Reviewerly/exHarmony huggingface dataset:
Energy efficient cloud computing using secure virtual machine migration: A taxonomy

Numerous cloud service enterprises have launched a broad variety of cloud services. Several businesses and people use these programs on a daily basis. The efficiency of cloud service depends on efficiency of the service providers. It is also important for cloud customers to analyses and review the available cloud resources thoroughly to pick the best cloud service. Secure virtual machine placement is a big issue during the migration of virtual machines in cloud computing. Secure virtual machine migration maximizes the throughput and reliability of the system and reduces Service Level Agreement (SLA) violations. This article focuses on secure virtual machine migration with energy-efficient techniques
A design thinking approach to creating explainable machine learning clinical decision support tools (Preprint)

<sec> <title>BACKGROUND</title> Though there has been considerable effort to implement machine learning methods for healthcare, clinical implementation has lagged. Incorporating explainable machine learning methods through the development of a decision support tool using a design thinking approach is expected to lead to greater uptake of such tools. </sec> <sec> <title>OBJECTIVE</title> The objective of this work was to explore how constant engagement of clinician end-users can address the lack of adoption of ML tools in clinical contexts due to their lack of transparency, and address challenges related to presenting explainability in a decision support interface. </sec> <sec> <title>METHODS</title> A Design Thinking framework was proposed that can be applied when developing an ML-based clinical decision support tool that incorporates explainability. The framework was then used in the development of a clinician-facing interface for the quantification of COVID-19 severity from chest x-ray images. We tested the resulting prototype with clinicians to verify domain appropriate representation, potential actionability, and consistency of the tool. </sec> <sec> <title>RESULTS</title> During testing with clinicians, there was an overall positive reception concerning the design, usefulness, and applicability of the tool. Results show that using the proposed approach can improve clinician’s receptivity of clinical decision support tools. </sec> <sec> <title>CONCLUSIONS</title> The Design Thinking approach was augmented by using frameworks to help inform the development focus and direction, and to continuously evaluate the design on elements that would improve clinician trust. The approach outlines a direction for machine learning experts, user experience designers, and clinician end users on how to collaborate in the creation of trustworthy and usable XML based clinical decision support tools. </sec> <sec> <title>CLINICALTRIAL</title> N/A </sec>
Envelope peak detection algorithm based on the CEEMDAN in white light interferometry

White light scanning interferometry (WLSI) is a key non-contact three-dimensional (3D) morphometry method. The traditional envelope peak detection (EPD) algorithm is a typical WLSI surface recovery algorithm without phase calculation, but its accuracy is susceptible to noise and fitting errors. In this paper, we propose an improved EPD algorithm based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to solve the above problem. The proposed algorithm can accurately locate the position of the envelope peak. The simulation and experiment results demonstrate that the proposed method is effective in improving the accuracy of the measurements.
Analysis and Synthesis of Music

Music and sound inSynthesis general areAnalysis pressure wavesPressure waves travelling through some medium, air most commonly. Waves are studied by physics, and can be represented, analyzed, approximated, codified, compressed, reduced, and synthesized with mathematics. These are the ideas discussed in this chapter.
A Republican Conception of Counterspeech

Abstract ‘Counterspeech’ is often presented as a way in which individual citizens can respond to harmful speech while avoiding the potentially coercive and freedom-damaging effects of formal speech restrictions. But counterspeech itself can also undermine freedom by contributing to forms of social punishment that manipulate a speaker’s choice set in uncontrolled ways. Specifically, and by adopting a republican perspective, this paper argues that certain kinds of counterspeech can dominate when they contribute to unchecked social norms that enable others to interfere arbitrarily with speakers. The presence of such domination can pose just as much a threat to freedom of speech as unchecked formal restrictions by threatening an individual’s discursive status, revealing a problem for those who defend counterspeech as a freedom-protecting alternative. Rather than rejecting both counterspeech and legislation outright, however, this paper argues that the republican principle of parsimony ought to be exercised when deciding on appropriate harmful speech response. While the principle of parsimony allows for suitably-checked formal punishment for some of the most egregious forms of harmful speech, citizen-led counterspeech must be guided by a reliable set of norms against the use of social punishment where those who do engage in social punishment face certain costs. The presence of robust, widely-known, and reliable norms thus supports both formal and informal responses to harmful speech while maintaining a secure discursive status for all.
Phishing Website Detection Using Machine Learning

Abstract: The growing internet user base and reliance on online platforms have led to a growing concern of phishing attacks. Conventional anti-phishing techniques struggle to keep up with evolving tactics. This research proposes a novel approach using machine learning algorithms to combat phishing attacks in real-time. The dataset includes legitimate and phishing websites, with various attack vectors and strategies. Data preprocessing, feature engineering, and machine learning models are trained on the dataset. The proposed approach achieves high accuracy and outperforms traditional rule-based methods. The ensemble models exhibit superior performance in handling both known and unseen phishing attacks. The real-time nature of the system allows for swift adaptation to new and emerging phishing techniques. The system's low computational overhead ensures seamless operation on various platforms without performance degradation.
Women Empowerment through Mobile Phone: Case Study of Bangladesh

Telecommunication particularly Mobile technology is one of the most powerful tools worldwide. Mobile phones play a foremost part in development programs around the world. The diffusion of mobile phones performs a great role in development programs along with overall empowerment in developing countries. Telecommunication is most powerful to combat poverty and give the people a chance to change their fate, their economy, their society and to empower themselves to change the world, especially for women. Large sections of women in Bangladesh are living in rural areas. The majority of them are still tradition-bound and are in a disadvantageous position. Because of inequality in women's access to and participation in all communication systems and their lack of sufficient mobility to promote their contribution to society, women are deprived in every aspect of life. Mobility is a very important factor for women's empowerment. The Mobile phone revealed the opportunities and freedom for women to make their own choices,improve their social network, change their attitudes and change their life. The objective of the study is to identify the impacts of mobile phones on empowering women of Bangladesh. Some successful case studies of women empowerment in Bangladesh are also discussed in the study.
Digital-Based Database Governance Development Management with Master Data Management UIN K.H. Abdurrahman Wahid Pekalongan

Academic and Student Service Management (AKMA) expects that there will be an information system data management that can be accessed by all parties. The research aims to analyze the influence of database governance development management and digitization on master data management. The research design is a positivistic quantitative descriptive approach with multiple regression analysis. The research population is all Lecturers, Students, AKMA Sections, UTIPD Sections, and LPPM. The sampling technique is the percentage of cluster random sampling. Research results the effect of database governance on master data management is 93%, and the effect is positive and significant. The effect of digitalization on master data management is 52.8%, the effect is positive and significant. The regression equation Y=0.930X1+0.528X2 with R2=0.875. Suggestion: AKMA can properly apply a digital-based database governance development management system with master data management.
Generalized implicit-key attacks on RSA

RSA (Rivest–Shamir–Adleman) is a fundamental algorithm in information security for public key cryptography. Recently, a novel attack scenario of RSA with two implicitly correlated private keys, i.e., implicit-key attack was formulated. The lattice-based cryptanalytic strategy was proposed to factor RSA moduli using given implicit hints referring to known quantities of unknown common bits distributed among unknown private keys. In this paper, we review the simple basic scenario in which two RSA instances share known amounts of MSBs (most significant bits) and LSBs (least significant bits). We extend it to a more complex situation, where the amounts of MSBs and LSBs shared along with a few common blocks of middle bits are known. In addition, based on the above theoretical analyses, we present a generalized implicit-key attack framework. Our results disclose the vulnerability of RSA using correlated private keys with implicit information. Furthermore, numerical computer experiments are conducted to assess the validity of basic and extended implicit-key attacks.
Compressed gate characterization for quantum devices with time-correlated noise

As quantum devices make steady progress towards intermediate scale and fault-tolerant quantum computing, it is essential to develop rigorous and efficient measurement protocols that account for known sources of noise. Most existing quantum characterization protocols such as gate set tomography and randomized benchmarking assume the noise acting on the qubits is Markovian. However, this assumption is often not valid, as for the case of 1/f charge noise or hyperfine nuclear spin noise. Here, we present a general framework for quantum process tomography (QPT) in the presence of time-correlated noise. We further introduce fidelity benchmarks that quantify the relative strength of different sources of Markovian and non-Markovian noise. As an application of our method, we perform a comparative theoretical and experimental analysis of silicon spin qubits. We first develop a detailed noise model that accounts for the dominant sources of noise and validate the model against experimental data. Applying our framework for time-correlated QPT, we find that the number of independent parameters needed to characterize one and two-qubit gates can be compressed by 10x and 100x, respectively, when compared to the fully generic case. These compressions reduce the amount of tomographic measurements needed in experiment, while also significantly speeding up numerical simulations of noisy quantum circuit dynamics compared to time-dependent Hamiltonian simulation. Using this compressed noise model, we find good agreement between our theoretically predicted process fidelities and two qubit interleaved randomized benchmarking fidelities of 99.8% measured in recent experiments on silicon spin qubits. More broadly, our formalism can be directly extended to develop efficient and scalable tuning protocols for high-fidelity control of large-arrays of quantum devices with non-Markovian noise.
FNReq-Net: A hybrid computational framework for functional and non-functional requirements classification

Requirements classification is a key component of software development life cycle. It enhances our understanding about project requirements, which in turn enables us to effectively identify and mitigate risks that could lead to project failure. Existing requirements classification predictors do not utilize feature selection methods competence in their predictive pipelines and lack in performance. To empower the process of automatic requirements classification, contributions of this paper are manifold. Firstly, it explores the potential of 7 filter-based feature selection techniques and 11 traditional machine learning classifiers. Secondly, for the first time it investigates combined potential of traditional feature selection and 9 diverse types of deep learning predictors. Thirdly, it presents a hybrid computational predictor namely FNReq-Net that reaps combine benefits of traditional feature selection and a novel deep learning predictor based on attention mechanism. Over two public benchmark datasets, large-scale experimental results reveal feature selection not only improves predictive performance of traditional machine learning predictors, but it also improves performance of deep learning predictors. The proposed FNReq-Net predictor outperforms state-of-the-art functional and non-functional requirements classification predictors by 4% and 1% in terms of F1-score over Promise and Promise-exp datasets, respectively.
Topic Modelling Using VSM-LDA For Document Summarization

Summarization is a process to simplify the contents of a document by eliminating elements that are considered unimportant but do not reduce the core meaning the document wants to convey. However, as is known, a document will contain more than one topic. So it is necessary to identify the topic so that the summarization process is more effective. Latent Dirichlet Allocation (LDA) is a commonly used method of identifying topics. However, when running a program on a different dataset, LDA experiences "order effects", that is, the resulting topic will be different if the train data sequence is changed. In the same document input, LDA will provide inconsistent topics resulting in low coherence values. Therefore, this paper proposes a topic modelling method using a combination of LDA and VSM (Vector Space Model) for automatic summarization. The proposed method can overcome order effects and identify document topics that are calculated based on the TF-IDF weight on VSM generated by LDA. The results of the proposed topic modeling method on the 1300 Twitter data resulted in the highest coherence value reaching 0.72. The summary results obtained Rouge 1 is 0.78, Rouge 2 is 0.67 dan Rouge L is 0.80.
Implementasi Metode RAD Untuk Pendaftaran Lowongan Kerja Melalui Bursa Kerja Berbasis Website

The system of recruiting workers in schools through special job fairs (BKK) is considered to be ineffective because the provision of job vacancies to alumni still uses information manually, such as using wall magazines as a place to post job vacancies and using social media such as Facebook as a means of information. Job opening is also considered less efficient due to limited users limited by social media itself. Registration is also done manually by alumni who come directly to BKK to register, and the vacancy data collection is still done manually, so it takes longer time to process registration data. The purpose of this research is to build a job vacancy registration system application through the BKK website based on the website to make it easier for BKK to provide job vacancy information and make it easier to recruit workers. This application development study uses the Rapid Application Development (RAD) method. Test the app using system black box and user usability testing methods.
The Impact of Instinct Motivation on Job Satisfaction: Analytical Study

The purpose of this research is to investigate the impact of instinct motivation on job satisfaction in private banks in Erbil.&nbsp; A quantitative research method via a questionnaire was applied to measure the current study.&nbsp; The researcher selected 18 private banks in Erbil city, however each bank had approximately 35 employees therefore the target researcher population was 630 employees.&nbsp; Researchers sent out 125 surveys but only received 112 back, of which only 98 were really filled out. The study used SPSS to analyze and measure the developed research hypothesis which stated that instinct motivation has a significant influence on job satisfaction. The findings revealed that there is a positive and significant influence of instinct on job satisfaction. The study recommended for the future study to obtain larger sample size to measure similar studies.
Hand interaction designs in mixed and augmented reality head mounted display: a scoping review and classification

Mixed reality has made its first step towards democratization in 2017 with the launch of a first generation of commercial devices. As a new medium, one of the challenges is to develop interactions using its endowed spatial awareness and body tracking. More specifically, at the crossroad between artificial intelligence and human-computer interaction, the goal is to go beyond the Window, Icon, Menu, Pointer (WIMP) paradigm humans are mainly using on desktop computer. Hand interactions either as a standalone modality or as a component of a multimodal modality are one of the most popular and supported techniques across mixed reality prototypes and commercial devices. In this context, this paper presents scoping literature review of hand interactions in mixed reality. The goal of this review is to identify the recent findings on hand interactions about their design and the place of artificial intelligence in their development and behavior. This review resulted in the highlight of the main interaction techniques and their technical requirements between 2017 and 2022 as well as the design of the Metaphor-behavior taxonomy to classify those interactions.
To me, to you: Towards Secure PLC Programming through a Community-Driven Open-Source Initiative

Over the last decade, industrial control systems (ICS) have experienced an increasing frequency of cyber attacks. At the heart of these systems are programmable logic controller (PLC), responsible for the monitoring, control, and automation of physical operational processes. As an increasing number of adversaries are attaining the capability to gain a foothold in ICS environments, with the goal of operational process manipulation, PLCs are becoming a primary target. Unlike conventional IT software, PLCs are programmed via unique industrial languages and the notion of secure PLC programming practices is in its infancy. This has led to vulnerabilities within the very logic PLCs use to interact with the physical world, notably in code provided by vendors, which is proprietary and unable to be viewed or edited to implement secure programming practices. These vulnerabilities then affords adversaries an attack surface to achieve their goals. In this positional paper, a conceptual framework is introduced positing the notion of a communitydriven hub. This hub incorporates a set of processes that draw from existing literature, to provide secure, verified, open-source PLC code. The goal of which is to not only provide PLC programmers with a convenient alternative to vulnerable vendor provided libraries, but increase the awareness and importance of secure PLC programming practices.
On Tracing Screen Photos-A Moiré Pattern-based Approach

Cyber-theft of trade secrets has become a serious business threat. Digital watermarking is a popular technique to help identify the source of the file leakage, whereby a unique watermark for each insider is hidden in sensitive files. However, malicious insiders may use smartphones to photograph the secret file displayed on screens to remove the embedded hidden digital watermarks due to the optical noises introduced during photographing. To identify the leakage source despite such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">screen-photo-based leakage attacks</i> , we leverage Moiré pattern, an optical phenomenon resulted from the optical interaction between electronic screens and cameras. As such, we present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mID</monospace> , a new watermark-like technique that can create a carefully crafted Moiré pattern on the photo when it is taken towards the screen. We design patterns that appear to be natural yet can be linked to the identity of the leaker. We implemented <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mID</monospace> and evaluated it with 7 display devices and 6 smartphones from various manufacturers and models. The results demonstrate that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mID</monospace> can achieve an average bit error rate (BER) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.2\%$</tex-math></inline-formula> and can successfully identify an ID with an average accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$98\%$</tex-math></inline-formula> , with little influence from the type of display devices, cameras, IDs, and ambient lights.
A Robust CycleGAN-L2 Defense Method for Speaker Recognition System

With the rapid development of voice technology, speaker recognition is becoming increasingly prevalent in our daily lives. However, with its increased usage, security issues have become more apparent. The adversarial attack poses a significant security risk to the speaker recognition model by making small changes to the input and thus causing the neural network model to produce an incorrect output. Nevertheless, there are currently limited defense techniques for speaker recognition models. To this end, we propose a robust CycleGAN-L2(CYC-L2) defense method. The method automatically adjusts the size of the dataset according to the learning of the generative adversarial networks on the dataset, and uses L2 loss functions to constrain the generative adversarial networks for better and faster training. In this paper, we will compare the effectiveness of defense against white-box attacks using existing defenses and the defenses proposed. The experimental results show that our defense method not only plays a better defense effect than the other defense methods mentioned under the x-vector model but also does not reduce the accuracy of benign examples in closed-set identification.