Reviewerly/exHarmony huggingface dataset:
Splitting, Renaming, Removing: A Study of Common Cleaning Activities in Jupyter Notebooks

Data scientists commonly use computational notebooks because they provide a good environment for testing multiple models. However, once the scientist completes the code and finds the ideal model, he or she will have to dedicate time to clean up the code in order for others to easily understand it. In this paper, we perform a qualitative study on how scientists clean their code in hopes of being able to suggest a tool to automate this process. Our end goal is for tool builders to address possible gaps and provide additional aid to data scientists, who then can focus more on their actual work rather than the routine and tedious cleaning work. By sampling notebooks from GitHub and analyzing changes between subsequent commits, we identified common cleaning activities, such as changes to markdown (e.g., adding headers sections or descriptions) or comments (both deleting dead code and adding descriptions) as well as reordering cells. We also find that common cleaning activities differ depending on the intended purpose of the notebook. Our results provide a valuable foundation for tool builders and notebook users, as many identified cleaning activities could benefit from codification of best practices and dedicated tool support, possibly tailored depending on intended use.
Taming Propagation Delay and Fork Rate in Bitcoin Mining Network

Bitcoin builds upon an unstructured peer-to-peer overlay network to disseminate transactions and blocks. Broadcast in such a network is slow and brings inconsistencies, i.e., peers have different views of the system state. Due to the delayed block propagation and the competition of mining, forking, i.e., the blockchain temporarily diverges into two or more branches, occurs frequently, which wastes computation power and causes security issues. This paper proposes an autonomous and distributed topology optimization mechanism to reduce block propagation delay and hence reduce the occurrence of blockchain forks. In the proposed mechanism, a node can autonomously update his neighbor set using the information provided by his current neighbors, since each neighbor will recommend a peer from his own neighbor set, i.e., a neighbor's neighbor, to this node. Each recommendation is based on a peer's propagation ability, which is characterized as a criteria function obtained through a combination of empirical analysis and machine learning. We further propose some metrics to evaluate a Bitcoin network topology. Experiment results reflect the effectiveness of the proposed mechanism and also indicate the correlation between block propagation time and fork rate.
Serious Game for Medical Imaging in Fully Immersive Virtual Reality

Fully immersive virtual reality systems allow the implementation of new immersive user interfaces and data visualization applications that offer effective ways to interact with multidimensional neuroimaging data.Learning to interpret 3D medical imaging scans can be an intimidating experience for students given the large range of pathologies to visualize and the relative placement(s) and location(s) of areas of interest, a process which is further complicated by interacting with, manipulating, and navigating through images across several possible views.Gamification and Serious Games (SG) have proved to have instructional benefits for teaching and learning.The adoption and use of virtual reality in education is growing, driven by the availability of more powerful and affordable virtual reality (VR) headsets, allowing novel ways to interact with and explore teaching material.This paper explores the use of VR to teach the fundamentals of medical imaging interpretation and the opportunities available in using immersive 3D visualizations to understand the spatial relationships between parts of the body, key organs, and their constituent parts.An integrated immersive learning environment is presented that orientates and guides the student through this process and assesses their subsequent level of understanding.The use of gamification in this context to increase student engagement and retention is discussed.
Fully Secure ID-Based Signature Scheme with Continuous Leakage Resilience

The side channel attacks will lead to the destruction of the security of the traditional cryptographic scheme. Leakage-resilient identity-based signature has attracted great attention. Based on the dual system encryption technology, we construct an identity-based signature scheme that can resist continuous private key leakage. In the standard model, the security of the scheme is proved. The key points of our leakage-resilient signature scheme are as follows: (1) The private key can be extended according to the security requirements. In other words, when the leakage is serious, we can select a bigger value n, where n is a parameter related to the leakage rate. (2) An elaborate key update algorithm makes the scheme resist continuous leakage attacks. Furthermore, the updated private key has the same distribution as the previous private key. (3) The proposed scheme is fully secure in the standard model rather than in the random oracle model or in the general group model. In order to achieve this goal, we use dual system encryption technology. Thus, the security of the constructed scheme does not depend on the number of queries of the attacker.
E-BATCH: Energy-Efficient and High-Throughput RNN Batching

Recurrent Neural Network (RNN) inference exhibits low hardware utilization due to the strict data dependencies across time-steps. Batching multiple requests can increase throughput. However, RNN batching requires a large amount of padding since the batched input sequences may vastly differ in length. Schemes that dynamically update the batch every few time-steps avoid padding. However, they require executing different RNN layers in a short time span, decreasing energy efficiency. Hence, we propose E-BATCH, a low-latency and energy-efficient batching scheme tailored to RNN accelerators. It consists of a runtime system and effective hardware support. The runtime concatenates multiple sequences to create large batches, resulting in substantial energy savings. Furthermore, the accelerator notifies it when the evaluation of an input sequence is done. Hence, a new input sequence can be immediately added to a batch, thus largely reducing the amount of padding. E-BATCH dynamically controls the number of time-steps evaluated per batch to achieve the best trade-off between latency and energy efficiency for the given hardware platform. We evaluate E-BATCH on top of E-PUR and TPU. E-BATCH improves throughput by 1.8× and energy efficiency by 3.6× in E-PUR, whereas in TPU, it improves throughput by 2.1× and energy efficiency by 1.6×, over the state-of-the-art.
On Creation of a Stablecoin Based on the Morini's Scheme of Inv&amp;Sav Wallets and Antimoney

Decentralized Finance (DeFi) is a popular topic in the blockchain and cryptocurrency industry in the early 2020s. Still, cryptocurrencies have not yet become Decentralized Payment Systems (DPS) because of the high volatility of bitcoin and many of the altcoins. We investigated a proposed method to form a non-collateralized stablecoin called the Morini's Scheme of Inv&Sav wallets. We figured out two equations for the rebasement for the Inv wallet balances and then compared the results. We found the second rebasement method to be fairer to the agents, but we found the issue of negative balances with both methods. We proposed novel solutions to overcome these issues. One of the proposed solutions was to freeze some money in the Sav wallet if there is a negative balance in the Inv wallet. Another proposed solution was to introduce a two-money economy of money and antimoney to turn the current centralized token distribution model decentralized and make transactions more probable even if agents do not have enough money funds; this could be seen as a decentralized version of credit cards.
Virtual Reality Enabled Human-Centric Requirements Engineering

Human-centric requirements engineering aims to gather, analyse, and validate software requirements with a focus on human factors. However, human-centric requirements engineering is facing many challenges due to the limitations of conventional document-based or face-to-face interview-based requirements elicitation methods. To tackle those challenges, we propose a novel idea of using a virtual reality (VR) platform to facilitate human-centric requirements engineering. The key features of such a VR platform include automatic creation of 3D avatars based on user persona documents, the simulation of different environmental factors (e.g., lights and weather conditions), the simulation of different user conditions with special accessibility requirements (e.g., colour blindness), and the embodiment of user&#x0027;s perspective via the first-person view. A prototype system is developed to demonstrate the feasibility and effectiveness of the proposed VR enabled human-centric requirements engineering platform. As an on-going work, we hope this paper will generate a series of new and interesting research in human-centric requirements engineering.
Graph-based Motion Planning with Primitives in a Continuous State Space Search

Motion primitives exploit symmetries of nonlinear systems to reduce the complexity of the motion planning problem. A graph search algorithm for motion primitives based on Hybrid <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{*}$</tex> was developed in a previous work; however, each continuous state is associated with discrete grid-cells and the planning is solved by a numerically complex optimization problem after the search is done. We extend this approach by considering directly the continuous states, which is the first pillar on a continuous state space search. The second pillar is adjusting some primitives' durations by an online optimization problem of reduced complexity. Then, our algorithm is able to solve motion planning tasks for leading the vehicle to an exact desired goal state, while respecting computation time constraints. Two numerical examples are given for a single-track vehicle model.
SAID: ECC-Based Secure Authentication and Incentive Distribution Mechanism for Blockchain-Enabled Data Sharing System

The fast digital transformation the world experienced in the past few decades has created a multitude of information resources distributed over the Internet and accessed by many different entities. The sharing of these resources needs to be secure and trustworthy to protect the privacy of data and data providers. Privacy and reliability are major concerns especially in blockchain-based data storage systems, as data are held by different owners. Each data provider tries to increase its share of profit by collaborating with other data providers. To ensure secure data sharing in such a decentralized environment, authentication and verification of shared data is needed. This paper proposes a mutual authentication mechanism based on Elliptic Curve Cryptography (ECC) using hash-based message authentication code (HMAC) to provide anonymity and integrity during the communication process in blockchain-based data sharing. To evaluate the fairness of profit sharing based on data providers' contributions and data utility, we propose the use of the Shapley value techniques. The performed security analysis demonstrates that our proposed approach is resilient against replay, secret disclosure, and traceability attacks.
Configurable Mixed-Radix Number Theoretic Transform Architecture for Lattice-Based Cryptography

Lattice-based cryptography continues to dominate in the second-round finalists of the National Institute of Standards and Technology post-quantum cryptography standardization process. Computational efficiency is primarily considered to evaluate promising candidates for final round selection. In lattice-based cryptosystems, polynomial multiplication is the most expensive computation and critical to improve the performance. This paper proposes an efficient number theoretic transform (NTT) architecture to accelerate the polynomial multiplication. The proposed design applies mixed-radix multi-path delay feedback architecture and flexibly adopts various polynomial sizes. Configurable NTT design is realized to perform forward and inverse NTT computations on a unified hardware, which is then used to develop an efficient polynomial multiplier. The proposed architectures were successfully accelerated on several Xilinx FPGA platforms to directly compare with state-of-the-art works. The implementation results show that the proposed NTT architectures have comparable area-time product and demonstrate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.7\sim 17\times $ </tex-math></inline-formula> performance improvement, and the proposed polynomial multipliers achieve higher performance compared with previous works. Experimental results confirmed the proposed design’s applicability for high-speed large-scale data cryptoprocessors.
A strategy for archives metadata representation on CIDOC-CRM and knowledge discovery

This paper presents a strategy for the semantic migration of Portuguese National Archives records into CIDOC-CRM standard, an ontology developed for museums, within the context of the EPISA project. The approach to automatically populate the CIDOC-CRM is based on Mapping Description Rules to semantically translate the archives descriptive information into CIDOC-CRM representation. The compliance of the CIDOC-CRM model recommendations guarantees that the populated CIDOC-CRM ontology of archives descriptive information verifies interoperability, and could be linked and integrated with other populated CIDOC-CRM ontologies. In the information modelling, requirements on the mapping representation, due to the intent of interpreting natural language text to automatically extract information of metadata text fields and to interpret natural language queries, are taken into account. To automatically interpret the Mapping Description Rules, OWL API was used to obtain the set of assertions that represents the information in the target ontology and two datasets are available with some migration examples. The exploration of the knowledge representation is done through some Description Logic queries to highlight the advantages of having this new representation of the National Archives. The evaluation of the resulting representation can be done automatically proving its correctness for the metadata that has a direct representation in CIDOC-CRM.
AR-Sanad 280K: A Novel 280K Artificial Sanads Dataset for Hadith Narrator Disambiguation

Determining hadith authenticity is vitally important in the Islamic religion because hadiths record the sayings and actions of Prophet Muhammad (PBUH), and they are the second source of Islamic teachings following the Quran. When authenticating a hadith, the reliability of the hadith narrators is a big factor that hadith scholars consider. However, many narrators share similar names, and the narrators’ full names are not usually included in the narration chains of hadiths. Thus, first, ambiguous narrators need to be identified. Then, their reliability level can be determined. There are no available datasets that could help address this problem of identifying narrators. Here, we present a new dataset that contains narration chains (sanads) with identified narrators. The AR-Sanad 280K dataset has around 280K artificial sanads and could be used to identify 18,298 narrators. After creating the AR-Sanad 280K dataset, we address the narrator disambiguation in several experimental setups. The hadith narrator disambiguation is modeled as a multiclass classification problem with 18,298 class labels. We test different representations and models in our experiments. The best results were achieved by finetuning BERT-Based deep learning model (AraBERT). We obtained a 92.9 Micro F1 score and 30.2 sanad error rate (SER) on the validation set of our artificial sanads AR-Sanad 280K dataset. Furthermore, we extracted a real test set from the sanads of the famous six books in Islamic hadith. We evaluated the best model on the real test data, and we achieved 83.5 Micro F1 score and 60.6 sanad error rate.
KPI anomaly detection method for Data Center AIOps based on GRU-GAN

The system architecture and application services of the data center are becoming increasingly large. To ensure the stable operation of the systems and businesses carried by the data center, the operations engineer needs to collect and monitor the generating KPIs during the operation of the systems and services. Traditional KPI anomaly detection methods are faced with the challenges of the huge amount of KPIs and constantly changing data characteristics, which are gradually no longer suitable for highly dynamic systems and services. With the popularity of artificial intelligence algorithms, machine learning and deep learning methods have also begun to be applied in operation and maintenance scenarios, that is the emergence of Artificial Intelligence for IT Operations (AIOps). KPI anomaly detection is the underlying core technology of AIOps. This paper proposes a hybrid model based on GRU-GAN (GGAN) for KPI anomaly detection in data center AIOps. The Gated Recurrent Unit (GRU) network is selected as the generator and discriminator of Generative adversarial network (GAN) in this model, which get the time correlation and data distribution of KPI through the adversarial training between the generator and the discriminator to make use of the reconstruction ability of the generator and the discriminant ability of the discriminator at the same time. At the anomaly detection stage, the anomaly score is formed by integrating reconstruction difference and discrimination loss to complete the anomaly detection task. Experimental results show that the proposed method can more accurately capture the variable data characteristics of KPI compared with the traditional KPI anomaly detection method and the general unsupervised method, as well as achieve better performance in the KPI anomaly detection task.
Model-agnostic Ensemble-based Explanation Correction Leveraging Rashomon Effect

Explainable artificial intelligence (AI) technology enables us to quantitatively analyze the whole prediction logic of AI as a global explanation. However, unwanted relationships learned by AI due to data sparsity, high dimensionality, and noise are also visualized in the explanation, which deteriorates confidence in the AI. Thus, establishing a methodology of correcting those unwanted relationships in an explanation has been developed. We propose a model-agnostic ensemble-based explanation correction (EBEC) leveraging the Rashomon effect of statistics and evaluated it. The evaluation results indicate that the EBEC can correct a targeted part of the global explanation of AI so that the part aligns with the domain knowledge given by the user while maintaining other parts of the explanation. The results also indicate that the EBEC can improve the prediction accuracy of AI when appropriate domain knowledge was given.
PRGAN: Personalized Recommendation with Conditional Generative Adversarial Networks

Most of the existing methods define recommendation as regression or classification for user-item interactions and apply discriminative models. However, recommender systems suffer from interaction data sparsity and data noise problems in reality. Recent Generative Adversarial Network-based recommender systems have the potential to solve the aforementioned problems. The negative sampling methods use the generator to collect effective signals from a large amount of unlabeled data to alleviate the data sparsity problem, while they suffer from sparse rewards in the policy gradient training process. The vector reconstruction methods generate user-related vectors for data augmentation to enhance robustness, but they lead to redundant calculation and only take the user as a condition and ignore information conveyed by items. To alleviate the limitations of these methods, we propose a novel framework termed Personalized Recommendation with Conditional Generative Adversarial Networks (PRGAN) to consider both the user and the item subset as conditions and formulate conditional rating vector generation as a user-item matching problem. The sparsity of conditional rating vectors can be controlled in our method, which simplifies the discriminator’s learning task. Experiments are conducted on four datasets to evaluate the effectiveness of the proposed framework.
Set-based Particle Swarm Optimization for Portfolio Optimization with Adaptive Coordinate Descent Weight Optimization

Set-based algorithms have been shown to successfully find optimal solutions to the portfolio optimization problem and to scale well to larger portfolio optimization problems. Set-based algorithms work by selecting a sub-set of assets from the set universe. These assets then form a new search space where the asset weights are optimized. Erwin and Engelbrecht purposed such an algorithm that was shown to perform similarly to a well known genetic algorithm for portfolio optimization. The proposed algorithm, set-based particle swarm optimization (SBPSO), used a meta-huerstic for the weight optimization process - unlike previous set-based approaches to portfolio optimization. Erwin and Engelbrecht also developed several modifications to SBPSO that improved its performance for portfolio optimization. This paper investgates an alternative weight optimizer for SBPSO for portfolio optimization, namely adaptive coordinate descent (ACD). ACD is a completely deterministic approach and thus ensures that, after a finite time, an approximation of a global optimum will be found. It is shown that SBPSO for portfolio optimization using ACD for weight optimization found higher quality solutions than the current SBPSO algorithm, albeit slightly slower.
A visual tool for monitoring and detecting anomalies in robot performance

Abstract In robotic systems, both software and hardware components are equally important. However, scant attention has been devoted until now in order to detect anomalies/failures affecting the software component of robots while many proposals exist aimed at detecting physical anomalies. To bridge this gap, the present paper focuses on the study of anomalies affecting the software performance of a robot by using a novel visualization tool. Unsupervised visualization methods from the machine learning field are applied in order to upgrade the recently proposed Hybrid Unsupervised Exploratory Plots (HUEPs). Furthermore, Curvilinear Component Analysis and t-distributed stochastic neighbor embedding are added to the original HUEPs formulation and comprehensively compared. Furthermore, all the different combinations of HUEPs are validated in a real-life scenario. Thanks to this intelligent visualization of robot status, interesting conclusions can be obtained to improve anomaly detection in robot performance.
A Greedy Approach to Ant Colony Optimisation Inspired Mutation for Permutation Type Problems

Meta-heuristics have demonstrated relative success when applied to permutation problems such as the Traveling Salesman Problem (TSP). Two successful meta-heuristics are Genetic Algorithms (GAs) and Ant Colony Optimisation (ACO) but have widely differing methodologies, combining both could be beneficial. This paper achieves this using a mutation operator based on the novel ACO variant Partial-ACO. Applied to a range of TSP instances significant gains are achieved over standard mutation operators. Furthermore, to increase performance consideration is given to operating mutation in a greedy manner enabling constant use, a fully combined GA and ACO meta-heuristic solution. Experiments demonstrate that a greedy approach to ACO mutation improves results enabling solutions to TSP instances of several thousand cities within a few percent of optimal to be achieved without any local search methods.