Reviewerly/exHarmony huggingface dataset:
Using Topic Modeling for Code Discovery in Large Scale Text Data

When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most popular algorithms is topic modeling. For a given text dataset, a topic model provides probability distributions of words for a set of “topics” in the data, which researchers then use to interpret meaning of the topics. A topic model also gives each document in the dataset a score for each topic, which can be used as a non-binary coding for what proportion of a topic is in the document. Unfortunately, it is often difficult to interpret what the topics mean in a defensible way, or to validate document topic proportion scores as meaningful codes. In this study, we examine how keywords from codes developed by human experts were distributed in topics generated from topic modeling. The results show that (1) top keywords of a single topic often contain words from multiple human-generated codes; and conversely, (2) words from human-generated codes appear as high-probability keywords in multiple topic. These results explain why directly using topics from topic models as codes is problematic. However, they also imply that topic modeling makes it possible for researchers to discover codes from short word lists.
Prediction for Diagnosing Liver Disease in Patients using KNN and Naïve Bayes Algorithms

There is a lot of data on patients who undergo medication or medical examinations at the hospital and this is information that must be extracted so that it can provide information for future improvement conditions, meaning that past data can be used as a prediction basis for liver disease in patients. This is very beneficial for medical personnel and also for patients if they experience symptoms that match the symptoms felt by a patient. This project uses machine learning because it involves big data and past data is used to predict future data. Referring to previous research in reference that the results of the evaluation are varied. So in this study, The proposed strategy to performance optimization is carried out based on training data and variables that affect the model. Based on the results of calculations and analysis, it was found that the performance evaluation values were area under the curve(AUC) for naïve Bayes algorithm is 72.5% and k-nearest neighbour (KNN) of 63.19%.
Narrative Transitions in Data Videos

Transitions are widely used in data videos to seamlessly connect data-driven charts or connect visualizations and non-data-driven motion graphics. To inform the transition designs in data videos, we conduct a content analysis based on more than 3500 clips extracted from 284 data videos. We annotate visualization types and transition designs on these segments, and examine how these transitions help make connections between contexts. We propose a taxonomy of transitions in data videos, where two transition categories are defined in building fluent narratives by using visual variables.
A study on attention-based LSTM for abnormal behavior recognition with variable pooling

Behavior recognition is a well-known computer vision mobile technology. It has been used in many applications such as video surveillance, motion detection on devices, human-computer interaction and sports video, etc. However, most of the existing works ignored the depth and spatio-temporal information so that they resulted in over-fitting and inferior performance. Consequently, a novel framework for behavior recognition is proposed in this paper. In this framework, we propose a target depth estimation algorithm to calculate the 3D spatial position information of the target, and take this information as the input of the behavior recognition model. Simultaneously, in order to obtain more Spatio-temporal information and better handle long-term video, combining with the idea of attention mechanism, we propose a skeleton behavior recognition model which is based on spatio-temporal convolution and attention-based LSTM (ST-CNN & ATT-LSTM). The deep spatial information is merged into each segment, and the model focuses on the key information extraction, which is essential for improving behavior recognition performance. Meanwhile, we use a feature compression method based on variable pooling to solve the problem of inconsistent input sizes caused by multi-person behavior recognition, so that the network can flexibly recognize multi-person skeleton sequences. Finally, the proposed framework is evaluated with real-world surveillance video data, and the results indicate that our framework is superior to existing methods.
Man-in-the-Middle Attack on Contactless Payment over NFC Communications: Design, Implementation, Experiments and Detection

A recent development emanating from RFID technology is Near Field Communication (NFC). Basically, NFC is a popular short range (<10 cm) wireless communication technology with applications in areas sensitive to security and privacy concerns such as contactless payment. Since NFC communications require very close proximity between two communicating devices (e.g., a smartcard and a terminal), it is generally believed that Man-in-the-Middle (MITM) attacks are practically infeasible here. Contrasting this belief, in this paper, we successfully establish MITM attack in NFC communications between a passive tag and an active terminal. We carefully present physical fundamentals of the attack, our engineering design, and results of successful attack implementation. Subsequently, we present the practical applicability of our MITM attack that exploits a potential vulnerability in EMV based contactless payment protocol, which arises due to separation between card authentication and transaction authorization phases. We demonstrate how an attacker can compromise the integrity of a contactless payment using a malicious MITM card, and also present multiple attack/victim scenarios to analyze different types of impacts of our attack. Further, we conduct rigorous experimental studies to analyze both hardware and practical ramifications of our attack. Finally, we propose a mechanism to detect the MITM attack based on experimental analysis that demands no additional hardware.
Realizing nonadiabatic holonomic quantum computation beyond the three-level setting

Nonadiabatic holonomic quantum computation (NHQC) provides a method to implement error resilient gates and that has attracted considerable attention recently. Since it was proposed, three-level {\Lambda} systems have become the typical building block for NHQC and a number of NHQC schemes have been developed based on such systems. In this paper, we investigate the realization of NHQC beyond the standard three-level setting. The central idea of our proposal is to improve NHQC by enlarging the Hilbert space of the building block system and letting it have a bipartite graph structure in order to ensure purely holonomic evolution. Our proposal not only improves conventional qubit-based NHQC by efficiently reducing its duration, but also provides implementations of qudit-based NHQC. Therefore, our proposal provides a further development of NHQC that can contribute significantly to the physical realization of efficient quantum information processors.
Xilinx Zynq FPGA for Hardware Implementation of a Chaos-Based Cryptosystem for Real-Time Image Protection

This paper proposes a well-optimized FPGA implementation of a chaos-based cryptosystem for real-time image encryption and decryption. A highly sensitive Pseudo-Random Number Generator (PRNG) based on the Lorenz chaotic system is designed to generate pseudo-random numbers. A high-quality encryption key is acquired by encrypting the numbers of a 128-bit counter using the PRNG. For image encryption, the image is first decomposed into 128-bit blocks. Then, the blocks are encrypted by XORing pixels with the key stream. [Formula: see text] cycles of encryption can be achieved to increase complexity. Finally, the blocks are concatenated to form an encrypted image. The algorithm is designed, implemented, and validated on the Xilinx Zynq FPGA platform using the Vivado/System Generator tool. The hardware design is well optimized for pipeline processing and low resource utilization. The experimental synthesis indicates that the provided architecture achieves high performance in terms of frequency and throughput. The encryption scheme is evaluated and analyzed by several tools and tests using different images. The experimental simulation results demonstrate that the hardware implementation is faster than a software implementation while maintaining the technique’s effectiveness. The proposed hardware implementation is extremely adopted for secret image encryption and decryption that can be used in real-time applications.
Deep Learning Based Question Generation Using T5 Transformer

Manual construction of questions is a tedious and complicated process. Automatic Question Generation (AQG) methods work towards diminishing these costs and to fulfil the requirement for a persistent supply of new questions. Current AQG techniques utilize complicated architectures, that require intensive computational resources as well as a deeper understanding of the subject. In this paper we propose an end-to-end AQG system that utilises the power of a recently introduced transformer, the Text-to-Text Transfer Transformer (T5). We use the pre-trained T5 model and fine-tune it for our down-stream task of question generation. Our model performs very well on unseen data and generates well-formed and grammatically correct questions. These questions can be used directly by students, to examine their own level of understanding, and teachers, to quickly reinforce key concepts whenever required. The model has also been deployed in the form of a web application for public access. This application serves as an educational tool using which any individual can assess their knowledge and identify areas of improvement.
Blockchain in agriculture

Blockchain has recently emerged as a promising digital technology aspiring to offer trusted financial transactions between various untrusted parties without the requirement for intermediaries in the process, such as banking institutes. This chapter focuses on a specific domain where the application of blockchain technology has significant potential as an essential facilitator of change, namely: agriculture and the food supply chain. A wide range of relevant recently finished or on-going projects and initiatives are introduced, noting relevant barriers, challenges, potential benefits, and opportunities, addressing the question of whether blockchain has matured enough as a technology to be effectively used in real-life applications within the agri-food industry. Our findings suggest the use of blockchain as a driver toward a transparent food supply chain, yet numerous barriers still exist, which hinder its broader adoption in agri-food systems. The outstanding barriers involve technical, policy, regulatory and educational issues.
A Feature Pyramid Fusion Detection Algorithm Based on Radar and Camera Sensor

Considering the development of object detection based on deep learning framework in recent years, it has brought a new scope for multi-source fusion in the field of autonomous driving. In this paper, we propose a new architecture with a feature pyramid attention module to fuse the projected radar data and camera data. In the proposed algorithm, the detection model of YOLOv3 is employed by us and the feature pyramid module is extended with the input interface of the radar projection image and attention module. Additionally, in order to reduce the interference information from different scales of radar projected block, a new generation mechanism of radar projection images is introduced. Finally, the radar projection image is fused in feature pyramid layers with an attention module. The result shows that the proposed fusion algorithm outperforms better than image-only network for the nuScenes dataset. The code for this research will be made available to the public at: https://github.com/yuanliangxie/nuscenes_data_process.
Physics-based shading reconstruction for intrinsic image decomposition

We investigate the use of photometric invariance and deep learning to compute intrinsic images (albedo and shading). We propose albedo and shading gradient descriptors which are derived from physics-based models. Using the descriptors, albedo transitions are masked out and an initial sparse shading map is calculated directly from the corresponding RGB image gradients in a learning-free unsupervised manner. Then, an optimization method is proposed to reconstruct the full dense shading map. Finally, we integrate the generated shading map into a novel deep learning framework to refine it and also to predict corresponding albedo image to achieve intrinsic image decomposition. By doing so, we are the first to directly address the texture and intensity ambiguity problems of the shading estimations. Large scale experiments show that our approach steered by physics-based invariant descriptors achieve superior results on MIT Intrinsics, NIR-RGB Intrinsics, Multi-Illuminant Intrinsic Images, Spectral Intrinsic Images, As Realistic As Possible, and competitive results on Intrinsic Images in the Wild datasets while achieving state-of-the-art shading estimations.
LEARNING-based Focused WEB Crawler

As the number of pages being published every day increases enormously, there is a consistent need to design an efficient crawler mechanism that can result in appropriate and efficient search results for the everyday query. Every day people face the problem of inappropriate or incorrect answers among search results. So, there is a strong need to develop enhanced methods to provide precise search results for the user in an acceptable time frame. Through this project, we exhibit an effective approach to building a crawler considering factors that have never been considered before. The main focus of the project would be designing an intelligent crawler that learns itself to improve the effective ranking of URLs using a focused crawler. Moreover, there exist many crawlers which first head to the seed URL, read the pages, and download the pages for further indexing to the search engines. In this, there is a problem that if a website/page which does not update regularly, is still crawled by the crawler even though it had already been downloaded in its previous visit. Due to this, there is a great loss of bandwidth, network, time, and storage. So, we aim to minimize these problems by making an effective system with a revisited policy for web crawlers. First, websites are divided into three categories frequently, frequent, static in the first crawl, and then the crawler decides its time that at what time it has to crawl again for that website.
Variational Measurement Update for Extended Object Tracking Using Gaussian Processes

We present an alternative inference framework for the Gaussian process-based extended object tracking (GPEOT) models. The method provides an approximate solution to the Bayesian filtering problem in GPEOT by relying on a new measurement update, which we derive using variational Bayes techniques. The resulting algorithm effectively computes approximate posterior densities of the kinematic and the extent states. We conduct various experiments on simulated and real data and examine the performance compared with a reference method, which employs an extended Kalman filter for inference. The proposed algorithm significantly improves the accuracy of both the kinematic and the extent estimates and proves robust against model uncertainties.
Hybrid IWD-GA: An Approach for Path Optimization and Control of Multiple Mobile Robot in Obscure Static and Dynamic Environments

SUMMARY In this article, hybridization of IWD (intelligent water drop) and GA (genetic algorithm) technique is developed and executed in order to obtain global optimal path by replacing local optimal points. Sensors of mobile robots are used for mapping and detecting the environment and obstacles present. The developed technique is tested in MATLAB simulation platform, and experimental analysis is performed in real-time environments to observe the effectiveness of IWD-GA technique. Furthermore, statistical analysis of obtained results is also performed for testing their linearity and normality. A significant improvement of about 13.14% in terms of path length is reported when the proposed technique is tested against other existing techniques.
Evolutionary continuous constrained optimization using random direction repair

To solve constrained optimization problems (COPs), it is crucial to guide the infeasible solution to a feasible region. Gradient-based repair (GR) is a successful repair strategy, where the forward difference is often used to estimate the gradient. However, GR has major deficiencies. First, it is difficult to deal with individuals falling into the local optima. Second, large amounts of fitness evaluations are required to estimate the gradient. In this paper, we proposed a new repair strategy, random direction repair (RDR). RDR generates a set of random directions, and calculates the repair direction and the repair step size of infeasible individual to reduce its constraint violation. Since the introduction of randomness, RDR could deal with individuals falling into the local optima. Furthermore, RDR only requires a few number of fitness evaluation. To demonstrate the performance of RDR, RDR was embedded into two state-of-the-art evolutionary continuous constrained optimization algorithms, tested on the Congress on Evolutionary Computation 2017 constrained real-parameter optimization benchmark. Experimental results demonstrated that RDR combined with evolutionary algorithms are highly competitive.
Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles

We propose a risk-aware framework for multi-robot, multi-demand assignment and planning in unknown environments. Our motivation is disaster response and search-and-rescue scenarios where ground vehicles must reach demand locations as soon as possible. We consider a setting where the terrain information is available only in the form of an aerial, georeferenced image. Deep learning techniques can be used for semantic segmentation of the aerial image to create a cost map for safe ground robot navigation. Such segmentation may still be noisy. Hence, we present a joint planning and perception framework that accounts for the risk introduced due to noisy perception. Our contributions are two-fold: (i) we show how to use Bayesian deep learning techniques to extract risk at the perception level; and (ii) use a risk-theoretical measure, CVaR, for risk-aware planning and assignment. The pipeline is theoretically established, then empirically analyzed through two datasets. We find that accounting for risk at both levels produces quantifiably safer paths and assignments.
A Geometric Perspective on Visual Imitation Learning

We consider the problem of visual imitation learning without human kinesthetic teaching or teleoperation, nor access to an interactive reinforcement learning training environment. We present a geometric perspective to this problem where geometric feature correspondences are learned from one training video and used to execute tasks via visual servoing. Specifically, we propose VGS-IL (Visual Geometric Skill Imitation Learning), an end-to-end geometry-parameterized task concept inference method, to infer globally consistent geometric feature association rules from human demonstration video frames. We show that, instead of learning actions from image pixels, learning a geometry-parameterized task concept provides an explainable and invariant representation across demonstrator to imitator under various environmental settings. Moreover, such a task concept representation provides a direct link with geometric vision based controllers (e.g. visual servoing), allowing for efficient mapping of high-level task concepts to low-level robot actions.
Artificial Intelligence Accelerators Based on Graphene Optoelectronic Devices

Optical and optoelectronic approaches of performing matrix–vector multiplication (MVM) operations have shown the great promise of accelerating machine learning (ML) algorithms with unprecedented performance. The incorporation of nanomaterials into the system can further improve the device and system performance thanks to their extraordinary properties, but the nonuniformity and variation of nanostructures in the macroscopic scale pose severe limitations for large‐scale hardware deployment. Here, a new optoelectronic architecture is presented, consisting of spatial light modulators and tunable responsivity photodetector arrays made from graphene to perform MVM. The ultrahigh carrier mobility of graphene, high‐power‐efficiency electro‐optic control, and extreme parallelism suggest ultrahigh data throughput and ultralow energy consumption. Moreover, a methodology of performing accurate calculations with imperfect components is developed, laying the foundation for scalable systems. Finally, a few representative ML algorithms are demonstrated, including singular value decomposition, support vector machine, and deep neural networks, to show the versatility and generality of the platform.
A Visual Analysis Approach to Understand and Explore Quality Problems of AIS Data

Low quality automatic identification system (AIS) data often mislead analysts to a misunderstanding of ship behavior analysis and to making incorrect navigation risk assessments. It is therefore necessary to accurately understand and judge the quality problems in AIS data before a further analysis of ship behavior. Outliers were filtered in the existing methods of AIS quality analysis based only on mathematical models where AIS data related quality problems are not utilized and there is a lack of visual exploration. Thus, the human brain’s ability cannot be fully utilized to think visually and for reasoning. In this regard, a visual analytics (VA) approach called AIS Data Quality visualization (ADQvis) was designed and implemented here to support evaluations and explorations of AIS data quality. The system interface is overviewed and then the visualization model and corresponding human-computer interaction method are described in detail. Finally, case studies were carried out to demonstrate the effectiveness of our visual analytics approach for AIS quality problems.
NEclatClosed: A vertical algorithm for mining frequent closed itemsets

Frequent closed itemsets provide a lossless and concise collection of all frequent itemsets to reduce the runtime and memory requirement of frequent itemsets mining tasks. This study presents an algorithm named NEclatClosed for fast mining of frequent closed itemsets. We introduce concepts and techniques based on the vertical database format and employ them in the mining process. The experimental results show that NEclatClosed outperforms the leading algorithms in terms of runtime and memory usage, especially runtime, in most cases.