in Canada's local elections, notably online voting, no regulation framework has been initiated. In particular, there are no guidelines or standards surrounding the use of online voting. While research documents online voting has positive effects for participation, implications for the integrity, accountability, and transparency of elections are stark. Canada's multilevel governance structure has meant municipalities mostly deliver elections on their own terms, resulting in a patchwork of online voting models and cybersecurity requirements. Many municipalities also lack the resources to vet vendor solutions adequately, and an increasing number of cities are eliminating paper voting. These conditions highlight an urgent need to regulate the design and procurement of election technology in Canada. To proactively respond to these developments, this article draws upon interviews with select officials and experts and regulation models in other jurisdictions to argue for a new model of electronic voting regulation that would be a good fit for Canada.
Deep Network with Pixel-Level Rectification and Robust Training for Handwriting Recognition

Offline handwriting recognition is a well-known challenging task in the optical character recognition field due to the difficulty caused by various unconstrained handwriting styles and limited training data. In order to learn invariant feature representations for handwriting, we propose a novel method to incorporate pixel-level rectification into a CNN- and RNN-based recognizer. We also propose an adjacent output mixup method for RNN layer's training to improve the generalization ability of the recognizer, i.e., the previous output of an RNN layer is added to the current output with random weights. We additionally adopt a series of techniques including pre-training, data augmentation and language model to significantly expand the training data scale, and further analyze their contributions to the improvement in the model performance. The proposed method performs well on four public offline handwriting benchmarks, including the IAM, Rimes, IFN/ENIT and CASIA-HWDB datasets.
Communication Efficient Framework for Decentralized Machine Learning

In this paper, we propose a fast, privacy-aware, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem. The proposed algorithm is based on the Alternating Direction Method of Multipliers (ADMM) algorithm. The key novelty in the proposed algorithm is that it solves the problem in a decentralized topology where at most half of the workers are competing the limited communication resources at any given time. Moreover, each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with a lower amount of communication overhead in each exchange. We prove that GADMM converges faster than the centralized batch gradient descent for convex loss functions, and numerically show that it converges faster and more communication-efficient than the state-of-the-art communication-efficient algorithms such as the Lazily Aggregated Gradient (LAG) and dual averaging, in linear and logistic regression tasks on synthetic and real datasets.
Trustworthy Method for Person Identification in IIoT Environments by Means of Facial Dynamics

In industrial Internet of Things (IIoT) environments, dependability of a complex manufacturing process in which human operators play a key role can be improved by identity recognition/authentication of whoever is involved in various stages of a production process, according to where and when he/she is supposed to be. To this aim, we propose an approach that exploits the dynamic appearance and the time-dependent local features characterizing the face of an individual during speech utterance with regard to their spatial and temporal components. The proposed method models these dynamic facial patterns captured from edge Internet of Things devices by means of the Local Binary Pattern on Three Orthogonal Planes descriptor, which effectively extract both face's local features and movement at the fog level of the architecture. A deep feedforward network available in the cloud is trained and optimized to match the extracted features to a reference database. The achieved results highlight state-of-the-art performances of the proposed method with regard to robustness and trustworthiness of identification, especially for challenging IIoT scenarios.
What Sparks Joy: The AffectVec Emotion Database

Affective analysis of textual data is instrumental in understanding human communication in the modern era of social media. A number of resources have been proposed in attempts to characterize the emotions tied to words in a text. In this work, we show that we can obtain a database that goes beyond the common binary scores for emotion classification provided by past work. Instead, we harness the power of Big Data by using neural vector space models trained with large-scale supervision from co-occurrence patterns. We modify the vector space to better account for emotional associations, which then enables us to induce AffectVec, a new emotion database providing graded emotion intensity scores for English language words with regard to a fine-grained inventory of over 200 different emotion categories. Our experiments show that AffectVec outperforms existing emotion lexicons by substantial margins in intrinsic evaluations as well as for affective text classification.
Wavelet based medical image super resolution using cross connected residual-in-dense grouped convolutional neural network

In clinical analysis and diagnosis, high resolution (HR) computed tomography (CT) images are required for proper treatment of a patient. Developing HR medical images by X-ray CT devices require extended radiation exposure with large radiative dosages, putting the patient at potential risk of inducing cancer. So, radiation exposure should be reduced. However, photon starvation and beam hardening in low-dose X-rays will cause severe artifacts. Thus, an accurate reconstruction of low-dose X-ray CT images is required. To this end, we propose a wavelet based multi-channel and multi-scale cross connected residual-in-dense grouped convolutional neural network (WCRDGCNN) for accurate super resolution (SR) of medical images. The adopted filter groups reduce the connection weights, thereby reducing the computational complexity. Gradient vanishing problem is tackled by using residual and dense skip connections. The extensive experimentation results on benchmark datasets show that our method outperforms the state-of-the-art SR methods.
Blood Cell Types Classification Using CNN

White Blood Cells also known as leukocytes plays an important role in the human body by increasing the immunity by fighting against infectious diseases. The classification of White Blood Cells, plays an important role in detection of a disease in an individual. The classification can also assist with the identification of diseases like infections, allergies, anemia, leukemia, cancer, Acquired Immune Deficiency Syndrome (AIDS), etc. that are caused due to anomalies in the immune system. This classification will assist the hematologist distinguish the type of White Blood Cells present in human body and find the root cause of diseases. Currently there are a large amount of research going on in this field. Considering a huge potential in the significance of classification of WBCs, we will be using a deep learning technique Convolution Neural Networks (CNN) which can classify the images of WBCs into its subtypes namely, Neutrophil, Eosinophil, Lymphocyte and Monocyte. In this paper, we will be reporting the results of various experiments executed on the Blood Cell Classification and Detection (BCCD) dataset using CNN.
Detecting Features of Middle Size Soccer Field using Omnidirectional Camera for Robot Soccer ERSOW

ERSOW (EEPIS Robot Soccer on Wheeled) is robot soccer developed by Politeknik Elektronika Negeri Surabaya that is designed and implemented on a Middle Size League division by following the rules of RoboCup, an international robot competition. One of the most famous division is a soccer robot, that is divided into two divisions: (1) SSL (Small Size League) and (2) MSL (Middle Size League). There are many research fields related to soccer robot which must be developed in robot ERSOW such as Artificial Intelligence (AI), Computer Vision, Embedded System, Mechanic Systems, and Hardware. This paper focuses on computer vision research for robot ERSOW, especially detecting features of the middle size soccer field, so that specific features of the field like X-junction, T-junction and L-junction can be detected to help robot positioning task where the result is represented into x and y in real-world coordinate. By knowing the position of the features, the robot position can be calculated. The localization system at robot ERSOW uses odometry, which has a large percentage of data errors. Therefore, we attempt to extract the feature of X-junction that is done to find its x and y coordinates and then the obtained coordinate can be used as a reference for correcting odometry data by AI.
Training Deep Neural Networks Using Posit Number System

With the increasing size of Deep Neural Network (DNN) models, the high memory space requirements and computational complexity have become an obstacle for efficient DNN implementations. To ease this problem, using reduced-precision representations for DNN training and inference has attracted many interests from researchers. This paper first proposes a methodology for training DNNs with the posit arithmetic, a type-3 universal number (Unum) format that is similar to the floating point(FP) but has reduced precision. A warm-up training strategy and layer-wise scaling factors are adopted to stabilize training and fit the dynamic range of DNN parameters. With the proposed training methodology, we demonstrate the first successful training of DNN models on ImageNet image classification task in 16 bits posit with no accuracy loss. Then, an efficient hardware architecture for the posit multiply-and-accumulate operation is also proposed, which can achieve significant improvement in energy efficiency than traditional floating-point implementations. The proposed design is helpful for future low-power DNN training accelerators.