Reviewerly/exHarmony huggingface dataset:
Cartesian Tree Matching and Indexing

We introduce a new metric of match, called Cartesian tree matching, which means that two strings match if they have the same Cartesian trees. Based on Cartesian tree matching, we define single pattern matching for a text of length n and a pattern of length m, and multiple pattern matching for a text of length n and k patterns of total length m. We present an O(n+m) time algorithm for single pattern matching, and an O((n+m) log k) deterministic time or O(n+m) randomized time algorithm for multiple pattern matching. We also define an index data structure called Cartesian suffix tree, and present an O(n) randomized time algorithm to build the Cartesian suffix tree. Our efficient algorithms for Cartesian tree matching use a representation of the Cartesian tree, called the parent-distance representation.
GENERALIZING GABOR FILTERS BASED ON ATEB-FUNCTIONS

Image filtering attempts to achieve greater resolution. There is a large number of filters that allows you to bring images with clear borders. In addition, noise is present when digitizing images. One of the most common types of filtering is the Gabor filter. It allows you to restore the image with the contour allocation at a certain frequency. Its core looks like elements of the Fourier basis, which is multiplied by Gaussian. The widespread use of Gabor filters for filtration is due to the fact that it gives a strong response at those points of the image where there is a component with local features of frequency in space and orientation. It is proposed to use the Ateb-Gabor filter, which greatly expands the well-known Gabor filter. The Ateb-Gabor filter combines all the properties of a harmonic function, which is multiplied by Gaussian. As a harmonic function, it is proposed to use the Ateb-functions that greatly extend the trigonometric effect. The developed filter is applied to the images. The Ateb-Gabor filter depends on the frequency and directions of the quasiperiodic structure of the image. Usually, to simplify the task, the average image frequency is calculated. It is unchanged at every point. Filtration of images is based on the generalized Ateb-Gabor filter. Influence of filtering parameters on images is investigated. The properties of periodic Ateb-functions are investigated. The value of the period from which the filtering results depend on is calculated. Ateb-Gabor filtering allowed for wider results than the classic Gabor filter. The one-dimensional Gabor filter based on the Ateb-functions gives the possibility to obtain more lenient or more convex forms of function at the maximum described in this study. In this way, filtration with a large spectrum of curves can be realized. This provides quick identification, since a more versatile kind of filtering has been developed.
To the Issue of Psychological Features of the Actor's Personality Development: the Experience of Typological Analysis

We present the results of the study of the actors’ personality development psychological characteristics. It was hypothesized that the analysis of the similarities between individual profiles of student-actors can reveal personal typological features at the stage of selection and training in theatrical college. Sample: students of the theatre college, 129 students at the beginning and 117 — at the end of the 1st year, 64 — second year, 54 — third year, and 41 — fourth year students. Methods: R. Cattell’s 16 Personality Factors Test. Results: we identified personal characteristics that manifested themselves at the stage of admission to college and inhibited successful professional development: rejection of authorities, desire to dominate, refusal to cooperate and work together. Cluster analysis revealed four personal types of student-actors related to theatrical roles. The following strategies of pedagogical influence manifested in the process of work with college students: individualization of training, transfer of professional knowledge through senior students. Conclusions: the study proved the efficiency and feasibility of using the method of cluster analysis and typological approach in the study of acting talent.
A New Approach to Modelling Centralised Reputation Systems

A reputation system assigns a user or item a reputation value which can be used to evaluate trustworthiness. Blömer, Juhnke and Kolb in 2015, and Kaafarani, Katsumata and Solomon in 2018, gave formal models for centralised reputation systems, which rely on a central server and are widely used by service providers such as AirBnB, Uber and Amazon. In these models, reputation values are given to items, instead of users. We advocate a need for shift in how reputation systems are modelled, whereby reputation values are given to users, instead of items, and each user has unlinkable items that other users can give feedback on, contributing to their reputation value. This setting is not captured by the previous models, and we argue it captures more realistically the functionality and security requirements of a reputation system. We provide definitions for this new model, and give a construction from standard primitives, proving it satisfies these security requirements. We show that there is a low efficiency cost for this new functionality.
Assertiveness in Self-Fulfillment and Professional Success. Interpersonal Dynamics in the Didactic Relation

Assertive communication is a basic tool in the acquirements of a debutant teacher and the development of communicational skills in this respect becomes vital for his professional success.Thus, this paper sets out the concepts of pedagogy of the group and assertive communication in the light of the newest theories and researches and argues about the need of an integrated development of the components of assertiveness-initiation of communication, setting boundaries, constructive expression of feelings, strategies for the increase of self-esteem-to students, future professors.It explains the necessity and benefits of developing assertiveness in self-success as well as in professional success.The purpose of the study is to investigate the perceptions of the students who are in the final years, regarding their own assertive behaviours and how they understand the aim of assertive communication in didactic relationship.In this respect, we organized and conducted three exploratory workshop activities based on focus group.Our results provide clarification regarding the students' personal perspectives on how to build a teacher-student relationship, on the models provided and the acquired skills, on the effective strategies of self-assertion they use consciously related to conflict management and injustice situations, as well as techniques used to manage emotions in the context of didactic relationships.It highlights the specific educational needs of students and the benefits of the democratic pedagogical style, based on assertiveness.
Feature attention based detection model for medical text

Recently, many breakthroughs have been achieved in the text detection field; however, printed text detection performance remains unsatisfactory. To address this issue, this paper proposes a refined feature attention based text detection model comprising a feature attention FCNs and text instance se gmentation. With the feature attention mechanism, the FCNs model is optimized effectively, which enables the network to learn more precise and accurate features. Therefore, the network can better detect noise-intensive and dense text in medical images. In addition, a centerline-based text region detection algorithm is proposed to process the output of network during text instance segmentation. This algorithm calculates each text region according to the geometric information of the text instance; thus, it is able to process multi-oriented text instances precisely. The proposed model can be trained end-to-end and does not require post-processing operations, which greatly increases detection efficiency. The proposed model achieved excellent results on a medical text image dataset. Compared to existing text detection models, the proposed model demonstrates significantly better performance in terms of F-meatures and detection speed.
Person re‐identification in the edge computing system: A deep square similarity learning approach

Summary The proliferation of mobile phones and webcams has led to an exponential increase in video data. One of the key technologies of video surveillance systems is Person Re‐identification (Re‐ID). The Re‐ID is used to identify whether the target pedestrian is the same person, and through scene matching, cross‐field tracking and track prediction of suspected pedestrians can be achieved. The edge computing has become the first choice for video analysis and processing, because of shorter response time and more efficient processing. In this paper, we propose a deep square similarity learning (DSSL), which considers the difference correlation, first‐order correlation, and two‐order correlation of image pairs. The training data automatically adjusts the network parameters and the weights of the three correlations to minimize the loss of the training set. Moreover, we conducted experiments on the challenging Re‐ID databases CuHK03 and Male1501. Compared with algorithm IDLA and DHSL, the first recognition rate is increased by 18% and 40%, respectively, in CuHK03, and 22% and 80% in Male1501. Then, we propose an online deep square similarity learning (ODSSL) algorithm to solve problem of data updating after the model is established by DSSL strategy. Meanwhile, ODSSL shows shorter update time and more efficient processing.
The Use of Folklore as Educational Entertainment Materials

Folklore is an ancient story that has become a special characteristic for every nation with diversity of cultures that embraces the cultural and historical richness of the nation. Usually the characters that appear in the folklore are in the form of animals, humans or gods. Generally, these folktales tell about an incident or the origin of a place. In this study, it focuses on the use of folklore as an educational entertainment in the classroom. The main method used to collect information is through a literature review by doing research from outside and within a country that is restricted to the educational entertainment of teaching and learning only. The finding of this study is that the use of folklore as a teaching material of educational entertainment can help students in increasing their interest and focus in the classroom. In fact, according to Rosliah Kiting, (2011), the use of storybook as a medium in Kadazandusun Language teaching and learning is able to enrich the vocabulary as well as inculcating of noble values ​​that are very effective through adapting to the activities in the classroom. The use of folklore and the humorous elements encompassed in educational entertainment in the classroom will create excitement in learning sessions and the learning will be more interactive and focused.
A Graphical Deep Inference System for Intuitionistic Logic

A graphical approach to intuitionistic propositional logic is presented. The system GrIn is a deep inference system and is formulated in terms of Peirce’s existential graphs. GrIn is shown to be sound and complete with respect to the class of all Heyting algebras. The system GrIn is shown to be equivalent to the Gentzen sequent calculus G3ip via translations between intuitionistic graphs and intuitionistic formulae. We also discuss the intuitionistic interpretation of logical constants proposed by the graphical approach.
Fall classification based on sensor data from smartphone and smartwatch

A fall is an unintentional event in which a person comes to rest on the ground/floor/a lower level. Falls are the second driving cause of inadvertent injury deaths around the world. Falls pose a major health risk to children and the elderly (especially above the age of 65). Injuries from falls make it difficult for an individual to move around, do regular activities, or live on their own. Hence, studies on methods able to recognize Activities of Daily Living (ADLs) and to identify falls is on the rise. The acknowledgment of ADLs may permit to gather the sum of physical activity that a subject performs daily, whilst the timely identification of falls may aid in diminishing their consequences. To improve the accuracy of the classifier, the data from smartwatches and smartphones are combined. There are not many publicly accessible datasets incorporating data from both smartwatchesand smartphones. Hence, the data will be collected independently. The Decision tree (J48) classifier will be used to classify the falls. The evaluation metrics include: Precision, Recall, F1 Score and the confusion matrix.
Identification of Handwritten Complex Mathematical Equations

The mathematical notation is well known and used throughout the world.Humanity has evolved from simple methods to represent accounts to the current formal notation capable of modeling complex problems.In addition, mathematical equations are a universal language in the scientific world, and many resources such as science and engineering technology, medical field also not an exception containing mathematics have been created during the last decades.However, to efficiently access all that information, scientific documents must be digitized or produced directly in electronic formats.Although most people are able to understand and produce mathematical information, introducing mathematical equations into electronic devices requires learning special notations or using editors.The proposed methodology is focused on developing a method to recognize intricate handwritten mathematical equations.For pre-processing, Gray conversion and Weiner filtering are used.Segmentation is performed using the morphological operations, which increase the efficiency of the subsequent image of equation.Finally Neural Network based template matching technique is used to recognize the image of handwritten mathematical equation.
The Opportunities and Limitations of Using Artificial Neural Networks in Social Science Research

Artificial Neural Networks (ANNs) are being increasingly used in various disciplines outside computer science, such as bibliometrics, linguistics, and medicine. However, their uptake in the social science community has been relatively slow, because these highly non-linear models are difficult to interpret and cannot be used for hypothesis testing. Despite the existing limitations, this paper argues that the social science community can benefit from using ANNs in a number of ways, especially by outsourcing laborious data coding and pre-processing tasks to machines in the early stages of analysis. Using ANNs would enable small teams of researchers to process larger quantities of data and undertake more ambitious projects. In fact, the complexity of the pre-processing tasks that ANNs are able to perform mean that researchers could obtain rich and complex data typically associated with qualitative research at a large scale, allowing to combine the best from both qualitative and quantitative approaches.
Cognition-based hybrid path planning for autonomous underwater vehicle target following

Intelligent path planning is one of the key techniques for autonomous underwater vehicles for the purpose of target detection, environmental survey and so on. In order to realize automatic motion plan, an intelligent cognitive architecture for autonomous underwater vehicle motion planning has been proposed to realize complicated target detection and mobile target following in the disturbance environment. A novel adaptive ant colony optimization and particle swarm optimization fusion-based fuzzy rules optimization algorithm has been proposed to generate optimized fuzzy rules. Through this optimization algorithm, the preliminary fuzzy rules can be optimized to realize intelligent motion planning for complicated operation tasks. Experiments of channel following for wall detection and mobile target following in the oceanic environment have verified the validity of path planning method in the implementation of detection and operation tasks.
Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection

The detection of nuclei is one of the most fundamental components of computational pathology. Current state-of-the-art methods are based on deep learning, with the prerequisite that extensive labeled datasets are available. The increasing number of patient cohorts to be analyzed, the diversity of tissue stains and indications, as well as the cost of dataset labeling motivates the development of novel methods to reduce labeling effort across domains. We introduce in this work a weakly supervised 'inter-domain' approach that (i) performs stain normalization and unpaired image-to-image translation to transform labeled images on a source domain to synthetic labeled images on an unlabeled target domain and (ii) uses the resulting synthetic labeled images to train a detection network on the target domain. Extensive experiments show the superiority of the proposed approach against the state-of-the-art 'intra-domain' detection based on fully-supervised learning.
Structure-Aware Residual Pyramid Network for Monocular Depth Estimation

Monocular depth estimation is an essential task for scene understanding. The underlying structure of objects and stuff in a complex scene is critical to recovering accurate and visually-pleasing depth maps. Global structure conveys scene layouts, while local structure reflects shape details. Recently developed approaches based on convolutional neural networks (CNNs) significantly improve the performance of depth estimation. However, few of them take into account multi-scale structures in complex scenes. In this paper, we propose a Structure-Aware Residual Pyramid Network (SARPN) to exploit multi-scale structures for accurate depth prediction. We propose a Residual Pyramid Decoder (RPD) which expresses global scene structure in upper levels to represent layouts, and local structure in lower levels to present shape details. At each level, we propose Residual Refinement Modules (RRM) that predict residual maps to progressively add finer structures on the coarser structure predicted at the upper level. In order to fully exploit multi-scale image features, an Adaptive Dense Feature Fusion (ADFF) module, which adaptively fuses effective features from all scales for inferring structures of each scale, is introduced. Experiment results on the challenging NYU-Depth v2 dataset demonstrate that our proposed approach achieves state-of-the-art performance in both qualitative and quantitative evaluation. The code is available at https://github.com/Xt-Chen/SARPN.
Heuristic construction of codeword stabilized codes

The family of codeword stabilized codes encompasses the stabilizer codes as well as many of the best known nonadditive codes. However, constructing optimal $n$-qubit codeword stabilized codes is made difficult by two main factors. The first of these is the exponential growth with $n$ of the number of graphs on which a code can be based. The second is the NP-hardness of the maximum clique search required to construct a code from a given graph. We address the second of these issues through the use of a heuristic clique finding algorithm. This approach has allowed us to find $((9,97\ensuremath{\le}K\ensuremath{\le}100,2))$ and $((11,387\ensuremath{\le}K\ensuremath{\le}416,2))$ codes, which are larger than any previously known codes. To address the exponential growth of the search space, we demonstrate that graphs that give large codes typically yield clique graphs with a large number of nodes. The number of such nodes can be determined relatively efficiently, and we demonstrate that $n$-node graphs yielding large clique graphs can be found using a genetic algorithm. This algorithm uses a crossover operation based on spectral bisection that we demonstrate to be superior to more standard crossover operations. Using this genetic algorithm approach, we have found ((13, 18, 4)) and ((13, 20, 4)) codes that are larger than any previously known code. We also consider codes for the amplitude damping channel. We demonstrate that for $n\ensuremath{\le}9$, optimal codeword stabilized codes correcting a single amplitude damping error can be found by considering standard form codes that detect one of only three of the ${3}^{n}$ possible equivalent error sets. By combining this error-set selection with the genetic algorithm approach, we have found ((11, 68)) and ((11, 80)) codes capable of correcting a single amplitude damping error and ((11, 4)), ((12, 4)), ((13, 8)), and ((14, 16)) codes capable of correcting two amplitude damping errors.
Optimal Path Planning for UAV Patrolling in Forest Fire Prevention

Path planning is one of the key aspects of autonomous unmanned aerial vehicles (UAVs). In this paper, an effective path planning approach based on a hybrid ant colony optimizations (ACO) algorithm for UAV patrolling in forest fire prevention missions is proposed. The proposed approach takes two steps, namely local path planning and global path planning, to find the shortest feasible path flying through multiple target points with obstacle avoidance. In local planning phase, a dubins-path based A* algorithm is applied to find the optimal path between every two target points, the resulting path would be flyable and safe. Later, the visiting order of each target point would be determined by an improved ACO algorithm in order to minimize the length of the final path. Simulation result shows the proposed algorithm can efficiently find a shortest flight path that fulfills the requirements of UAV based patrolling task in forest fire prevention mission.
Decision Process in Human-Agent Interaction: Extending Jason Reasoning Cycle

The main characteristic of an agent is acting on behalf of humans. Then, agents are employed as modeling paradigms for complex systems and their implementation. Today we are witnessing a growing increase in systems complexity, mainly when the presence of human beings and their interactions with the system introduces a dynamic variable not easily manageable during design phases. Design and implementation of this type of systems highlight the problem of making the system able to decide in autonomy. In this work we propose an implementation, based on Jason, of a cognitive architecture whose modules allow structuring the decision-making process by the internal states of the agents, thus combining aspects of self-modeling and theory of the mind.
The Prediction of Breast Biopsy Outcomes Using Two Data Mining Algorithms Based on Parameter Variations

Objective: Breast cancer is the type of cancer that causes the most death in women in the United States after lung cancer. The objective of this study was to predict breast biopsy results using age, BI-RADS, Mass Shape, Mass Margin, Mass Density by Multilayer Perceptron and Random Forest algorithms. Material and Methods: The dataset contains a BI-RADS assessment, the patient's age and three BI-RADS attributes together with the ground truth for 516 benign and 445 malignant masses. WEKA software was used for data mining. The data mining methods of the Multilayer Perceptron and Random Forest were used to predict the severity of cancer. Results: Accuracy, F-measure and Root Mean Squared Error values of the Multilayer Perceptron model were found 0.837, 0.833 and 0.352, respectively while accuracy, F-measure and Root Mean Squared Error values of the Random Forest model were found 0.816, 0.814 and 0.396, respectively. The Multilayer Perceptron method provided a better prediction of breast cancer diagnosis than the Random Forest method and a software was developed based on the best model created by the Multilayer Perceptron method. Conclusion: Consequently, a model that was built with the MLP method can be used as an alternative in the diagnosis of patients and be an assistant tool for physicians.