Reviewerly/exHarmony huggingface dataset:
Target-based calibration of 3D LiDAR and binocular camera on unmanned vehicles

Unmanned vehicles need to gather the surrounding information comprehensively. Perception of automotive information is one of the important information. In the field of automotive perception, the stereo vision plays a vital role and stere-vision can calculate the length, width, and height, making the object more specific. However, under the existing technology, it is impossible to obtain accurate detection in a complex environment by relying on a single sensor. Therefore, it is particularly important to study the calibration technology based on multi-sensor fusion. This paper proposes a method based on feature point pair matching. Two rectangular planks are used to extract the 3D point cloud of the edge of the board in stereo vision and LiDAR coordinate systems, which is then used to obtain the corner coordinates. Finally, the Kabsch algorithm is used to solve the coordinate transformation between the paired feature points, and a clustering method is used to remove outliers from the multiple measurements and obtain the average value. By setting up an experiment, this method can be implemented on the Nvidia Jetson Tx2 embedded development board, and accurate registration parameters can be obtained, thus verifying the theoretical method’s feasibility. It finishes calibration of the LiDAR and binocular camera based on present methods. The result shows that, it can reduce the effects of noise, and acquire registration parameters accurately of LiDAR and cameras. Compared with the approved method of the same type, our proposed method has less errors and good practical value.
Human Activity Recognition with Videos Using Deep Learning

Human activity recognition is a fascinating problem that can be addressed in ample ways. This review is centered around acknowledging actual human exercises dependent on the understanding of sensor information which likewise incorporates one-dimensional time-series information. Different methods have been implemented during the past years. Various ways are offered to recognize various types of activities, such as whether the individual is running, walking, dancing, jogging, or falling, to list a few. For each technique, multiple methodologies and datasets are employed, with data obtained in various ways such as sensors, accelerometers, gyroscopes, pictures, and so on. The desired outcomes by each approach and dataset type are then compared. To categorize, machine learning techniques such as K-nearest neighbors (KNN), support vector machines (SVM), decision trees, hidden Markov models, and other Deep Learning architectures are used.
Human Context Recognition: A Controllable GAN Approach

Human activity recognition (HAR), the use of sensor data to identify activities performed by an individual, has garnered substantial research attention. However, obtaining data for HAR is costly. As such, recent studies have used Generative Adversarial Networks (GANs) to create synthetic HAR data. Problematically, these solutions cannot generate personalized data that match the behavior of a user, limiting potential HAR applications as prior work has shown that personalized HAR models outperform generalized ones. To address this problem, we propose a novel controllable GAN where user and activity attributes can be explicitly requested for generation under one model. The GAN can learn these attributes independently and infer context pairs never seen during training: an impossibility for existing methodologies. We show that while our model is comparable to standard HAR GANs, its ability to consider user differences and generate novel context pairs outperforms these state-of-the-art GANs.
Raspberry Pi Forensic Investigation and Evidence Preservation using Blockchain

The adoption of Internet of Things (IoT) devices in numerous industries is rising rapidly these days, particularly in health care industry. The volume of data transmitted by these devices is enormous, and it will be in variety of formats. Furthermore, there is no guarantee that the data and gadgets are secure. If the equipment malfunctions as a result of one of the attacks, it may endanger human life if there is a direct connection. Therefore, it is required to have extensive investigation if any IoT-related cybercrime is discovered. This entails gathering adequate evidence from these IoT devices in order to undertake a thorough inquiry and obtained evidentiary data has to be protected. The primary purpose of this work is to extract evidences from Raspberry Pi and record it in the public digital ledger. This work also focuses on gathering data from wide range of IoT devices, that are in different data formats. After precisely examining the relationships between evidence, provenance, traceability, and auditability, artefacts from IoT devices are extracted and published in the IoT Forensics Network ledger.
Multi-factor Behavioral Authentication Using Correlations Enhanced by Neural Network-based Score Fusion

In recent years, personal behavioral authentication has been proposed as a new authentication method to support traditional knowledge-based, possession-based, and biometrics-based authentication. Most of the previous behavioral authentication research relied on historical behavioral patterns or trained classification models, thus requiring a large amount of preliminary data. In addition, while many studies utilized multiple authentication factors, authentication scores of each element were calculated independently without using correlations between them. This paper proposes a new authentication approach that uses behavioral correlations between each factor. In order to demonstrate the effectiveness of our proposal, we constructed a correlation-based authentication method using the data collected from smartphones and wearable activity trackers, including GPS locations, Wi-Fi access points, and activity types inferred from the metabolic equivalent of task (MET). Since the proposed method matches the data from multiple sensors to verify the request rather than utilizing the pattern extracted from the historical data, it does not require a large amount of preliminary data. We also employed a neural network-based score fusion method that aggregates the three authentication scores to improve the final authentication accuracy. The experimental result showed that the proposed method could achieve a half total error rate (HTER, an arithmetic average of a false rejection rate and false acceptance rate) of merely 8.0% that is much lower than other classification methods using the same dataset.
FPGA based Implementation of Binarized Neural Network for Sign Language Application

In the last few years, there is an increasing demand for developing efficient solutions for computer vision-related tasks on FPGA hardware due to its quick prototyping and computing capabilities. Therefore, this work aims to implement a low precision Binarized Neural Network (BNN) using a Python framework on the Xilinx PYNQZ2 embedded platform to tackle the challenging problem of Sign Language recognition. More specifically, the FINN framework is adopted and the BNN topology is modified to adapt large resolution (i.e 64x64) to perform classification of proposed Indian Sign Language (ISL) gestures into corresponding numbers. In addition, data augmentation techniques are also applied to improve the overall performance of the neural network. Furthermore, hardware/software co-verification of BNN topology is performed to validate the accuracy after implementing it onto hardware. Extensive experimental results show that it achieves a classification rate of 843.8 frames per second (FPS) on PYNQ-Z2 FPGA which delivers higher performance as compared to previous works. Also, the post-implementation results are analyzed in terms of resource utilization and power consumption.
Neural Question Generation Using Question Type Guidance

Question generation aims to generate questions from a context. The quality of generated questions is related to the context, the corresponding answer, and the question type. Most of previous works seldom pay attention to the question type. In this paper, we focus on how to exploit question type to guide the question generation task. The question type prediction task is introduced into our multi-task framework to discover the underlying relationship between the context-answer pair and the question. In additional, we also apply metric learning mechanism to improve the semantic relevance between the generated question and the context. Our model achieves a better performance than the baseline systems on SQuAD benchmark. Experimental results demonstrate that the guidance of question type is significant for question generation.
Text Summarization of Articles Using LSTM and Attention-Based LSTM

This research work focuses on developing a model for performing text summarization of articles, depending on the novelty of the information that is arranged in the final summary, where summarization is roughly categorized into two types: extractive and abstractive summarization. In today's fast-paced society, most people disregard if they are deemed to be rather extensive due to a lack of time. Therefore, having a brief overview of this article would not only encourage readers to read it, but if the topic is of interest, it would but also allow them to go deeper into it. As a result, this research work intends to bridge this research gap with the proposed model. Initially, this research project developed a reference RNN model and tested its accuracy. It was discovered that the summary loses a lot of real terms and reduces and confuses the overall meaning, therefore it was decided to build an LSTM model for the same and test it with three distinct datasets. The first experiment is carried out with a small dataset, whereas the second experiment is carried out with a comparatively bigger dataset by applying LSTM to it. We decided to carry it forward and improve the model by increasing the size of our dataset and choosing a large dataset with an attention layer to the proposed LSTM model by comparing the accuracy of all the models with the existing model, and it will be evaluated by using ROUGE metrics and proving the proposed solution to the world. Furthermore, the future research directions will be provided in Future Scope regarding where and how the proposed model can achieve more efficiency in the future.
Understanding the Role of Political Micro-influencers in Pakistan

The explosive growth of social networks, computing technologies, and natural language processing tools have enabled online marketing, brand promotions, and campaigns. In this regard, social media influencers have received great attention due to their excessive audience reach. However, automatic identification and analysis of such users face challenges of noisy text and computation which are further exacerbated for multilingual content. In this paper, we propose a novel method for the identification of micro-influencers and examine the evolution of such users in the digital landscape of Pakistan. First, we create labelled datasets for the classification of hashtags and micro-influencers using machine learning methods. Next, we build and classify a dataset of 14.4 million tweets into political and non-political categories to identify political micro-influencers. In addition, we present an analysis on the hashtag usage and location of micro-influencers. Moreover, we perform a longitudinal analysis of micro-influencers by capturing two snapshots. The analysis shows that the micro-influencers exhibit highly dynamic behaviour as 40&#x0025; accounts of micro-influencers from 2018 does not exist in 2021. The location analysis revealed 37&#x0025; micro-influencers are not from Pakistan. This research is useful for journalists, political scientists, and social media analysts.
Integrative Applications of Blockchain and Contemporary Technologies from a Big Data Perspective

Blockchain is not a standalone technology. It is a ledger that ensures the trustworthiness and traceability of data stored on it. Given the Industry 4.0 environment, it will work in integration with state-of-the-art technologies such as the internet of things (IoT), 3D printing (3DP), fog computing, geotagging, artificial intelligence (AI), and swarm robotic systems (SRS). These technologies' combined function would create large amounts of data on blockchain, popularly known as big data. This chapter contributes by highlighting the role of contemporary Industry 4.0 technologies in creating big data. The chapter features the managerial and policy implications of big data from service operations management perspective.
Towards OS-Independent Memory Images Analyzing: Using Paging Structures in Memory Forensics

Memory forensics is an approach for inspecting and analyzing the memory snapshots or dumps to understand the current state of a physical or virtual machine. Unfortunately, existing memory forensics tools still rely on kernel information of the target operating system (OS) to work properly, which usually depends on the availability of the kernel source code. This dependency prevents these tools from being usable against any close source OS. The goal of the research described in this paper is to make investigations of OS-independent methodology using x86 and x86_64 CPUs paging mechanism. After completing the algorithm, we implement them into a tool called MemInspect. MemInspect partially retrieves running process information from memory dumps without using any kernel information. We validated the efficacy of MemInspect under different kernels of Linux, Windows, FreeBSD, and BeOS. For all the OSes have been tested, MemInspect can successfully retrieve the hidden processes that spawn from the rootkit. We also present a detailed case study demonstrating our technique's ability to retrieve the KBeast rootkit's hidden process successfully.
AGBM: An Adaptive Gradient Balanced Mechanism for the End-to-End Steering Estimation

End-to-end steering estimation is one of the important deep regression tasks. However, driving datasets are always imbalanced on the distribution of the steering value, which makes end-to-end learning models perform bad steering estimation accuracy, especially for the sharp steering value estimation, which finally, leads to an imbalanced training problem. In this paper, the essential reason for the imbalanced training problem for the deep regression task is first revealed as the gradient disharmony. To hedge the gradient disharmony, a general gradient balanced mechanism for the deep regression task is proposed in this paper. Meanwhile, the general gradient balanced mechanism is applied for the steering estimation task. According to the steering distribution, a novel Adaptive Gradient Balanced Mechanism (AGBM) is proposed to hedge the gradient disharmony based on the Laplace distribution with the general gradient balanced mechanism theory. Further, a new loss function embedding with AGBM is proposed to balance the gradient disharmony for the training process. AGBM makes the process of gradient hedging with adaptive style without parameters finetuning. Based on five driving datasets, four experiments are conducted for demonstrations. The experiment results show the AGBM enables the end-to-end models to perform the best accuracy of the steering estimation, including the sharp steering samples. Meanwhile, AGBM is generalized with different end-to-end learning models.
Digitalization of Administrative Documents A Digital Transformation Step in Practice

Digital transformation is one of the most popular keyword in recent years. It is not only a trend in science research based on the development of information technology, but also a proposed duty that applied in the companies or organizations nowadays. Digitalization of administrative documents is therefore considered as the first step in digital transformation of public organization. Through the digitizing process, the information that were in written format or hard copies will be converted into digital format (e.g. document files) to serve for storing, mining, processing and managing the documents. This paper presents a method to build a web application for digitizing the administrative documents applied in most public organizations. The method is based on the OCR (Optical Character Recognition) combined with the image processing techniques. Our digital process is implemented as following steps. (i) Scanning the hard copies of the administrative documents. (ii) Removing noise data and filtering necessary information in the content based on image processing technique. (iii) Classifying automatically the acquired contents into the respective components of a template form following the structured format of Vietnam Government. (iv) Generating automatically a document file. The application can process a document with a single or multiple pages. To compare with similar applications, our application is processed very fast, without limitation of pages for each document and obtained accuracy as our expectation.
A Mobile Based Waste Classification Using MobileNets-V1 Architecture

In Indonesia, waste is a very serious problem. According to research in handling and processing waste is classified into three types, namely recyclable, nonorganic, and organic waste. Organic and inorganic waste will generally be transported and stockpiled at the Final Disposal Site (TPA). So far, the only available waste bins are waste bins with manual sorting done by the community. As it is known that currently there are many people who do not understand the different types of waste to be disposed of, so that even though organic and inorganic types of waste have been provided, people still dispose of waste in inappropriate types. This of course will be very inconvenient in the effort to sort waste in the waste whereas the first place for garbage to gather. Because of the need for a tool that can help the community in distinguishing the types of waste before putting it into the waste with an accurate classification method Based on the problems in classifying the types of waste that have been described previously, we need a system that is able to classify waste according to its type, The MobileNets-V1 architecture is used in this research to classify images. The models generated by the architecture will then be deployed into mobile-based applications. The dataset used in this study consists of 3 classes, namely N (Non-Recyclable), O (Organic), R (Recyclable). Because the data is highly imbalanced, we conduct undersampling in order to balance the data. This undersampling process is done only in the training set after splitting the whole dataset into training, validation, and testing set. After the balancing process, each class has 1822 sample data, totalling of 5466 sample data in the trianing set. The pretrained MobileNets-V1 model is able to classify types of waste very well. The best model obtained is a model that uses dropout value of 0.4 which provides testing accuracy of 88.26%, training accuracy of 92.44% and validation accuracy of 89.00%.
Object Detection for Autonomous Vehicle using Single Camera with YOLOv4 and Mapping Algorithm

In this paper, we propose a new algorithm combined with the existing object recognition algorithm. Multi-object recognition algorithms are now various, with their respective advantages and disadvantages according to their uses. However, these algorithms can only detect and recognize objects without being able to know the location of the object relative to the sensor. The ability to know the location of the object is needed so that the autonomous car can make the right decisions without harming the driver. Since it requires fast and precise object detection and recognition capabilities, the algorithm used in object recognition is YOLOv4 with CSPDarknet-53. And because object recognition uses a neural network, the algorithm in determining the location of the object needs to be made as efficient as possible without affecting the performance of the object recognition algorithm, so that the mapping algorithm is used. The YOLOv4 model used has a precision value of 57.23 percent with a detection capability of 0.03785 seconds without a mapping algorithm, and if it is added with a mapping algorithm, the detection time becomes 0.03792 seconds. Since it has fast detection time, thus it can be applied to a real-time application.
Key Sequences based on Cyclic Elliptic Curves over <i>GF</i>(2<sup>8</sup>) with Logistic Map for Cryptographic Applications

Abstract The chaotic functions and cyclic elliptic curves (CEC) has attracted many researchers, especially cryptographers, since decades. As CEC and chaotic functions meet the requirements of the cryptography, they are widely used either in key sequence generation or encryption/decryption process. Since in symmetric key encryption, stream ciphers need a large key sequence to encrypt/decrypt the information, authors propose a hybrid model to generate pseudo random sequence using CEC and logistic map. The hybrid model is developed using CEC points over and an one‐dimensional logistic map. In the proposed system, binary chaotic sequence and CEC points are given to AES S‐box to get two random sequences separately. Byte wise parity is computed for both the sequences. Based on the parity of both the bytes, one of the byte is left shifted and the other is right shifted by two positions in a circular manner. Later, both the bytes are combined using the XOR operation to get the random binary sequence. The generated sequences are analysed for randomness using various test suites like NIST, Correlation, FIPS, and TestU01. It has been observed that all the sequences pass the randomness tests. Using additive stream cipher, image encryption is implemented by considering the generated sequence as key sequence. The performance evaluation and security analysis of the encryption process is conducted. The results obtained for the proposed system indicate that the system is robust to withstand many cryptanalytic attacks.
Attribute Assailability and Sensitive Attribute Frequency based Data Generalization Algorithm for Privacy Preservation

The data publishing and analysis expose users’ and users group’s sensitive data and their identities. As a result, the adversary initiate inference attack using exposed users’ identifies and associated sensitive information. Some of the users’ attribute and users’ group attributes values are highly vulnerable and their contiguity increase privacy breach. The traditional k-anonymity based privacy models prevent identity disclosure but fails to prevent sensitive attribute values disclosure and ignore repercussion of assailable attributes on users’ group privacy. This research propose a novel data generalization algorithm that enhances users’ group privacy and data utility. The proposed method calculate the susceptibility of quasi-identifier attributes, frequency of sensitive attributes and their association. These values are used for form equivalence class that satisfies k-anonymity and β-likeness property. The proposed generalization algorithm considers both susceptibility, frequency of sensitive attributes and their association while anonymizing data. The extensive experiments are carried out on open source Apache Spark environment. The simulation results demonstrate that efficiency and effectiveness of proposed algorithm. This research work outperform other state-art data privacy model in terms of thwarting inference attack, information loss and accuracy of model.