Reviewerly/exHarmony huggingface dataset:
Customer Perceptions of Overall Service Quality (Low-cost Carriers in Saudi Arabia)

The civilized and industrial development in The Kingdom of Saudi Arabia, through achieving the 2030 vision and creating a suitable environment for business, has attracted a lot of investments, businesses, and enterprises, which have created a highly competitive environment. This competitive environment has led company managers to search for ways to ensure continuity and superiority over their competitors. This research aims to assist people with interest in low-cost carriers (LCCs) in Saudi Arabia such as managers of LCCs and other stakeholders by providing them with information on the travelers’ perceptions that might be overlooked and could help to find solutions that lead to increase customer satisfaction and improve performance of the firm, or to abolish activities that could damage the reputation and performance. Survey questionnaires were distributed to 111 participants in The Kingdom of Saudi Arabia to assess their perceptions of overall service quality via applying modified SERQUAL model on LCCs (LCCSQUAL). Data were analyzed using SPSS software; it was found that there are several SERQUAL dimensions that need to be improved especially those items with the lowest scores. The research concludes with providing some proposals that may assist managers and interested readers in achieving their goals towards sustainability.
Fault Tolerant Triplet Networks for Training and Inference

&lt;p&gt;This paper deals with the fault tolerance of Triplet Networks (TNs). Results based on extensive analysis and simulation by fault injection are presented for new schemes. As in accordance with technical literature, stuck-at faults are considered in the fault model for the training process. Simulation by fault injection shows that the TNs are not sensitive to this type of fault in the general case; however, an unexcepted failure (leading to network convergence to false solutions) can occur when the faults are in the negative subnetwork. Analysis for this specific case is provided and remedial solutions are proposed (namely the use of the loss function with regularized anchor outputs for stuck-at 0 faults and a modified margin for stuck-at 1/-1 faults). Simulation proves that false solutions can be very efficiently avoided by utilizing the proposed techniques. Random bit-flip faults are then considered in the fault model for the inference process. This paper analyzes the error caused by bit-flips on different bit positions in a TN with Floating-Point (FP) format and compares it with a fault- tolerant Stochastic Computing (SC) implementation. Analysis and simulation of the TNs confirm that the main degradation is caused by bit-flips on the exponent bits. Therefore, protection schemes are proposed to handle those errors; they replace least significant bits of the FP numbers with parity bits for both single- and multi-bit errors. The proposed methods achieve superior performance compared to other low-cost fault tolerant schemes found in the technical literature by reducing the classification accuracy loss of TNs by 96.76% (97.74%) for single-bit (multi-bit errors). &lt;/p&gt;
Essay-BR: a Brazilian Corpus to Automatic Essay Scoring Task

Automatic Essay Scoring (AES) is the computer technology that evaluates and scores the written essays, aiming to provide computational models to grade essays automatically or with minimal human involvement. While there are several AES studies in a variety of languages, few of them are focused on the Portuguese language. The main reason is the lack of a corpus with manually graded essays. In order to bridge this gap, in this paper we extended a corpus of essays written by Brazilian high school students in an online platform. All of the essays are argumentative and were scored across five competences by experts. Moreover, we conducted an experiment with the extended corpus to show some challenges posed by the Portuguese language. The corpus are publicly available at https://github.com/lplnufpi/essay-br.
Fitting Parameters for Procedural Plant Generation

We propose a novel method to obtain a 3D model of a tree based on a single input image by fitting parameters for some procedural plant generator. Unlike other methods, our approach can work with any plant generator, treating it as a black-box function. It is also possible to specify the desired characteristics of the plant, such as the geometric complexity of the model or its size. We propose a similarity function between the given image and generated model, that better catches the significant differences between tree shapes. To find the appropriate parameter set, we use a specific variant of a genetic algorithm designed for this purpose to maximize similarity function. This approach can greatly simplify the artist's work. We demonstrate the results of our algorithm with several procedural generators, from a very simple to a fairly advanced one.
3S-LEADERSHIP MODEL AS A TOOL OF USING DESIGN THINKING IN THE SYSTEM OF PUBLIC GOVERNANCE

The problem of integrated implementation of technology and techniques of design thinking, design management and design leadership into public administration should be considered a priority, as this area is the institutional component that determines the level of either innovative development of a society or its stagnation and decline. The dynamic changes in the intellect, mentality, experience, life values and orientations of modern people set new requirements for leaders in the age of intellectual economy, therefore traditional theories and models of leadership no longer meet the needs of group design-thinking. The purpose of this study is to determine a leadership model most adequate to the principles and criteria of design thinking so as to adapt it to the priority use in the processes and procedures of public administration. The tasks of the study are to analyze the expediency of changes in traditional leadership models and tools oriented to new requirements for design leaders; to outline the conditions for the effective management in the “leader-follower” pair in order to substantiate the role of a leader in design-thinking process and identify the leader’s influence on the group’s performance depending on the level of his/her mastery of using design thinking methodology; to assess the level of adequacy of the 3S leadership model to the criteria and methods of design thinking. To reach the objectives of the article the method of comparative analysis was used. The results of the analysis of the known models of design thinking processes revealed that this process is a fundamental condition and basis for leaders to create an atmosphere of effective use of their own intellect and the intellect of their followers in “leader-follower” pair for joint creativity and innovation. The conclusions of the study draw attention to the feasibility of and conditions for integrating the intellect of “leader-follower” pair and using for this purpose a model of 3S-leadership, based on common behavioral phenomena of synarchy, syntelect and synergy of its members. The main condition for the group to use design thinking technology efficiently has been defined as the mastery of design thinking methodology by its leader with the simultaneous use of the 3S-leadership model to influence followers, which will allow all group members to re-orient to the criteria of the 3S-model, their intellectual activity to be organized according to the recommendations of design thinking, and the leader to transform into a real design-leader who ensures a collective synergistic effect, i.e. a joint innovative product. Authors believe that the results of this study will contribute to the development of programs for training design-thinking leaders for the national system of public administration.
The Effect of Unemployment and Poverty on Criminality in East Java Province in Supporting State Defense

The purpose of this study is to determine the effect of unemployment and poverty on crime, especially in the province of East Java in an effort to maintain national defense. This research entirely uses secondary data obtained from BPS East Java. This study uses the analisis Ordinary Least Square (OLS) approach with Eviews. The results of this study indicate that: (1) Unemployment has a positive and significant effect on crime in East Java. (2) Poverty has a positive and significant effect on crime in East Java. (3) Unemployment and Poverty have a simultaneous or joint effect on crime in East Java. Based on the results of this study, the authors suggest to the East Java Provincial Government to make policies that are oriented towards reducing the number of unemployment and poverty in East Java Province.
Albert Einstein, World of Dices and Hydrocarbon System Analysis

Albert Einstein, Caesar and others have used dices as a metaphor for risks and probabilities; indirectly reverting to the experience human kind may have with natural processes in its environment contemporarily with human evolution. It will be shown for example by concentrating on the exploration of hydrocarbons that the rules of a dice-game can be used to better understand the importance of the number of ruling parameters (dices), in this case geological parameters. Especially the Rotliegend Gas Play of the North German Basin belongs to the very complex hydrocarbon systems with more than 70 independent parameters. The Dutch Rotliegend Play for comparison can be characterized by only 10 parameters and is therefore of a simple type. Processes on earth like the formation of systems of hydrocarbon fields as well as environmental systems (e.g. river systems, lakes, islands, sedimentary basins) are subordinated to the dice of nature like in a Casino and are steered invisibly by a selection of rules of the game that one understands as natural laws. The complexity of a system as well as the variedness of its “members” that may be found in anthropogenic systems as well (different properties in thinking, self-reflection, feedback-capabilities, combative and ambitious behavior of individuals with the target to climb upwards in a ranking matrix) is decided by the number of the influencing parameters, represented by dices. Like in a dice-game the exploration of hydrocarbons is unsolvable connected to luck and bad-luck, coincidence and necessity, and to past and future.
Quantum voting protocol without quantum memory

Most of the quantum voting protocols are impractical due to the currently limited quantum storage capabilities. In this article, based on the interference principle of light, we proposed a new quantum voting protocol without quantum memory. In our protocol, the ballot is a sequence of non-orthogonal coherent states, the voting information is encoded by implying different phase shifts on the coherent states, and the vote counting is carried out by performing USD measurement on the coherent states. Particularly, the design of USD measurement on coherent states eliminates the need for quantum storage. Our protocol satisfies the general security requirements of quantum voting protocols and can resist various attacks. In addition, our protocol can be implemented by only linear optics and thus can be experimentally achieved with current technology.
Teaching Design of English Writing Based on UMU

With the development of time, traditional teaching methods cannot meet the needs of education and society for innovative talents. Blended learning conforms to the reform of education and teaching and is becoming the new normal of future education. Blended learning is a kind of teaching mode that integrates face-to-face teaching and online learning after people reflect on online learning to improve the learning effect. This kind of teaching mode that transcends the limitation of time and space brings great convenience to students and teachers. Blended learning can not only satisfy students’ personalized learning style and the independent construction of personalized knowledge but also fully reflect the characteristics of students as cognitive subjects in the learning process. Compared with traditional education, blended learning pays more attention to the perspective of students and creates a truly highly participatory and personalized learning experience for students by using the Internet, mobile technology, and face-to-face teaching, which is the promotion and improvement of classroom teaching. In mixed learning in the Internet age, various online teaching platforms can provide students with diversified learning resources and learning methods. Supported by information technology, the UMU interactive learning platform provides independent learning methods, open learning space, and two-way interaction. The platform has the basic functions of online discussion, uploading and downloading learning resources, and submitting assignments. At the same time, it can be used on the Web and mobile phones to organize classes and interactions at any time. Teachers can create a variety of activities and courses on the platform and can also add interaction in the content link to improve the quality and experience of traditional education through mobile Internet technology.
Towards a generalized Regev's cryptosystem and its related signing cryptosystem: An implementation for hashes and simple ASCII texts in Python

Abstract Quantum computing makes most of nowadays protocols breakable. Fortunately, this technology is still too expensive to be used by anybody. The cryptosystems relying on the LWE problem are still an excellent choice to prepare for the democratization of quantum computing. The motivation of this work is to implement a post-quantum complete and complex asymmetric cryptosystem using the learning with error (LWE) problem. In fact, it is impossible or hard to find a cryptosystem in Python in the literature like the implemented one. As a contribution, this work proposes an implementation of a generalized Regev's Cryptosystem and its related signature and verification functions for achieving the quantum-safety property.\\ It also tests the validity of a list of primes for specified values. Finally, this work completes the cryptosystem with the signing and verification algorithms. These algorithms are news for the scientific community. Errors and omissions excepted. These implementations are based on hard problems related to lattices
Breast Tumor Segmentation in Ultrasound Images Based on U-NET Model

Breast cancer is the second leading cause of cancer death among women in the United States. If detected and diagnosed in the early stages, it increases survival rates and lowers treatment costs. Emerging deep learning methods have enabled researchers to achieve remarkable results in the classification and segmentation of natural images and computer vision tasks. However, medical images continue to face challenges due to a lack of ground truth for segmentation tasks. As a result, this study proposed and developed automatic methods for segmenting breast lesions from ultrasound images with few label images. The proposed U-NET model employs GELU activation function and has been tested on a total of 620 breast ultrasound images, which includes 310 benign and 310 malignant cases. The quantitative analysis includes accuracy, loss function, dice similarity coefficient, and precision. The qualitative analysis compares original images, masks, image predicted, and the output result, which is mask predicted binary image. The proposed U-NET model outperformed previous methods, achieving a DSC of 99.62%, a loss of 3.01%, an accuracy of 98.15%, and a precision of 91.82%. In addition, we have identified some of the innovations made to the original U-NET model.
Open Quantum Random Walks and Quantum Markov chains on Trees II: The recurrence

In the present paper, we construct QMC (Quantum Markov Chains) associated with Open Quantum Random Walks such that the transition operator of the chain is defined by OQRW and the restriction of QMC to the commutative subalgebra coincides with the distribution of OQRW. Furthermore, we first propose a new construction of QMC on trees, which is an extension of QMC considered in Ref. [9]. Using such a construction, we are able to construct QMCs on tress associated with OQRW. Our investigation leads to the detection of the phase transition phenomena within the proposed scheme. This kind of phenomena appears first time in this direction. Moreover, mean entropies of QMCs are calculated.
On Variability due to Local Minima and K-Fold Cross Validation

Abstract Resampling methods such as cross validation or bootstrap are often employed to estimate the uncertainty in a loss function due to sampling variability, usually for the purpose of model selection. In models that require nonlinear optimization, however, the existence of local minima in the loss function landscape introduces an additional source of variability that is confounded with sampling variability. In other words, some portion of the variability in the loss function across different resamples is due to local minima. Given that statistically sound model selection is based on an examination of variance, it is important to disentangle these two sources of variability. To that end, a methodology is developed for estimating each, specifically in the context of K -fold cross validation, and neural networks (NN) whose training leads to different local minima. Random effects models are used to estimate the two variance components—that due to sampling and that due to local minima. The results are examined as a function of the number of hidden nodes, and the variance of the initial weights, with the latter controlling the “depth” of local minima. The main goal of the methodology is to increase statistical power in model selection and/or model comparison. Using both simulated and realistic data, it is shown that the two sources of variability can be comparable, casting doubt on model selection methods that ignore the variability due to local minima. Furthermore, the methodology is sufficiently flexible so as to allow assessment of the effect of other/any NN parameters on variability.
Neural Embeddings for Text

We propose a new kind of embedding for natural language text that deeply represents semantic meaning. Standard text embeddings use the outputs from hidden layers of a pretrained language model. In our method, we let a language model learn from the text and then literally pick its brain, taking the actual weights of the model's neurons to generate a vector. We call this representation of the text a neural embedding. We confirm the ability of this representation to reflect semantics of the text by an analysis of its behavior on several datasets, and by a comparison of neural embedding with state of the art sentence embeddings.
MSCDP: Multi-Step Crowd Density Predictor in Indoor Environment

Abstract Monitoring and predicting crowd movements in indoor environments are of great importance in crowd management to prevent crushing and trampling. Existing works mostly focused on individual trajectory forecasting in a less crowded scene, or crowd counting and density estimation. Only a very few works predict the crowd density distribution. However, this study is failing to realize multi-step prediction or exploiting only density heatmaps modality ignores the information complementation with corresponding video frames. Therefore, we are motivated to predict crowd density distribution in multiple time steps to facilitate long-term prediction. In this paper, a Multi-Step Crowd Density Predictor (MSCDP) to fuse video frame sequences and corresponding density heatmaps, is proposed to accurately forecast the future crowd density heatmaps. To capture long-term periodic movement features, the long-term optical flow context memory (LOFCM) module is designed to store learnable patterns. We conducted extensive experiments on two real-world datasets. Evaluation results show that our MSCDP outperforms the state-of-the-art baseline techniques and MSCDP variants in terms of various prediction errors, demonstrating the effectiveness of MSCDP and each of its key components in multi-step crowd density prediction.
The future of mixed reality is adaptive

In a future where we replace our smartphones and notebooks with mixed reality headsets, the way we create user interfaces will change drastically. Future interfaces will need to adapt automatically to users' context, guided by optimization-based methods and machine learning, to become beneficial for end-users.
PARAMETER ESTIMATION OF COVID-19 COMPARTMENT MODEL IN INDONESIA USING PARTICLE SWARM OPTIMIZATION

Background: The government established a vaccination program to deal with highly reactive COVID-19 cases in Indonesia. In obtaining accurate predictions of the dynamics of the compartment model of COVID-19 spread, a good parameter estimation technique was required.. Purpose: This research aims to apply Particle Swarm Optimization as a parameter estimation method to obtain parameters value from the Susceptible-Vaccinated-Infected-Recovered compartment model of COVID-19 cases. Methods: This research was conducted in April-May 2020 in Indonesia with exploratory design research. The researchers used the data on COVID-19 cases in Indonesia, which was accessed at covid19.go.id. The data set contained the number of reactive cases, vaccinated cases, and recovered cases. The data set was used to estimate the parameters of the COVID-19 compartment model. The results were shown by numerical simulations that apply to the Matlab program. Results: Research shows that the parameters estimated using Particle Swarm Optimization have a fairly good value because the mean square error is relatively small compared to the data size used. Reactive cases of COVID-19 have decreased until August 21, 2021. Next, reactive cases of COVID-19 will increase until the end of 2021. It is because the virus infection rate of the vaccinated population is positive . If occurs before the stationary point, then the reactive cases of COVID-19 will decrease mathematically. Conclusion: Particle Swarm Optimization methods can estimate parameters well based on mean square error and the graphs that can describe the behavior of COVID-19 cases in the future.
Digitalisasi Cagar Budaya Kota Pekanbaru menggunakan web

Recognition of cultural heritage and its preservation raises innovative ideas for digitizing. Digitization began to be discussed as an alternative solution to solving the problem of cultural heritage. Pekanbaru has a cultural heritage that includes buildings, artifacts, and structures. The cultural heritage found in the city of Pekanbaru started from the time of the Siak Kingdom, including the Railway Monument, Sultan Siak Shelter House, Ground Zero Pekanbaru, Red and White Monument, Marhum Pekan Tomb Complex, Pekanbaru Great Mosque, Surau Al Irhash, Tuan Khadi Shelter and Havenmaster's house. This study is intended to digitize the crocodile reserve in the city of Pekanbaru using the PHP and MySql programming languages ​​as data storage. The results of this study contribute to the preservation and recognition of cultural heritage as the nation's heritage. With the existence of a website about cultural heritage, this cultural heritage will not be forgotten by the masses.