Reviewerly/exHarmony huggingface dataset:
Score refinement for confidence-based 3D multi-object tracking

Multi-object tracking is a critical component in autonomous navigation, as it provides valuable information for decision-making. Many researchers tackled the 3D multi-object tracking task by filtering out the frame-by-frame 3D detections; however, their focus was mainly on finding useful features or proper matching metrics. Our work focuses on a neglected part of the tracking system: score refinement and tracklet termination. We show that manipulating the scores depending on time consistency while terminating the tracklets depending on the tracklet score improves tracking results. We do this by increasing the matched tracklets’ score with score update functions and decreasing the unmatched tracklets’ score. Compared to count-based methods, our method consistently produces better AMOTA and MOTA scores when utilizing various detectors and filtering algorithms on different datasets. The improvements in AMOTA score went up to 1.83 and 2.96 in MOTA. We also used our method as a late-fusion ensembling method, and it performed better than voting-based ensemble methods by a solid margin. It achieved an AMOTA score of 67.6 on nuScenes test evaluation, which is comparable to other state-of-the-art trackers. Code is publicly available at: https://github.com/cogsys-tuebingen/CBMOT.
Enabling Smart Education System Using Blockchain Technology

Blockchain Technology is gaining lot of attention in recent days due to its distinct characteristics like decentralization, reliability, security and data integrity. Many companies are researching the possibility of adapting Blockchain Technology in their respective domains to utilize the potential of blockchain to the fullest. In spite of its rapid growth very little is known about the state of the art of blockchain in educational sector. The book keeping process of degrees and certificates can be one such potential area where blockchain can play a major role. This chapter presents the detailed analysis of Blockchain Technology and its application in education. Its emphases on (i) Blockchain Technology and its diversified application (ii) Digital Signatures and (iii) Blockchain based educational solutions. This chapter also highlights the challenges of blockchain in education based on an intensive research.
All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on iteratively using the model to generate artificial labels for the unlabeled data to train against. A common property among its various methods is that they only rely on the model’s prediction to make labeling decisions without considering any prior knowledge about the visual similarity among the classes. In this paper, we demonstrate that this degrades the quality of pseudo-labeling as it poorly represents visually similar classes in the pool of pseudo-labeled data. We propose SemCo, a method which leverages label semantics and co-training to address this problem. We train two classifiers with two different views of the class labels: one classifier uses the one-hot view of the labels and disregards any potential similarity among the classes, while the other uses a distributed view of the labels and groups potentially similar classes together. We then co-train the two classifiers to learn based on their disagreements. We show that our method achieves state-of-the-art performance across various SSL tasks including 5.6% accuracy improvement on Mini-ImageNet dataset with 1000 labeled examples. We also show that our method requires smaller batch size and fewer training iterations to reach its best performance. We make our code available at https://github.com/islam-nassar/semco.
OIFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning

Occlusion is an inevitable and critical problem in unsupervised optical flow learning. Existing methods either treat occlusions equally as non-occluded regions or simply remove them to avoid incorrectness. However, the occlusion regions can provide effective information for optical flow learning. In this paper, we present OIFlow, an occlusion-inpainting framework to make full use of occlusion regions. Specifically, a new appearance-flow network is proposed to inpaint occluded flows based on the image content. Moreover, a boundary dilated warp is proposed to deal with occlusions caused by displacement beyond the image border. We conduct experiments on multiple leading flow benchmark datasets such as Flying Chairs, KITTI and MPI-Sintel, which demonstrate that the performance is significantly improved by our proposed occlusion handling framework.
Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems: Part 1—Fundamentals and Applications in Games, Robotics and Natural Language Processing

The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems. Reinforcement Learning (RL) is an approach to simulate the human’s natural learning process, whose key is to let the agent learn by interacting with the stochastic environment. The fact that the agent has limited access to the information of the environment enables AI to be applied efficiently in most fields that require self-learning. Although efficient algorithms are being widely used, it seems essential to have an organized investigation—we can make good comparisons and choose the best structures or algorithms when applying DRL in various applications. In this overview, we introduce Markov Decision Processes (MDP) problems and Reinforcement Learning and applications of DRL for solving POMDP problems in games, robotics, and natural language processing. A follow-up paper will cover applications in transportation, communications and networking, and industries.
Zero trust: Never trust, always verify

This short paper argues that current conceptions in trust formation scholarship miss the context of zero trust, a practice growing in importance in cyber security. The contribution of this paper presents a novel approach to help conceptualize and operationalize zero trust and a call for a research agenda. Further work will expand this model and explore the implications of zero trust in future digital systems.
Blockchain technology: A <scp>DNN</scp> token‐based approach in healthcare and <scp>COVID</scp>‐19 to generate extracted data

The healthcare technologies in COVID-19 pandemic had grown immensely in various domains. Blockchain technology is one such turnkey technology, which is transforming the data securely; to store electronic health records (EHRs), develop deep learning algorithms, access the data, process the data between physicians and patients to access the EHRs in the form of distributed ledgers. Blockchain technology is also made to supply the data in the cloud and contact the huge amount of healthcare data, which is difficult and complex to process. As the complexity in the analysis of data is increasing day by day, it has become essential to minimize the risk of data complexity. This paper supports deep neural network (DNN) analysis in healthcare and COVID-19 pandemic and gives the smart contract procedure, to identify the feature extracted data (FED) from the existing data. At the same time, the innovation will be useful to analyse future diseases. The proposed method also analyze the existing diseases which had been reported and it is extremely useful to guide physicians in providing appropriate treatment and save lives. To achieve this, the massive data is integrated using Python scripting language under various libraries to perform a wide range of medical and healthcare functions to infer knowledge that assists in the diagnosis of major diseases such as heart disease, blood cancer, gastric and COVID-19.
LESS-FM: Fine-Tuning Signatures from the Code Equivalence Problem

Code-based cryptographic schemes are highly regarded among the quantum-safe alternatives to current standards. Yet, designing code-based signatures using traditional methods has always been a challenging task, and current proposals are still far from the target set by other post-quantum primitives (e.g. lattice-based). In this paper, we revisit a recent work using an innovative approach for signing, based on the hardness of the code equivalence problem. We introduce some optimizations and provide a security analysis for all variants considered. We then show that the new parameters produce instances of practical interest.
Path Planning Method Based on D* lite Algorithm for Unmanned Surface Vehicles in Complex Environments

In recent decades, path planning for unmanned surface vehicles (USVs) in complex environments, such as harbours and coastlines, has become an important concern. The existing algorithms for real-time path planning for USVs are either too slow at replanning or unreliable in changing environments with multiple dynamic obstacles. In this study, we developed a novel path planning method based on the D* lite algorithm for real-time path planning of USVs in complex environments. The proposed method has the following advantages: (1) the computational time for replanning is reduced significantly owing to the use of an incremental algorithm and a new method for modelling dynamic obstacles; (2) a constrained artificial potential field method is employed to enhance the safety of the planned paths; and (3) the method is practical in terms of vehicle performance. The performance of the proposed method was evaluated through simulations and compared with those of existing algorithms. The simulation results confirmed the efficiency of the method for real-time path planning of USVs in complex environments.
Application of blockchain in dealing with sustainability issues and challenges of financial sector

Blockchain network and technology is a peer-to-peer distributed system that operates in a decentralized manner. The two concepts enable to create a secure environment and allow users to exchange transactions, contracts and data thereby establishing trust amongst the users. The current study aims to investigate the issues faced in the financial sector by means of applying blockchain technology. For this, the current study provides an overview of blockchain technology and analyze the major issues and challenges faced in financial sector due to the application of the blockchain technology. It is believed that traditional system tends to be cumbersome, error-prone and slow and unsafe, in contrast, users find the blockchain as cheaper, transparent and more effective. Particularly blockchain technology provides the banking sector, security, performance, and cost reduction in many of their processes and provides quality of services to their users.
Autonomous Aerial Filming With Distributed Lighting by a Team of Unmanned Aerial Vehicles

This letter describes a method for autonomous aerial cinematography with distributed lighting by a team of unmanned aerial vehicles (UAVs). Although camera-carrying multi-rotor helicopters have become commonplace in cinematography, their usage is limited to scenarios with sufficient natural light or of lighting provided by static artificial lights. We propose to use a formation of unmanned aerial vehicles as a tool for filming a target under illumination from various directions, which is one of the fundamental techniques of traditional cinematography. We decompose the multi-UAV trajectory optimization problem to tackle non-linear cinematographic aspects and obstacle avoidance at separate stages, which allows us to re-plan in real time and react to changes in dynamic environments. The performance of our method has been evaluated in realistic simulation scenarios and field experiments, where we show how it increases the quality of the shots and that it is capable of planning safe trajectories even in cluttered environments.
Designing a Central Bank Digital Currency with Support for Cash-Like Privacy

Most central banks in advanced economies consider issuing central bank digital currencies (CBDCs) to address the declining use of cash as a means of payment and to position themselves against increased competition from Big Tech companies, cryptocurrencies, and stablecoins. One crucial design dimension of a CBDC is the degree of transaction privacy. Existing solutions are either prone to security concerns or do not provide full (cash-like) privacy. Moreover, it is often argued that a fully private payment system and, in particular, anonymous transactions cannot comply with anti-money laundering (AML) and countering the financing of terrorism (CFT) regulation. In this paper, we follow a design science research approach (DSR) to develop and evaluate a holistic software-based CBDC system that supports fully private transactions and addresses regulatory constraints. To this end, we employ zero-knowledge proofs (ZKPs) to enforce limits on fully private payments. Thereby, we are able to address regulatory constraints without disclosing any transaction details to third parties. We evaluate our artifact through interviews with leading economic, legal, and technical experts and find that a regulatorily compliant CBDC system based on ZKPs that supports full (cash-like) privacy is feasible.
A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing

To boost the object grabbing capability of underwater robots for open-sea farming, we propose a new dataset (UDD) consisting of three categories (seacucumber, seaurchin, and scallop) with 2,227 images. To the best of our knowledge, it is the first 4K HD dataset collected in a real open-sea farm. We also propose a novel Poisson-blending Generative Adversarial Network (Poisson GAN) and an efficient object detection network (AquaNet) to address two common issues within related datasets: the class-imbalance problem and the problem of mass small object, respectively. Specifically, Poisson GAN combines Poisson blending into its generator and employs a new loss called Dual Restriction loss (DR loss), which supervises both implicit space features and image-level features during training to generate more realistic images. By utilizing Poisson GAN, objects of minority class like seacucumber or scallop could be added into an image naturally and annotated automatically, which could increase the loss of minority classes during training detectors to eliminate the class-imbalance problem; AquaNet is a high-efficiency detector to address the problem of detecting mass small objects from cloudy underwater pictures. Within it, we design two efficient components: a depth-wise-convolution-based Multi-scale Contextual Features Fusion (MFF) block and a Multi-scale Blursampling (MBP) module to reduce the parameters of the network to 1.3 million. Both two components could provide multi-scale features of small objects under a short backbone configuration without any loss of accuracy. In addition, we construct a large-scale augmented dataset (AUDD) and a pre-training dataset via Poisson GAN from UDD. Extensive experiments show the effectiveness of the proposed Poisson GAN, AquaNet, UDD, AUDD, and pre-training dataset.
Performance evaluation of machine learning-based classification with rock-physics analysis of geological lithofacies in Tarakan Basin, Indonesia

This study aims to put a supervised learning method for automatically classifying lithofacies in well-logging dataset, where several machine learning algorithms were compared in this study that took place in the Tarakan Basin, Indonesia. The predicted lithofacies in this study including shale, shaly sandstone, sandstone, and coal, where coal is considered as the unique lithofacies in the study area. As training and testing datasets, we used two separate well log datasets from the Tarakan Basin. The first well, named Omnicron, was used to train the model, while the second well, named Kay, was used to test it. Random Forest and Gradient Boosting outperformed the other models in the experiment, with the accuracy of 87.49% and 87.01%, respectively. When it came to classifying coal, however, both approaches had issues. The Pr-Recall curve revealed that the coal score was under average precision in each facies, with values of roughly 0.52 and 0.38, respectively, which explaining why, even with high accuracy, the machine learning algorithm predicted poorly in one lithofacies class. In order to evaluate this coal misclassification, we used rock physics to analyze the machine learning prediction in this report. As result, we found that each facies is well-differentiated by physical properties, and the predicted lithofacies have a distribution that is close to the original facies however, coal may be potentially misclassified as other lithofacies as some of the coals have similar rock physical properties with the surrounding lithology (e.g. coal with a mixture of shale may have similar DT and GR responses). Based on this research, the use of machine learning in the Tarakan Basin effectively provides lithofacies data with a high degree of precision and accuracy in a much shorter time.
A Resource Efficient Integer-Arithmetic-Only FPGA-Based CNN Accelerator for Real-Time Facial Emotion Recognition

Recently, many researches have been conducted on recognition of facial emotion using convolutional neural networks (CNNs), which show excellent performance in computer vision. To obtain a high classification accuracy, a CNN architecture with many parameters and high computational complexity is required. However, this is not suitable for embedded systems where hardware resources are limited. In this paper, we present a lightweight CNN architecture optimized for embedded systems. The proposed CNN architecture has a small memory footprint and low computational complexity. Furthermore, a novel hardware-friendly quantization method that uses only integer-arithmetic is proposed. The proposed hardware-friendly quantization method maps the scale factors to power-of-two terms and replaces multiplication and division operations using scale factors with shift operations. To improve the generalization and classification performance of the CNN, we create the FERPlus-A dataset. This is a new training dataset created using a variety of image processing algorithms. After training with FERPlus-A, quantization has been performed. The size of a quantized CNN parameter is about 0.39 MB, and the number of operations is about 28 M integer operations (IOPs). By evaluating the performance of the quantized CNN that uses only integer-arithmetic on the FERPlus test dataset, the classification accuracy is approximately 86.58%. It achieved higher accuracy than other lightweight CNNs in prior studies. The proposed CNN architecture that uses only integer-arithmetic is implemented on the Xilinx ZC706 SoC platform for real-time facial emotion recognition by applying parallelism strategies and efficient data caching strategies. The FPGA-based CNN accelerator implemented for real-time facial emotion recognition achieves about 10 frame per second (FPS) at 250 MHz and consumes 2.3 W.
A text-mining based cyber-risk assessment and mitigation framework for critical analysis of online hacker forums

Online hacker communities are meeting spots for aspiring and seasoned cybercriminals where they engage in technical discussions, share exploits and relevant hacking tools to be used in launching cyber-attacks on business organizations. Sometimes, the affected organizations can detect these attacks in advance, with the help of cyber-threat intelligence derived from the explicit and implicit features of hacker communication in these forums. Herein, we proposed a novel text-mining based cyber-risk assessment and mitigation framework, which performs the following critical tasks. (i) Cyber-risk Assessment - to identify hacker expertise (i.e., newbie, beginner, intermediate, and advanced) using explicit and implicit features applying various classification algorithms. Among these features, cybersecurity keywords, sharing of attachments, and sentiments emerged as significant. Further, we found that expert hackers demonstrate leadership in the online forums that eventually serve as communities of practice. Consequently, novice hackers gradually develop their cyber-attack skills through prolonged observations, interactions, and external influences in this social learning process. (ii) Cyber-risk mitigation – computes financial impact for every {hacker expertise, attack-type} combination, and then by ranking them on a {likelihood, impact} decision-matrix to prioritize mitigation strategies in affected organizations. Through these novel recommendations, our framework can guide managers to decide on appropriate cybersecurity controls using an {expected loss, probability, attack-type, hacker expertise} metric against financial losses due to cyber-attacks.
Particle distance rank feature selection by particle swarm optimization

This paper presents a feature selection method in multi-objective particle swarm optimization space. For this task, a novel particle ranking is proposed based on particle distance from dominated and non-dominated particles and then used for feature rank computation. Position and velocity of particles are updated by a new update rule relies in feature ranks encoded in a vector. Properties of the proposed method are proven mathematically and supported in experiments. The proposed feature selection method is evaluated on 12 UCI datasets and 4 datasets from real-world applications compared with 5 state-of-the-art feature selection methods. As a visual comparison, the proposed method finds better non-dominated particles in two-dimensional optimization space with lower run time. Experiments also showed that the proposed method outperforms existing feature selection methods with regard to Success Counting Measure, C_Metric, Hyper-Volume Indicator and Statistical Analysis.
Variational Bayesian-Based Filter for Inaccurate Input in Underwater Navigation

Autonomous underwater vehicle (AUV) has been employed in oceanography applications based on a reliable navigation. The complex underwater environment leads to more velocity measurement errors of AUV, so it is difficult to determine the accurate navigation and positioning information. To solve the problem, a novel variational Bayesian-based filter for inaccurate input (VBFII) is proposed to determine the state information under the complex marine condition of inaccurate input. Firstly, the velocities are assumed to follow the Gaussian distribution, which can better describe the model of input information. Secondly, the augmentation method is used to augment the state vector and error covariance matrix to simplify calculation. The augmented state vector, the augmented predicted error covariance and measurement error noise matrices are derived more accurately based on the variational Bayesian (VB) approach. The experiment results show that the proposed VBFII has better estimation accuracy and robustness than other comparison algorithms.
Maintaining Social Distancing in Pandemic Using Smartphones With Acoustic Waves

The awareness of &#x201C;social distancing&#x201D; is something we have heard because of the coronavirus (COVID-19) pandemic. Second wave of COVID-19 is started, and maintaining social distance and managing our psychological and physical well-being are the new challenges today. We are constantly reminded to maintain a safe distance from other people around us to stop the spread of COVID-19. But what if there was any solution that could make us aware if someone is in your social distance radius and alert us to maintain enough distance? Our article describes a solution using smartphones that delivers near-field peer-to-peer communication by using the present hardware to convert the messages in sound waves.