Reviewerly/exHarmony huggingface dataset:
Rectifying Administrated ERC20 Tokens

ERC20 token is the most popular type of Ethereum smart contract. The daily transaction volume of these tokens exceeds 100 billion dollars, which agitates the popular notions of “decentralized banking” and “tokenized economy”. Yet, it is a common misconception to assume that the decentralization of blockchain entails the decentralization of smart contracts deployed on this blockchain. In practice, the developers of smart contracts implement administrating patterns, such as censoring certain users, creating or destroying balances on demand, destroying smart contracts, or injecting arbitrary code. These routines, which are designed to tightly control the operation of these smart contracts, turn an ERC20 token into an administrated token—the type of Ethereum smart contract that we scrutinize in this research.We discover that many smart contracts are administrated, which means that their owners solely possess an omnipotent power over these contracts. Moreover, the owners of these tokens carry lesser social and legal responsibilities compared to the traditional centralized actors that those tokens intend to disrupt. This entails two major problems: a) the owners of the tokens have the ability to quickly steal all the funds and disappear from the market; and b) if the private key of the owner’s account is stolen, all the assets might immediately turn into the property of the attacker. Therefore, the administrated ERC20 tokens are not only dissimilar to the traditional centralized asset management tools, such as banks, but they are also more vulnerable to adversarial actions by their owners or attackers. We develop a pattern recognition framework based on 9 syntactic features characterizing administrated ERC20 tokens, which we use to analyze existing smart contracts deployed on Ethereum Mainnet. Our analysis of 84,062 unique Ethereum smart contracts reveals that nearly 58% of them are administrated ERC20 tokens, which accounts for almost 90% of all ERC20 tokens deployed on Ethereum.To protect users from the frivolousness of unregulated token owners without depriving the ability of these owners to properly manage their tokens, we introduce SafelyAdministrated—a library that enforces a responsible ownership and management of ERC20 tokens. The library introduces three mechanisms: deferred maintenance, board of trustees and safe pause. We implement and test SafelyAdministrated in the form of Solidity abstract contract, which is ready to be used by the next generation of safely administrated ERC20 tokens.
Dynamic Margin for Federated Learning with Imbalanced Data

Federated Learning (FL) enables plenty of edge computing devices to jointly learn a model without data sharing. However, in real-world federated datasets, traditional FL algorithms perform poorly with non-IID data. To better learn from non-IID data, we propose DMFL (Dynamic Margin for Federated Learning) as a strategy to improve the performance of federated learning with highly skewed imbalanced dataset. Accordingly, we perform a theoretical analysis for dynamic margin loss in FL, demonstrating that it is applicable to yield a lower generalization error bound. DMFL encourages clients to have a large margin for minority classes by adding a dynamic term into margins. With a proper margin shift on the fixed margin, the imbalance between clients can be alleviated. Our extensive experiments indicate that DMFL outperforms the other baselines on test accuracy. The results also indicate that each client in DMFL not only performs well locally but also has a good generalization on global test data.
Entanglement generation in a quantum network with finite quantum memory lifetime

We simulate entanglement sharing between two end-nodes of a linear chain quantum network using SeQUeNCe, an open-source simulation package for quantum networks. Our focus is on the rate of entanglement generation between the end-nodes with many repeaters with a finite quantum memory lifetime. Numerical and analytical simulations show limits of connection performance for a given number of repeaters involved, memory lifetimes, the distance between the end-nodes, and an entanglement management protocol. Our findings demonstrate that the performance of quantum connection depends highly on the entanglement management protocol, which schedules entanglement generation and swapping, resulting in the final end-to-end entanglement.
Coarse-grained intermolecular interactions on quantum processors

Variational quantum algorithms (VQAs) are increasingly being applied in simulations of strongly bound (covalently bonded) systems using full molecular orbital basis representations. The application of quantum computers to the weakly bound intermolecular and noncovalently bonded regime, however, has remained largely unexplored. In this work, we develop a coarse-grained representation of the electronic response that is ideally suited for determining the ground state of weakly interacting molecules using a VQA. We require qubit numbers that grow linearly with the number of molecules and derive scaling behavior for the number of circuits and measurements required, which compare favorably to traditional variational quantum eigensolver methods. We demonstrate our method on IBM superconducting quantum processors and show its capability to resolve the dispersion energy as a function of separation for a pair of nonpolar molecules---thereby establishing a means by which quantum computers can model Van der Waals interactions directly from zero-point quantum fluctuations. Within this coarse-grained approximation, we conclude that current-generation quantum hardware is capable of probing energies in this weakly bound but nevertheless chemically ubiquitous and biologically important regime. Finally, we perform experiments on simulated and real quantum computers for systems of three, four, and five oscillators as well as oscillators with anharmonic onsite binding potentials; the consequences of the latter are unexamined in large systems using classical computational methods but can be incorporated here with low computational overhead.
A Hybrid PSO with Rao Algorithm for Classification of Wisconsin Breast Cancer Dataset

This paper proposes a hybrid PSO algorithm with Rao algorithms for breast cancer data classification. In this study, the proposed algorithm improved the process of updating the velocity, and the new position of particle was selected from the best particle classification of efficiency. The Rao algorithm was applied to update the velocity and the new position of particle was selected from the best particle classification of efficiency values. The algorithm was evaluated on Wisconsin Breast Cancer Dataset (WBCD) from UCI Machine Learning Repository. The computational results of the proposed algorithm were compared with PSO, J48, SMO, Random Forest, Multilayer perceptron, and Naïve Bayes. The results of the comparison revealed that the proposed algorithm showed high performance for classification at 98%, better than PSO, J48, SMO, Random Forest, and Multilayer perceptron, and Naive Bayes.
Anonymity of NIST PQC Round 3 KEMs

This paper investigates anonymity of all NIST PQC Round 3 KEMs: Classic McEliece, Kyber, NTRU, Saber, BIKE, FrodoKEM, HQC, NTRU Prime (Streamlined NTRU Prime and NTRU LPRime), and SIKE. We show the following results: Those answer the open problem to investigate the anonymity and robustness of NIST PQC Round 3 KEMs posed by Grubbs, Maram, and Paterson (EUROCRYPT 2022). We use strong disjoint-simulatability of the underlying PKE of KEM and strong pseudorandomness and smoothness/sparseness of KEM as the main tools, which will be of independent interest.
Unveiling process insights from refactoring practices

Context: Software comprehension and maintenance activities, such as refactoring, are said to be negatively impacted by software complexity. The methods used to measure software product and processes complexity have been thoroughly debated in the literature. However, the discernment about the possible links between these two dimensions, particularly on the benefits of using the process perspective, has a long journey ahead. Objective: To improve the understanding of the liaison of developers' activities and software complexity within a refactoring task, namely by evaluating if process metrics gathered from the IDE, using process mining methods and tools, are suitable to accurately classify different refactoring practices and the resulting software complexity. Method: We mined source code metrics from a software product after a quality improvement task was given in parallel to (117) software developers, organized in (71) teams. Simultaneously, we collected events from their IDE work sessions (320) and used process mining to model their processes and extract the correspondent metrics. Results: Most teams using a plugin for refactoring (JDeodorant) reduced software complexity more effectively and with simpler processes than the ones that performed refactoring using only Eclipse native features. We were able to find moderate correlations (≈43%) between software cyclomatic complexity and process cyclomatic complexity. Using only process driven metrics, we computed ≈30,000 models aiming to predict the type of refactoring method (automatic or manual) teams had used and the expected level of software cyclomatic complexity reduction after their work sessions. The best models found for the refactoring method and cyclomatic complexity level predictions, had an accuracy of 92.95% and 94.36%, respectively. Conclusions: We have demonstrated the feasibility of an approach that allows building cross-cutting analytical models in software projects, such as the one we used for detecting manual or automatic refactoring practices. Events from the development tools and support activities can be collected, transformed, aggregated, and analyzed with fewer privacy concerns or technical constraints than source code-driven metrics. This makes our approach agnostic to programming languages, geographic location, or development practices, making it suitable for challenging contexts, such as, in modern global software development where many projects adopt agile methodologies, and low/no code platforms. Initial findings are encouraging, and lead us to suggest practitioners may use our method in other development tasks, such as, defect analysis and unit or integration tests.
AI Explainability 360: Impact and Design

As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, have different explanation needs. To address these needs, in 2019, we created AI Explainability 360, an open source software toolkit featuring ten diverse and state-of-the-art explainability methods and two evaluation metrics. This paper examines the impact of the toolkit with several case studies, statistics, and community feedback. The different ways in which users have experienced AI Explainability 360 have resulted in multiple types of impact and improvements in multiple metrics, highlighted by the adoption of the toolkit by the independent LF AI &amp; Data Foundation. The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.
A caster-wheel-aware MPC-based motion planner for mobile robotics

Differential drive mobile robots often use one or more caster wheels for balance. Caster wheels are appreciated for their ability to turn in any direction almost on the spot, allowing the robot to do the same and thereby greatly simplifying the motion planning and control. However, in aligning the caster wheels to the intended direction of motion they produce a so-called bore torque. As a result, additional motor torque is required to move the robot, which may in some cases exceed the motor capacity or compromise the motion planner's accuracy. Instead of taking a decoupled approach, where the navigation and disturbance rejection algorithms are separated, we propose to embed the caster wheel awareness into the motion planner. To do so, we present a caster-wheel-aware term that is compatible with MPC-based control methods, leveraging the existence of caster wheels in the motion planning stage. As a proof of concept, this term is combined with a model-predictive trajectory tracking controller. Since this method requires knowledge of the caster wheel angle and rolling speed, an observer that estimates these states is also presented. The efficacy of the approach is shown in experiments on an intralogistics robot and compared against a decoupled bore-torque reduction approach and a caster-wheel agnostic controller. Moreover, the experiments show that the presented caster wheel estimator performs sufficiently well and therefore avoids the need for additional sensors. Video: https://youtu.be/NXXZKEZUi30
A novel hybrid approach of gravitational search algorithm and decision tree for twitter spammer detection

With the increasing popularity of online social networking platforms, the amount of social data has grown exponentially. Social data analysis is essential as spamming activities and spammers are escalating over online social networking platforms. This paper focuses on spammer detection on the Twitter social networking platform. Although existing researchers have developed numerous machine learning methods to detect spammers, these methods are inefficient for appropriately detecting spammers on Twitter due to the imbalance of spam and nonspam data distribution, the involvement of diverse features and the applicability of data mechanisms by spammers to avoid their detection. This research work proposes a novel hybrid approach of the gravitational search algorithm and the decision tree (HGSDT) for detecting Twitter spammers. The individual decision tree (DT) algorithm is not able to address the challenges as it is unstable and ineffective for the higher level of favorable data for a particular attribute. The gravitational search algorithm (GSA) constructs the DTs with improved performance as the gravitational forces act as the information-transferring agents through mass agents. Moreover, the GSA is efficient in handling the data of higher dimensional search space. In the HGSDT approach, the construction of the DT and splitting of nodes are performed with the heuristic function and Newton’s laws. The performance of the proposed HGSDT approach is determined for the Social Honeypot dataset and 1KS-10KN dataset by conducting three different experiments to analyze the impact of training data size, features and spammer ratio. The result of the first experiment shows the need of a higher proportion of training data size, the second experiment signifies the more importance of textual content-based features compared to the other feature categories and the third experiment indicates the requirement of balanced data to attain the effective performance of the proposed approach. The overall performance comparison indicates that the proposed HGSDT approach is superior to the incorporated machine learning methods of DT, support vector machine and back propagation neural network for detecting Twitter spammers.
Minimal-Configuration Anomaly Detection for IIoT Sensors

The increasing deployment of low-cost IoT sensor platforms in industry boosts the demand for anomaly detection solutions that fulfill two key requirements: minimal configuration effort and easy transferability across equipment. Recent advances in deep learning, especially long-short-term memory (LSTM) and autoencoders, offer promising methods for detecting anomalies in sensor data recordings. We compared autoencoders with various architectures such as deep neural networks (DNN), LSTMs and convolutional neural networks (CNN) using a simple benchmark dataset, which we generated by operating a peristaltic pump under various operating conditions and inducing anomalies manually. Our preliminary results indicate that a single model can detect anomalies under various operating conditions on a four-dimensional data set without any specific feature engineering for each operating condition. We consider this work as being the first step towards a generic anomaly detection method, which is applicable for a wide range of industrial equipment.
Different eigenvalue distributions encode the same temporal tasks in recurrent neural networks

Different brain areas, such as the cortex and, more specifically, the prefrontal cortex, show great recurrence in their connections, even in early sensory areas. {Several approaches and methods based on trained networks have been proposed to model and describe these regions. It is essential to understand the dynamics behind the models because they are used to build different hypotheses about the functioning of brain areas and to explain experimental results. The main contribution here is the description of the dynamics through the classification and interpretation carried out with a set of numerical simulations. This study sheds light on the multiplicity of solutions obtained for the same tasks and shows the link between the spectra of linearized trained networks and the dynamics of the counterparts. The patterns in the distribution of the eigenvalues of the recurrent weight matrix were studied and properly related to the dynamics in each task.
Controlling BigGAN Image Generation with a Segmentation Network

GANS have been used for a variety of unconditional and conditional generation tasks; while class-conditional generation can be directly integrated into the training process, integrating more sophisticated conditioning signals within the training is not as straightforward. In this work, we consider the task of sampling from P(X) such that the silhouette of (the subject of) X matches the silhouette of (the subject of) a given image; that is, we not only specify what to generate, but we also control where to put it: more generally, we allow a mask (this is actually another image) to control the silhouette of the object to be generated. The mask is itself the result of a segmentation system applied to a user-provided image. To achieve this, we use pre-trained BigGAN and State-of-the-art segmentation models (e.g. DeepLabV3 and FCN) as follows: we first sample a random latent vector z from the Gaussian Prior of BigGAN and then iteratively modify the latent vector until the silhouettes of \(X=G(z)\) and the reference image match. While the BigGAN is a class-conditional generative model trained on the 1000 classes of ImageNet, the segmentation models are trained on the 20 classes of the PASCAL VOC dataset; we choose the “Dog” and the “Cat” classes to demonstrate our controlled generation model.
Multi-UAV Cooperative 3D Coverage Path Planning Based on Asynchronous Ant Colony Optimization

UAVs carrying visual sensors to capture the defects on the building surface has the advantages of high efficiency, low cost, flexibility and convenience. This kind of inspection task is called coverage path planning problem in 3D space. When the target to be detected is a large complex building, multi-UAV collaboration is usually required. So, how to obtain the optimal path of multi-UAV becomes a great challenge. In order to solve the problem, Asynchronous Ant Colony Optimization (AACO), which makes multiple ant colonies move forward asynchronously, is proposed here to conquer the difficulty. Firstly, the random sampling method is used to get the potential UAV waypoints in the 3D free environment, based on which a Primitive Coverage Graph (PCG) is constructed. Also, the visibility matrix and coverage rate are defined to quantify the coverage performance of UAV path primitives. Next, Asynchronous Ant Colony Optimization combined with reward strategy is proposed to solve the problem by selecting jump cities in turn. Finally, several simulations are provided to verify the feasibility and effectiveness of the algorithm.
The pareto distribution of software features and no-code

This research analyzes the top-performing software applications to identify the Pareto distribution of the features and accordingly proposes the use of no-code tools to increase the efficiency of software developers.
Ensemble of Counterfactual Explainers

In eXplainable Artificial Intelligence (XAI), several counterfactual explainers have been proposed, each focusing on some desirable properties of counterfactual instances: minimality, actionability, stability, diversity, plausibility, discriminative power. We propose an ensemble of counterfactual explainers that boosts weak explainers, which provide only a subset of such properties, to a powerful method covering all of them. The ensemble runs weak explainers on a sample of instances and of features, and it combines their results by exploiting a diversity-driven selection function. The method is model-agnostic and, through a wrapping approach based on autoencoders, it is also data-agnostic.
Longitudinal Analysis of Teacher Technology Acceptance and Its Relationship to Resource Viewing and Academic Performance of College Students during the COVID-19 Pandemic

Due to COVID-19, teachers quickly changed their courses from traditional face-to-face modality to emergency remote teaching (ERT), relying on learning management systems (LMS). In this simple prospective design study, we analyzed the relation of the level of teachers’ technological acceptance at the beginning of ERT (March 2020) considering three variables: the time spent by teachers in the LMS during that semester, the percentage of LMS’s resources their students viewed during the semester, and the final academic performance of the same students at the end of that semester (September 2020). This study included 251 teachers (57% male) and 12,185 students (45% male). We measured the teachers’ level of acceptance with the Spanish version of the Questionnaire Technology Acceptance Model (TAM). We found that the relation between the teacher’s acceptance and their time spent on the LMS was significant and positive (rho = 0.24, p &lt; 0.001). In addition, teachers’ perception of LMS’s easiness is related to the percentage of educational resources their students utilized (rho = 0.26, p &lt; 0.001). Finally, we found a relation between the usefulness dimension of the TAM to the academic performance of the students at the end of that semester (rho = 0.18, p &lt; 0.01). Considering these results, we discuss practices for implementing quality education.
QoE-Driven Secure Video Transmission in Cloud-Edge Collaborative Networks

Video transmission over the backhaul link in cloud-edge collaborative networks usually suffers security risks, which is ignored in most of the existing studies. The characteristics that video service can flexibly adjust the encoding rates and provide acceptable encoding qualities, make the security requirements more possible to be satisfied but tightly coupled with video encoding by introducing more restrictions on edge caching. In this paper, by considering the interaction between video encoding and edge caching, we investigate the quality of experience (QoE)-driven cross-layer optimization of secure video transmission over the wireless backhaul link in cloud-edge collaborative networks. First, we develop a secure transmission model based on video encoding and edge caching. By employing this model as the security constraint, then we formulate a QoE-driven joint optimization problem subject to limited available caching capacity. To solve the optimization problem, we propose two algorithms: a near-optimal iterative algorithm (EC-VE) and a greedy algorithm with low computational complexity (Greedy EC-VE). Simulation results show that our proposed EC-VE can greatly improve user QoE within security constraints, and the proposed Greedy EC-VE can obtain the tradeoff between QoE and computational complexity.