Reviewerly/exHarmony huggingface dataset:
Multi-stage Fusion for One-Click Segmentation

Segmenting objects of interest in an image is an essential building block of applications such as photo-editing and image analysis. Under interactive settings, one should achieve good segmentations while minimizing user input. Current deep learning-based interactive segmentation approaches use early fusion and incorporate user cues at the image input layer. Since segmentation CNNs have many layers, early fusion may weaken the influence of user interactions on the final prediction results. As such, we propose a new multi-stage guidance framework for interactive segmentation. By incorporating user cues at different stages of the network, we allow user interactions to impact the final segmentation output in a more direct way. Our proposed framework has a negligible increase in parameter count compared to early-fusion frameworks. We perform extensive experimentation on the standard interactive instance segmentation and one-click segmentation benchmarks and report state-of-the-art performance.
Mixture-Based Unsupervised Learning for Positively Correlated Count Data

The Multinomial distribution has been widely used to model count data. However, its Naive Bayes assumption usually degrades clustering performance especially when correlation between features is imminent, i.e., text documents. In this paper, we use the Negative Multinomial distribution to perform clustering based on finite mixture models, where the mixture parameters are to be estimated using a novel minorization-maximization algorithm, thriving in high-dimensionality optimization settings. Furthermore, we integrate a model-based feature selection approach to determine the optimal number of components in the mixture. To evaluate the clustering performance of the proposed model, three real-world applications are considered, namely, COVID-19 analysis, Web page clustering and facial expression recognition.
Modeling Humanoid Robots Using Fuzzy Set Extensions

Humanoid robots will be very usable in human society to improve the quality of human life in the future. They can be used for assisting the sick and elderly people, and dirty or dangerous jobs. Human behaviors are mostly emotional and based on the degrees of feelings such as being strongly sad or being slightly happy. Fuzzy sets theory can be very useful in modeling humanoid robots’ emotions and behaviors. Pythagorean fuzzy setsPythagorean fuzzy sets (PFS) are an extension of intuitionistic fuzzy setsIntuitionistic fuzzy sets introduced by Atanassov (Atanassov in Fuzzy Sets Syst. 20:87–96, [1]). PFSs have the advantage of providing larger domains for assigning membership and non-membership degrees satisfying that their squared sum is at most equal to one. PFS have been often used in modeling the problems under vagueness and impreciseness in order to better define the problems together with the hesitancy of decision makers. Different human emotions and behaviors can be modeled in humanoid robots (HR) by fuzzy sets. In this paper, facial expressionsFacial expression of a humanoid robot are modeled depending on the degrees of the emotions. Larger degree of emotion causes a stronger indicator of the facial mimic.
DEVELOPING HoPALM AS TEACHING MATERIALS FOR SECONDARY HIGH SCHOOL STUDENTS

This research was aimed to develop a product named HoPALM, hot-potatoes assisted listening material for secondary high school students. This research applied development research and ADDIE procedure. In the analysis stage, the researcher found the fundamental problems and needs of the students as well as the opportunity as the background of developing HoPALM. In the designing and developing stage, the researcher collected the sources that needed and develop HoPALM by using the features available on the hot-potatoes program. After the product assessed by the material content and the multimedia experts, HoPALM considered as valid and applicable to use, the researcher implemented the product to the two school targets, SMA Negeri 1 and 2 Sungai Kakap. According to 208 students from two schools, as many as 156 students or 75% of them feel more motivated and prefer to learn English. As many as 123 students or 59.1% of them were able to overcome difficulties encountered. 120 students or 57.7% admitted that their listening ability increased. Furthermore, although not spontaneously, as many as 196 students or 95.6%, confessed being able to understand what they are listening to, especially if there are images in that section. Most importantly, as many as 120 students or 57.7% more motivated in learning English.
Evolutionary Strategies with Analogy Partitions in P-Guessing Games

In Keynesian Beauty Contests notably modeled by p-guessing games, players try to guess the average of guesses multiplied by p. Theoretical and experimental research in the spirit of level k models has characterized the behavior of agents with different levels of reasoning when p is persistently above or below 1. Convergence of plays to Nash equilibrium has often been justified by agents' learning. However, interrogations remain on the origin of reasoning types and equilibrium behavior when learning takes place in unstable environments. When successive values of p can take values above and below 1, bounded rational agents may learn about their environment through simplified representations of the game, reasoning with analogies and constructing expectations about the behavior of other players. We introduce an evolutionary process of learning to investigate the dynamics of learning and the resulting optimal strategies in unstable p-guessing games environments with analogy partitions. As a validation of the approach, we first show that our genetic algorithm behaves consistently with previous results in persistent environments, converging to the Nash equilibrium. We characterize strategic behavior in mixed regimes with unstable values of p. Varying the number of iterations given to the genetic algorithm to learn about the game replicates the behavior of agents with different levels of reasoning of the level k approach. This evolutionary process hence proposes a learning foundation for endogenizing existence and transitions between levels of reasoning in cognitive hierarchy models.
First Language and Second Language Proficiency: A Case of Bisaya Learners

This study purported to determine the L1 and L2 proficiency of the respondents and to determine if a significant relationship can be drawn between proficiencies in the two languages. The study employed a descriptive-quantitative-correlational design using respondents’ ratings for the two subjects. The 113 respondents are third graders, nearly half are males and more than half are females with age means of 8.56 and 8.63 respectively. The analysis of the data showed that the respondents’ level of proficiency in the L1 and L2 are relatively similar and both characterized as ‘approaching proficiency’. Moreover, the data exposed a strong positive significant correlation between the respondents L1 and L2 proficiencies.
Organization and Complexity

The universe, in its diversity, has atoms; molecules; stars; galaxies; and—at least on one small planet—living organisms, humans, and societies. These different entities, which are different from each other and not reducible to each other, have in common the fact that each is made up of organized elements that form a whole. What we perceive as the world around us are these wholes. The significance of the general notion of organization is not immediately evident. It is not in a search for a common minimum that we need to concentrate our efforts. It is in the manner of perceiving, conceiving, and thinking about the things of our world in an organized way. To obtain a generic, rather than general, concept, it is the idea of organization that we have to delve into. What is organization? Organization binds elements (particles, atoms, molecules, cells, individuals, etc.) in relationships that thus become components of a whole. In the first definition, organization is a structure of relations between components to produce a whole with qualities unknown to these components outside the structure. Hence, organization connects parts to each other and parts to the whole. This gives rise to the complex character of the relation between the parts and the whole. Dilthey1 had already stated, “A whole cannot be understood except by understanding its constituent parts, which cannot be understood except by understanding the whole.” Two centuries earlier, Pascal2 referred to this circular relation, “I consider it impossible to know the parts without knowing the whole, or to know the whole without knowing the parts.” There is a close relation among the concepts of organization, interrelation, and system.b These three terms, although inseparable, can be distinguished from each other. The concept of interrelation refers to the types and forms of links between elements and between the elements and the whole. The concept of system refers to the complex unit of an interrelated whole, to its characters and properties. The concept of organization refers to the structuring of the parts within, with and through a whole. The two notions, organization and system, are linked by that of interrelation: the whole interrelation, if it has stability or regularity, acquires an organized character and produces a system. There is a circular reciprocity between these three terms. When the notion of system disperses the quality of being and existing (to say “living systems” tends to take the emphasis away from living beings and their existential dimension), the notion of organization refers to to something concrete.
Microsoft: Mixed Reality, the Future of Computing and Human Interactions

In this keynote, Ash will talk about how emerging MR use cases impact MR hardware developments.
Compositional Cyber-Physical Systems Modeling.

Assuring the correct behavior of cyber-physical systems requires significant modeling effort, particularly during early stages of the engineering and design process when a system is not yet available for testing or verification of proper behavior. A primary motivation for `getting things right' in these early design stages is that altering the design is significantly less costly and more effective than when hardware and software have already been developed. Engineering cyber-physical systems requires the construction of several different types of models, each representing a different view, which include stakeholder requirements, system behavior, and the system architecture. Furthermore, each of these models can be represented at different levels of abstraction. Formal reasoning has improved the precision and expanded the available types of analysis in assuring correctness of requirements, behaviors, and architectures. However, each is usually modeled in distinct formalisms and corresponding tools. Currently, this disparity means that a system designer must manually check that the different models are in agreement. Manually editing and checking models is error prone, time consuming, and sensitive to any changes in the design of the models themselves. Wiring diagrams and related theory provide a means for formally organizing these different but related modeling views, resulting in a compositional modeling language for cyber-physical systems. Such a categorical language can make concrete the relationship between different model views, thereby managing complexity, allowing hierarchical decomposition of system models, and formally proving consistency between models.
Feature-Wise Opinion Summarization of Consumer Reviews Using Domain Ontology

There is a rapid increase of contents generated by users such as reviews and comments over the Internet. Analyzing a review is critical for data-driven decision making and corporate intelligence of individuals and organizations. This paper focuses on feature-wise sentiment analysis of reviews which results in a summary based on the important features of a specific domain. The proposed approach consists of two major processes: data acquisition and preprocessing to aggregate reviews from different sources. Ontology is generated based on the domain-specific knowledge, and it is updated with new features, opinions and sentiment orientation. Sentiment determination process uses the lexicon, SentiWordNet to discovery the sentiment orientation of each opinion word and PMI value to find the sentiment expectation of each new feature. The summarization of reviews represents the feature-level summary of reviews to the user. Evaluation results show high precision and recall values which measures the exactness and completeness of the approach.
A Study on the Actual Condition and Perception of Special Education Online Learning due to COVID-19

This study was conducted to investigate the current status of e-learning for special education students and to examine how special education teachers and parents perceive e-learning. To this end, we conducted a survey with special education teachers and parents. A total of 164 special education teachers and 127 parents participated in this survey, and the questionnaire was developed by referring to previous studies. To achieve the objectives of the study, the results of the survey were analyzed by performing frequency analysis, exploratory factor analysis and reliability analysis, cross-analysis, and an independent t-test. As a result of the study, we determined that both special education teachers and parents were dissatisfied with online learning—they responded negatively concerning the effectiveness of online learning. The platforms that are mainly used include LMS, band, classting, EBS, and other online learning centers. We also determined that participants mainly used self-made learning materials, study papers, and textbooks. The reason that e-learning was not effective was mostly due to special education students' personal difficulties and home e-learning difficulties. The target of online learning was that the most integrated students were the most suitable. For effective e-learning to take place, there must be customized learning environments and content for students with disabilities that is developed at the national level. The comfort level with e-learning was found to be lower than the average, and the parent group was less comfortable than the special education teacher group. Based on these results, we suggested ways to improve online education in special education in the future.
Pathobox: The Collaborative Tele-pathology Platform with Access Management

Recent technological advances in medical informatics led to the adoption of new technologies in pathology clinical area. Digital pathology is allowing the acquisition, storage, and distribution of pathological digital samples, which are gathered by scanners and displayed in network workstations. This paper discusses the opportunities and challenges of digital pathology, and how it is changing education, training, and medical practice nowadays. A new paradigm of collaborative telepathology is proposed through a cloud-based architecture, fully compliant with the DICOM standard, that integrates a cross-platform pathology viewer, collaborative tools, virtual work-spaces, and personal storage areas. Data management and privacy are ensured through the implementation of a role-based access control mechanism. The solution was designed to serve distinct use cases, including telepathology and e-academy.
Motivation as preparatory department students' personal readiness to learn Russian as a foreign language

The article covers motivation as a complex psychopedagogical process reflecting the level of preparatory department students' personal readiness to learn Russian as a foreign language. The article shows that teaching Russian is a complex multi-dimensional process closely linked to an individual's motivational abilities. The author analyzes educational and cognitive motivation along with motivation for foreign-language communication. The article describes the results of research on preparatory department students' personal motivation.
Teachers’ Competence in Information and Communications Technology (ICT) as an Educational Tool in Teaching: An Empirical Analysis for Program Intervention

Information and Communications Technology (ICT) provides access to information through telecommunication technologies to interact in the digital world and continues to evolve to bring remarkable changes in the educational system. In this context, descriptive research determined and described the level of teachers’ ICT competence in different skill-set through a standardized survey questionnaire adapted from the National Information and Communications Technology Competency Standards NICS-Basic of the Commission of Information and Communication Technology. A total of seventy-two secondary teachers in one selected Integrated High School in the Philippines for the school year 2018-2019 were chosen purposively as respondents of the study. To ensure a one-hundred percent retrieval of data, the researchers personally administered the survey questionnaire to the respondents. Frequency and Percentage, Mean, T-test, and Analysis of Variance (ANOVA) were used as a statistical tool to analyze the results using the Statistical Package for the Social Sciences (SPSS) software. The findings revealed that teachers’ Information and Communications Technology competence in different skill-set is intermediate. Moreover, age, gender, the highest educational attainment, and teaching position has no significant effect to the ICT competence of teachers. However, the number of teachers ICT-related seminars and training attended in ICT basics, spreadsheet, computer ethics and security were significant. Thus, the researchers proposed to integrate ICT in the In-Service Training (INSET) for teachers anchored on the skill-set that has an intermediate level by introducing the NICS-Advance to enhance and improve teachers’ ICT competence.
Quantum state verification in the quantum linear systems problem

We analyze the complexity of quantum state verification in the context of solving systems of linear equations of the form $A \vec x = \vec b$. We show that any quantum operation that verifies whether a given quantum state is within a constant distance from the solution of the quantum linear systems problem requires $q=\Omega(\kappa)$ uses of a unitary that prepares a quantum state $\left| b \right>$, proportional to $\vec b$, and its inverse in the worst case. Here, $\kappa$ is the condition number of the matrix $A$. For typical instances, we show that $q=\Omega(\sqrt \kappa)$ with high probability. These lower bounds are almost achieved if quantum state verification is performed using known quantum algorithms for the quantum linear systems problem. We also analyze the number of copies of $\left| b \right>$ required by verification procedures of the prepare and measure type. In this case, the lower bounds are quadratically worse, being $\Omega(\kappa^2)$ in the worst case and $\Omega(\kappa)$ in typical instances with high probability. We discuss the implications of our results to known variational and related approaches to this problem, where state preparation, gate, and measurement errors will need to decrease rapidly with $\kappa$ for worst-case and typical instances if error correction is not used, and present some open problems.
Frameworks for HCI Research

The chapter proposes a general definition of the concept of framework, together with its derivation and exemplification. The definition and an existing conception are then applied to HCI research. The resulting core HCI research framework comprises discipline (as an academic field of study), general problem (as the design of human–computer interactions), particular scope (as the design of human–computer interactions to do something as desired), research (as the diagnosis of design problems and the prescription of design solutions, as they relate to performance, for the acquisition and for the validation of knowledge to support practices), knowledge (as acquired and validated, supporting practices) and practices (as supported by knowledge, acquired and validated by research). The core framework is sufficiently well specified for its application to approaches.
AirMatch: An Automated Mosaicing System with Video Preprocessing Engine for Multiple Aerial Feeds

Surveillance through aerial systems is in place for years. Such systems are expensive, and a large fleet is in operation around the world without upgrades. These systems have low resolution and multiple analog cameras on-board, with Digital Video Recorders (DVRs) at the control station. Generated digital videos have multi-scenes from multi-feeds embedded in a single video stream and lack video stabilization. Replacing on-board analog cameras with the latest digital counterparts requires huge investment. These videos require stabilization and other automated video analysis prepossessing steps before passing it to the mosaicing algorithm. Available mosaicing software are not tailored to segregate feeds from different cameras and scenes, automate image enhancements, and stabilize before mosaicing (image stitching). We present "AirMatch", a new automated system that first separates camera feeds and scenes, then stabilize and enhance the video feed of each camera; generates a mosaic of each scene of every feed and produce a super quality mosaic by stitching mosaics of all feeds. In our proposed solution, state-of-the-art video analytics techniques are tailored to work on videos from vintage cameras in aerial applications. Our new framework is independent of specialized hardware requirements and generates effective mosaics. Affine motion transform with smoothing Gaussian filter is selected for the stabilization of videos. A histogram-based method is performed for scene change detection and image contrast enhancement. Oriented FAST and rotated BRIEF (ORB) is selected for feature detection and descriptors in video stitching. Several experiments on a number of video streams are performed and the analysis shows that our system can efficiently generate mosaics of videos with high distortion and artifacts, compared with other commercially available mosaicing software.
The Analysis of the Test for English Vocabulary Learning Strategies under Relativism

Abstract English words are the foundation and necessary prerequisites for English learning. The amount of English vocabulary that students memorized and mastered directly affects students’ ability to use language such as listening, speaking, reading and writing. The author tried to seek out the ways and strategies which help English learners to mater vocabularies from the point of words relativism. To carry out the process of the research, the author adopted sample surveys, interviews, and word tests to investigate individuals or team-work vocabulary learning. From the result of word tests, the team-work vocabulary learning is superior to that of individuals’. Thus, the author concentrates on analyzing the outer connection of words in the context network, and finds effective ways to establish word exogenous connections. Meanwhile, the inner elements that affect vocabulary learning is equally important, including brain science, learning science, semantic network.