Reviewerly/exHarmony huggingface dataset:
Advanced machine learning methods for autonomous classification of ground vehicles with acoustic data

This paper presents a distributed multi-class Gaussian process (MCGP) algorithm for ground vehicle classification using acoustic data. In this algorithm, the harmonic structure analysis is used to extract features for GP classifier training. The predictions from local classifiers are then aggregated into a high-level prediction to achieve the decision-level fusion, following the idea of divide-and-conquer. Simulations based on the acoustic-seismic classification identification data set (ACIDS) confirm that the proposed algorithm provides competitive performance in terms of classification error and negative log-likelihood (NLL), as compared to an MCGP based on the data-level fusion where only one global MCGP is trained using data from all the sensors.
Water and conflict on internal displacement: network analysis of Somalia case

&amp;lt;p&amp;gt;Recent decades have witnessed an increasing trend of displacement&amp;amp;#8212;forced movements of people, e.g., refugee, internal displacement, asylum. The greatest portion of global displaced populations are internally displaced persons (IDP) who travel within a country's boundary. IDPs are relocated due to varying reasons such as conflict, drought, flood, etc. Somalia is particularly renowned for protracted internal displacement due to long-lasting conflicts, extreme droughts, and flooding events. Despite the severity and continuity of the problem, we still lack an understanding of how water (here, droughts and floods) and conflict build IDP networks respectively. This research answers the following questions to solve the gap: (1) What are the underlying push and pull mechanisms in water-induced and conflict-induced IDP networks?; (2) How are water-induced and conflict-induced IDP networks structured and characterized?; (3) How do geographical locations cluster differently in two IDP networks? The analysis was conducted on the yearly IDP flow data at the district level in Somalia. We compared water-induced and conflict-induced IDP networks in Somalia using multiple network metrics, motif analysis, and community detection algorithms. From the analysis, conflict-induced IDP networks followed a power law for both indegree and outdegree. Though the in-degree networks of water-induced IDPs were weakly scale-free, the out-degree case was a random network. Both water-induced and conflict-induced IDP networks shared a similar mesoscopic network structure through the motif analysis. Closed triads were more frequently observed, supporting the importance of social linkages such as social homophily or information/knowledge sharing. Through the community detection, we found that water drove IDPs to move to nearby locations and led neighboring locations to clustered. Conflict, however, facilitated IDP flows between remote locations, building geographically-dispersed clusters. These findings offer an in-depth insight into commonalities and differences between water-induced and conflict-induced IDP networks in Somalia.&amp;lt;/p&amp;gt;
Comparative performance evaluation of island particle swarm algorithm applied to solve constrained and unconstrained optimization problems

Distributed evolutionary computation has been efficiently used, in last decades, to solve complex optimization problems. Island model (IM) is considered as a distributed population paradigm employed by evolutionary algorithms to preserve the diversification and, thus, to improve the local search. In this article, we study different island model techniques integrated in to particle swarm optimization (PSO) algorithm in order to overcome its drawbacks: premature convergence and lack of diversity. The first IMPSO approach consists in using the migration process in a static way to enhance the police migration strategy. On the other hand, the second approach, called dynamic-IMPSO, consists in integrating a learning strategy in case of migration. The last version called constrained-IMPSO utilizes a stochastic technique to ensure good communication between the sub-swarms. To evaluate and verify the effectiveness of the proposed algorithms, several standard constrained and unconstrained benchmark functions are used. The obtained results confirm that these algorithms are more efficient in solving low-dimensional problems (CEC’05), large-scale optimization problems (CEC’13) and constrained problems (CEC’06), compared to other well-known evolutionary algorithms.
EXPLORING STUDENTS’ DIFFICULTIES IN READING USING QUESTION AND ANSWER RELATIONSHIP

The aim of the research is to identify students' difficulties in reading using Question-Answer Relationship. The researchers employed descriptive qualitative research. The participants of the research were seventh-grade students of SMPN 2 Parongpong. To collect data for this study, questionnaire and test were used as the instruments of this research. Then, the data were analyzed qualitatively. The data of the research showed that the students faced several difficulties in reading deal with lack of vocabulary, difficulty in determining main ideas and finding specific information. Keywords: Sudents’ Difficulties, Question Answer Relationship, Reading
Bidirectional Long Short-Term Memory for Entailment Identification in Requirement Specifications Using Information from Use Case Diagrams

Text entailment is a field of natural language processing research that concerned with understanding the meanings or semantics of sentences or text fragments. An entailment statement is a relationship between sentences where the truth of one sentence necessarily implies the truth of another. In requirements engineering (RE), identifying entailments between requirements is crucial for modeling the dependencies. Current entailment methods are not suitable for this purpose. This research proposes a new architecture for classifying entailments between requirements in the software specification document. The proposed architecture builds a training model using use case diagram and its description. The training model is later used for classifying entailments between requirements statements. Based on the case study, the training model can identify entailments between requirement statements.
Adaptation of Lampung Families in Reconstructing 21st Century Skills Implementation During Pandemic

The government’s policy of Learning From Home (LFH) during pandemic emphasizes the importance of 21st Century Literacy Skills due to the ability needed to access media, information and technology. The urgency to increase family adaptive behavior appears as a demanding condition whilst the literacy skills also oppose great challenges for parents as students companions. This study is a cross-sectional survey with quantitative data collection, through questionnaires for 123 respondents at State Elementary School 3 Rajabasa, Bandar Lampung City. Data analysis is done by descriptively and inferentially method. The survey results showed that 87.8% of respondents have facilities to support online access to information. It is also found that the 21 st Century Literacy Skills are mandatory capital owned by each family during LFH activities. The family adaptive abilities in supporting children 21st century literacy skills implementation can be seen through the literacy skills of the students’ families.
Semantic Augmentation Transformer Model for Ad-hoc Retrieval

We propose SATM, a Semantic Augmentation Transformer Model for ad-hoc retrieval. SATM adapts contrastive learning, which improves the performance of semantic similarity and the ranking results of ad-hoc retrieval. It can also augment the semantic representation of sentence embeddings. Specifically, we first use an unsup-ervised contrastive learning augmentation module to learn query similarity so that the projection head can accurately capture query semantics. Then we use the trained encoder network to map queries and perform semantic similarity calculations and rankings with document embeddings. We use BERT to extract the contextual representation of the sentence, and use the augmentation module to enhance the semantics and eliminate the anisotropy of sentence embedding. Experimental results show that in the TrecQA data set, SATM has 4% and 1.4% improvements in MRR and MAP over Bert-base.
On the Capabilities of Quantum Machine Learning

Machine learning techniques give impressive results in many areas. However, due to the physical limitation of integrated circuits which restricts their computational power growth, and the rapid advances in quantum computing, lots of research studies on quantum machine learning (QML) have been done recently. QML is a technique that uses quantum algorithms as parts of the implementation. Quantum algorithms use quantum mechanics and have the potential to outperform classical algorithms for a given problem. In this paper, three widely used machine learning algorithms are discussed and their quantum versions are presented, namely: quantum neural network, quantum autoencoder, and quantum kernel method. In addition, we discuss the potential capabilities of these QML algorithms and review recent work employing them. Moreover, a quantum neural network prototype is implemented using Qiskit as a proof of concept and tested on a real quantum computer. Empirical results show that quantum neural networks can be trained efficiently.
The Study of the Psychological Culture of Future Teachers Psychologists in the Context of the Formation of Professional Orientation

The author of the article reveals the concept of the professional orientation of educational psychologists through a coordinated set of methods and means for designing, implementing and analyzing the results of educational activities.The created program of empirical research reflects the sequence of tasks to be solved in studying the relationship between self-relationship and the psychological culture of future educational psychologists.The article substantiates the relevance of the formation of readiness for professional orientation and, in this context, the psychological culture of future educational psychologists is studied.The presented program is aimed at testing the hypothesis put forward about the relationship between the level of self-attitude and the level of psychological culture of the personality of future psychologists in the framework of forming their readiness for professional orientation.The article reflects the results of studying the psychological culture of future specialists of educational psychologists who receive higher education at the bachelor's level of 3-4 courses and at the master's level of 1-2 courses, full-time and part-time forms of education.
The Mechanics of Authorship

How to prepare a manuscript, acquire illustrations and permissions, review copyediting, proofread, and index a book.
FURNIT-AR : AUGMENTED REALITY INTERIOR DESIGN STORE APPLICATION

Furniture is something that fills the house more than people , it gives you sound sleep , support when you are back home tired from work. When you are out there purchasing a furniture for yourself or your loved ones , you always want something that looks appealing and is of the best quality as well . Purchasing something without a trial is a very common mistake that you encounter in the traditional method. But with FURNIT AR : AUGMENTED REALITY INTERIOR DESIGN STORE APPLICATION and with the help of Augmented Reality you can try things virtually before making any move. This project is a composition of an android application and Augmented Reality technology, with the help of the camera of your smartphone you can try out different furniture, interior design items virtually inside the android application. The project is free to download and no needs to pay a penny forit. Keywords: Android, Android Studio, Sceneform, ARCORE, Blender, Augmented reality, GLB, GLTF 2.0
Classifying earthquakes and mining activity with deep neural networks

&amp;lt;p&amp;gt;Earthquake detection and phase picking are central problems of seismic activity analysis. Traditional approaches [1] and machine learning methods [2] are applied in this domain, typically performing well on commonly investigated standard datasets reaching above 99% accuracy in seismic activity detection.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Unfortunately, most databases in the literature contain only earthquake data as detectable activities and spurious activities such as mining are not included in these datasets. We have investigated a recently published deep neural network-based method [3] and found that these detectors are fooled by mining activity.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To solve this problem, we have created a complex dataset that contains 1200 independently recorded mining and earthquake activities from Central Europe. Our dataset poses a more complex problem than commonly investigated datasets such as the STanford EArthquake Dataset and can be viewed as an extension of that.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We have trained a convolutional neural network containing five convolutional and three fully-connected layers to classify these signals on this dataset and reached a 94% classification accuracy, which demonstrates that the categorization of mining activity and earthquakes is possible with modern machine learning approaches.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[1] Galiana-Merino, J. J., Rosa-Herranz, J. L., &amp;amp; Parolai, S. (2008). Seismic P Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain. IEEE Transactions on Geoscience and Remote Sensing, 46(11), 3815-3826.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[2] Zhu, W., &amp;amp; Beroza, G. C. (2019). PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261-273.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[3] Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., &amp;amp; Beroza, G. C. (2020). Earthquake transformer&amp;amp;#8212;an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature communications, 11(1), 1-12.&amp;lt;/p&amp;gt;
PENGARUH FEE AUDIT, AUDIT TENURE DAN DUE PROFESSIONAL CARE TERHADAP KUALITAS HASIL AUDIT

The preparation of a financial report is an obligation for an industry both on a small, medium and large scale. Then another obligation is an assessment of the fairness of a financial report, where the assessment is carried out by an external party, namely the Public Accounting Firm (KAP). KAP as an external party or third party is employed by a company and becomes a liaison between the agent and the principal. Therefore, with this situation, there is an agency theory, which will cause agency costs for a company that uses its services. In providing an audit service in financial statements, an auditor who works at KAP needs to provide the best quality for the implementation of the audit carried out. This study presents factors such as audit fees, audit tenure and professional dues that will have an influence on audit quality. This research was conducted by distributing questionnaires to auditors at KAP in Java. Based on the results of research conducted, it is stated that the audit fee variable has no effect on the quality of the audit produced by the auditor, in other words, the audit fee given cannot predict the audit result that will be given. The second hypothesis states that the number of auditor engagements with clients (audit tenure) has a significant negative effect on the quality of the audit produced by the auditor. This means that the longer the audit time between the client and the auditor, for example more than three years, it will have a negative impact on the quality of the resulting audit due to weak independence. The third hypothesis gives the result that Due Professional Care has a significant and positive effect on audit quality, which means that the prudential principle of an auditor in obtaining evidence will provide better audit quality.
3 Co-TEAM: A logic model for pharmacy health coaching among substance use disorders patients

ABSTRACT&#x0D; Background: Pharmacy health coaching on various chronic diseases has been widely used with promising results. Drug addiction is a chronic disease, however, the intervention model of pharmacy health coaching in these patients has not been previously reported.&#x0D; Objective: Developing a logic model for pharmacy health coaching among substance use disorders patients that can be used by community pharmacists as a tool to support development, innovation, monitoring, and evaluating the performance of pharmacy health coaching.&#x0D; Methods: A logic model used to develop an intervention model of pharmacy health coaching following a process consisting of four steps: (1) a literature review to identify key values, scope, delivery &amp; tools, the content of the session, and competencies for pharmacy health coaching; (2) focus groups discussion (FGD) to obtain information from specific sources and discuss the results of the literature review; (3) developing a logic model based on the finding from steps 1 and 2; and (4) FGD to discuss and fine-tune the model.&#x0D; Results: A logic model was successfully constructed and generated 3 Co-TEAM models for pharmacy health coaching among substance use disorders patients. The 3 Co-TEAM consists of communication, collaboration, consultation, training, education, attitude, and motivational interviewing.&#x0D; Conclusion: The 3 Co-TEAM model for pharmacy health coaching provides steps to implement this service in an objective, organized and comprehensive manner. The proposed model can function as a tool that provides guidance and reference for pharmacists in the process of implementing pharmacy health coaching among substance use disorders patients and collaborating with other health professionals.&#x0D; &#x0D; Keywords: logic model, pharmacy health coaching, substance use disorders, pharmaceutical services, patient-centered care
Hubungan Video Pembelajaran dengan Motivasi Belajar Siswa Kelas V di SD Negeri 24 Macanang

The problem in this study is that in the learning process the teacher is still dominated by using printed teaching materials in the form of student books and teacher books, therefore the need for several learning resources that can stimulate and increase student learning motivation, one of which is the use of learning videos. This research is a quantitative correlational study that aims to determine the relationship between video learning and the learning motivation of fifth grade students at SD Negeri 24 Macanang. The research data were obtained by distributing questionnaires to students. The results showed that there was a significant relationship between learning videos and students' learning motivation at SD Negeri 24 Macanang with a value of (3.6559) greater (&gt;) value (1.67469) which means H0 is rejected and H1 is accepted. Based on the results of the study, it can be concluded that the learning video will have an impact on students' learning motivation. So the more often learning videos are used, it will increase students' learning motivation. From these conclusions, the impact resulting from this study is known that a good learning video will increase students' learning motivation
Design Learning Activities for the Topic of Microbiology - Biology 10 toward Self-Study Competence Development

Nowadays, the world’s enormous knowledge resources has become more accessible than ever thanks to the support of the Internet and technology. Students and learners are enabled to learn whenever and wherever they want. Teachers have encouraged students to improve their self-study competence to exploit this advantage. Designing the teaching process and activities to develop students’ self-study competence plays an important role in many subjects, including Biology. Therefore, this study proposes a four-step learning process to develop students’ self-study competence. The study also presents an example to illustrate this learning process and help teachers understand what they should do in designing a particular lesson to develop students’ self-study competence.
Writing and verifying a Quantum optimizing compiler (keynote)

As quantum computing hardware evolves, it will continue to face four key limitations: low qubit counts, limited connectivity, high error rates, and short coherence times. Quantum compilers play a key role in addressing these issues, reducing the number of qubits needed to perform a computation, mapping those qubits to the desired hardware, and minimizing the number of costly operations, both in terms of error rates and execution time. However, we cannot afford for compilers to become another source of bugs: Quantum computing is an inherently probabilistic and error-prone process and any additional sources of error are unlikely to be properly diagnosed. To address this, we present VOQC, a verified optimizing compiler for quantum circuits. VOQC heavily optimizes quantum programs while guaranteeing that the output is quantum-mechanically indistinguishable from the input program, up to permutation of qubits. This ensures that compilation produces an equivalent program that is executable on the given hardware. In this talk, we will address the key differences between classical and quantum compilation and the challenges unique to the latter. We will discuss the design decisions that underlie VOQC and how they enable its most powerful optimizations. Finally, we will discuss the developments since VOQC was first published, both within the VOQC toolchain and competing compilers, verified and unverified.
Selecting Trustworthy Clients in the Cloud

With the recent increase demand for cloud services, handling clients’ needs is getting increasingly challenging. Responding to all requesting clients with no exception could lead to security breaches, and since it is the provider’s responsibility to secure not only the offered cloud services but also the data, it is important to ensure clients reliability. Although filtering clients in the cloud is not so common, it is required to assure cloud safety. In this paper, we made use of multi agent system interaction mechanisms to handle the cloud interactions for the providers’ side, and of Particle Swarm Optimization to select the and determine the accurate trust weight and therefore to filtrate the clients by determining malicious and untrustworthy clients. The selection depends on previous knowledge and overall rating of trusted peers. The conducted experiments proves that the combination between MAS, PSO and trust outputs relevant results with different number of peers, the solution is able to converge to the best result after small number of iterations. To explore more space and scenarios, synthetic data was generated to improve the model’s precision .