Reviewerly/exHarmony huggingface dataset:
Investigations on Distributed Differential Evolution Framework with Fault Tolerance Mechanisms

This chapter presents an investigation on fault tolerance capability of the distributed island model when framed with differential evolution (DE) algorithm. To tackle the difficulties raised by the increased complexity of the problems, various parallel and distributed frameworks have been adapted by the evolutionary computing (EC) society. Incorporating the parallelism concept with the distributed framework had been proven to be effective while solving highly complex real-world problem. The two main purposes behind adapting this approach are to lessen the computation time with improved population diversity and to enhance the solution quality. Nonetheless, there exist only few research works focusing on the problems evoked by the faults that are habitually occurring when instigating the parallel algorithms in distributed frameworks. This chapter analyzes the behavioral nature of the DE based distributed framework when different type of faults arises, in the distributed environment. A distributed differential evolution (dDE) algorithm framework was implemented as a base structure for the investigation. Three possible faulty scenarios were simulated in algorithm level. The framework was then tested on three possible fault scenarios. The dDE framework with and without the simulated faulty scenarios is experimented in solving benchmarking functions from CEC-2017. An empirical comparative study was carried out with the performance metrics depicting the solution quality and speed of the framework. The virtuous results obtained from each faulty scenario depicted the reliability of the distributed island model, considering DE to be a fault-tolerant parallel algorithm.
On the common population diversity measures in metaheuristics and their limitations

Maintaining population diversity is one of the fundamental challenges for metaheuristic algorithms. With the emergence of adaptive and self-adaptive methods, the population diversity is frequently used as an indicator of the population state and feedback for the adaptive mechanism. In literature, several methods for quantification of the population diversity were proposed over the years. However, expressing the overall complexity of a metaheuristic population state by a single number inherently leads to simplification and distortion. As we show in this paper, lower diversity value does not automatically mean worse conditions for the emerging of new feasible solutions.
Joint Crypto-Blockchain Scheme for Trust-Enabled CCTV Videos Sharing

The declaration of COVID-19 as pandemic affected almost all public and private functionaries by enforcing them to operate remotely, and the judiciary is no exception. The trend seems to continue for the foreseeable future. The judicial proceedings where a piece of evidence, particularly the digital one, holds undisputed importance. Its preservation and intact transmission to the jury have gained an enhanced significance in this era which is highly susceptible to manipulation. This research proposes joint encryption and blockchain-based solution for immutable decentralised evidence management for CCTV footage. The permissioned chain of evidence system is implemented using Hyperledger Fabric to ensure the validity of the video metadata. The hash values are calculated on videos (recorded per session) and stored in the blockchain, while Region of Interest (ROI) based selective encryption of videos adds optimized security to individuals' privacy and protects from illegal access of stored videos. The experimental results provide a proof of concept that the proposed scheme facilitates the remote court services by maintaining a legal chain of evidence for CCTV videos.
Background and Current Situation of Geosciences in Peru

In this chapter, an overview of geoscientific development based on Peru is discussed. It recounts the history of the Peruvian geoscientific community, showing that over time it has been linked to mining and civil engineering. It is also shown that in the past, it has belonged to the power-wielding elite. The main problems facing the Peruvian geoscientific academy, associated with bias toward geologists dedicated to mining activity, their participation in the management of geological risks, the quality of geo-education and others, are also summarized. This discussion includes proposals for action that should be considered to improve the visibility of geosciences in the country. The role of Peruvian geoscientists in raising awareness of the general population and the usefulness of spreading geo-ethical values are also analyzed.
How to use corpus linguistics in forensic linguistics

The chapter explores in the ways in which corpus linguistics has been, and can be, applied to forensic linguistics. Drawing upon examples from both real-life casework and academic research, this chapter illustrates how the range of corpus-based methods (frequency information, concordances, collocation and keyword analysis) can each be employed for forensic purposes. In particular, it focuses on how specialised corpora are used within authorship attribution cases and how reference corpora are used to determine the meaning of a word that may be unclear or contested. It also explores how corpus-based methods have been used to study traditional topics such as courtroom discourse and deception detection but also emerging areas such as disinformation. It concludes by offering some thoughts about the future of the field.
A Novel Image Encryption Approach for IoT Applications

The rapid developments observed in the field of Internet of Things (IoT), along with the recently increasing dependence on this technology in home and financial applications, have made it necessary to pay attention to the security of information sent through these IoT applications. The present article proposes a new encryption method for important messages that are sent via IoT applications. The proposed method provides four levels of security for the confidential message (in this case, an image). The first level is represented by applying the Conformal Mapping on the secret image. The second level is represented by encoding the resulting image from the first level using the encryption and decryption (RSA) method, while the third level is the use of Less Significant Bit (LSB) as the hiding method to hide the message inside the cover image. The compression of the stego image using GZIP is the last level of security. The peak signal-to-noise (PNSR) metric was used to measure the quality of the resulting image after the steganography process. The results appear promising and acceptable. Therefore, it is suggested that this method can be applied to send secret messages through applications of special importance across the IoT.
Hyperledger Fabric Framework with 5G Network for Blockchain-based Security of IoT Smart Home Applications

In today's world, Internet of Things (IoT) device and data security is still a significant problem. IoT devices are small and light, with little or no processing capability to run complicated and computationally intensive security algorithms. In their natural state, IoT devices are centralized and have a single point of failure. Therefore, when we exchange data, there is a chance of fake authenticity and phishing. Due to its decentralized nature and resilience, this study tries to eliminate data security problems utilizing the phenomenon known as the blockchain. The Hyperledger framework was employed for block chaining, which employs its fabrication technology for the security of IoT devices and data. We have chosen an IoT-based smart home environment to test our data security solutions because of the diversified nature of 10T devices.
Ontologies of Action and Object in Home Environment towards Injury Prevention

It is one of the critical applications for human-centric artificial intelligence that surveys the risky situation and inferring ways to prevent it by storing the situation information from surveillance cameras. Recognition of human activities in daily situations is an emerging topic in the computer vision domain. Significantly, the context information, such as objects involved in activities and the relationships between the objects and the activities, are attractive to improve the accuracy of the activity recognition task. However, the existing labels for actions and objects are not well considered for describing daily activities. This short research paper provides the ontologies of actions and objects in the home environment, so-called Primitive Action ontology, and Home Object ontology. The Primitive Action ontology contains a minimal set of primitive actions designed to abstract actions and discards objects and methods. The Home Object ontology has object types and properties such as affordance and attributes to describe daily situations. The properties represent both normal and abnormal effects, including intentional function and incidents in the home environment. We also discuss the prospect of using these ontologies as the conclusion of this paper.
MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series

Medication recommendation for chronic diseases based on the complex historical electronic medical records (EMR) is an important and challenging research problem in medical informatics because the medical records are often irregularly sampled and contain many missing data. However, most existing approaches fail to explore the irregular time-series dependencies and ignore the consecutive correlation in dynamic prescription history. To fill this gap, we propose the MEdication Recommendation network on Irregular Time-Series (MERITS), which captures the irregular time-series dependencies with the neural ordinary differential equations (Neural ODE). Meanwhile, it leverages a drug-drug interaction knowledge graph and two learned medication relation graphs to explore the co-occurrence and sequential correlations of the medications. We further propose an attention-based encoder-decoder framework to combine the historical information of patients and medications from EMR. Besides, we collect and annotate a diabetes inpatient medication dataset and demonstrate the effectiveness of MERITS by comparing it with several state-of-the-art methods of medication recommendations.
Discovering the Research Issues of Classical Chinese Segmentation via Modern Chinese Segmentation System

Word segmentation is the specific issue for Chinese in the natural language processing(NLP) domain. After several decades, there are some Chinese segmentation systems, such as CKIP and Jeiba, which are available for Chinese NLP applications. However, these systems are designed for the modern Chinese, not for the classical Chinese. In this work, we tried to apply the modern Chinese segmentation systems on the classical Chinese articles to find the research issues for the classical Chinese segmentation system. One classical Chinese articles dataset has been carefully selected, processed, and organized as the experimental benchmark by the experts’ professional knowledge.
Issues Related to EHR Blockchain Applications

One of the most significant opportunities offered by the blockchain technology is to finally integrate the different processes in the healthcare ecosystem. Several companies are exploring the use of blockchain to manage digital medical data, and several solutions, leveraging the benefits of the blockchain, have been proposed for the implementation of the Electronic Health Record (EHR). However, the application of blockchain technology in the medical domain is not free from challenges. This paper describes the issues related to the adoption of the blockchain technology in the development of an EHR management system.
Development of Secured Smart Display for Art Industry

There exists a yawning gap in the information base on the Art Industry. It is particularly worrisome against the fact that there is noticeable development with the mode of transmission of art. However, this development doesn’t include traditional artists who do not have access modern materials for exhibiting their art. This study designs an application of digital technology and its design and implementation, which is proposed to bridge the gap present in the Art Industry. The focus is on the process and various integrations of computing concepts to create a working system for the digitization of traditional 2-dimensional art. The development shall also revolve around the User experience that the buyer of the art shall be getting, as well as the protection of the intellectual property of the artist as his works get digitized. The researcher used a web-based model for the front-end design, and used a straightforward encryption library executable in JavaScript. Image datasets were locally stored on the test system to minimize complexity of the proof-of-concept model. There has been a various image encryption algorithm aimed at repelling simple and mildly complex piracy attacks, but generally, these systems tend to leave out code optimization and efficiency at runtime, therefore exposing the plain images to newer attacks. This paper means to bridge the efficiency gap, utilising a faster model at launch.
Multipopulation Particle Swarm Optimization Algorithm with Neighborhood Learning

Particle swarm optimization (PSO) algorithm is widely used due to its fewer control parameters and fast convergence speed. However, as its learning strategy is only learning from the global optimal particle, the algorithm has the problem of low accuracy and easily falling into local optimization. In order to overcome this defect, a multipopulation particle swarm optimization algorithm with neighborhood learning (MPNLPSO) is proposed in this article. In MPNLPSO, a small-world network neighborhood learning strategy is proposed to make particles learn from the neighborhood optimal particles instead of only the global optimal particle. Furthermore, the concept of multipopulation cooperation is introduced to balance the ability of global exploration and local exploration. In addition, a dynamic opposition-based learning strategy is proposed to effectively activate the particles in the search stagnation state. Moreover, in order to improve the accuracy of the algorithm and, to some extent, avoid the population diversity decreases too fast, as the searching process continues, Lévy flight is introduced to randomly perturb the particles of historical optimal and neighborhood optimal. To verify the performance of the proposed algorithm experimentally, twenty benchmark functions are solved. Experimental results show that the proposed multipopulation particle swarm optimization algorithm with neighborhood learning presents high efficiency and performance with a certain robustness.
The 16,384-node Parallelism of 3D-CNN Training on An Arm CPU based Supercomputer

As the computational cost and datasets available for deep neural network training continue to increase, there is a significant demand for fast distributed training on supercomputers. However, porting and tuning applications for new advanced supercomputers requires tremendous amount of development efforts. Therefore, we present software tuning best practice for a 3D-CNN model training on a new Arm CPU based supercomputer, Fugaku. We (i) tune computation in DL by a JIT translator for aarch64, (ii) optimize collective communication such as Allreduce for 6D mesh/torus network topology, (iii) tune I/O by data staging with compression and data loader with caching, and (iv) parallelize training in data and model parallelism. We apply the proposed methods to a CosmoFlow 3D-CNN model, and achieve the training in 30 minutes using 16,384 nodes consisting of 4096 data- and 4 model-parallelism. This is the fastest result of any CPU-based systems in MLPerf HPC v0.7 in the world.
Mcore: Multi-Agent Collaborative Learning for Knowledge-Graph-Enhanced Recommendation

Recently, knowledge-graph-enhanced recommendation systems have attracted much attention, since knowledge graph (KG) can help improving the dataset quality and offering rich semantics for explainable recommendation. However, current KG-enhanced solutions focus on analyzing user behaviors on the product level and lack effective approaches to extract user preference towards product category, which is essential for better recommendation because users shopping online normally have strong preference towards distinctive product categories, not merely on products, according to various user studies. Moreover, the existing pure embedding-based recommendation methods can only utilize KGs with a limited size, which is not adaptable to many real-world applications. In this paper, we generalize the recommendation problem with preference mining as a compound knowledge reasoning task and propose a novel multi-agent system, called Mcore, which can promote model performance by mining users’ high-level interests and is adaptable to large KGs. Specifically, we split the overall problem and allocate sub-task to each agent: Coordinate Agent takes charge of recognizing the product-category preference of current user, while Relation Agent and Entity Agent perform KG reasoning cooperatively from a user node towards the preferred categories and terminate at a product node as recommendation. To train this heterogeneous multi-agent system, where agents own various functionalities, we propose an asynchronous reinforcement training pipeline, called Multi-agent Collaborative Learning. The extensive experiments on real datasets demonstrate the effectiveness and adaptability of Mcore on recommendation tasks.
Generating Structural Node Representations via Higher-order Features and Adversarial Learning

Role of node is defined on structural similarity or local connective pattern, describing the functions of node in the network. In real-world situation, it can denote person’s identity and status. It has been studied over the past decades, and learning role-based network representations is crucial to many downstream tasks. In this field, the important step for is extracting some measurements to evaluate structural similarity. Although some methods have been developed to capture the role features to learn the structural similarities between nodes, they all design the features of fixed types, such as global, local, and higher-order features. These features can only discover single type of roles, and simply combing them may cause damage to performance. It is very difficult to model the complex relationship between different scale features in the field of role-based network embedding. Therefore, we propose a novel adversarial framework to generate structural node representations via higher-order features and adversarial learning (SHOAL). We leverage the Auto-Encoder on higher-order features and some GNNs on its outputs to aggregate local neighbors. We believe that higher-order and local features can denote roles, and effectively integrating them will help for role discovery. So we consider the GNNs as the generator and design an adversarial game between these features, which can also improve the robustness. The experiments on real-world networks demonstrate the superiority and efficiency of our model, and the results also prove the effectiveness of integrating higher-order and local features.
Arithmetic Circuit Homomorphic Encryption Key Pairing Comparisons and Analysis between Elliptic Curve Diffie Hellman and Supersingular Isogeny Diffie Hellman

This project is an extension of ongoing research on Fully Homomorphic Encryption - Arithmetic Circuit Homomorphic Encryption. This paper focus on the implementation of pairing algorithm Supersingular Isogeny Diffie Hellman Key Exchange into Arithmetic Circuit Homomorphic Encryption as well as comparison and analyse with Elliptic Curve Diffie Hellman. Next, the paper will discuss on the latencies incurred due to pairing sessions between machines, key generations, key sizes, CPU usage and overall latency for the two respective key exchange methods to be compared against each other.
Fuzzy Search of Knowledge Graph with Link Prediction

In SPARQL, only actual connected nodes and properties are retrieved as fact. However, many knowledge graphs expressed in RDF are incomplete, with knowledge graph embedding and graph neural networks used to compensate for the incompleteness.
Toward a Smell-aware Prediction Model for CI Build Failures

During the last years, researchers have explored the potential factors behind Continuous integration (CI) build failures focusing mainly on metrics related to code changes, statistics about the project etc. However, code quality indicators such as the presence of bad smells have been rarely discussed in the context of CI. In this paper, we aim at investigating the extent to which CI build failures prediction can be improved by the detection of bad smells. Specifically, we evaluate the contribution of 28 well-known bad smells when added to BF-DETECTOR, an existing tool for CI build failures prediction. We conduct a case study on a dataset of 15,041 Travis CI builds extracted from five GitHub projects. The obtained results demonstrate the efficiency of the smell-aware prediction to improve the F1-score of BF-DETECTOR by 4% on average. In particular, we found that Excessive Parameter List (EPL), Sensitive Equality (SE) and Lazy Test (LT) are the most contributing to the prediction.
Ultrastrong Capacitive Coupling of Flux Qubits

A flux qubit can interact strongly when it is capacitively coupled to other circuit elements. This interaction can be separated into two parts, one acting on the qubit subspaces and one in which excited states mediate the interaction. The first term dominates the interaction between the flux qubit and an $LC$ resonator, leading to ultrastrong couplings of the form ${\ensuremath{\sigma}}^{y}(a+{a}^{\ifmmode\dagger\else\textdagger\fi{}})$, which complement the inductive ${\ensuremath{\sigma}}^{x}i({a}^{\ifmmode\dagger\else\textdagger\fi{}}\ensuremath{-}a)$ coupling. However, when coupling two flux qubits capacitively, all terms need to be taken into account, leading to complex nonstoquastic ultrastrong interaction of types ${\ensuremath{\sigma}}_{1}^{y}{\ensuremath{\sigma}}_{2}^{y}$, ${\ensuremath{\sigma}}_{1}^{z}{\ensuremath{\sigma}}_{2}^{z}$, and ${\ensuremath{\sigma}}_{1}^{x}{\ensuremath{\sigma}}_{2}^{x}$. Our theory explains all these interactions, describing them in terms of general circuit properties---coupling capacitances, qubit gaps, inductive, Josephson and capacitive energies---that apply to a wide variety of circuits and flux qubit designs.
Full-field strain measurement using optimal path search method with error control

Digital image correlation (DIC) is one of the main means of optical measurement of the strain field. However, there has been little discussion on the calculation errors at positions with abrupt change. This study seeks to address how to reduce the full-field strain calculation errors and improve the strain accuracy. An optimal path search method (OPSM) with error control for a full-field strain solution is proposed by optimizing the algorithm of the traditional DIC method, using the material displacement field with abrupt change. The strain calculation considering the calculation zone boundary, hole, and shock wave front is discussed. A comparison of the results using the proposed method with the theoretical results shows that this method can effectively obtain full-field strain information, especially at positions with abrupt change in displacement. The application of OPSM to the cellular structure in a quasi-static compression experiment again verifies the validity. This method not only is comparable with the original method in the noise suppression effect but also ensures the approximation of the results to the true values in the displacement zone with abrupt change.