Reviewerly/exHarmony huggingface dataset:
An automatic speech segmentation tool based on multiple acoustic parameters

Speech segmentation is required not only for linguistic research based on oral corpora, but had become essential for natural language processing. Many researchers have developed different approaches to deal with the need of automatic segmentation of speech data. In this paper we discuss some of the prosodic parameters used as cues for boundary identification and present an ongoing project for the automatic segmentation of spontaneous speech developed for Brazilian Portuguese.
Challenges of Creating a New Engineering Education Model in Russia

The engineering education model in the Russian Federation has its roots in the Soviet system of higher education where students would study for 5, or even 6, years according to a very strict pre-determined curriculum which earned them a diploma of engineer in a narrow area of specialization, e.g.polymer engineering or petroleum engineering.Today, after signing the Bologna agreement which is aimed at standardizing the rigor of the bachelor and masters degrees in Europe, the Russian Federation is transforming its system to meet the requirements of the declaration.Universities are developing four year bachelor curricula and two year masters curricula.This implies a restructuring of the sequence and content of courses which will form a new generation of engineers having a solid foundation in engineering principles plus the soft skills needed to meet the challenges of this global world.This paper concentrates on the development of a new engineering education model in Russia and the challenges that universities face in this process.Areas where the best practices of the U.S. engineering education system could benefit and shorten the process are suggested in the future.
Showing ‘digital’ objects in web-based video chats as a collaborative achievement

Abstract Showing material objects by bringing them to the camera or turning the camera toward them are pervasive practices in domestic and recreational video-mediated communication (VMC). We here discuss a set of specific showing practices characteristic of digitally embedded video-mediated settings, which may be called ‘digital showings’. These involve participants’ collaboration to retrieve a digital object so as to ensure a shared perceptual experience on screen of said object. We draw on data from multiparty Google Hangouts On Air (HOAs) to show that while digital and material showings share an overall sequential organization, the former display the emergence of unique collaborative practices that at times become collective performances of computer literacy. We focus on three instances of digital showings: (a) screenshares of pictures – showing an image by sharing one’s screen; (b) screenshares of videos – showing a running video by sharing one’s screen; and (c) link-share showings – showing by sharing the link to a showable content that may be independently retrieved while experienced jointly.
Mobile Studio Pedagogy, Part 2: Self-regulated Learning and Blended Technology Instruction

This paper presents findings from a series of experiments underway in an electronics class for engineering students in which several approaches are currently being implemented to promote self-regulated learning using mobile studio pedagogy.An overview of the project is provided, followed by the presentation of students' self-reported data collected on learning outcomes.Data indicate of the four approaches to learning used, students who used a deep learning approach (overall and autonomously) had greater gains in course content and content specific outcomes.Students who participated in the deep learning-all modalities group (which included autonomous and peer practice in class, lab, and via homework) had the greatest gains in general learning and transfer skills.Deep learning that included only peer collaboration did not appear to be any more successful than traditional surface learning.
Predicting Student Preparedness In Calculus

Five years ago Louisiana Tech University began using a web-based tutorial program, ALEKS, in an effort to provide more effective mathematics tutoring for its students.Results on performance indicate that, for students in Math 240 (our Calculus I), strong student use of ALEKS highly correlates with student retention and success.Moreover, data shows that approximately 1500 mathematics students (at the level of calculus and below) are using ALEKS each term.At present, students are placed into Calculus I based on their Math ACT score, although there are always students whose Math ACT scores and success in Calculus I do not correlate well.Students are permitted to take the ACT multiple times and use their highest score for placement.Because Louisiana implemented high-stakes standardized testing seven years ago in the public school system for 3 rd through 12 th grades, many students are now approaching the ACT with a substantial amount of long-term training and practice in taking standardized tests.Therefore, we wanted to investigate whether or not the Math ACT score is still a sufficiently accurate method of placement into Calculus I.This study looks at student performance on the initial ALEKS assessment to see if it might be a more accurate predictor of student success in Calculus I than the Math ACT or if it, together with the Math ACT, might be more reliable than the Math ACT data alone.In this preliminary report, we focus on whether or not the Math ACT accurately reflects student preparedness for calculus.
A Computational Model Based on Symmetry for Generating Visually Pleasing Maps of Platform Games

In this paper we introduce a computational model based on the concept of symmetry to generate visually pleasing maps of platform games. We cast the problem of generating symmetrical maps as an optimization task and propose a heuristic search algorithm to solve it. A user study using a platform game shows the advantage of our method over other approaches in terms of visual aesthetics and enjoyment. Another user study shows that our method is able to generate maps as visually pleasing as maps created by professional designers.
Generating Relaxed, Obvious, and Dilemma Choices with Dunyazad

Dunyazad is a system which creates narrative choices à la Choose-Your-Own-Adventure books. It attempts to generate choices that achieve specific poetic effects. This paper demonstrates Dunyazad’s ability to manage player expectations by having it generate three distinct choice structures: obvious choices, relaxed choices, and dilemmas. Using answer set programming, Dunyazad’s choice generation system directly encodes a theory of choice poetics, so flaws in its output can inform both the system and the theory itself. Survey data presented here thus not only validate that players’ perceptions match Dunyazad’s intentions, but also have implications for the theory of choice poetics. Statistical analysis of our data indicates that Dunyazad can successfully construct obvious choices, relaxed choices, and dilemmas.
Feasibility Study: Subgoal Graphs on State Lattices

Search using subgoal graphs is a recent preprocessing-based path-planning algorithm that can find shortest paths on 8-neighbor grids several orders of magnitude faster than A*, while requiring little preprocessing time and memory overhead. In this paper, we first generalize the ideas behind subgoal graphs to a framework that can be specialized to different types of environments (represented as weighted directed graphs) through the choice of a reachability relation. Intuitively, a reachability relation identifies pairs of vertices for which a shortest path can be found quickly. A subgoal graph can then be constructed as an overlay graph that is guaranteed to have edges only between vertices that satisfy the reachability relation, which allows one to find shortest paths on the original graph quickly. In the context of this general framework, subgoal graphs on grids use freespace-reachability (originally called h-reachability) as the reachability relation, which holds for pairs of vertices if and only if their distance on the grid with blocked cells is equal to their distance on the grid without blocked cells (freespace assumption). We apply this framework to state lattices by using variants of freespace-reachability as the reachability relation. We provide preliminary results on (x,y,theta)-state lattices, which shows that subgoal graphs can be used to speed up path planning on state lattices as well, although the speed-up is not as significant as it is on grids.
Learning Abstract Information Theory On Visual Data: An Integrated Course On Wavelet Based Image Compression

We describe the implementation of and our experiences with a capstone course on wavelet based image compression held at the University of Technology Berlin in the years 2002 to 2006.This course has been designed as an "integrated project", which means that it combines lectures, seminar talks to be prepared and held by the students, and a programming part.The design goal of this course has been to provide all the necessary theoretical knowledge to understand the concepts behind image compression technologies, such as JPEG2000.We are also aiming at simulating the work-flow as found within an IT company as realistically as possible, preparing electrical engineers and computer scientists as well as possible for their professional life.This training does not only include the technical, but also the social skills required to successfully complete larger projects.The subject of image compression offers the advantage of requiring a solid knowledge on terms of information science such as entropy, distortion, quantization, Fourier and wavelettransformation, but also offering a direct visual feedback of how these techniques perform.Therefore, we believe that image compression is an attractive topic to be used for a capstone course.Traditionally, a course would assign weekly programming exercises to the students; however, we believe this to be unsuitable for a capstone course as it does not simulate the work-flow of a professional software development team; furthermore, it does not require the degree of teamwork we deem critical to modern software development.Thus, we divide students into groups of two to four people and assign each team to one sub-task of an image codec and provide some boiler-plate code of our own.Much to their astonishment, students soon find themselves spending a considerable amount of time with project management and coordination activities.That means, teams have to design interfaces and data structures to combine their efforts to create a working project, which adds an often underestimated social component to the course.With some guidance from the teachers, students have always been able to supply a working code at the end of the semester.Needless to say, the thrill of having a nontrivial working program at the end of the course is a major source of motivation for our students and adds much to the satisfaction and positive feedback we receive.
Predictable remote invocations for distributed stream processing

Typical infrastructure for big-data includes multiple machines with data accessed remotely with request–response patterns from different remote locations. Currently, most of the state-of-the-art remote invocation techniques are focused on models for distributed interactions, which have not explored the advantages given by parallel computing, such as those offered to run on distributed stream processors. In this context, the article is focused on the definition of a predictable remote procedure call (RPC) able to take advantage from the distributed stream processing technology. Potentially, this type of infrastructure enables efficient parallel computations, reducing the effective response-time of end-to-end invocations linearly with the number of resources assigned to the system, as the data increases. The article describes a predictable model which defines maximum response-times for different types of applications described in the context of Apache Storm. Evaluation also offers clues on the performance one may expect from this type of infrastructure.
Improved Space Forest: A Meta Ensemble Method

The performance of the ensemble algorithms is related with the individual accuracy of the base learners and their results diversity. Individual accuracy of a base learner is directly related to the similarity between the original training set and the base learner's training set. When a modified training set by randomly selecting features/classes/samples is given to the base learners, the diversity is created but the individual accuracy is decreased. From this point of view, different ensemble algorithms can be seen as a selection between having more accurate but less diverse base learners and having more diverse but less accurate base learners. We propose a meta ensemble method named as improved space forest which adds generated and (hopefully) more accurate features to the original features. The new features are obtained from randomly selected original features. When the new features are more distinctive than the original ones, they are selected by the learners. So, the ensemble may have more accurate base learners. However, a different improved space is generated for each learner to create diversity. The proposed method can be used with different ensemble methods. We compared original and improved space versions of bagging, random forest, and rotation forest algorithms. Improved space versions have generally better or comparable results than the original ones. We also present a theoretical framework to analyze the individual accuracies and diversities of the base learners.
Investigating Whether Transfer of Learning in Pharmacy Students Depends More on Knowledge Storage or Accessibility

<b>Objective.</b> To investigate whether prior knowledge or reactivated marginal knowledge alone is sufficient for student pharmacists to transfer foundational science content to pharmacy application. <b>Methods.</b> In two experiments, transfer of foundational science knowledge was examined. Far transfer was examined by investigating the relationship between prior knowledge of chemistry and physiology to pharmacokinetic application. Near transfer was examined by investigating the relationship between foundational physiology content and its application to pharmacy. Participants completed fill-in-the-blank (FIB) items to assess accessible prior knowledge. Half the participants also received multiple-choice formats of the FIB questions to assess stored well but not readily retrievable information. Participants then answered application-type questions. <b>Results.</b> Participants were more successful in correctly answering multiple-choice questions than FIB questions. Participants could not apply stored and accessible nor stored but inaccessible knowledge in the far transfer condition (pharmacokinetics application). Participants were more successful in applying foundational science knowledge to the near transfer condition (pharmacy application of physiology) <b>Conclusion.</b> This study suggests some students may be able to transfer their prerequisite knowledge to pharmacy application, but most students probably cannot successfully transfer without formal instruction. In addition, reactivating the foundational knowledge through multiple-choice testing has been shown to be sufficient to cause retention of the knowledge, but this seems to be an insufficient stimulus to allow for transfer to occur. What this confirms is that even if students have the prior knowledge, transfer cannot be assumed to be an automatic process and probably requires specific instruction.
In search of the recurrent units of translation

Translation memory systems aim to reuse previously translated texts. Be-cause the operational unit of the first-generation translation memory sys-tems is the sentence, such systems are only useful for text types in which full-sentence repetition frequently occurs. Second-generation sub-sentential translation memory systems try to remedy this problem by providing addi-tional translation suggestions for sub-sentential chunks. In this paper, we compare the performance of a sentence-based translation memory system (SDL Trados Translator’s Workbench) with a sub-sentential translation memory system (Similis) on different text types. We demonstrate that some text types (in this case, journalistic texts) are not suited to be translated by means of a translation memory system. We show that Similis offers useful additional translation suggestions for terminology and frequent multiword expressions.
Optimized projection for hashing

Hashing, which seeks for binary codes to represent data, has drawn increasing research interest in recent years. Most existing Hashing methods follow a projection-quantization framework which first projects high-dimensional data into compact low-dimensional space and then quantifies the compact data into binary codes. The projection step plays a key role in Hashing and academia has paid considerable attention to it. Previous works have proven that a good projection should simultaneously 1) preserve important information in original data, and 2) lead to compact representation with low quantization error. However, they adopted a greedy two-step strategy to consider the above two properties separately. In this paper, we empirically show that such a two-step strategy will result in a sub-optimal solution because the optimal solution to 1) limits the feasible set for the solution to 2). We put forward a novel projection learning method for Hashing, dubbed Optimized Projection (OPH). Specifically, we propose to learn the projection in a unified formulation which can find a good trade-off such that the overall performance can be optimized. A general framework is given such that OPH can be incorporated with different Hashing methods for different situations. We also introduce an effective gradient-based optimization algorithm for OPH. We carried out extensive experiments for Hashing-based Approximate Nearest Neighbor search and Content-based Data Retrieval on six benchmark datasets. The results show that OPH significantly outperforms several state-of-the-art related Hashing methods.
Операторные $E$-нормы и их использование

We consider a family of norms (called operator E-norms) on the algebra $B(H)$ of all bounded operators on a separable Hilbert space $H$ induced by a positive densely defined operator $G$ on $H$. Each norm of this family produces the same topology on $B(H)$ depending on $G$. By choosing different generating operator $G$ one can obtain operator E-norms producing different topologies, in particular, the strong operator topology on bounded subsets of $B(H)$. We obtain a generalised version of the Kretschmann-Schlingemann-Werner theorem, which shows continuity of the Stinespring representation of CP linear maps w.r.t. the energy-constrained $cb$-norm (diamond norm) on the set of CP linear maps and the operator E-norm on the set of Stinespring operators. The operator E-norms induced by a positive operator $G$ are well defined for linear operators relatively bounded w.r.t. the operator $\sqrt{G}$ and the linear space of such operators equipped with any of these norms is a Banach space. We obtain explicit relations between the operator E-norms and the standard characteristics of $\sqrt{G}$-bounded operators. The operator E-norms allow to obtain simple upper bounds and continuity bounds for some functions depending on $\sqrt{G}$-bounded operators used in applications.
On Causal Identification under Markov Equivalence

In this work, we investigate the problem of computing an experimental distribution from a combination of the observational distribution and a partial qualitative description of the causal structure of the domain under investigation. This description is given by a partial ancestral graph (PAG) that represents a Markov equivalence class of causal diagrams, i.e., diagrams that entail the same conditional independence model over observed variables, and is learnable from the observational data. Accordingly, we develop a complete algorithm to compute the causal effect of an arbitrary set of intervention variables on an arbitrary outcome set.
A Diversity-Accuracy Measure for Homogenous Ensemble Selection

Several selection methods in the literature are essentially based on an evaluation function that determines whether a model M contributes positively to boost the performances of the whole ensemble.In this paper, we propose a method called DIversity and ACcuracy for Ensemble Selection (DIACES) using an evaluation function based on both diversity and accuracy.The method is applied on homogenous ensembles composed of C4.5 decision trees and based on a hill climbing strategy.This allows selecting ensembles with the best compromise between maximum diversity and minimum error rate.Comparative studies show that in most cases the proposed method generates reduced size ensembles with better performances than usual ensemble simplification methods.
Medical Image Magnification Based on Original and Estimated Pixel Selection Models

The issue of medial image resolution enhancement is one of the most important topics for medical imaging that helps improve the performance of many post-processing aspects like classification and segmentation towards medical diagnosis.Our aim in this paper is to evaluate different types of pixel selection models in terms of pixel originality in medical image reconstruction problems. A previous investigation showed that selecting far original pixels has highly better performance than using near unoriginal/estimated pixels while magnifying some benchmarks in digital image processing.In our technical study, we apply two classical interpolators, cubic convolution (CC) and bi-linear (BL), in order to reconstruct medical images in spatial domain. In addition to the interpolators, we use some geometrical image transforms for creating the reconstruction models.The results clearly demonstrate that despite the absolute preference of the original pixel selection model in the first research, we cannot see this preference in medical dataset in which the results of BL interpolator for both tested models (original and estimated pixel selection models) are approximately the same as each other and for CC interpolator, we only see a relatively better preference for the original pixel selection model.The current research reveals the fact that selection models are not a general factor in reconstruction problems, and the structure of the basic interpolators is also a main factor which affects the final results. In other words, some interpolators in medical dataset can be affected by the selection models, while, some cannot.