Reviewerly/exHarmony huggingface dataset:
A Brief Survey of Offline Explainability Metrics for Conversational Recommender Systems

Conversational recommendation systems (CRS) are being embedded into search engines, streaming services, and commercial products. Explanations in conversational recommendation systems can increase user trust and satisfaction and help system designers fine-tune and improve the system. [1–2] Evaluating the quality of explanations for a recommendation system is a current challenge due to its relative newness as an attribute to optimize for, a small number of conversational recommendation datasets, and also due to a lack of enough annotated data. [1, 3–4] To facilitate the improvement of conversational recommendation systems explain-ability we survey and propose the creation of an automated metric to score how well a conversational recommender system explains itself for multiple levels of stakeholders, which evaluates the explain-ability of recommendation output and for each turn dialog output.
The epidemiological impact of digital and manual contact tracing on the SARS-CoV-2 epidemic in the Netherlands: Empirical evidence

The Dutch government introduced the CoronaMelder smartphone application for digital contact tracing (DCT) to complement manual contact tracing (MCT) by Public Health Services (PHS) during the 2020–2022 SARS-CoV-2 epidemic. Modelling studies showed great potential but empirical evidence of DCT and MCT impact is scarce. We determined reasons for testing, and mean exposure-testing intervals by reason for testing, using routine data from PHS Amsterdam (1 December 2020 to 31 May 2021) and data from two SARS-CoV-2 rapid diagnostic test accuracy studies at other PHS sites in the Netherlands (14 December 2020 to 18 June 2021). Throughout the study periods, notification of DCT-identified contacts was via PHS contact-tracers, and self-testing was not yet widely available. The most commonly reported reason for testing was having symptoms. In asymptomatic individuals, it was having been warned by an index case. Only around 2% and 2–5% of all tests took place after DCT or MCT notification, respectively. About 20–36% of those who had received a DCT or MCT notification had symptoms at the time of test request. Test positivity after a DCT notification was significantly lower, and exposure-test intervals after a DCT or MCT notification were longer, than for the above-mentioned other reasons for testing. Our data suggest that the impact of DCT and MCT on the SARS-CoV-2 epidemic in the Netherlands was limited. However, DCT impact might be enlarged if app use coverage is improved, contact-tracers are eliminated from the digital notification process to minimise delays, and DCT is combined with self-testing.
Gaussian denoising in spatial heterodyne interferograms by convolutional neural network

Spatial heterodyne spectral technology is a hyperspectral remote sensing technique. With the improvement in detection accuracy, new demands have emerged for denoising methods in spatial heterodyne interferograms. Convolutional neural networks (CNNs) is a currently hot research topic. they have unique advantages in extracting abstract features from data. In recent years, CNNs have demonstrated outstanding performance in the field of image denoising. In this paper, we construct a Spatial heterodyne interferograms denoise CNN(SHI-DnCNN) using batch normalization and residual learning. We utilize the trained SHI-DnCNN to denoise spatial heterodyne interferograms contaminated with Gaussian noise. The results show that SHI-DnCNN exhibits excellent Gaussian noise denoising capability for spatial heterodyne interferograms. Furthermore, we evaluate the denoising results using PSNR, SSIM, and residual spectra, further confirming the superior denoising performance of SHI-DnCNN. This work provides a new and effective solution for denoising spatial heterodyne interferograms.
Learning Latent Representation with Limited Labels for IoT Anomaly Detection

Abstract— Malware detection is a critical challenge in the current era, especially for IoT devices. Previous studies have applied analytic techniques to reduce data size and extract valuable information. However, most of these studies count on a considerable quantity of outliers to perform anomaly detection. In this paper, we propose an enhanced method (named FeaWAD*) that improves the data encoding strategy based on the FeaWAD network [1]. These models require only a small fraction of anomalies for training. We evaluate the FeaWAD* method on the N-BaIoT dataset with various test scenarios for detecting known attacks as well as unknown future attacks. The experimental results demonstrate that the FeaWAD* method outperforms the original model FeaWAD and other popular anomaly detection methods such as Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine. We also measure the time-based effectiveness of the model to assess its practical applicability.
Using Machine Learning: Consumer Attitudes Toward the New Facebook Currency

As digital currencies continue to take center stage in the world’s financial system, IT behemoths are more interested than ever in how they can alter established payment methods. Facebook, a popular social media network, developed its own cryptocurrency called Libra as one of these initiatives with the goal of revolutionizing how people exchange and hold value online. This study uses cutting-edge machine learning techniques to assess a dataset of user emotions in order to examine consumer attitudes around the launch of this new Facebook money. A survey-based methodology was used to collect information from a simple random sample of Palestinian consumers (302 responses). The results of this study suggested that, in some situations, consumers have favorable attitudes about the new Facebook currency, which we covered in more depth throughout this paper. Policymakers, financial institutions, and technology businesses can learn important lessons from this study's findings about consumer attitudes about digital currencies and their potential effects on conventional financial systems. Understanding the elements that influence consumer attitudes of a digital currency within the context of a well-known social media site like Facebook may help to build successful implementation methods, fix issues, and promote universal adoption.
A novel indirect optical method for rock stress measurement using micro-deformation field analysis

Stress measurement plays a crucial role in geomechanics and rock engineering, especially for the design and construction of large-scale rock projects. This paper presents a novel method, based on the traditional stress relief approach, for indirectly measuring rock stress using optical techniques. The proposed method allows for the acquisition of full-field strain evolution on the borehole's inner wall before and after disturbance, facilitating the determination of three-dimensional (3D) stress information at multiple points within a single borehole. The study focuses on presenting the method's theoretical framework, laboratory validation results, and equipment design conception. The theoretical framework comprises three key components: the optical imaging method of the borehole wall, the digital image correlation (DIC) method, and the stress calculation procedure. Laboratory validation tests investigate strain field distribution on the borehole wall under varying stress conditions, with stress results derived from DIC strain data. Remarkably, the optical method demonstrates better measurement accuracy during the unloading stage compared to conventional strain gauge methods. At relatively high stress levels, the optical method demonstrates a relative error of less than 7% and an absolute error within 0.5 MPa. Furthermore, a comparative analysis between the optical method and the conventional contact resistance strain gauge method highlights the optical method's enhanced accuracy and stability, particularly during the unloading stage. The proposed optical stress measurement device represents a pioneering effort in the application of DIC technology to rock engineering, highlighting its potential to advance stress measurement techniques in the field.
Privacy Preserving Provable Data Possession: A FHE-based Hash Approach

The data privacy nowadays is of importance for every user of cloud service. Meanwhile, proof of data possession (PDP) is also a well-known security issue. In our work, we propose a PDP framework that contains 1) a PDP procedure combine with Fully Homomorphic Encryption (FHE) over Hash (such as MD5 and SHA256) that has capability to check integrity of single file efficiently. 2) A FHE merkle hash tree uses FHE hash function to check the integrity of multiple files under encryption. The proposed approach has been implemented using TFHE library. In the experiment stage, though it takes longer time to do our PDP process, the time complexity is polynomial and thus it is an usable work.
Pengelolaan Arsip Dalam Meningkatkan Tertib Administrasi Di Uin Sayyid Ali Rahmatullah Tulungagung

UIN Sayyid Ali Rahmatullah Tulungagung encounters obstacles in its archival management due to a shortage of human resources. The objective of this research is to illustrate how the management of archives can be improved to promote better administrative structure at UIN Sayyid Ali Rahmatullah Tulungagung and identify the challenges it faces. This study utilizes a descriptive approach to qualitative research methodology. Data collection involves observation, interviews with one archivist and two archive users, and documentation. The validity of the data is ensured through the triangulation technique. The research findings reveal that archive management at UIN Sayyid Ali Rahmatullah Tulungagung operates effectively, adopting a decentralized approach where each faculty assumes responsibility for receiving, recording, distributing, and retrieving archives. The implementation of digital systems such as Sistem Informasi Tata Naskah Dinas (Si-TANDA) has made a significant contribution to maintaining well-organized administration. In the future, UIN Sayyid Ali Rahmatullah Tulungagung needs to prioritize the augmentation of its human resources.
Intelligent Evaluation and Optimization of Postgraduate Education Comprehensive Ability Training under the Mode of “One Case, Three Systems”

This study aims to explore the intelligent evaluation and optimization methods for the comprehensive ability training of graduate students under the mode of "one case, three systems" to improve the quality and effect of graduate training. Firstly, a weighted clustering algorithm for mixed attributes is designed. Secondly, an evaluation model of postgraduate training quality based on sampling method and ensemble learning is established. Finally, the algorithm's and the model's performance are compared and tested. The test results show that with the increase in the number of experiments, the accuracy of the proposed weighted clustering algorithm can reach more than 90%, which is improved by 10%. The average number of iterations is 276, and the accuracy and F1 value can achieve the highest level with fewer iterations and stable algorithm performance. Compared with the R1 model, F1 and the accuracy of the model proposed in this study are enhanced by 3.29% and 6.75%, respectively. The feature-weighted clustering algorithm and the training quality evaluation model designed here complement each other and jointly construct a more elaborate and comprehensive training system. The feature-weighted clustering algorithm oriented to mixed attributes for the first time combines sampling methods and ensemble learning in the education ability training. Moreover, a multi-dimensional and intelligent postgraduate training evaluation framework is constructed, which provides a new idea for improving the quality of postgraduate training.
Measuring customer satisfaction of an e-commerce company based on opinion mining using SVM algorithm, CSI and IPA

This study was carried out to examine the perceived service quality of an e-commerce company app users and the measures that may be taken to improve customer satisfaction. The analysis techniques used were opinion mining to analyse reviews on the Google Play Store, e-SERVQUAL and Customer Satisfaction Index (CSI) to assess customer satisfaction, and Importance Performance Analysis (IPA) to identify elements that require improvement. According to the findings of opinion mining, 65.86% of e-commerce company application users were satisfied, while 34.14% were dissatisfied. Customer satisfaction levels were classified as satisfied for the dimensions of efficiency and reliability, and fairly satisfied for the dimensions of fulfilment, compensation, and responsiveness, based on questionnaire distribution. The Importance Performance Analysis method was used to determine the priority for service quality improvement. Findings showed that delivery time, product suitability, refund and return policy, and service time were the top priorities for service improvement.
How did the Teachers Talk? Investigating Politeness Strategies in Teachers’ Utterances during Classroom Interactions in English Subject

&lt;p&gt;Analyzing politeness strategies in classroom interaction can reveal the communications between teachers and students during classes. Furthermore, it can reflect the relationship of both parties and thus can be used as a mean to improve the quality of the teaching-learning process. This research is conducted to analyze the types of politeness strategies found in the teachers’ utterances in English teaching-learning process. Observations are conducted in two English classes at one of public schools in Karanganyar, Central Java, Indonesia. The subjects of the research are two English teachers. The data were analyzed using data reduction, data display, conclusion, and verification. The data were classified under the theory of politeness strategies as proposed by Brown and Levinson. The results revealed that the English teachers performed four types of politeness strategies with a total of 239 data. The different dominant politeness strategies from the two teachers showed that the intentions aimed by the teachers in classroom interactions are different. The first teacher showed the use of bald on record strategy as the dominant strategy showing the emphasis on directness and the use of simple utterances. Meanwhile, the second teacher tended to use politeness strategy to show closeness and friendliness to the students.&lt;/p&gt;
Insight of Yoga Nidra: Scientific Basis

When awareness is separate and distinct from mental activity, When waking, dream and deep sleep pass like clouds, Yet awareness of Self remains, This is the experience of total relaxation…That is why, in tantra, Yoga Nidra is said to be the doorway to Samadhi.1”- Swami Satyananda
Initial Progress of Identification of the Appropriate NLP Technique for Content Evaluation in Textual Conversations of People Infected by Sars-Cov-2

When Covid-19 became a pandemic on March 2020, an urgent need arose for reliable info and advice, so Virtual Assistants were created to help teach the public how to avoid the Alpha variant. But when new variants like Beta, Delta, and Omicron appeared with different symptoms, they caused new waves of infections and deaths. To tackle this, a Natural Language Processing prototype was created to analyze experiences of 4422 people, who had been infected in Ecuador, and to detect which symptoms were most common in their conversations. For this purpose, Python language was used, Google Collab platform, and several combinations of text processing techniques with various classifiers were tested. Finally, the results were measured using quality metrics, accuracy, precision, Recall, F1, to identify the most appropriate model, finding that the combination of Stop Word, Tokenization, stemming techniques together with the LSTM classifier reached high effectiveness among the options tested for a classifier model with multi-label output.
SIGNRL: A Population-Based Reinforcement Learning Method for Continuous Control

In engineering processes that require continuous control, it is common to face significant challenges. Addressing these challenges through explicit modeling can take much work and effort. For this reason, Reinforcement Learning (RL) has gained popularity as a feasible strategy for solving this problem. In this context, various value-based methodologies, policies, or combinations have been employed to obtain an optimal learning policy. However, problems such as convergence to local maxima and high variance in training persist. In addition, computational time and cost increase in complex environments, so more robust RL methodologies are required. This paper proposes a Swarm Intelligence Guided Neural Reinforcement Learning (SIGNRL) algorithm, which uses Particle Swarm Optimization as a multi-agent parameter explorer to find the optimal policy. Numerical results obtained in the OpenAI Gym Cart-Pole environment show that SIGNRL, with its gradient-free learning, exhibits good convergence and lower variance in continuous control tasks.
Human-Inspired Audiovisual Inducement of Whole-Body Responses

For service humanoid robots, it is crucial to design human-robot interaction behaviors that avoid eliciting negative responses from users. Drawing inspiration from the natural human response of turning attention when hearing one's own name, this paper introduces a system that enables users to effortlessly capture a robot's attention, fostering a more natural and intuitive interaction. The proposed system consists of an integrated audio-visual perception system and a sophisticated whole-body controller, effectively merging audio and vision data to stimulate comprehensive and coordinated robot motion during human robot interaction scenario. The system has been successfully implemented and tested on the service humanoid robot GARMI. The resultant whole-body motion emulates the natural human process of localizing sound and visual cues, enabling the robot to track and follow the user who initiated the interaction. Moreover, the system's capabilities have been extended to include an object-reaching use-case, effectively demonstrating the versatility of the whole-body controller. To enhance user-friendliness, we have incorporated a natural language command interface, allowing users to effortlessly control the activation of the proposed system while serving as an attention-switching mechanism. Moreover, the robot employs an intuitive audio-visual feedback mechanism, offering transparency about its current state to users. Lastly, the system's performance has been rigorously evaluated through a series of experiments, confirming its effectiveness and reliability.
Efficiency Analysis of Microservices Based on Queueing Models

This study proposes an efficiency analysis framework based on queuing models to analyze the efficiency difference of breaking down traditional large services into n microservices. For generalization, this study considers different service time distributions (e.g. exponential distribution of service time and fixed service time) and explores the system efficiency in the worst-case and best-case scenarios through queuing models (i.e. M/M/1 queuing model and M/D/1 queuing model). In each experiment, it was shown that the total time required for the original large service was higher than that required for breaking it down into multiple microservices, so breaking it down into multiple microservices can improve system efficiency. Therefore, this study's findings can serve as a reference guide for future development of microservice architecture.
Evaluation and Analysis of Standard Security Techniques in V2X Communication: Exploring the Cracking of ECQV Implicit Certificates

The IEEE 1609.2 and 1609.2.1 standards have used several security techniques based on elliptic curve cryptography (ECC) for vehicle-to-everything (V2X) Communication. For improving the performance of security credential management system (SCMS), this study evaluates the computation time of key generation, key expansion, signature generation, and signature verification under different security strengths. The lengths of uncompressed elliptic curve (EC) points, compressed EC points, explicit certificates, and implicit certificates based on elliptic curve Qu-Vanstone (ECQV) are analyzed and discussed. Furthermore, a mathematical model is proposed to prove the ECQV cracking probability, and suggestions are given for avoiding the potential threats of ECQV cracking.
Pull Requests Acceptance: A Study Across Programming Languages

Context: The pull-based development is a modern way to support distributed software development, helping to produce high-quality software with increased involvement from the software development community. Objectives: We investigate the effect of source code quality on Pull Request (PR) acceptance in different programming languages, looking at the quality flaws that can be more relevant. Method: We mine software repositories to analyze over 40K PRs from 100 open-source projects in five different programming languages: Python, Java, Kotlin, Haskell, and C/C++. The code quality of the individual PRs was evaluated using static code analysis. Quality flaws were inputted into classification models to predict PRs acceptance and evaluate the fitting. Results: There is a low impact of code quality on PRs acceptance. No major quality flaws can be used to predict reliably PRs acceptance. Conclusion: Source code quality plays a marginal role in accepting PRs in the analyzed projects. Additional factors might impact the acceptance of PRs, such as the reputation and popularity of the submitters.
Theory of Evolutionary Systems Engineering

Evolutionary approaches to engineering design involve generating populations of candidate solutions that compete via a selection process iteratively, to improve measures of performance over many generations. Although the attractive properties of biological evolutionary systems have motivated researchers to investigate emulating them for engineering design, there has been an emphasis on using encodings of the technical artefacts themselves, rather than encoding a complete bio-inspired system which is capable of producing such artefacts. It is the latter approach which is the subject of this contribution: how might a bio-inspired system be designed that self-organises the process of engineering design and manufacture? To make progress in the application of evolutionary processes to problems in engineering design, the evolutionary model must encompass the complexity of systems engineering. A new theory of evolutionary systems engineering is presented, based on von Neumann's Universal Constructor Architecture (UCA), drawing from more recent understanding of biology and applying the resulting system to the task of engineering design. It demonstrates how individual bioinspired algorithms fit into a coherent whole, and how they can be combined to drive open-endedness in automated design. The resulting system provides a common language for multidisciplinary applications in generative design, whereby industrial systems engineering approaches can be developed using principles from the UCA for the first time.