Reviewerly/exHarmony huggingface dataset:
A Conceptual Study on Holochain and Blockchain Technology

In this paper, we study and describe the conceptual differences of a new and upcoming technology called Holochain Technology along with that of widely used, Blockchain Technology. The working and architecture of both of these technologies have been studied purely at the conceptual level in order to ascertain the differences among them and decide the more efficient technology of the two. Our focus during the study was on the creation of chain, validation of the data uploaded on the chain and its integrity. Moreover, one of the important factors that we considered during the study was the effect of increased number of transactions in the current chain due to the addition of more nodes. Hence, in this paper, scalability is considered as one of the major factors that plays part in the performance of these two technologies. Thus, at the end of the paper, we will find out which of the two technologies studied here, will be used with more prevalence in the future.
Using Contamination Indices for Assessments of Heavy Metals Status of Soils around Mercury Mine, in Kurşunlu, (Konya) Province

The objective of this study was to establish contamination level for, Pb, Zn, Cu, Ni, Cr, As, Hg, Sb, Co, and Cd in soils around the old Hg mine area central part of Turkey. 34surface samples from mine area were collected. Samples were digested and the metals were determined by ICP-OES and assess metal contamination in soils by using Enrichment factor (EF), contamination factor (Cf), Geoaccumulation index (Igeo), hazard quotient (HQ) (individual metal), contamination index (Cd), modified contamination index (mCd), pollution load index (PLI), and hazard load index (HLI) (multi metal). Enrichment factor (EF), contamination factor (Cf), Geoaccumulation index (Igeo), hazard quotient (HQ)indicate that the soil in the most of studied points were unpolluted, slightly polluted or moderate degree of pollution with respect to Pb, Cr, Cd, Zn, Cu, Co and Ni, while the soils in all studied stations were extremely contaminated with respect to Hg, Sb and As. When the obtained result to by evaluated the cumulative effect of metal pollution load index (PLI), the contamination degree (Cd) and modified Contamination Degree (mCd), and hazard load index were found to be very high high pollution, ultra-high degree pollution and medium pollution respectively indicating that the studied soils in were polluted by total of studied heavy metals.
Quantum-Inspired Distributed Memetic Algorithm

This paper proposed a novel distributed memetic evolutionary model, where four modules distributed exploration, intensified exploitation, knowledge transfer, and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality. Distributed exploration evolves three independent populations by heterogenous operators. Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches. Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents. Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably. Quantum computation is a newly emerging technique, which has powerful computing power and parallelized ability. Therefore, this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm, referred to as quantum-inspired distributed memetic algorithm (QDMA). In QDMA, individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace. The QDMA integrates the superiorities of distributed, memetic, and quantum evolution. Computational experiments are carried out to evaluate the superior performance of QDMA. The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon's rank-sum test. The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model, but also to superior designs of each special component.
Investigation of the Optimal PID-Like Fuzzy Logic Controller for Ball and Beam System with Improved Quantum Particle Swarm Optimization

Fuzzy Logic Controllers (FLCs) are intelligent control methods, where membership functions and corresponding rules are defined to get a proper control signal. The parameters were defined for these controllers, and they are named as PID-like FLC since the input and output parameters are connected to the Fuzzy controller with integral and derivative action of the error signal to change the behavior/performance of FLC. In this research, three different rule sets for Fuzzy controllers; 3 × 3, 5 × 5, and 7 × 7 are used and parameters are optimized with; differential evolution, genetic algorithm, particle swarm optimization and quantum-behaved particle swarm optimization. In addition to these controllers, a novel algorithm named as improved quantum particle swarm optimization is proposed as a part of this research. The simulation and real-life implementation on the experimental set results of these controllers are discussed in this paper.
3D Brain Tumor Volume Reconstruction and Quantification using MRI Multi-modalities Brain Images

Brain tumor volume quantification is not possible with magnetic resonance imaging (MRI) non-invasive imaging systems. Usually, the brain MR imaging modality is based on four modalities T1, T1ce (contrast-enhanced), T2, and FLAIR (Fluid-Attenuated Inversion Recovery) highlighting various complex brain structure. The aim of this study is to develop an automated brain tumor segmentation method to allow an effective 3D tumor reconstruction and volume quantification. The Local Graph Cut (LGC) and Flood fill (FF) segmentation tools are used. A 3D reconstruction is performed using the open-source ImageJ image processing package. To assess the brain tumor segmentation accuracy a 3D Dice score is used. This extensive study is conducted on the MICCAI BraTS2020 dataset and shows that the proposed model obtains good results. To evaluate the final full segmentation of 3D volume accuracy, we use three brain modalities, namely T1 and T2 weighted, and T1ce scans. Also, to measure accuracy of segmentation we take the advantage of the existing ground truth image dataset. We find an average Dice score of 0.501 ± 0.128 for T2-weighted images and LGC algorithm. Also, there are not important differences between the results provided by investigated algorithms for all images types. The 3D reconstruction and volumetric analysis via 2D segmented slices produce realistic shape samples. The computed volumes are in the limit of 71% from the tumor volume computed using the ground truth images.
Patient Similarity Learning with Selective Forgetting

Patient similarity learning aims to use patient information such as electronic medical records and genetic data as input to calculate the pairwise similarity between patients, and it is becoming increasingly important in healthcare applications. However, in many cases, patient similarity learning models also need to forget some patient data. From the perspective of privacy, patients desire a tool to erase the impacts of their sensitive data from the trained patient similarity models. From the perspective of utility, if a patient similarity model’s utility is damaged by some bad patient data, the patient similarity model needs to forget such patient data to regain utility. Although some researchers have studied the problem of machine unlearning, existing methods cannot be directly applied to patient similarity learning as they fail to consider the comparative relationships among patients. In addition, they also fail to identify the optimal conditions of the local objective functions. In this paper, we fill in this gap by studying the unlearning problem in patient similarity learning. To unlearn the knowledge of a specific patient, we propose a novel erasable patient similarity learning framework, which enjoys the provable data removal guarantee and achieves high unlearning efficiency while keeping high model utility in patient similarity learning. We also conduct extensive experiments on real-world patient disease datasets to verify the desired properties of the proposed erasable framework.
Formalizing CCS and π-calculus in Guarded Cubical Agda

Dependent type theories with guarded recursion have shown themselves suitable for the development of denotational semantics of programming languages. In particular, Ticked Cubical Type Theory (TCTT) has been used to show that, for guarded labeled transition systems (GLTS), interpretation into the denotational semantics maps bisimilar processes to equal values. In fact the two notions are proved equivalent, allowing one to reason about equality in place of bisimilarity. We extend that result to the Calculus of Communicating Systems (CCS) and the π-calculus. For the latter, we pick early congruence as the syntactic notion of equivalence between processes, showing that denotational models based on guarded recursive types can handle the dynamic creation of channels that goes beyond the scope of GLTSs. Hence we present fully abstract denotational models for CCS and the early π-calculus, formalized as an extended example for Guarded Cubical Agda: a novel implementation of Ticked Cubical Type Theory based on Cubical Agda.
Fast instruments and tissues segmentation of micro-neurosurgical scene using high correlative non-local network

Surgical scene segmentation provides critical information for guidance in micro-neurosurgery. Segmentation of instruments and critical tissues contributes further to robot assisted surgery and surgical evaluation. However, due to the lack of relevant scene segmentation dataset, scale variation and local similarity, micro-neurosurgical segmentation faces many challenges. To address these issues, a high correlative non-local network (HCNNet), is proposed to aggregate multi-scale feature by optimized non-local mechanism. HCNNet adopts two-branch design to generate features of different scale efficiently, while the two branches share common weights in shallow layers. Several short-term dense concatenate (STDC) modules are combined as the backbone to capture both semantic and spatial information. Besides, a high correlative non-local module (HCNM) is designed to guide the upsampling process of the high-level feature by modeling global context generated from the low-level feature. It filters out confused pixels of different classes in the non-local correlation map. Meanwhile, a large segmentation dataset named NeuroSeg is constructed, which contains 15 types of instruments and 3 types of tissues that appear in meningioma resection surgery. The proposed HCNNet achieves the state-of-the-art performance on NeuroSeg, it reaches an inference speed of 54.85 FPS with the highest accuracy of 59.62% mIoU, 74.7% Dice, 70.55% mAcc and 87.12% aAcc.
Dataset and Methods for Recognizing Care Activities

A major challenge in stationary care in hospitals is the limited amount of time for each patient due to a large overhead being created by manual documentation efforts. Studies show that it is common for caregivers to spend more than one hour per day for documentation efforts.
CFPNet: A Denoising Network for Complex Frequency Band Signal Processing

The recent development of deep learning has brought breakthroughs in image denoising. However, the recovery of image detail, especially high-frequency weak information, still needs to be improved. Firstly, the noise mainly concentrates on the high-frequency signal, and the high-frequency signal is easy to be disturbed, which makes it difficult to recover; Secondly, in the process of image denoising with deep learning, feature extraction of model is used to smooth the noise for image restoration, resulting in a poor recovery effect of high-frequency signal. To solve the above problems and improve the overall image denoising performance, we propose a denoising network for complex frequency band signal processing (CFPNet), which contains three insights: 1) the image input node uses a cosine transform to segment the image noise frequency and divides different image features into signals in different frequency bands for targeted noise reduction; 2) targeted noise reduction is carried out for different frequency band signals via a fine-grained scheme; 3) different frequency band signals are fused and high-frequency signals are enhanced to improve the recovery of detailed signals. The experimental results show that the proposed CFPNet can achieve state-of-the-art performance on both real-world datasets and Gaussian noise fitting datasets.
Ethereum Based Smart Contract for Event Management System

Ethereum is a polpular blockchain platform with its native cryptocurrency, Ether (ETH). Further blockchain development and deployment creates new opportunities that enable its use in various applications and allow it to be further integrated into everyday life. A smart contract is a small set of instructions and a self-executing program stored in blockchain. Thus blockchain is impervious and eliminates the requirement of a central authority. This paper focuses on blockchain based event management smart contract, which is created in solidity and deployed on Rinkeby test network, and also described the differences between smart-contract and traditional-contract.
Decentralized Governance and Energy Carbon Footprint: Opportunity for Re-Empowering People?

Given the emergence of shared databases and distributed ledger technologies such as blockchain, this paper presents a short discussion about how the electric power sector can benefit from the inclusion of such systems and contribute to pushing efforts toward achieving the United Nations Goals for the Sustainable Development. With increasing attention to community-backed energy projects focused on the procurement of green energy, the development of a transparent and reliable medium for the record and publication of carbon emissions of different generators, utilities, and aggregators can promote a technological update of the current local electricity market platforms and enable the participation of different agent archetypes. Given this, this paper provides an overview of blockchain applications and decentralized governance models, as well as their potential for the transition to renewable energies. The authors then propose a framework that connects these concepts to the negotiation infrastructures of present energy markets in a form that may empower electricity customers and have benefits that transcend the local economy, environment, and democracy.
Fast Multi-Contact Motion Planning Based on Best-Neighbor Search of Contact Sequences

This paper presents a computationally efficient method for planning dynamic multi-contact motion. A low-dimensional dynamical model of a robot equipped with multiple contact points is developed, and a motion planning problem is formulated as a optimal control problem with continuous and discrete variables. An extension to the differential dynamic programming (DDP) framework enables efficient computation of optimal cost for a large number of locally modified contact sequences. Based on this finding, a novel algorithm for multi-contact motion planning is developed, and its performance is evaluated in simulations.
A Role Based Model Template for Specifying Virtual Reality Software

Research in hardware and software support for Virtual Reality (VR) has significantly increased over the last decade. Given the software platform fragmentation and hardware volatility, there is an apparent disconnect among practitioners while building applications in the VR domain. This paper proposes a role-based model template as a meta-model to specify the bare minimum VR software system. We conducted a grounded-theory-based qualitative study on prevailing and phased-out VR SDKs and standards to propose this meta-model. This model template can help VR practitioners build open-source tools to develop, design, and test VR software systems.
Parental Involvement and Students’ Motivation in Learning English

This study was to determine the level of parental involvement, the level of students' motivation in learning English, and the correlation between parental involvement and students' motivation in learning English. This study used quantitative designs. The instruments of this study were questionnaires adapted from Ekiz and Kulmetov (2015) and Amin, Juriah, and Isa (2001) for measuring parental involvement and an adapted questionnaire for students’ motivation from the Attitude/Motivation Test Battery (2005). This study was conducted to 52 grade 10 – 12 Science students at a private school in Balikpapan enrolled in the first semester of the academic year 2021/2022. The results showed that parental involvement was moderate (M= 3.38). Specifically, parental involvement in terms of parents as motivators was high (M=3.55), while parents as educators (M=3.34) and parents as facilitators (M=3.12) were moderate. Moreover, the results showed that students’ motivation in learning English was high (M=3.82). Specifically, intrinsic motivation was high (M=4.17) and extrinsic motivation was moderate (M=3.40). Lastly, it was found that there was a significant correlation between parental involvement and students’ motivation in learning English as the results show p=.000 &lt; 0.005, r=.537. Therefore, it is recommended that parents be more involved so students can be more motivated in learning English.
IMPLEMENTATION OF QR CODES ON ICHIBAN SUSHI RESTAURANT'S DISH MENU ON ORDER TIME EFFICIENCY AND CUSTOMER SATISFACTION LEVEL

This research aims to find out how the QR code affects the efficiency of consumer ordering time and the level of consumer satisfaction with the QR code. This is a qualitative research which describes the QR code research on the Ichiban Sushi restaurant menu. Data collection techniques using observation and interviews. The research carried out on "Ichiban Sushi" restaurant which is located in Plaza Asia, Tasikmalaya City in West Java. The results of this study focus on solving problems by simplifying the queue management process for the Ichiban Sushi menu and simplifying restaurant menu ordering and how it affects customer satisfaction. The findings reveal that there was an efficiency in ordering time because using this QR code was felt to be faster and more flexible, consumers could order simultaneously and consumers also did not need to queue to order restaurant menus. QR codes on the ordering menu for Ichiban Sushi Restaurant greatly affects the level of consumer satisfaction, where the majority of consumers feel satisfied with the use of QR codes on the ordering menu for Ichiban Sushi Restaurant which is certainly very beneficial for consumers.
HierTTS: Expressive End-to-End Text-to-Waveform Using a Multi-Scale Hierarchical Variational Auto-Encoder

End-to-end text-to-speech (TTS) models that directly generate waveforms from text are gaining popularity. However, existing end-to-end models are still not natural enough in their prosodic expressiveness. Additionally, previous studies on improving the expressiveness of TTS have mainly focused on acoustic models. There is a lack of research on enhancing expressiveness in an end-to-end framework. Therefore, we propose HierTTS, a highly expressive end-to-end text-to-waveform generation model. It deeply couples the hierarchical properties of speech with hierarchical variational auto-encoders and models multi-scale latent variables, at the frame, phone, subword, word, and sentence levels. The hierarchical encoder encodes the speech signal from fine-grained features into coarse-grained latent variables. In contrast, the hierarchical decoder generates fine-grained features conditioned on the coarse-grained latent variables. We propose a staged KL-weighted annealing strategy to prevent hierarchical posterior collapse. Furthermore, we employ a hierarchical text encoder to extract linguistic information at different levels and act on both the encoder and the decoder. Experiments show that our model performs closer to natural speech in prosody expressiveness and has better generative diversity.
A Textual Data-Oriented Method for Doctor Selection in Online Health Communities

As doctor–patient interactive platforms, online health communities (OHCs) offer patients massive information including doctor basic information and online patient reviews. However, how to develop a systematic framework for doctor selection in OHCs according to doctor basic information and online patient reviews is a challenged issue, which will be explored in this study. For doctor basic information, we define the quantification method and aggregate them to characterize relative influence of doctors. For online patient reviews, data analysis techniques (i.e., topics extraction and sentiment analysis) are used to mine the core attributes and evaluations. Subsequently, frequency weights and position weights are respectively determined by a frequency-oriented formula and a position score-based formula, which are integrated to obtain the final importance of attributes. Probabilistic linguistic-prospect theory-multiplicative multiobjective optimization by ratio analysis (PL-PT-MULTIMOORA) is proposed to analyze patient satisfactions on doctors. Finally, selection rules are made according to doctor influence and patient satisfactions so as to choose optimal and suboptimal doctors for rational or emotional patients. The designed textual data-driven method is successfully applied to analyze doctors from Haodf.com and some suggestions are given to help patients pick out optimal and suboptimal doctors.
Private Anomaly Detection in Linear Controllers: Garbled Circuits vs. Homomorphic Encryption

Anomaly detection can ensure the operational integrity of control systems by identifying issues such as faulty sensors and false data injection attacks. At the same time, we need privacy to protect personal data and limit the information attackers can get about the operation of a system. However, anomaly detection and privacy can sometimes be at odds, as monitoring the system’s behavior is impeded by data hiding. Cryptographic tools such as garbled circuits and homomorphic encryption can help, but each of these is best suited for certain different types of computation. Control with anomaly detection requires both types of computations so a naive cryptographic implementation might be inefficient. To address these challenges, we propose and implement protocols for privacy-preserving anomaly detection in a linear control system using garbled circuits, homomorphic encryption, and a combination of the two. In doing so, we show how to distribute private computations between the system and the controller to reduce the amount of computation–in particular at the low-power system. Finally, we systematically compare our proposed protocols in terms of precision, computation, and communication costs.
Distributed online estimation of biophysical neural networks

In this work, we propose a distributed adaptive observer for a class of nonlinear networked systems inspired by biophysical neural network models. Neural systems learn by adjusting intrinsic and synaptic weights in a distributed fashion, with neuronal membrane voltages carrying information from neighbouring neurons in the network. We show that this learning principle can be used to design an adaptive observer based on a decentralized learning rule that greatly reduces the number of observer states required for exponential convergence of parameter estimates. This novel design is relevant for biological, biomedical and neuromorphic applications.