Reviewerly/exHarmony huggingface dataset:
Fast and Robust Geometric Two-Qubit Gates for Superconducting Qubits and beyond

Quantum protocols based on adiabatic evolution are remarkably robust against imperfections of control pulses and system uncertainties. While adiabatic protocols have been successfully implemented for quantum operations such as quantum state transfer and single-qubit gates, their use for geometric two-qubit gates remains a challenge. In this paper, we propose a general scheme to realize robust geometric two-qubit gates in multi-level qubit systems where the interaction between the qubits is mediated by an auxiliary system (such as a bus or coupler). While our scheme utilizes Stimulated Raman Adiabatic Passage (STIRAP), it is substantially simpler than STIRAP-based gates that have been proposed for atomic platforms, requiring fewer control tones and ancillary states, as well as utilizing only a generic dispersive interaction. We also show how our gate can be accelerated using a shortcuts-to-adiabaticity approach, allowing one to achieve a gate that is both fast and relatively robust. We present a comprehensive theoretical analysis of the performance of our two-qubit gate in a parametrically-modulated superconducting circuits comprising two fluxonium qubits coupled to an auxiliary system.
Motorcycle contra robos device with mobile-based application

&#x0D; &#x0D; &#x0D; As a result of motorcycle theft, many motorcycle riders are experiencing troubles with their motorcycles. Thieves are growing more sophisticated when stealing motorcycles from riders in today's world. Many outcomes, ramifications, and possible solutions for motorcycle riders and the anti-carnapping task force where anti-theft systems in the Philippines are increasingly developed were addressed in this study, such components related to preventing theft.This study was conducted to help motorcycle owners and riders prevent their bikes from being stolen by developing and designing a Motorcycle Contra Robos Device with a Mobile-Based Application.This research also sought to establish the system's project success in terms of the ISO 25010 software quality model's primary features, which include functionality suitability, compatibility, usability, security, maintainability, and portability.The descriptive method was used by the researchers to describe the respondent’s assessments of the Motorcycle Contra Robos Device with Mobile-based Application in Barangay Maharlika Village Taguig City. The participants' evaluations were based on the Anti-Carnapping Task Force and Motorcycle riders. The researchers used a systematic approach to system development from idea to implementation.The test of differences in the assessment of the two groups of participants (Motorcycle Rider and Anti-carnapping Task Force) regarding the measuring tool using T-Test achieved at 0.008 T ratio where there are two groups of respondents, and there are six criteria per group. The computed value was compared to the tabulated/T-critical value at a 0.05 level of significance which shows the T-ratio is less than the computed value. Thus, the null hypothesis is accepted that “There is no significant difference between the perceptions of the two groups.” This means that the group participants agreed upon the testing of the system based on the criteria of ISO 25010.” This shows that the participating groups of respondents agreed on the criteria set by ISO 25010.&#x0D; &#x0D; &#x0D;
An empirical study of scoring functions for learning Bayesian networks in model averaging

Scoring functions for Bayesian network (BN) structure learning can conflict in their rankings and previous work has empirically studied their effectiveness with an aim to provide recommendations on their use.However, previous studies on scoring functions are limited by the small number and scale of the instances used in the evaluation and by a focus on learning a single network.Often, a better alternative to committing to a single network is to learn multiple networks and perform model averaging as this method provides confidence measures for knowledge discovery and improved accuracy for density estimation.In this paper, we empirically study a selection of widely used and also recently proposed scoring functions.We address design limitations of previous empirical studies by scaling our experiments to larger BNs, comparing on an extensive set of both ground truth BNs and real-world datasets, considering alternative performance metrics, and comparing scoring functions on two model averaging frameworks: the bootstrap and the credible set.Contrary to previous recommendations based on finding a single structure, we find that for model averaging the BDeu scoring function is the preferred choice in most scenarios for the bootstrap framework and a recent score called quotient normalized maximum likelihood (qNML) is the preferred choice for the credible set framework.
ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset

Benchmark datasets play an important role in evaluating Natural Language Understanding (NLU) models. However, shortcuts -- unwanted biases in the benchmark datasets -- can damage the effectiveness of benchmark datasets in revealing models' real capabilities. Since shortcuts vary in coverage, productivity, and semantic meaning, it is challenging for NLU experts to systematically understand and avoid them when creating benchmark datasets. In this paper, we develop a visual analytics system, ShortcutLens, to help NLU experts explore shortcuts in NLU benchmark datasets. The system allows users to conduct multi-level exploration of shortcuts. Specifically, Statistics View helps users grasp the statistics such as coverage and productivity of shortcuts in the benchmark dataset. Template View employs hierarchical and interpretable templates to summarize different types of shortcuts. Instance View allows users to check the corresponding instances covered by the shortcuts. We conduct case studies and expert interviews to evaluate the effectiveness and usability of the system. The results demonstrate that ShortcutLens supports users in gaining a better understanding of benchmark dataset issues through shortcuts, inspiring them to create challenging and pertinent benchmark datasets.
Binary Classification with Minimum Observations

Binary classification with minimum observations is an important task in applications where enough training data are not available.In this paper, we propose a binary classification approach that is based on an unsupervised ranking algorithm for objects with numerical attributes.The ranking algorithm takes normalized attributes of numerical objects as input and returns the weights of attributes.These weights are used to rank the objects.We propose a class labelling algorithm that labels each side of the ranked objects as a class using less than or equal to 15 labeled data objects or observations.Evaluation on six different data sets shows that the proposed approach is comparable to the state-of-the-art binary classification algorithms that use 70 percent observations compared to less than one percent observations (on average) used by the proposed approach.
End-users in recovery from substance use disorders as designers of Personas and digital games with therapeutic potential

Thirty-five million people worldwide are affected by Substance Use Disorders (SUDs). Digital games can exert therapeutic effect in this domain and the development of these games by the end users themselves tends to potentiate the adherence and effectiveness of the games. Personas is a consolidated technique to support design solutions. There are studies in the literature that examine the creation of personas with the participation of health professionals. However, patients are generally just information providers and a more active participation is a desirable outcome to improve the design of digital systems for the healthcare domain. This paper investigates the co-creation of personas and digital games with therapeutic potential by patients recovering SUDs. Twenty-one patients, one healthcare professional and four computer science professionals took part in the workshops. In total, were created seven personas and four digital games with therapeutic potential. By conducting a qualitative analysis, we were able to determine eleven lessons learned from the process.
THE LOGICAL ESSENCE OF ABDUCTION AS A METHOD OF CONSTRUCTION OF VERSIONS

The article is devoted to the study of the main points of abduction. Pierce's abduction is tried to be interpreted as a logical reasoning, which is responsible for creating explanatory hypotheses. Abduction - the search for a single factor that explains the causal phenomenon. the process of abduction consists of three stages: result - cause - explanation. Analysis of works of art shows that a number of considerations that have traditionally been considered deductive are abductions. Abduction is not used enough, as it remains unknown to both practitioners and scientists.
Design of Modified RNS-PPA Based FIR Filter for High-Speed Application

The primary motive of this paper is to give the design and implementation of RNS (Residue Number System) based Area efficient and excessive-overall performance FIR filter of 4-tap, eight-tap, 16-tap of input eight bit. Additionally, RNS mathematics is a treasured device for theoretical research of the limits of fast mathematics. These proposed strategies additionally have a few additions operation, through the use of convention adder will decrease the speed of operation and additionally increase the number of logic gates. So, to conquer the one's issues we are using Ladner Fischer parallel prefix adder to lower the delay and area. First, the multiplier is designed through the use of RNS approach. In which the delay is decreased through 78.57% and power dissipation is likewise reduced to 64.65% for the RNS_PPA multiplier. A combination of those algorithms generates a brand-new structure of excessive speed and low implementation area in a single multiplier for FIR filter using Xilinx 14.7.
Deep Contrastive Multiview Network Embedding

Multiview network embedding aims at projecting nodes in the network to low-dimensional vectors, while preserving their multiple relations and attribute information. Contrastive learning approaches have shown promising performance in this task. However, they neglect the semantic consistency between fused and view representations and have difficulty in modeling complementary information between different views. To deal with these deficiencies, this work presents a novel Contrastive leaRning framEwork for Multiview network Embedding (CREME). In our work, different views can be obtained based on the various relations among nodes. Then, we generate view embeddings via proper view encoders and utilize an attentive multiview aggregator to fuse these representations. Particularly, we design two collaborative contrastive objectives, view fusion InfoMax and inter-view InfoMin, to train the model in a self-supervised manner. The former objective distills information from embeddings generated from different views, while the latter captures complementary information among views to promote distinctive view embeddings. We also show that the two objectives can be unified into one objective for model training. Extensive experiments on three real-world datasets demonstrate that our proposed CREME is able to consistently outperform state-of-the-art methods.
SCIENTIFIC RESEARCHES AND STUDYING AT HIGHER EDUCATION INSTITUTIONS AS A NECESSITY DURING THE DISTANCE LEARNING IN THE DIFFICULT COUNTRY CIRCUMSTANCES

The article evaluates the use of research activities and higher education in distance learning format and proves their need during difficult circumstances in the country. The main methods and methods of research are indicated. The views and points of view of modern scientists on distance learning and education and their practical application are analyzed. In order to ensure its practicality, the scientific article describes the experimental work which we have conducted in the form of an online survey among specialists of the first and fourth years studying for the specialties «Agronomy» and «Ecology». According to the results of the experiment, the main forms of research activity of applicants of higher education institutions are emphasized. At higher educational institutions distance learning is based on rationally selected methods of studying, information technologies, principles of studying, etc. In practical use, they act as forms and methods of organizing educational and scientific activities. The main methods are described: the method of individualized learning, the method of problem presentation of educational material, the research method, the method of projects. Proposals were made in the educational process to attract innovative and individual games, business games, problem-oriented games, situation modeling. The article highlights the main tasks that are being solved due to the introduction of distance learning and education at higher institution, as well as the list of opportunities that can be obtained in the end. It is emphasized the need for motivation, which should stimulate the applicant of education to positive action in the scientific and educational process and further development of it as a specialist. The importance of the form of feedback from applicants and the role of a teacher in the management work of scientific activities of educational applicants is stated. It is concluded that the remote format of research and training at higher education institutions is primarily different from traditional ones, in parallel with that it has a future in the education system, but should not replace traditional education. Key words: distance format, scientific research, education, students of education, research work.
HOW DOES INTANGIBLE HUMAN CAPITAL IMPACT ON ECONOMIC GROWTH: A RESEARCH ON PRIVATE COMPANIES IN YEMEN

The apparent contribution of human capital in economic growth in an economy cannot be overemphasized.Human capital is the vital resource that is needed in every sector of an economy in a nation.Unfortunately, for many years' human capital development has been crippled with multifarious difficulties that have hindered economic growth in many economies; with underdeveloped economies suffering the effect of poorly developed human capital.This research was conducted to investigate the impact of intangible human capital on economic growth in Yemen.The researcher used primary data from 250 respondents and was analyzed using Microsoft excel, graphical representation and SPSS.In the end, it was discovered that human capital, whether developed or underdeveloped, has a significant effect in the growth of an economy both in the long and short term.
STUDENT ATTITUDE TOWARD ACTIVATING BACKGROUND KNOWLEDGE IN READING

&#x0D; &#x0D; &#x0D; Reading is one of the skills that every student must have in order to comprehend what they are reading. Background knowledge is required for students to use in their reading strategy. Background knowledge is information that a person has about a topic that is based on his experience, for example. The purpose of this study is to investigate the impact of reading strategies on background knowledge among English Education Departments at State Islamic University of North Sumatera. The researcher's sample consists of several in third semester students of English Education Departments at State Islamic University of North Sumatera. In this study, researchers used quantitative methods such as questionnaires and tests to conduct surveys. The questionnaire was an open questionnaire with two sessions. And the data found in this study revealed that the students attitude toward using the Activating background knowledge strategy in third semester students of English Education at State Islamic University of North Sumatera is still immature. They have not been able to successfully apply this strategy when reading a text. Students' attitudes toward using this strategy remain weak and negative. Students are still having difficulty activating their background knowledge.&#x0D; &#x0D; &#x0D;
Implementation of Learning in Elementary Schools Is Related to the Implementation of Literacy and Numeracy Skills

The purpose of this research to know and describe the learning process in elementary school, does the learning that has been applied in the classroom already include literacy and numeracy skills. In connection with this, a Minimum Competency Assessment will be held whose material includes literacy and numeracy competencies. The approach taken by the researcher is descriptive qualitative with 4th grade teacher participants and 4th grade elementary school students. Data was collected by means of observation, interviews, and documentation. The data analysis technique used in this study refers to the Miles and Huberman model. The results showed that teachers at SDN 2 Tuguraja Tasikmalaya and SD IT Qatrunnada Jakarta.
YOLO-V3 Based Real-time Drone Detection Algorithm

Drones are currently being used in a wide range of useful tasks that are too dangerous or/and expensive to be performed by humans. However, this is increasingly developing security breaching issues due to the possibility of misuse of unmanned aircraft in illegal activities such as drug smuggling, terrorism etc. Thus,thedetection and tracking of dronesare becoming a crucial topic. Unfortunately, due to the drone’s small size, its’ detection methods are generally unreliable: high false alarm rate, low accuracy rate and low detection speed are well-known aspects of this detection. The newemerging real-time algorithm based on the improved “You Only Look Once - version 3” (YOLO-V3) algorithm is proposed here for drone detection. This newly designed algorithm is consisting of three phases and has shown the potential to outperform the traditional detection approaches. The newly designed algorithm is trained and evaluated on the designed drone dataset. The evaluation results of our algorithm obtain 96% on average precision and 95.6% on accuracy.
Bahasa Indonesia sebagai Alat Pemersatu Bangsa

Language is a national identity. The Indonesian nation has a unified language that is used by all its people to communicate. A great nation needs language as a unifying agent so that communication becomes easier and fosters a sense of nationalism in every area it covers. Indonesian is the language of unity that is upheld by all Indonesians. This is reflected in the third pledge of the 1928 Youth Pledge which reads, "We sons and daughters of Indonesia uphold the language of unity (national language), Indonesian", Indonesian is also the only official language nationally in Indonesia. This is in accordance with Article 36 of the 1945 Constitution. Language is the most important component in human life. Humans will not be able to continue their survival properly and regularly without language. They can not interact easily and better if they do not master the language between each other and with the absence of the continuity they can not capture the expression of psychiatric or intentions expressed by opponents of communication. It also causes the septum and less emotional affiliated with one another. It could be said that language as one of the primary needs that have a role as a regulator of the circulation of survival that is as a means of communication which might be expected with good communication Indonesian national unity can be realized. In fact, the language can also be categorized as the most powerful weapon to fortify themselves and the country from the threats of disintegration.
Universidad virtual e inclusión la bimodalidad

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
Invariant Inference With Provable Complexity From the Monotone Theory

Invariant inference algorithms such as interpolation-based inference and IC3/PDR show that it is feasible, in practice, to find inductive invariants for many interesting systems, but non-trivial upper bounds on the computational complexity of such algorithms are scarce, and limited to simple syntactic forms of invariants. In this paper we achieve invariant inference algorithms, in the domain of propositional transition systems, with provable upper bounds on the number of SAT calls. We do this by building on the monotone theory, developed by Bshouty for exact learning Boolean formulas. We prove results for two invariant inference frameworks: (i) model-based interpolation, where we show an algorithm that, under certain conditions about reachability, efficiently infers invariants when they have both short CNF and DNF representations (transcending previous results about monotone invariants); and (ii) abstract interpretation in a domain based on the monotone theory that was previously studied in relation to property-directed reachability, where we propose an efficient implementation of the best abstract transformer, leading to overall complexity bounds on the number of SAT calls. These results build on a novel procedure for computing least monotone overapproximations.
THE TYPES OF ERRORS IN DESCRIPTIVE TEXT COMMITTED BY THE STUDENTS OF STMIK STIKOM INDONESIA

&#x0D; &#x0D; &#x0D; This study was focused on analyzing the types of errors in descriptive text committed by the students of STMIK STIKOM Indonesia. Descriptive qualitatively was chosen in this research which was done through an on-line observation. The students were assigning to write descriptive text through Google Classroom and the result was taken as the sample data. The obtained data were analyzed descriptively. The research finding shows six errors namely the topic sentence, the developing sentences, the coherence, the diction, the grammar, and the mechanics. Some errors are indefinite concerning to previous criteria. The outcome defines undeniability for the teacher to apply any methods to minimize errors in writing descriptive text. At the same time, the students should obtain knowledge about indicators and practice in writing descriptive text. Furthermore, the next researchers have to gain other preference in finding types of errors.&#x0D; &#x0D; &#x0D;