Reviewerly/exHarmony huggingface dataset:
SmartIdOCR: Automatic Detection and Recognition of Identity card number using Deep Networks

Identity authentication is much needed and required in this digital age where the information can be utilized in many areas like banking, finance, insurance, education, etc. The long time in the manual authentication process is tiresome for both sides due to the exchange of data. The challenge lies in verification and information extraction from the ID card during the authentication process. There is an AI-based solution needed to reduce the authentication time. This paper aims to solve this problem by doing real-time authentication of identity cards like PAN and UIDAI using AI techniques with good accuracy. Real-time authentication is done by text detection and text recognition. The text detection is done using a differentiable binarization algorithm. We do not have a real annotated dataset for an ID number. We generated approximately 90000 identity number images synthetically with noise and blur using two fonts. This dataset is divided into training, validation, and testing sets. We present a neural encoder-decoder model with attention for converting ID number line images into editable text. Our method is evaluated based on the text output of the line image. An attention-based approach can tackle this problem in a better way in comparison to other neural techniques using CTC-based models. This paper describes the usage of OpenNMT architecture for recognition due to the flexibility of hyperparameter tuning. We evaluated the text recognition performance on scanned as well as the camera-captured identity card number images. We also compared the current recognition performance of OpenNMT with Tesseract (LSTM) on the same testbed containing 36000 images containing ID numbers only. The proposed approach outperformed the Tesseract in ID number recognition.
A Forensic Methodology for the Analysis of Cloud-Based Android Apps

The widespread use of smartphones has made the gadget a prime source of evidence for crime investigators. The cloud-based applications on mobile devices store a rich set of evidence in the cloud servers. The physical acquisition of Android devices reveals only minimal data of cloud-based apps. However, the artifacts collected from mobile devices can be used for data acquisition from cloud servers. This paper focuses on the forensic acquisition and analysis of cloud data of Google apps on Android devices. The proposed methodology uses the tokens extracted from the Android devices to get authenticated to the Google server bypassing the two-factor authentication scheme and access the cloud data for further analysis. Based on the investigation, we have also developed a tool to acquire, preserve and analyze cloud data in a forensically sound manner.
Recognition of violent actions on streets in urban spaces using Machine Learning in the context of the Covid-19 pandemic

Currently, recognition systems based on Artificial Intelligence and Computer Vision have enabled various applications in fields such as Medicine, Industrial Engineering, and in an emerging way in the field of Public Safety as a useful and necessary tool in smart cities that favours the control, management and prevention of criminal acts. Given that violence is a very frequent social problem in Latin American countries. A pilot case has been proposed in the city of Iquitos, Peru, with a tool generated to recognise violent actions from a video or image captured from a mobile phone. This work proposes the application of a mobile tool that facilitates the recognition of high-frequency violent actions on public roads. A bank of 500 images has been generated for each class of violent action prioritised in this work, then a manual labelling tool called &#x201C;LabelImg&#x201D; has been used with the extraction of FPS from videos, and the convolutional neural network algorithm YOLO v3 has been used with the Darknet variant. The results of the experiment achieved an accuracy of 94&#x0025; in the detection of 4 violent actions: punching, kicking, grappling and strangling.
Efficient-Grad: <u>Efficient</u> Training Deep Convolutional Neural Networks on Edge Devices with <u>Grad</u> ient Optimizations

With the prospering of mobile devices, the distributed learning approach, enabling model training with decentralized data, has attracted great interest from researchers. However, the lack of training capability for edge devices significantly limits the energy efficiency of distributed learning in real life. This article describes Efficient-Grad, an algorithm-hardware co-design approach for training deep convolutional neural networks, which improves both throughput and energy saving during model training, with negligible validation accuracy loss. The key to Efficient-Grad is its exploitation of two observations. Firstly, the sparsity has potential for not only activation and weight, but gradients and the asymmetry residing in the gradients for the conventional back propagation (BP). Secondly, a dedicated hardware architecture for sparsity utilization and efficient data movement can be optimized to support the Efficient-Grad algorithm in a scalable manner. To the best of our knowledge, Efficient-Grad is the first approach that successfully adopts a feedback-alignment (FA)-based gradient optimization scheme for deep convolutional neural network training, which leads to its superiority in terms of energy efficiency. We present case studies to demonstrate that the Efficient-Grad design outperforms the prior arts by 3.72x in terms of energy efficiency.
Comparison of Distance Metrics on Fuzzy C-Means Algorithm Through Customer Segmentation

Distance metrics are often used in a similarity-based algorithm like clustering to improve the performance when deciding to group data based on similarities. It has a crucial role when building machine learning models. Therefore, this research would like to examine the optimal distance metrics method in the clustering algorithm. The algorithm that will be used in this research is Fuzzy C-Means clustering by applying several data distance measurement methods (Euclidean Distance, Manhattan Distance, Chebyshev Distance, and Minkowski Distance). Then, the resulting cluster will be evaluated using a validity index including partition coefficient index (PC), modified partition coefficient index (MPC), and RMSE. The results represent that the most optimal distance of the 2 clusters dataset was obtained using Manhattan Distance measurement methods. The most optimal distance of the 3 clusters dataset was obtained using Minkowski Distance measurement methods. From a series of conducted experiments of the dataset, the Manhattan and Minkowski measurement methods represented the optimal results for the FCM algorithm.
Robust and Efficient Data Security Solution for Pervasive Data Sharing in IoT

Pervasive sensing is shaping up modern societies and opening the door for many unconventional applications. Instead of the contemporary access model where sensor data is disseminated to a single user, multi-access scenarios are becoming more prevalent, which raises the issue of how to authenticate users, how to ensure access authorization, and how to prevent information leakage. To address these issues, this paper presents a novel lightweight protocol that promotes a data-driven methodology. The idea is to employ hardware primitives to support authentication of legit data recipients and to factor in the previously shared data samples in generating encryption keys. Our protocol in essence generates encryption keys that vary per packet and in an implicitly synchronized manner between the data source and each recipient. The generated key is also a function of the hardware primitive and thus effectively prevents data access to unauthorized recipients. We analyze the resilience of our protocol to impersonation and message replay, and hardware primitive modeling attacks. The security properties of our solution is validated using the AVISPA toolset and its performance is compared to the asymmetric cryptography approaches.
A UAV Detection and Tracking Algorithm Based on Image Feature Super-Resolution

UAV is difficult to detect by visual methods at a long distance, so a UAV detection and tracking algorithm is proposed based on image super-resolution. Firstly, a saliency transformation algorithm is built to focus on the suspected area. Then, a generative adversarial network is established on the basis of ROI to realize the super-resolution of weak targets and restore the high-resolution details of target features. Finally, the cooperative attention module is built to recognize and track UAV. Our experiments show that the proposed algorithm has strong robustness.
Audio Watermarking Using Modified Least Significant Bit Technique

In this paper, a digital audio watermarking system based on a modified LSB Technique has been proposed. The main objective of the proposed method is to embed the watermark image in the Least Significant Bit plane without affecting the perceived quality of the original audio signal. Watermark bits are embedded throughout the signal instead of being localized to a particular set of bits. The proposed methodology for both embedding and extraction processes has resulted in an efficient technique that can be of great help for musicologists in maintaining their copyrights. The performance of the proposed method is evaluated by comparing it with the standard LSB Technique using the signal-to-noise ratio (SNR) as the parameter. The watermarked audio is then subjected to certain signal processing attacks and the variations are analyzed to evaluate its robustness.
Deep Learning Transceiver for Terahertz Band Communication System with 1-bit ADC and Oversampling

In this study, with the aim of reducing power consumption for terahertz band wireless communication, we present a deep learning-based solution for transceiver design with 1-bit quantization and oversampling at the receiver, and Faster- than-Nyquist transmission. Our simulation results illustrate that the studied system with 1-bit quantization achieves bit-error- rate performance comparable to that of an end-to-end channel autoencoder without the constraint of 1-bit quantization subject to the same spectral efficiency. It is also demonstrated that reliable communication can be achieved at rates exceeding 5.3 bits/sec/Hz which corresponds to 80% of the achievable capacity at adequate SNR.
Causality in requirements artifacts: prevalence, detection, and impact

Abstract Causal relations in natural language (NL) requirements convey strong, semantic information. Automatically extracting such causal information enables multiple use cases, such as test case generation, but it also requires to reliably detect causal relations in the first place. Currently, this is still a cumbersome task as causality in NL requirements is still barely understood and, thus, barely detectable. In our empirically informed research, we aim at better understanding the notion of causality and supporting the automatic extraction of causal relations in NL requirements. In a first case study, we investigate 14.983 sentences from 53 requirements documents to understand the extent and form in which causality occurs. Second, we present and evaluate a tool-supported approach, called CiRA, for causality detection. We conclude with a second case study where we demonstrate the applicability of our tool and investigate the impact of causality on NL requirements. The first case study shows that causality constitutes around 28 % of all NL requirements sentences. We then demonstrate that our detection tool achieves a macro- $$\hbox {F}_{1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mtext>F</mml:mtext><mml:mn>1</mml:mn></mml:msub></mml:math> score of 82 % on real-world data and that it outperforms related approaches with an average gain of 11.06 % in macro-Recall and 11.43 % in macro-Precision. Finally, our second case study corroborates the positive correlations of causality with features of NL requirements. The results strengthen our confidence in the eligibility of causal relations for downstream reuse, while our tool and publicly available data constitute a first step in the ongoing endeavors of utilizing causality in RE and beyond.
An Interaction Provenance-based Trust Management Scheme For Connected Vehicles

Connected vehicles (CVs) have facilitated the development of intelligent transportation system that supports critical safety information sharing with minimum latency. However, CVs are vulnerable to different external and internal attacks. Though cryptographic techniques can mitigate external attacks, preventing internal attacks imposes challenges due to authorized but malicious entities. Thwarting internal attacks require identifying the trustworthiness of the participating vehicles. This paper proposes a trust management framework for CVs using interaction provenance that ensures privacy, considers both in-vehicle and vehicular network security incidents, and supports flexible security policies. For this purpose, we present an interaction provenance recording and trust management protocol. Different events are extracted from interaction provenance, and trustworthiness is calculated using fuzzy policies based on the events.
Special Issue: Regularization Techniques for Machine Learning and Their Applications

Over the last decade, learning theory performed significant progress in the development of sophisticated algorithms and their theoretical foundations. The theory builds on concepts that exploit ideas and methodologies from mathematical areas such as optimization theory. Regularization is probably the key to address the challenging problem of overfitting, which usually occurs in high-dimensional learning. Its primary goal is to make the machine learning algorithm “learn” and not “memorize” by penalizing the algorithm to reduce its generalization error in order to avoid the risk of overfitting. As a result, the variance of the model is significantly reduced, without substantial increase in its bias and without losing any important properties in the data.
Multi-scale spatial-temporal transformer for 3D human pose estimation

Most existing video-based 3D human pose estimation methods focus on single-scale spatial and temporal feature extraction. However, many human motions are only related to local joints, which suggests that we need to pay attention to the local pose of the human body for 3D pose estimation. In this paper, we propose a novel multi-scale spatial-temporal transformer framework to tackle the problem of 3D human pose estimation. Our framework mainly consists of two separate modules: a multi-scale spatial transformer module and a multiscale temporal transformer module. The first module is designed to enhance the spatial dependencies by the joint-level and part-level spatial transformers. The goal for the second module is to capture the temporal correlation of human pose by the local part-level and global whole-level temporal transformer. Then we apply a weight fusion module to predict accurate 3D human pose of the center frame. Experimental results show that our method achieves excellent performance.
Implementation of Chatbot on University Website Using RASA Framework

Chatbots are increasingly being utilized to help human performance to boost communication ease and provide better and faster services. A chatbot was built to make it easier to give better and more conveniently available information services 24 hours a day, seven days a week. The suggested chatbot design is constructed using the Rasa framework and is based on the Dual Intent and Entity Transformer (DIET). DIET is a multi-tasking transformer architecture that is both advanced and lightweight. The chatbot will be implemented on the “Politeknik Siber dan Sandi Negara” website, focusing on addressing questions about new student admittance. The chatbot is built with Docker and put as a Chat Widget on the website. The dataset utilized to train the algorithm combines past chat data and data from university social media. The built-in chatbot is evaluated using accuracy metrics and F1 scores. Model evaluation metrics and functionality tests are used in the evaluation. Testing with evaluation results in a precision value of 0.99. a recall value of 1.0, and an F1 score of 0.99. While the functioning test percentage is 90.625 %.
Swimmer’s Posture Recognition and Correction Method Based on Embedded Depth Image Skeleton Tracking

With the continuous emergence of depth image recognition technology, human motion recognition technology has gradually come to life. However, because the current technology of image recognition and skeletal tracking is too backward, people cannot use gesture recognition robots to perform gesture recognition and correction with high accuracy for athletes. In this study, the skeletal tracking depth image is obtained through the Kinect sensor. When using the Kinect sensor to acquire images, the requirements for the environment around the measured object are very low, and it will not be affected by conditions such as light, shadow, and object occlusion, and the pose can be segmented in real time in a complex background. In this study, the depth map feature and the bone information feature are selected for fusion. The HOD feature is difficult to deal with the occlusion problem, and it is not easy to detect the excessive range of the human body gesture or the change of the object direction. Based on this, the HOD feature is improved in many places to form a new 3D-HOD feature and DMM-HOG feature. In this study, the technical research on posture recognition and correction of swimmers in the experiment has realized posture collection, posture segmentation, posture analysis, posture modeling, and posture recognition. In this study, random forests are combined with HOD features to classify pixel-by-pixel points in depth images. The accuracy of training pixel classification is as high as 85%, and the average recognition time of the decision tree for each pose is about 9% higher than that of random forests. The use of Meanshift algorithm to cluster the classified pixels to form skeletal joint points is fast and efficient, and can quickly and accurately find joint points. It has also achieved good experimental effects on human motion recognition and continuous motion recognition, is more suitable for real life, and has commercial practical value.
Interactive ChatBots for Software Engineering: A Case Study of Code Reviewer Recommendation

Recommendation systems have played a large role in the Software Engineering research landscape. Applications have ranged from source code elements, APIs and reviewer recommendations, with techniques borrowed from the Information Retrieval, and Machine Learning domains. In recent times, there has been work into a new method of interaction, which is ChatBots, especially for Software Engineering. Early work has been aimed at using bots for mining software repositories, providing task-oriented feedback for the software developer. In this work, we would like to take the ChatBots one step forward, but using them inconjunction with recommendation systems to provide an interactive experience for recommendations. As a case study, we focus on the existing reviewer recommendation systems, and propose how using a ChatBot may enhance the solution, to provide a more accurate and realistic recommendation for the practitioner. In the end, we highlight the potential and next steps to utilize ChatBots into existing Software Engineering recommendation systems.
Automatic Speaker Verification and Replay Attack Detection System using novel Glottal Flow Cepstrum Coefficients

With the growing number of devices that use voice biometric system for verification purposes, it becomes mandatory to accurately verify the target speaker. Moreover, intruders use different spoofing attacks to fool an automatic speaker verification (ASV) system. Voice replay attack being the easiest to generate among other spoofing attacks is frequently employed in front of the ASV systems of these devices to get access to the system for malicious purposes. Thus, we need to develop robust systems that are capable of classifying the target speaker and differentiate between the bonafide and replay voice. To combat the vulnerabilities of ASV systems, we proposed an automatic speaker verification and replay attack detection (ASVRAD) system. For this purpose, we proposed a novel audio feature i.e., Glottal Flow Gammatone Cepstrum Coefficients (GLGTCC), an enhanced version of Glottal Flow Cepstrum Coefficients (GLFCC) for accurate classification of the bonafide speakers and voice replay attacks detection. Our GLGTCC features can reliably capture the speaker-specific attributes from their vocal excitation patterns in the audio. Later, we employed a multiclass support vector machine (SVM) to classify the bonafide speakers, and a binary SVM for classification of bonafide and replay audios. Experimental results of our system on the standard ASVspoof 2019 PA dataset achieved the lowest EER of 3.00&#x0025; for speaker verification and 18.38&#x0025; on voice replay attacks over the comparative methods.
Software Requirements Prioritization in the context of Global Software Development

Requirements engineering (RE) is the initial and most crucial phase of the Software Development Life Cycle (SDLC). This phase involves multiple activities such as gathering requirements, analyzing, prioritizing, documenting, and validating user requirements. Requirements prioritization plays a crucial role in the successful execution of this phase. It helps us to decide which requirements are to be delivered first. Therefore, identifying user requirements and prioritizing them is a complex activity and needs frequent communication. It is a complex task and becomes even more complex while working with globally dispersed team members which is the main motivation behind this research. Our research aims to overcome the communication gap between stakeholders during requirements prioritization in GSD. To achieve the targeted objective, the practices of &#x201C;Communicate-bond-belong theory (CBB)&#x201D; have been incorporated into the requirements prioritization framework. The results ensure effective communication among the globally dispersed team members. Moreover, the validation of the proposed model is done with the help of a control group experiment. Later on, the Goal-Question Metric (GQM) approach is used to evaluate the obtained results with the help of statistical evaluation techniques. The proposed technique could successfully assist the practitioners in prioritizing requirements in the GSD context.
A pointer meter reading recognition method based on improved ORB algorithm for substation inspection robot

Abstract Currently, pointer meters in substations are read manually, which causes a waste of human resources. To realize automatic reading of such meters, an accurate method for recognizing and reading of pointer meters was proposed for the inspection robot in substation. The new method consists of three steps. Step 1: the image is preprocessed with grayscale, noise reduction and piecewise linear grayscale transformation. Step 2: the Gaussian scale space is used to enhance the scale invariace of the ORB algorithm. Meanwhile, the RANSAC algorithm is used to screen the matching point pairs, which improves the accuracy of the feature pointer matching. Step 3: the Hough transform algorithm is utilized to fit the pointer. Finally, the reading result is obtained according to the angle method and the prior information of the pointer meter. The proposed approach can recognize and read the meter accurately. It has high reliability and engineering application value.