Reviewerly/exHarmony huggingface dataset:
Unstoppable DAOs for web3 disruption

Decentralised Autonomous Organisations (DAOs) have the capability of being a disruptive Web3 technology. Their usage of cryptographically secure distributed ledgers shows promise of replacing existing technical and financial intermediaries. However, this promise has not been fully materialised yet: existing attempts typically rely on centralisation as the required decentralised components do not exist or are not mature enough. We present our Web3 Deployment Experiment around a robust decentralised economy to address these issues. Our economy is unique due to the removal of all centralised components and governance. It is resilient against legal and economic attacks as no individual or organisation can compromise its functioning. We dub this characteristic extreme decentralisation. Similar to BitTorrent and Bitcoin, our extreme decentralisation DAOs carefully avoid single points of failure and are effectively unstoppable. Within our experiment around a music economy, we bypass all intermediaries in finance, technology, and the music industry itself with a direct donation to musicians. We demonstrate the viability of collective decision-making within our decentralised economy and present a set of principles for Web3 DAOs. Our implementation shows that the DAO ecosystem is fully deployable on smartphones, allowing anyone to create a DAO without reliance on central authorities or components.
Trans-Compiler-Based Database Code Conversion Model for Native Platforms and Languages

Cross-platform mobile application development frameworks are now widely used among software companies and developers. Despite their time and cost-effectiveness, they still lack the performance and experience of natively developed applications. Many research tools have been proposed to solve this problem by converting a natively developed application from one platform to another. The Trans-Compiler Based Android to iOS Converter (TCAIOSC) was proposed to convert the front-end and back-end code of Android Java applications to iOS applications. Since databases are essential for mobile applications, this paper proposes a new database code conversion model based on trans-compilation and pattern matching. It proposes a model that can be used to support database code conversion between native languages and platforms and applies the proposed model to support the conversion of Firebase Firestore database code from Android (Java) to iOS (Swift) using TCAIOSC. The enhanced tool’s results show high accuracy for the converted database code and a noticeable improvement in the overall conversion results for TCAIOSC.
Benchmarking Concept Drift Detectors for Online Machine Learning

Concept drift detection is an essential step to maintain the accuracy of online machine learning. The main task is to detect changes in data distribution that might cause changes in the decision boundaries for a classification algorithm. Upon drift detection, the classification algorithm may reset its model or concurrently grow a new learning model. Over the past fifteen years, several drift detection methods have been proposed. Most of these methods have been implemented within the Massive Online Analysis (MOA). Moreover, a couple of studies have compared the drift detectors. However, such studies have merely focused on comparing the detection accuracy. Moreover, most of these studies are focused on synthetic data sets only. Additionally, these studies do not consider drift detectors not integrated into MOA. Furthermore, None of the studies have considered other metrics like resource consumption and runtime characteristics. These metrics are of utmost importance from an operational point of view. In this paper, we fill this gap. Namely, this paper evaluates the performance of sixteen different drift detection methods using three different metrics: accuracy, runtime, and memory usage. To guarantee a fair comparison, MOA is used. Fourteen algorithms are implemented in MOA. We integrate two new algorithms (ADWIN++ and SDDM) into MOA.
Upper bound on device-independent quantum key distribution with two way classical postprocessing under individual attack

Abstract Device-independent quantum key distribution (DI-QKD) can guarantee the security even with untrusted devices. Unfortunately, conventional DI-QKD protocols can tolerate low noises only and require high detection efficiencies to achieve positive key rates. To improve the noise robustness, one promising solution is implementing the two-way classical postprocessing, which has the advantage of reducing the bit errors. In this paper, we study the DI-QKD with B-step two-way classical postprocessing under individual attacks. We adopt the tool of convex combination attack, i.e. an optimal individual attack, to upper bound the Devetak-Winter key rate. We show that, by using the B-step procedure, our protocol can tolerate detection efficiencies as low as <?CDATA $81.0\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>81.0</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> and depolarising noise of 0.799, which is better than the thresholds for the protocol with one-way error correction. This result can serve as the lower bounds on the critical noise and detection efficiency for the scenarios under general attacks. Our work justifies the advantage of two-way classical postprocessing for DI-QKD, thus offering a step towards its applications.
Rocket Recovery Carbin Section Passive location based on whale optimization algorithm

Abstract The accurate location of the rocket recovery cabin section is one of the key technologies to realize the reliable recovery rockets. Based on the multi-station time difference of arrival (TDOA) passive location principle, a technical solution for the recovery and positioning of the rocket recovery cabin section is designed. Through the active radiation signal of the recovery cabin, the whale optimization algorithm (WOA) is used to locate the position of the rocket recovery cabin section. The recovery process trajectory of the rocket's stage 1 cabin was adopted to verify the feasibility of the designed recovery positioning technical scheme.
VDI/VDE 2634–1 performance evaluation tests and systematic errors in passive stereo vision systems

In this paper, we explore the sensitivity of the length tests described in the VDI/VDE 2634-1 guideline to systematic errors in a passive stereo vision system. The results reported here are based on a stereo vision system set up on the table of a Cartesian coordinate measuring machine (CMM) and simulations that match the experimental conditions. The stereo vision system is calibrated using a checkerboard pattern resulting in estimates for 26 camera model parameters. Errors in these model parameters cause errors in the triangulated 3D points, thus resulting in length errors evaluated from those points. In the first part of this study, we consider a grid of points in the measurement volume of the stereo vision system and simulate the effect of induced systematic errors in each model parameter to determine the resulting error in the length for all lines (i.e., between all pairs of points) in that grid. We identify the lines that provide the maximum error in the length for each model parameter. Thus, we identify a collection of lines, which we refer to as model-based lines, that provide maximum sensitivity to the model parameters. Also, through simulations, we assess whether the lines defined in the VDI/VDE guideline are sufficient to detect the systematic errors in the stereo vision system. In the second part of this study, we evaluate the performance of the stereo vision system, i.e., assess the effect of errors in the model parameters, by using the CMM as the reference. For this purpose, the CMM is commanded to move to the set of points in the rectangular grid previously considered in the simulations with the stereo vision system tracking a small checkerboard target installed on the ram of the CMM. We compare the VDI/VDE and model-based lines in terms of the experimentally obtained length errors. The studies show that selection of lines based on the model (determined through simulations and verified experimentally) is advantageous because of the superior ability in detecting systematic errors in stereo vision systems.
Informed anytime Bi-directional Fast Marching Tree for optimal motion planning in complex cluttered environments

Practical applications favor anytime asymptotically-optimal algorithms that find a feasible solution and improve it toward the optimal solution within limited time. This paper proposed Informed Anytime Bi-directional Fast Marching Tree (IABFMT*), an anytime asymptotically-optimal sampling-based algorithm that combines the strength of BFMT* (bi-directional variant of FMT*) and IAFMT* (an anytime variant of FMT*). IABFMT* performs a bi-directional "lazy" search to efficiently find a feasible solution and quickly improve it towards optimal. In addition, we implemented a graph pruning method to simplify the planning problem and heuristic cost evaluation to reject samples, which avoided a lot of unnecessary computations and improved the convergence rate. Different simulations in OMPL have been carried out to demonstrate the superior efficiency of IABFMT* in comparison with other state-of-the-art algorithms in complex cluttered environments.
BS-Join: A novel and efficient mixed batch-stream join method for spatiotemporal data management in Flink

The new computing model, mixed batch-stream data processing, plays a crucial role in big spatiotemporal data managements. As the core of the above computing method, mixed batch-stream data join has high requirements on the throughput and latency due to the coexistence of two types of data sources. Apache Flink is the most suitable distributed system for mixed batch-stream data join, with lower latency than the join calculation model based on Hadoop and Spark, and it simulates remote real-time reading of batch data sources and completes join calculation with the DataStream API. However, as the degree of parallelism increases, frequent remote data reads will cause huge disk and communication pressure, thereby reducing the job efficiency and scalability. To make things trickier, the above effects are further amplified when simulating complex operations such as range joins. Aiming at the above difficulties and the characteristics of mixed batch-stream data join, a cache-based framework supporting mixed batch-stream join computing natively is proposed, which increases the search speed in the process of data join by building indexes in batch data sources. Meanwhile, for equijoin and range join, an optimization mechanism based on hotspot awareness and an optimization mechanism based on skip list are proposed respectively to further improve the job efficiency. In summary, the advantages of our work are highlighted as follows: (1) The proposed framework enables Flink to natively support mixed batch-stream data join, and can improve throughput by 5 times and speedup by 4 times; (2) The optimization mechanism based on hotspot awareness can further improve the efficiency of equijoin; (3) Compared with range queries by traditional Operators in Flink, the throughput can be increased by 6 times while the latency is reduced by 45%.
Comparative analysis of deep convolution neural network models on small scale datasets

Deep Learning is a representational model that proves significant impact in computer vision applications by automating the feature extraction process with the advancement and availability of high computing power. Conventional machine learning algorithm works efficiently on large scale data and trained for specific applications that cannot be used for other task. Deep neural networks require large amounts of data, resources, time and high computing devices. Complex deep learning models for a new application domain could be developed using transfer learning with less resources and few labeled training data. An important challenge is negative transfer, which means the initial and target problems are similar. Also, there are no research findings on choosing the right pre-trained model which are closely related to the target dataset and there is no proper measure to analyze the performance. Convolutional Neural Networks models impart significant advancement to the ImageNet visual recognition challenge in the last decade. This work focuses on the use of pre-trained models for small datasets. It analyzes the initialization of parameters, tuning of hyperparameters, parameter optimization, regularization methods, and computation complexity of pre-trained convolutional neural network models. Experiments are performed on three different data sets COREL1k, COIL20 and Garbage images. It shows that the time to re-train the existing pre-trained models was greatly reduced by transfer learning. Transferring weights, instances and layers from models trained in ImageNet dataset could produce better performance in similar small scale datasets, but the performance of dataset which differs in ImageNet dataset was not satisfactory.
X-ray microtomography analysis of urban soils of the Rostov region

Urban soils are exposed to transformations of physical properties which are not captured by the conventional approaches due to the presence of impurities that can distort the obtained data. Advanced visualization techniques such as X-ray computed tomography (CT) offer new opportunities of exploring heterogeneity of soil properties at horizon or aggregate scales. The studies of the structure and pore space of different types of urban soils formed on chernozems were carried out using a SkyScan 1172G tomograph. The tomographic study was carried out in the humus-accumulative horizons for six main types of urban soils. The volumetric parameters for the pore space were calculated, and pores orientation and shape parameters were calculated in 2D sections. The closed pore value was obtained as the most informative diagnostic indicator of the urban soils structural condition. A morphometric analysis of the shape and orientation of fine soil macropores with a diameter of 0.3–2.0 mm also showed significant structural differences between natural soils under woody or grassy vegetation and urban anthropogenically transformed soils. All studied soils at the aggregate scales slightly differ from each other in the value of the internal aggregate tomographic porosity. This shows that the compaction because of anthropogenic transformation occurs due to the interaggregate pore space. At the aggregate porous spaces, no changes can be traced.
Designing Controlled Chinese Rules for MT Pre-Editing of Product Description Text

The study aims to investigate how pre-editing based on controlled Chinese rules can be an effective approach to improving Chinese-to-English machine translation (MT) output. Based on the analysis of comparable texts, and by considering the rules of modern business writing and the differences in sentence structure between Chinese and English languages, four controlled Chinese rules for product description text are proposed: 1. Every sentence should have an explicit subject; 2. There are no repetitive complimentary expressions; 3. Sentences should be short; and 4. The Chinese sentence structure should be complete, with clear logical relationships within and between sentences. In accordance with the four rules, five corresponding pre-editing methods are introduced. Then, taking Xiaomi Air2 SE earphones' product description as an experimental text, the study examines the influence of pre-editing in accordance with controlled Chinese rules on MT output quality. The results show that such pre-editing can significantly improve MT output in dimensions of adequacy, fluency, and style.
Improving Data Stream Classification using Incremental Yeo-Johnson Power Transformation

Data transformation plays an essential role as a preprocessing step in learning models. Several classification techniques have premises about the underlying data distribution, such as normal distribution assumed in Bayesians classifiers. However, applying data transformation in a streaming setting requires processing an infinite and continuous flow of data. In this paper, we propose the Incremental Yeo-Johnson Power Transformation, a variant of the well-known batch Yeo-Johnson transformation that is tailored for streaming settings, i.e., it supports streaming data via statistical sampling and hypothesis testing. Experimental results show that our proposal achieves the same data normality as its batch counterpart. In addition, it improves the prediction performance of a data stream classifier based on Bayesian statistical models. Overall, learning models obtained 3 percentage points improvement.
Dynamic Parameter Identification of a 7-DoF Lightweight Robot Manipulator Using Probabilistic Differential Optimization

The dynamic identification of a robot manipulator is a canonical problem in robotics and is usually solved by employing the least squares technique, considering the so-called base parameters of the robot. Such parameters, however, differ from those to which we really want to have access, such as the center of mass and the moments of inertia with respect to this center. Furthermore, the solution obtained by this technique can lead to unsuitable parameters from a physical point of view, which requires a later stage of physical feasibility analysis. In this article, we seek to overcome these drawbacks by identifying the parameters of a 7-DoF lightweight robot using only population-based metaheuristics. We make a study between different techniques and compare them with Bayesian optimization, another black-box approach. We also propose a new algorithm called Probabilistic Differential optimization and show that this algorithm is more efficient than the others considered and can find a physically feasible solution because of its probabilistic exploration ability. We also study the need for more than one excitation trajectory to overcome measurement noise and overfitting, and finally obtain accurate parameters of the Kinova Gen3 arm equipped with a Robotiq 2F-85 gripper.
Using attack graphs to defend healthcare systems from cyberattacks: a longitudinal empirical study

Cyber security encompasses a variety of financial, political, and social aspects with significant implications for the safety of individuals and organisations. Hospitals are among the least secure and most vulnerable organisations in terms of cybersecurity. Protecting medical records from cyberattacks is critical for protecting personal and financial records of those involved in medical institutions. Attack graphs, like in other systems, can be used to protect medical and hospital records from cyberattacks. In the current study, a total of 352 real-life cyberattacks on healthcare institutions using common vulnerability scoring system (CVSS) data were statistically examined to determine important trends and specifications in regard to those attacks. Following that, several machine learning techniques and an artificial neural network model were used to model industrial control systems (ICS) vulnerability data of those attacks. The average vulnerability score for attacks on healthcare IT systems was found to be very high. Moreover, this score was found to be higher in healthcare institutions which have experienced cyberattacks in the past and no mitigation actions were implemented. Using Python programming software, the most successful model that can be used in modelling cyberattacks on IT systems of healthcare institutions was found to be the K-nearest neighbours (KNN) algorithm. The model was then enhanced further and then it was tried to make predictions for future cyberattacks on IT systems of healthcare institutions. Results indicate that the overall score is critical indicating that medical records are, in general, at high risk and that there is a high risk of cyberattacks on medical records in healthcare institutions. It is recommended, therefore, that those institutions should take urgent precautionary measures to mitigate such a high risk of cyberattacks and to make them more secure, reliable, and robust.
RFID Scheme for IoT Devices Based on LSTM-CNN

As an essential branch of physical layer authentication research, radio frequency identification (RFID) has advantages in achieving lightweight and highly reliable authentication. However, in the Internet of Things (IoT) environment, where a large scale of devices are connected to the network, there is an issue that the difference of the RF fingerprints is less distinct among the same type of devices. To this end, in this paper, we propose an RFID scheme for IoT devices based on long-short term memory and convolutional neural network (LSTM-CNN). This scheme combines the excellent learning ability of LSTM and CNN to perceive the context information and extract the local feature of RF data. Specifically, RF data is first fed into LSTM to obtain long-term dependency features containing temporal information. Then, CNN is designed for secondary feature extraction to enlarge RF differences and further used for device classification. The experiment results on the open RF data set ORACLE indicate that the identification accuracy of the proposed scheme can reach over 99%. Compared with other schemes, the performance is improved by 6%-30%.
A lightweight YOLOv5 garbage detection and classification method

Aiming at the problems of unclear, difficult, and inefficient classification of traditional manual waste, and the difficulty of deploying large existing garbage classification network models, a lightweight garbage detection and classification network S-YOLOv5 is designed based on YOLOv5s. First, a garbage dataset containing 18 types of common household garbage is constructed and labeled according to the principles of garbage classification; secondly, a module combining shufflenetv2 and CoordAttention was introduced to replace the YOLOv5s backbone network, and the ReLU activation function in the shufflenet module was substituted by FReLU; finally the PANet structure was replaced by the BiFPN structure, so as to reduce the model complexity and achieve lightweight while maintaining a high mAP. The experimental results show that the size of S-YOLOv5 is only 2.6MB, which is about 1/6 of the original network size, and the mAP is 80.2%. The size of the proposed network is reduced while maintaining high accuracy, making it more suitable for deployment in smart devices.
Data Efficient Safe Reinforcement Learning

Applying reinforcement learning (RL) methods for real world applications pose multiple challenges - the foremost being safety of the system controlled by the learning agent and the learning efficiency. An RL agent learns to control a system by exploring the available actions in various operating states. In some states, when the RL agent exercises an exploratory action, the system may enter unsafe operation, which can lead to safety hazards both for the system as well as for humans supervising the system. RL algorithms thus must learn to control the system respecting safety. In this work, we formulate the safe RL problem in the constrained off-policy setting that facilitates safe exploration by the RL agent. We then develop a sample efficient algorithm utilizing the cross-entropy method. The proposed algorithm’s safety performance is evaluated numerically on benchmark RL problems.