Reviewerly/exHarmony huggingface dataset:
How Does Domino Card Help Children to Insight Numbering

This study explores the differences in the effectiveness of conventional educational domino media and contemporary digital educational domino media on the cognition of children aged 4-6 years. This research was conducted using R&D (research Research and Development) using the ASSURE model. 34 children aged 4-6 years and their parents were involved in the small socialization stage using a questionnaire conducted by Google Form. In this test, the researcher used SPSS 23 to test the feasibility of the media. The first test is the Validity Test of parents' perceptions and the results of children's cognitive abilities related to the ability to match number symbols with numbers for children aged 4-6 with a sig level of 0.05 with the results of r above (0.17) on each item being declared valid because r table > r count. The second test is the Validity Test of parental feedback on conventional educational domino media and contemporary digital education domino media with the results of r above (0.17) for each item being declared valid because r table > r count. The third test is the Cronbach's Alpha Reliability Test which results in the effectiveness of conventional domino media being 0.631, while the effectiveness of contemporary domino media is 0.516. The results of the cognitive abilities of children who use conventional media are 0.740, while the cognitive abilities of children who use contemporary media are 0.635. The fourth test is the Classical Assumption Test with the Normality Test, the sig value is above 0.05, the data is normally distributed, the homogeneity test is the sig value above 0.05, then the data is said to have the same variance (homogeneous). The fifth test is the Correlation Test with results less than 0.05 which means it has a significant strong relationship for conventional domino media 0.671 while for digital domino media 0.922. The sixth test is the Sample T-test with the results of the same contemporary media significance, which is less than sig 0.05. The result of the average score is that there is a difference in scores between conventional and contemporary domino media. Where the average score of contemporary digital domino media (27.76) is greater than conventional domino media (25.47). In this test, it can be decided if conventional and contemporary digital domino media are very effective in the ability to match number symbols with numbers in children aged 4-6 years. Recent research confirms that the conventional and contemporary design domino media model has contributed to the improvement of children's cognition so that it is feasible and effective to use. Furthermore, this research has implications for the term positive benefits of digital media, where children's cognitive development is formed through game stimulation. However, gamification learning will be effectively implemented in fostering children's cognitive development.
Aplikasi Android Pengelolaan Informasi Kebersihan Keamanan Ketertiban Masyarakat di Lingkungan Kelurahan Setonogedong Kota Kediri

The society of Setonogedong Village has carried out activities in order to maintain cleanliness, security and environmental order. The activities of maintaining cleanliness, security and environmental order are carried out through community cooperation. However, efforts to maintain cleanliness, security and environmental order have not been carried out optimally, this is proved by the existence of people who do not know about how to submit complaints about cleanliness, security and environmental order. This community service activity produces an e-government android application that is expected to help the community to convey information related to efforts to maintain cleanliness, security and environmental order in the Setonogedong Village, Kediri City.
@Flora &amp; Ulysses

The film follows 10-year-old Flora and her journey in protecting her superhero CG squirrel named Ulysses.
O2MD²: A New Post-Quantum Cryptosystem With One-to-Many Distributed Key Management Based on Prime Modulo Double Encapsulation

Polynomial-time attacks designed to run on quantum computers and capable of breaking RSA and AES are already known. It is imperative to develop quantum-resistant algorithms before quantum computers become available. Computationally hard problems defined on lattices have been proposed as the fundamental security bases for a new type of cryptography. The National Institute of Standards and Technology (NIST) recently hosted the Post-Quantum Cryptography Standardization project, aiming to create a roster of innovative post-quantum cryptosystems. These candidates have been publicly available for testing since early 2017. As they are currently under analysis, new proposals are still desirable. As such, we use the ring learning with errors (RLWE) problem combined with arithmetic functions to propose the O2MD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> cryptosystem, which provides a one-to-many private/public key architecture having a distributed key refresh for a network of users while working on multiple polynomial rings over different prime order fields. Our solution has three different frameworks that reach AES-256 equivalent security, and provides message integrity and message authenticity verifications. We compare our solution's speed against the speed of the twenty-six different implementations from seven popular candidates in the NIST project, and our cryptosystem performs from 2 to 4 orders of magnitude faster than them. We also propose six different implementations that reach the security levels 1, 3 and 5 proposed in the NIST competition. Finally, we used the NIST Statistical Test Suite to verify the indistinguishability of our produced ciphertexts against randomly generated noise.
Public Opinion of Transgender Rights in Serbia

Author(s): Luhur, Winston; Brown, Taylor N.T.; Silva-Santisteban, Alfonso | Abstract: There are no laws in Peru that protect people against discrimination based on their gender identity. This report analyzes data collected in the 2017 Global Attitudes Toward Transgender People survey to examine public opinion of transgender rights and status in Peru.
Adversarial OcclusionAugmentation: Guided Occlusions for Improving Object Detector

In recent years, deep-learned object detectors have achieved great success in the computer vision domain and reached promising results on benchmark datasets. However, in real-world applications (e.g. robotics) they still face problems, mainly due to the gap between the training data recorded under lab conditions and the complex and unknown test environments.Besides the well-known data thirst, one of the mentioned challenging problems is dealing with occlusions, which leads to missed and/or false detection and thus decreases the performance. Although there exist already some works on occlusion handling [5, 40], most of them focus on image classification and are evaluated on relatively simple datasets.In this work a novel Adversarial Occlusion Augmentation approach is proposed, focusing on the improvement of the object detector performance in highly cluttered environments, while at the same time keeping additional effort of data processing low. Inspired by Generative Adversarial Networks (GAN) [12] where both generator and discriminator can be optimized simultaneously, an adversarial neural network with a GAN-like architecture is designed to bring in guided occlusions on a synthetically generated dataset. A so-called Occlusion Generating Network (OccNet), representing the generator, and an object detector as discriminator are jointly trained in a competing manner. This enables the OccNet to learn to generate challenging samples due to occlusions, while the object detector is further improved by the enhanced training dataset. The occlusion types are modified step by step, from artificial to real-world occlusions. Integrated with the Dual Attention Mechanism [7], OccNet is able to search the optimal occlusion areas in a more efficient way. Furthermore, due to the jointly training strategy, the generated occlusions change from simple to hard which enables the detector to adapt itself epoch-wise. Therefore, the overall detection performance of an off-the-shelf object detector canbe improved accordingly by learning from such an easy-to-hard occlusion schema.Experiments on the T-LESS Dataset [15] prove that the adversarial occlusion augmentation approach can effectively improve the performance of a deep object detector without additional data effort.
Comparison of container-based platforms for quantum computing simulation

Quantum Computing allows for substantial speed-ups e.g. for integer factorization or database search, compared to conventional computation. However, since real quantum computers are an emerging technology, a significant amount of research in this domain still relies on simulations of quantum computations on conventional machines. This paper proposes a containerized environment to simulate quantum circuits, making this environment quick and easy to share within research teams. Also test the environment implementing an arithmetic circuit (Vedral’s adder) and compares the performance of two of the current more used environments: IBM QISKit and Google Cirq.
Reasoning About Quality and Fuzziness of Strategic Behaviours.

Temporal logics are extensively used for the specification of on-going behaviours of reactive systems. Two significant developments in this area are the extension of traditional temporal logics with modalities that enable the specification of on-going strategic behaviours in multi-agent systems, and the transition of temporal logics to a quantitative setting, where different satisfaction values enable the specifier to formalise concepts such as certainty or quality. We introduce and study FSL---a quantitative extension of SL (Strategy Logic), one of the most natural and expressive logics describing strategic behaviours. The satisfaction value of an FSL formula is a real value in [0,1], reflecting `how much' or `how well' the strategic on-going objectives of the underlying agents are satisfied. We demonstrate the applications of FSL in quantitative reasoning about multi-agent systems, by showing how it can express concepts of stability in multi-agent systems, and how it generalises some fuzzy temporal logics. We also provide a model-checking algorithm for our logic, based on a quantitative extension of Quantified CTL*.
PRIORITIES OF INNOVATIVE DEVELOPMENT OF THE UNIVERSITY: THE EXPERIENCE OF ATYRAU UNIVERSITY NAMED AFTER KH. DOSMUKHAMEDOVA

Within the framework of this work, the innovative educational environment of the Atyrau University named after Kh. Dosmukhamedov (AU) is considered. The role and the effects of the development of the innovation system of the university are analyzed and shown, the main strategic goals are revealed. The model for the development of educational infrastructure was substantiated, which implies an increase in the number of students, the modernization of educational programs, the launch of international educational programs in English, the implementation of innovative projects, including the commercialization of the results of scientific activities based on the business incubator “i-Space”, the promotion of the university for more high positions in national rankings of multidisciplinary universities, in international rankings, including the world ranking of universities for sustainable development “Green Metric”. The results of the study confirm the importance of the Development Strategy of AU for 2020-2025 as an alternative and an important step in creating an innovative university environment for solving problems of strengthening scientific potential, raising the status of a scientist, teacher of AU, increasing the contribution of university science to the needs of social partners, modernizing the content of academic activities in the context world trends, realizing their potential in striving for higher positions in the national ratings of multidisciplinary universities, in the international ratings of sustainable development "Green Metric".
THE EFFECTIVENESS OF BEHAVIORISM LEARNING MODEL IN IMPROVING STUDENTS’ SPEAKING SKILLS

This research aims to answer two issues: 1) How is the improvement of students’ speaking skills using behaviourism method in the boarding house of International Class Program IAIN Salatiga; 2) how significant is the implementation of behaviourism learning model in improving students’ speaking skills in the boarding house of International Class Program IAIN Salatiga. By incorporating quantitative approach and pre-experimental model, this research acquires the number of improvement of students’ speaking skills and respectively finds the level of significance of the improvement. The research context is at the boarding house of International Class Program IAIN Salatiga involving 39 students of batch 2018 and 2019. The researchers compare the score of pre-test and post-test during approximately 3-month time of treatment in the purpose of improving speaking skills by using behaviourism method. Having analysed, the researchers find that students’ speaking skills have improved. In average, students of bath 2018 reach 11.37 points of improvement on their speaking skills, whilst students of batch 2019 achieve 24.30 points of improvement. In conclusion, the implementation of behaviourism learning model is relatively effective by calculating the score of N-Gain of the improvement of students’ speaking skills. Based on the table of N-Gain criteria, the score of N-Gain 0.36 for batch 2018 and 0.49 for batch 2019 belong to medium category of 0.3 <= g <= 0.7. By referring to the result of this research, the use of behaviourism learning model can be applied to support the improvement of students’ speaking skills when they life in dormitory or boarding schools.
Determinants of Organizational Commitment towards Employee Performance among Libyan Banks

In the rapid changing business environment and the competition between organizations, employees’ performance is the most critical issues for overall organizational performance in Libyan commercial bank. Therefore, the study was aimed to examine the effect of human resource management practice and leadership style on employee performance, and the mediating role of organizational commitment. The research was conducted under positivism research philosophy, deductive approach, explanatory research design, and quantitative method of data collection. Thus, a survey questionnaire was developed for collecting samples of 415 from the target population 18526. A stratified simple random sampling technique was employed for the selection of samples. The collected data were analyzed through SPSS V23 and Smart-PLS 3.3.3. A total of seven hypotheses were proposed where five hypotheses were revealed accepted, however, two hypotheses were rejected. The findings will help the bank managers, customers, employees, and government for policymaking and improving the employee performance in the banking sector in Libya. The study focused on only commercial banks in Libya and the study was carried out based on the quantitative methodology. Therefore, further, studies can be done collecting data from all types of banks and the mixed-method study can employ to have more insight into the issues.
Nearly-frustration-free ground state preparation

Solving for quantum ground states is important for understanding the properties of quantum many-body systems, and quantum computers are potentially well-suited for solving for quantum ground states. Recent work [1] has presented a nearly optimal scheme that prepares ground states on a quantum computer for completely generic Hamiltonians, whose query complexity scales as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>&amp;#x03B4;</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>&amp;#x2212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math>, i.e. inversely with their normalized gap. Here we consider instead the ground state preparation problem restricted to a special subset of Hamiltonians, which includes those which we term "nearly-frustration-free": the class of Hamiltonians for which the ground state energy of their block-encoded and hence normalized Hamiltonian <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>&amp;#x03B1;</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>&amp;#x2212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:mi>H</mml:mi></mml:math> is within <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>&amp;#x03B4;</mml:mi><mml:mi>y</mml:mi></mml:msup></mml:math> of -1, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&amp;#x03B4;</mml:mi></mml:math> is the spectral gap of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>&amp;#x03B1;</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>&amp;#x2212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:mi>H</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>0</mml:mn><mml:mo>&amp;#x2264;</mml:mo><mml:mi>y</mml:mi><mml:mo>&amp;#x2264;</mml:mo><mml:mn>1</mml:mn></mml:math>. For this subclass, we describe an algorithm whose dependence on the gap is asymptotically better, scaling as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>&amp;#x03B4;</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>y</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>2</mml:mn><mml:mo>&amp;#x2212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math>, and show that this new dependence is optimal up to factors of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>log</mml:mi><mml:mo>&amp;#x2061;</mml:mo><mml:mi>&amp;#x03B4;</mml:mi></mml:math>. In addition, we give examples of physically motivated Hamiltonians which live in this subclass. Finally, we describe an extension of this method which allows the preparation of excited states both for generic Hamiltonians as well as, at a similar speedup as the ground state case, for those which are nearly frustration-free.
Stakeholder’s Training Process for GSD Based Requirements Elicitation Frameworks

The requirements elicitation frameworks define a systematic way of performing requirements elicitation tasks at pre-stages of the requirements engineering process. The requirements elicitation is a human-centered activity which critically depends upon the selection and training of the most appropriate stakeholders. The requirements elicitation process becomes challenging in global software development projects where stakeholders of the requirements elicitation process have high diversities in their geographical location, working time zones and local cultures. In such global software development scenarios, the need for the trainings of the involved stakeholders becomes most important aspect to create smoothness in the working setups. This article presents a stakeholder’s training process for global software development-based requirements elicitation frameworks to address the emergent need in software development industry. The proposed stakeholder’s training process provides a mechanism to comprehensively train requirements elicitation stakeholder’s about situational context and global software development context. The application of the proposed stakeholder’s training process in software development projects highlighted a significant improvement in the quality of the whole requirements elicitation process for global software development projects.
Beliefs of Adults with Disabilities About Literacy Skills’ Roles on Their Livelihood and Their Influence on Their Endeavor of Becoming Literate in Ethiopia

The roles of literacy skills in improving the livelihood of individuals have been extensively researched, but little is known about their impact on the lives of persons with disabilities. This qualitative case study tried to explore people with disabilities’ beliefs about literacy skills contributions for their livelihood and how these determine the commitment of perusing literacy skills. To gather data, semi-structured interviews and focus group discussions were used. The findings of the study indicated that it was believed by respondents that literacy skills can potentially improve their livelihood. As a result of their beliefs, their strive for becoming literate was imperative.
Finding the Hardest Formulas for Resolution (Extended Abstract)

A CNF formula is harder than another CNF formula with the same number of clauses if it requires a longer resolution proof. We introduce resolution hardness numbers; they give for m=1,2,... the length of a shortest proof of a hardest formula on m clauses. We compute the first ten resolution hardness numbers, along with the corresponding hardest formulas. To achieve this, we devise a candidate filtering and symmetry breaking search scheme for limiting the number of potential candidates for hardest formulas, and an efficient SAT encoding for computing a shortest resolution proof of a given candidate formula.
Designing an improved Adaptive Neuro-Fuzzy Inference System based on Whale Optimization Algorithm to predict blood donation

Artificial neural networks and fuzzy sets theory is one of appropriate techniques to solve engineering problems in order to predict variables of supply chains and, also, of systems with high complexity and implicitly which provide no sufficient data. The main advantage of this technique over others, which lies in the short time of data examination and algorithm discovery, is in the line with that prediction and/or its influence on the future. Adaptive neuro-fuzzy inference system (ANFIS) combines neural networks with fuzzy logic concepts and is able to use capabilities of both in one framework, the inference system of which is in conformity to fuzzy if-then rules having potential for learning how to approximate non-linear functions. Among applications of this technique are to define variables based on the past data and their impacts on the past temporal sequences in order to predict future conditions. This research, therefore, uses neuro-fuzzy technique in order to blood donation based on data from the past years. Since each technique has its own error rates, Metaheuristic Whale Algorithm is used to reduce errors of ANFIS by improving the parameter values of neuro-fuzzy systems. The obtained results show a reduction of the RMSE of prediction from 0.00261 to 0.00153 in the ANFIS-WOA and a 41% improvement over the ANFIS method.