Reviewerly/exHarmony huggingface dataset:
The The Implementation of Standard Operating Procedures of Restaurant Services in Developing English Material

This research aims at finding out 1) the characteristics of Food and Beverage Service Management students of Makassar Tourism Polytechnic, 2) the students’ needs in English language for Food and Beverage Service based on Standard Operational Procedure, 3) the designed and developed English language materials based on Standard Operational Procedure for Food and Beverage Service Students, 4) the validity of the developed English Materials based on needs analysis of Standard Operational Procedure for Food and Beverage Service Students. This research applies Research and Development (R&amp;D) method by using the ADDIE Model by taking 117 students of FBS Management department, Makassar Tourism Polytechnic as the respondents. The results of data analysis show that 1) The English competency level of the students of FBS Management department are mostly in the intermediate level (81.20%) 2) The language skills mostly needed by the students of FBS management department is speaking skill and they prefer to study English in the pair work or role play activity. In terms of the SOP of the restaurant services, the results have shown that all of the procedures for each SOP needed by the students of FBS as the English material for FBS Management department. 3) The English material which is designed and developed based on the need analysis of SOP restaurant services, 4) the results of the last validation for the designed and developed material indicate that the material is ready to be used. The quality of the developed materials is categorized as good material. Thus, it can be used as a main source to learn English Food and Beverage Service Management at Makassar Tourism Polytechnic
Counting permutations where the difference between entries located r places apart can never be s (for any given positive integers r and s)

Given positive integers r and s, we use inclusion-exclusion, weighted-counting of tilings, and dynamical programming, in order to enumerate, semi-efficiently, the classes of permutations mentioned in the title.In the process, we revisit the beautiful previous work of Enrique Navarrete, Roberto Tauraso, David Robbins (to whose memory this article is dedicated), and John Riordan.We also present two new proofs of John Riordan's recurrence (from 1965) for the sequence enumerating permutations without rising and falling successions (the r = 1, s = 1 case of the title in the sense of absolute value).The first is fully automatic using the (continuous) Almkvist-Zeilberger algorithm, while the second is purely human-generated via an elegant combinatorial argument.We then state some open questions and pledge donations to the OEIS in honor of the solvers.We conclude with a postscript describing a clever bijection by Rintaro Matsuo, that we were made aware of after the first version was written, that suggests an alternative approach to our counting problem, and that implies that there always exists, for each specific r and s, a polynomial time algorithm for computing these sequences, and also gives strong evidence (but not yet proves) some of the open problems.But as r and s get larger, the implied degree of the "polynomial time algorithm" grows large enough to guarantee that our original, "exponential time" algorithms, are still useful.
Finding your Way Out: Planning Strategies in Human Maze-Solving Behavior

In many situations encountered in our daily lives where we have several options to choose from, we need to balance the amount of planning into the future with the number of alternatives we want to consider to achieve our long-term goals. A popular way to study these planning problems in controlled environments is maze-solving tasks, since they can be precisely defined and controlled in terms of their topology. In our study, participants solved mazes that differed systematically in topological properties regulating the number of alternatives and depth of paths. Replicating previous results, we show the influence of these spatial features on performance and stopping times. Longer and more branched solution paths lead to more planning effort and longer solution times. Additionally, we measured subjects’ eye movements to investigate their planning horizon. Our results suggest that people decrease their planning depth with increasing number of alternatives.
METHODOLOGICAL FOUNDATIONS OF THE PSYCHOLOGY OF THEATRICAL ACTIVITY

Background Th e article reveals acting as an activity, the defi nition of the method of “psychophysical actions” in the context of mastering the acting role as a process of development of higher mental functions of actors. Objective. Th is article attempts to off er a psychological interpretation of the technologies of acting presented in the works of K.S. Stanislavsky, as well as his followers V.O. Toporkov, M.A. Chekhov, E.B. Vakhtangov, V.E. Meyerhold, M.O. Knebel, etc. from the point of view of L.S. Vygotsky’s cultural-historical approach, A.N. Leontiev’s theory of activity, psychological theories of communication and personality. Methods. The authors of the article analyze stage communication and personality development during the development of the role from the point of view of the basics of social psychology and personality psychology, and also reveal scientifi cally based psychological interpretations of the acting profession as a conscious continuous activity aimed at achieving the credibility and organic existence of an actor on stage. Results. Th e results of the theoretical analysis allow us to identify the mediation of theatrical activity by psychological patterns and technologies of analysis of the proposed circumstances of the role in the profession of the artist. Conclusion. As a conclusion, a reasoned position is given on the need for scientifi c study of acting using the methodology of psychological science in order to develop the most eff ective method for the development of the acting profession as a process of formation of higher mental functions and the choice of adequate psychophysical actions in the proposed circumstances of the role.
THE PROBLEM OF CROSS-CULTURAL COMMUNICATION IN FOREIGN RESEARCHES

Background. Intercultural interaction is the basis of positive development in the modern world. However, situations of confl icts and crises seriously disrupt cross-cultural communication. An analysis of the results of studies conducted in diff erent countries and in diff erent situations makes it possible to identify the psychological foundations for overcoming the negative consequences of violations of intercultural interaction. Objective. Present the experience of researching the problem of cross-cultural communication. Methods. Review of foreign literature. Results. An analysis of modern research on the problems of cross-cultural communication has shown that, with the unconditional recognition of the characteristics of the cultures to which the studied groups belong as the leading determinant of the study, there is no socio-psychological typology of cultures, which does not allow building a predictive analysis of the interaction of representatives of diff erent cultures. Th e studies describe various types of indirect, substitutive contact, the conditions of which allow establishing positive relationships, which is especially important in situations of protracted systemic political and economic crises. Researchers believe that objective conditions have developed for the functioning of intercultural psychology and that broad interdisciplinary cooperation is necessary to evaluate the eff ectiveness of the means of cross-cultural communication off ered by psychologists. Conclusion. Th e role of the semiotic approach in the study of cross-cultural communication is revealed. Since cultural diff erences are combined at the level of cultural codes, the semantic aspect of the combination of diff erent cultures is a source of possible misunderstandings and contradictions. Mediated, substitute, indirect contacts of people help to overcome misunderstandings and create a basis for active intercultural cooperation. In this case, conditions are created not only for understanding the representatives of contacting cultures, but the understanding of all representatives of a diff erent culture improves. Th e attitude towards representatives of cultures that do not participate in substitutive contact also improves.Th is eff ect is considered as intercultural competence.
Splitting NegP in Standard Arabic: A Syntactic Study into VSO Word Order in Negative Sentences

This paper strives to overcome the issues of achieving a VSO word order in negated sentences in Standard Arabic. A possible solution would be to split the Negative projection into two, where the higher head hosts the negative particle, and the lower head has the ability to attract and host the verb, which falls within the scope of the negative particle. Splitting the negative projection proved to be sufficient to maintain the integrity and validity of the other theories available in the Minimalist Program. Moreover, it allows for a distinction between the A-movement operation, where verbs move over the Subject to a higher position resulting in a VSO word order, and the A’-Movement operation of a fronted Determiner Phrase (DP) is moved to the utmost left for focal purposes.
Blended High School English Teaching and Multiple Assessment System Construction Strategies

In the post-epidemic era, the teaching mode of high school English has altered from a single face-to-face teaching mode to a hybrid online and offline teaching mode. This mode has a positive significance in maintaining the education order, ensuring the teaching quality of high school English teaching. The objectives of the research are the teaching method and the corresponding multiple assessment systems for high school students, and this paper applies methods of document analyses, investigation and experience summary. Based on the blended high school English teaching, the research combines process evaluation and terminal evaluation organically and diversifies evaluation subjects, evaluation contents, and evaluation manner based on the actual learning situation, building an evaluation system with adaptability and rationality. In this way, it is helpful to inspire teachers to design their teaching process in a more efficient and scientific way and solve the present obstacle that the traditional teaching evaluation system no longer meets the evaluative needs of the new teaching model.
SEA TURTLE CONVERSATION USING ARTIFICIAL INTELLIGENCE

More than half of the world's turtle species are now threatened with extinction, according to a new study published by a group of 51 global turtle experts.Loss of habitat is the biggest threat to turtles globally.Other threats include the pet trade, overconsumption for food and medicine, pollution, invasive species, and climate change.Turtles grow slowly, mature late, and live a long time.This slow and steady lifestyle strategy has served them well for millions of years, but now, in the face of modern pressures, it can be a big liability.There are only seven species of marine turtle on the planet.Sadly, six of them are now considered threatened with global extinction due to a combination of human, environmental and predator threats.Two notable characteristics of pre-Anthropocene turtles were their massive population sizes and correspondingly high biomasses, the latter among the highest values (over 855 kilograms per hectare) ever reported for animals.As a result of their numerical dominance, turtles have played important roles as significant bioturbators of soils, infaunal miners of sea floors, dispersers and germination enhancers of seeds, nutrient cyclers, and consumers.However conservation concerns, the collapse of turtle populations on a global scale has greatly diminished their ecological roles.
Riemannian Stochastic Approximation for Minimizing Tame Nonsmooth Objective Functions

In many learning applications, the parameters in a model are structurally constrained in a way that can be modeled as them lying on a Riemannian manifold. Riemannian optimization, wherein procedures to enforce an iterative minimizing sequence to be constrained to the manifold, is used to train such models. At the same time, tame geometry has become a significant topological description of nonsmooth functions that appear in the landscapes of training neural networks and other important models with structural compositions of continuous nonlinear functions with nonsmooth maps. In this paper, we study the properties of such stratifiable functions on a manifold and the behavior of retracted stochastic gradient descent, with diminishing stepsizes, for minimizing such functions.
Hybrid Probabilistic Logic Programming: Inference and Learning

This thesis focuses on advancing probabilistic logic programming (PLP), which combines probability theory for uncertainty and logic programming for relations. The thesis aims to extend PLP to support both discrete and continuous random variables, which is necessary for applications with numeric data. The first contribution is the introduction of context-specific likelihood weighting (CS-LW), a new sampling algorithm that exploits context-specific independencies for computational gains. Next, a new hybrid PLP, DC#, is introduced, which integrates the syntax of Distributional Clauses with Bayesian logic programs and represents three types of independencies: i) conditional independencies (CIs) modeled in Bayesian networks; ii) context-specific independencies (CSIs) represented by logical rules, and iii) independencies amongst attributes of related objects in relational models expressed by combining rules. The scalable inference algorithm FO-CS-LW is introduced for DC#. Finally, the thesis addresses the lack of approaches for learning hybrid PLP from relational data with missing values and (probabilistic) background knowledge with the introduction of DiceML, which learns the structure and parameters of hybrid PLP and tackles the relational autocompletion problem. The conclusion discusses future directions and open challenges for hybrid PLP.
Developing Hands-on Labs for Source Code Vulnerability Detection with AI

As the role of information and communication technologies gradually increases in our lives, source code security becomes a significant issue to protect against malicious attempts Furthermore with the advent of data-driven techniques, there is now a growing interest in leveraging machine learning and natural language processing as a source code assurance method to build trustworthy systems Therefore training our future software developers to write secure source code is in high demand In this thesis we propose a framework including learning modules and hands on labs to guide future IT professionals towards developing secure programming habits and mitigating source code vulnerabilities at the early stages of the software development lifecycle In this thesis our goal is to design learning modules with a set of hands on labs that will introduce students to secure programming practices using source code and log file analysis tools to predict and identify vulnerabilities In a Secure Coding Education framework we will improve students skills and awareness on source code vulnerabilities detection tools and mitigation techniques integrate concepts of source code vulnerabilities from Function API and library level to bad programming habits and practices leverage deep learning NLP and static analysis tools for log file analysis to introduce the root cause of source code vulnerabilities
Experimental entanglement generation using multiport beam splitters

Multi-photon entanglement plays a central role in optical quantum technologies. One way to entangle two photons is to prepare them in orthogonal internal states, for example, in two polarisations, and then send them through a balanced beam splitter. Post-selecting on the cases where there is one photon in each output port results in a maximally entangled state. This idea can be extended to schemes for the post-selected generation of larger entangled states. Typically, switching between different types of entangled states require different arrangements of beam splitters and so a new experimental setup. Here, we demonstrate a simple and versatile scheme to generate different types of genuine tripartite entangled states with only one experimental setup. We send three photons through a three-port splitter and vary their internal states before post-selecting on certain output distributions. This results in the generation of tripartite W, G and GHZ states. We obtain fidelities of up to $(87.3 \pm 1.1)\%$ with regard to the respective ideal states, confirming a successful generation of genuine tripartite entanglement.
Order-Preserving Squares in Strings

An order-preserving square in a string is a fragment of the form $uv$ where $u\neq v$ and $u$ is order-isomorphic to $v$. We show that a string $w$ of length $n$ over an alphabet of size $\sigma$ contains $\mathcal{O}(\sigma n)$ order-preserving squares that are distinct as words. This improves the upper bound of $\mathcal{O}(\sigma^{2}n)$ by Kociumaka, Radoszewski, Rytter, and Wale\'n [TCS 2016]. Further, for every $\sigma$ and $n$ we exhibit a string with $\Omega(\sigma n)$ order-preserving squares that are distinct as words, thus establishing that our upper bound is asymptotically tight. Finally, we design an $\mathcal{O}(\sigma n)$ time algorithm that outputs all order-preserving squares that occur in a given string and are distinct as words. By our lower bound, this is optimal in the worst case.
Sample Complexity of Kernel-Based Q-Learning

Modern reinforcement learning (RL) often faces an enormous state-action space. Existing analytical results are typically for settings with a small number of state-actions, or simple models such as linearly modeled Q-functions. To derive statistically efficient RL policies handling large state-action spaces, with more general Q-functions, some recent works have considered nonlinear function approximation using kernel ridge regression. In this work, we derive sample complexities for kernel based Q-learning when a generative model exists. We propose a nonparametric Q-learning algorithm which finds an $ε$-optimal policy in an arbitrarily large scale discounted MDP. The sample complexity of the proposed algorithm is order optimal with respect to $ε$ and the complexity of the kernel (in terms of its information gain). To the best of our knowledge, this is the first result showing a finite sample complexity under such a general model.
Visually Grounded Keyword Detection and Localisation for Low-Resource Languages

This study investigates the use of Visually Grounded Speech (VGS) models for keyword localisation in speech. The study focusses on two main research questions: (1) Is keyword localisation possible with VGS models and (2) Can keyword localisation be done cross-lingually in a real low-resource setting? Four methods for localisation are proposed and evaluated on an English dataset, with the best-performing method achieving an accuracy of 57%. A new dataset containing spoken captions in Yoruba language is also collected and released for cross-lingual keyword localisation. The cross-lingual model obtains a precision of 16% in actual keyword localisation and this performance can be improved by initialising from a model pretrained on English data. The study presents a detailed analysis of the model's success and failure modes and highlights the challenges of using VGS models for keyword localisation in low-resource settings.
User Study for Improving Tools for Bible Translation

Technology has increasingly become an integral part of the Bible translation process. Over time, both the translation process and relevant technology have evolved greatly. More recently, the field of Natural Language Processing (NLP) has made great progress in solving some problems previously thought impenetrable. Through this study we endeavor to better understand and communicate about a segment of the current landscape of the Bible translation process as it relates to technology and identify pertinent issues. We conduct several interviews with individuals working in different levels of the Bible translation process from multiple organizations to identify gaps and bottlenecks where technology (including recent advances in AI) could potentially play a pivotal role in reducing translation time and improving overall quality.
Vision Transformer-based Feature Extraction for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes using the image attribute. In this paper, we put forth a new GZSL approach exploiting Vision Transformer (ViT) to maximize the attribute-related information contained in the image feature. In ViT, the entire image region is processed without the degradation of the image resolution and the local image information is preserved in patch features. To fully enjoy these benefits of ViT, we exploit patch features as well as the CLS feature in extracting the attribute-related image feature. In particular, we propose a novel attention-based module, called attribute attention module (AAM), to aggregate the attribute-related information in patch features. In AAM, the correlation between each patch feature and the synthetic image attribute is used as the importance weight for each patch. From extensive experiments on benchmark datasets, we demonstrate that the proposed technique outperforms the state-of-the-art GZSL approaches by a large margin.
Applications and resource reductions in measurement-based variational quantum eigensolvers

We discuss the procedure for obtaining measurement-based implementations of quantum algorithms given by quantum circuit diagrams and how to reduce the required resources needed for a given measurement-based computation. This forms the foundation for quantum computing on photonic systems in the near term. To demonstrate that these ideas are well grounded we present three different problems which are solved by employing a measurement-based implementation of the variational quantum eigensolver algorithm (MBVQE). We show that by utilising native measurement-based gates rather than standard gates, such as the standard CNOT, MBQCs may be obtained that are both shallow and have simple connectivity while simultaneously exhibiting a large expressibility. We conclude that MBVQE has promising prospects for resource states that are not far from what is already available today.
Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index

Safety goes first. Meeting and maintaining industry safety standards for robustness of artificial intelligence (AI) and machine learning (ML) models require continuous monitoring for faults and performance drops. Deep learning models are widely used in industrial applications, e.g., computer vision, but the susceptibility of their performance to environment changes (e.g., noise) \emph{after deployment} on the product, are now well-known. A major challenge is detecting data distribution shifts that happen, comparing the following: {\bf (i)} development stage of AI and ML models, i.e., train/validation/test, to {\bf (ii)} deployment stage on the product (i.e., even after `testing') in the environment. We focus on a computer vision example related to autonomous driving and aim at detecting shifts that occur as a result of adding noise to images. We use the population stability index (PSI) as a measure of presence and intensity of shift and present results of our empirical experiments showing a promising potential for the PSI. We further discuss multiple aspects of model monitoring and robustness that need to be analyzed \emph{simultaneously} to achieve robustness for industry safety standards. We propose the need for and the research direction toward \emph{categorizations} of problem classes and examples where monitoring for robustness is required and present challenges and pointers for future work from a \emph{practical} perspective.