Reviewerly/exHarmony huggingface dataset:
Efficient sampling of noisy shallow circuits via monitored unraveling

We introduce a classical algorithm for sampling the output of shallow, noisy random circuits on two-dimensional qubit arrays. The algorithm builds on the recently-proposed "space-evolving block decimation" (SEBD) and extends it to the case of noisy circuits. SEBD is based on a mapping of 2D unitary circuits to 1D {\it monitored} ones, which feature measurements alongside unitary gates; it exploits the presence of a measurement-induced entanglement phase transition to achieve efficient (approximate) sampling below a finite critical depth $T_c$. Our noisy-SEBD algorithm unravels the action of noise into measurements, further lowering entanglement and enabling efficient classical sampling up to larger circuit depths. We analyze a class of physically-relevant noise models (unital qubit channels) within a two-replica statistical mechanics treatment, finding weak measurements to be the optimal (i.e. most disentangling) unraveling. We then locate the noisy-SEBD complexity transition as a function of circuit depth and noise strength in realistic circuit models. As an illustrative example, we show that circuits on heavy-hexagon qubit arrays with noise rates of $\approx 2\%$ per CNOT, based on IBM Quantum processors, can be efficiently sampled up to a depth of 5 iSWAP (or 10 CNOT) gate layers. Our results help sharpen the requirements for practical hardness of simulation of noisy hardware.
Building Self-Confidence in English Public Speaking through YouTube? Why Not?

&lt;p&gt;&lt;span lang="EN-US"&gt;Self-confidence plays a pivotal role in public speaking, especially in English. Therefore, building students’ self-confidence is needed by using media including YouTube. The present study aims to elaborate on the role of YouTube in developing students’ self-confidence in English public speaking viewed from the student’s perspective. Further, some strategies used in developing students’ self-confidence are discussed. Using a qualitative method, the data of this study were gathered through a semi-structured interview with six students from the English Education Department of an Indonesian Islamic university. The findings reveal that most students have positive responses towards the use of YouTube as a medium to develop their self-confidence in English public speaking. In addition, they reported that YouTube plays a very crucial role in the development of their confidence in English public speaking. Importantly, they can comprehend the material more quickly, and develop their interest and motivation to learn. Finally, this study promotes the need for socialization from educational institutions in the development of English public speaking and the need to integrate the content of English public speaking with media and technology to enhance students’ learning success.&lt;/span&gt;&lt;/p&gt;
Enhancing Arabic Sentiment Analysis in E-Commerce Reviews on Social Media Through a Stacked Ensemble Deep Learning Approach

Sentiment analysis (SA) employs natural language processing techniques to extract opinions from textual data.Applying SA to the Arabic language presents numerous challenges, including ambiguity, the presence of multiple dialects, a need for additional resources, and morphological variation.The domain of Arabic SA has witnessed significant advancements with the application of deep learning (DL) approaches, such as convolutional neural networks (CNNs).The performance of single DL models has been further improved by hybrid models combining CNNs with bidirectional long short-term memory (Bi-LSTM) or bidirectional gated recurrent units (Bi-GRU).It is anticipated that the accuracy of these DL models can be enhanced through stacked deep learning ensembles.In this study, a stacked ensemble approach is proposed that accurately predicts Arabic sentiment by leveraging the predictive capabilities of CNN, Bi-GRU, Bi-LSTM, and hybrid DL models (CNN-Bi-GRU and CNN-Bi-LSTM).The proposed model's efficacy is evaluated using four extensive datasets: the HARD dataset, the BRAD dataset, the ARD dataset, and a real dataset composed of 71,583 Arabic reviews.Experimental results demonstrate the suitability of the proposed model for analyzing sentiments in Arabic texts.The method's first step involves feature extraction using the AraBERT model.Subsequently, five DL models are developed and trained, including CNN, Bi-GRU, Bi-LSTM, a hybrid CNN-Bi-GRU model, and a hybrid CNN-LSTM model.Finally, the outputs of the base classifiers are concatenated using the multilayer perceptron algorithm.Our approach achieves an improved accuracy of 0.9256 compared to basic and hybrid deep learning methods.
Enhancement of ChatGPT using API Wrappers Techniques

This study looks at how API (Application Programming Interface) wrapper technology can make it easier to use complex functions by putting together a lot of API calls. These packages have non-real-time interfaces that are hard to use. ChatGPT is a chatbot-specific GPT-3 language paradigm. It lets developers create chatbots that respond intelligently to natural language user input, creating a more engaging user experience. This article shows that ChatGPT, Python, and API wrapper technology may be used to develop a smart chatbot. We show how to use the OpenAI API library to add ChatGPT to Python programs. This makes it easier for developers to make chatbots that sound and act more like real people when they talk. Our contribution to this field is showing that it is possible to make smart chatbots with ChatGPT and API wrapper technology. To reach this goal, we use a system that combines the OpenAI API with ChatGPT and Python. This gives us valuable information about how to make smart chatbots. The efficiency of the system has been tested many times while applying it to different environments, and the results are satisfactory.
Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints

Purpose: We consider the problem of route planning of multiple rechargeable heterogeneous UAVs with multiple trips under mission time and payload carrying constraints. The goal is to determine the types and number of UAVs to be deployed and their flying paths that minimizes the monetary cost, which is a sum of the recharging energy cost of each UAV, the UAV rental cost, and the cost of violating the mission time deadline.Design/methodology/approach: The problem is formulated as a mixed integer programming (MIP). Then, the genetic algorithm (GA) is developed to solve the model and the solutions are compared to those obtained from the exact method (Branch-and-Bound). Novel chromosome encoding and population initializations are designed, and standard procedures for crossover and mutation are adapted to this work. Test problems on grid networks and real terrains are used to evaluate the runtime efficiency and solution optimality, and the sensitivity of GA parameters is studied based on two-level factorial experiments.Findings: The proposed GA method can find optimal solutions for small problem sizes but with much less computation time than the exact method. For larger problem sizes, the exact method failed to find optimal solutions within the limits of time and disk space constraints (24 hours and 500 GB) while the GA method yields the solutions within a few minutes with as high as 49% better objective values. Also, the proposed GA method is shown to well explore the solution space based on the variation of the total costs obtained.Originality/value: The unique aspects of this work are that the model optimizes the sum of three different costs – the electricity recharging cost, the UAV rental cost, the penalty cost for mission deadline violation, and the recharging period based on the remaining energy, the payload capacity, and the heterogeneity of UAVs are incorporated into the model. The model is formulated as a mixed integer programming and the genetic algorithm is developed to solve the program. Novel chromosome encoding and population initializations are designed, and standard procedures for crossover and mutation are adapted to this work.
Mobile Augmented Reality in Biological Education: Perceptions of Austrian Secondary School Teachers

Today's teachers play a critical role in preparing students for the integration of educational technologies, such as augmented reality (AR), into their lessons. It is thought that AR implementation improves collaboration, motivation, and learning outcomes. Considering this, this study aims to determine the teachers’ perceptions of the benefits and obstacles of employing mobile AR applications (mAR) in their biology education, along with suggestions for practice, app developers, and policymakers. Therefore, a mixed-methods study was used to examine Austrian secondary school biology teachers’ opinions. A questionnaire containing open-ended and closed-ended questions was distributed to 35 teachers. Descriptive statistics were employed to process quantitative data, whereas grounded theory was utilized to process qualitative data. According to the findings, biology teachers likely utilize mAR apps to teach about human anatomy or to identify living things (e.g., plant determination). According to the teachers, mAR can improve students’ learning outcomes, motivation, and collaboration, and further their enthusiasm for learning biology. The main obstacles that teachers encounter whilst integrating mAR into their lessons are lack of technical devices, Internet issues, inconsistency with the curriculum, and questionable scientific accuracy of information. Despite the promising results, additional future studies with larger sample sizes are needed.
Digital-Based Flash Card to Increase Social Studies Learning Outcomes for Elementary School Students in The Fourth Grade

The development of innovative learning media is needed to provide a different and varied learning experience. This is required to stimulate student interest in learning to achieve maximum learning outcomes. The aim of this study is to develop and analyze the effect of digital-based flashcard media to improve social studies learning outcomes for fourth-grade elementary school students. This research uses R&amp;D (Research and Development) methods and adapts the ADDIE development model. The population in this study were fourth-grade students. This study randomly took a sample of 126 fourth-grade students and calculated using the Slovin formula with a margin of error of 5%. The results of this study show that the results of material expert validation 83.3% in the worthy category. The results of media experts 93.3% in the very worthy category, and the results of the effectiveness of learning media are obtained through a questionnaire given to students at the pretest and posttest. This study concludes that digital-based flashcard media positively impacts social studies learning outcomes for fourth-grade elementary school students.
The Effect of Workplace Stress on Employee Engagement, the Mediation Role of Leadership Style

The aim of the current study was to investigate the impact of workplace stress on employee engagement within Iraqi universities through the mediator role of leadership style. Work-related stress and non-work-related stress were identified as representing workplace stress. Employee engagement has been measured through three types: social engagement, affective engagement, and intellectual engagement. Two types of leadership styles have been tested: directive and participative leadership. the structural equation Modeling was used to test the study model. A sample of 245 employees in Iraqi universities, including university professors, was targeted. Only 214 respondents participated in the survey. The results showed that non-work-related stress is the most influential factor in employee engagement. Furthermore, it has been found that the participative leadership style mediates the relationship between work-related and non-work-related stress and employee engagement. There is one exception; participative leadership did not mediate the impact of non-work-related stress on intellectual.
Multi-task deep cross-attention networks for far-field speaker verification and keyword spotting

Abstract Personalized voice triggering is a key technology in voice assistants and serves as the first step for users to activate the voice assistant. Personalized voice triggering involves keyword spotting (KWS) and speaker verification (SV). Conventional approaches to this task include developing KWS and SV systems separately. This paper proposes a single system called the multi-task deep cross-attention network (MTCANet) that simultaneously performs KWS and SV, while effectively utilizing information relevant to both tasks. The proposed framework integrates a KWS sub-network and an SV sub-network to enhance performance in challenging conditions such as noisy environments, short-duration speech, and model generalization. At the core of MTCANet are three modules: a novel deep cross-attention (DCA) module to integrate KWS and SV tasks, a multi-layer stacked shared encoder (SE) to reduce the impact of noise on the recognition rate, and soft attention (SA) modules to allow the model to focus on pertinent information in the middle layer while preventing gradient vanishing. Our proposed model demonstrates outstanding performance in the well-off test set, improving by 0.2%, 0.023, and 2.28% over the well-known SV model emphasized channel attention, propagation, and aggregation in time delay neural network (ECAPA-TDNN) and the advanced KWS model Convmixer in terms of equal error rate (EER), minimum detection cost function (minDCF), and accuracy (Acc), respectively.
Depositional Setting of the Shiranish Formation (Campanian-Maastrichtian) in Selected Sections from Northern Iraq

The Shiranish Formation (Campanian-Maastrichtian) was studied in two surface sections (Diana and Garuta) in Erbil Governorate. Both sections lie within the High Folded Zone of Zagros Fold-Thrust Belt in Iraq. Stratigraphically, the formation is divided into three lithostratigraphic units depending on the field characteristics with varied thicknesses of the formation in both sections. The lower unit (Unit A) consists of blue to grey well-bedded marly massive limestone, the middle unit (Unit B) is composed of blue to grey marl, marly limestone and stratified shale, and finally, the upper unit (Unit C) is composed of grey limestone and blue marly limestone. The lower contact of the formation is unconformable with the Bekhme Formation in the Diana section and conformable with the Kometan Formation in the Garuta section, while the upper contact is conformable with both Tanjero and Kolosh formations in Diana and Garuta sections respectively. X-ray diffraction analysis has revealed that calcite is the main mineralogical component with less of quartz and clay minerals as represented by chlorite and illite. Four lithofacies were distinguished (Marl facies (Sml), Shale facies (Ssl), Conglomerate limestone facies (Scl)) and red clay layers (Srl), whereas, three main microfacies were recognized; Fossiliferous Lime Mudstone Microfacies (Sml), Lime Wackestone Microfacies (Sw) and the Packstone Lime Microfacies (Sp), which in turn were subdivided into seven secondary facies, according to their components, which are; Planktonic Foraminiferal Lime Wackestone Submicrofacies (Sw1), Keeled Planktonic Foraminiferal Lime Wackestone Submicrofacies (Sw2), Calcispheres Lime Wackestone Submicrofacies (Sw3), Bioclastic Lime Wackestone Submicrofacies (Sw4), Benthonic Foraminiferal Lime Wackestone Submicrofacies (Sw5), Keeled Planktonic Foraminiferal lime Packstone Submicrofacies (Sp1) and Planktonic Foraminiferal Globular Chamber Packstone Submicrofacies (Sp2 ). Based on these facies, the formation was deposited in a relatively deep marine environment and within two secondary environments; the outer shelf and upper bathyal environments as a result of the widespread transgression in the beginning of the Campanian.
Optimizing University Plans through Information System Strategic Planning

This study is motivated by the policy of a university related to Information System (IS) or Information Technology (IT) which was prepared without careful planning so that the information system that had been built did not fully support the university’s plan according to the university strategic plan. Therefore, the purpose of this research is to be able to implement an information system that is optimal and in line with the university strategic policies, so strategic planning of IS is needed. This is done so that the investment spent on IS development is accordance with the needs and can provide measurable benefits in accordance with the goals and objectives of the university. This study uses PEST, SWOT, Value Chain, and Critical Success Factor analysis. Then determine the IS/IT strategic plan and it is expected to produce outputs in the form of IS/IT strategic planning proposals at Campus X. Therefore, based on the results of the McFarlan Strategic Grid mapping which states that of the 6 existing IS applications, none of the applications are in the Strategic quadrant and the High Potential quadrant. The result of the IS/IT strategic planning shows that there are 12 new IS applications, the development of new network infrastructure as well as the addition of new human resources.
A Cost Sensitive SVM and Neural Network Ensemble Model for Breast Cancer Classification

Breast Cancer has surpassed all categories of cancer in incidence and is the most prevalent form of cancer in women worldwide. The global incidence rate is seen to be highest in the country of Belgium as per statistics of WHO. In the case of developing countries specifically, India, it has overtaken other cancers and stands first in incidence and mortality. Major factors identified as impacting the prognosis and survival in the country is chiefly the late diagnosis of the disease and diverse situations prevailing in different parts of the country including lack of diagnostic facilities, lack of awareness, fear of undergoing existing procedures and so on. This is also true for many other countries in the world. Early diagnosis is a vital factor for survival. The implementation of machine learning techniques in cancer prediction, diagnosis and classification can assist medical practitioners as a supplementary diagnostic tool. In this work, an ensemble model of a polynomial kernel-based Support Vector machines and Gradient Descent with Momentum Back Propagation Artificial Neural Networks for Breast Cancer Classification is proposed. Feature selection is applied using Genetic Search for identifying the best feature set and data sampling techniques such as combination of oversampling and undersampling and cost senstivke learning are applied on the individual Neural Network and Support Vector Machine classifiers to deal with issues related with class imbalance. The ensemble model is seen to show superior performance in comparison with other models producing an accuracy of 99.12%.
Multi-scale Avatars in a Shared Extended Reality Between AR and VR Users

This paper introduces multi-scale avatar sizes in Extended Reality (XR) allowing the avatars to shrink down into a small size or enlarge into a giant size. Collaborative interfaces are when one or more interfaces have merged into one shared environment with a stable network connection and the cloud-based interaction happened between users. The users from different locations or separate platforms were joining one room to interact. Multi-scale in collaborative MR is significant in some situations that required precise avatar measurements to interact and manipulate the content. However, the correct or appropriate scaling for multi-scale still remains unsolved, and very less people work to explore the method. Therefore, this chapter aims to produce a correct multi-scale technique between VR and AR users. This paper discusses the development of a multi-scale interaction between VR and AR users and implementation in a shared or remote application. The remote MR application involves different interaction techniques.
Implementing Kahoot! for Japanese Language Learning in Indonesian High School

Kahoot! is an individual and collaborative gamified learning platform that encourages curiosity, promotes lifelong learning, and makes learning enjoyable. Given the numerous advantages of using Kahoot! in language acquisition, several academic institutions have included gamification to enhance their learning experience. However, there are always issues and hurdles encountered throughout technology deployment. The present study aimed to analyze how the teacher implements Kahoot! in Japanese learning, the Indonesian high school teachers and students' perceptions of using Kahoot! and the constraints they encountered in using Kahoot! The study was designed as qualitative research, enrolling one Japanese language teacher and 34 students in a state senior high school. Semi-structured interviews, non-participatory observation, and a mixed questionnaire were used to collect data. Despite specific problems in the teacher's implementation of Kahoot!, the research indicated that both the teacher and the students had an "excellent" impression of the platform, with a poor internet connection being recognized as the biggest stumbling block to their utilizing Kahoot! efficiently. While Kahoot! may be a realistic alternative for inspiring high school students to learn Japanese, it requires a solid internet connection to guarantee that the learning process runs well.
Early Detection of Breast Cancer Using Machine Learning and Ensemble Techniques

Breast Cancer is found as the most dangerous and most commonly affecting diseases in the world by WHO. The severity of breast cancer and early diagnosis of it has gained the attention of researchers to save humankind from such devastating disease. Early prediction of breast cancer has geared up its journey after the introduction to machine learning supervised algorithms. In the paper, the use of various machine learning algorithms along with the ensemble algorithms is shown. The results obtained are highly accurate to help one correctly predict cancer. The paper aims at early diagnosis of breast cancer with a humble motto of saving patients suffering from the disease by allowing them to know whether the diagnosed tumor is cancerous or non-cancerous, being Malignant and Benign respectively. This paper would be useful and aiding for those who are novel researchers in prediction and diagnosis of breast cancer using machine learning.
Cognitive Dissonance on Sustainable Mobility

This article aims to define sustainable mobility, particularly, reference travel time recognition and its economic and cognitive impact with a particular focus on infocommunication.The paper focuses on the social surplus of mobility and its effect on consumer time budget and cognitive recognition of sustainability.Nowadays, the added value of mobility is unquestionable; meanwhile, the increasing amount of information causes cognitive load.Travel time seems to be constant, and different mobility modes have different (environment)mental loads.Therefore, a (environ)mental impact assessment is required.An always-changing environment must lead to an analysis of the effects of rational decisions.The work focused on travel time, as a decision parameter and its impact on (environ)mental load and sustainability.