Reviewerly/exHarmony huggingface dataset:
Visual Word Recognition as a Means of Addressing Top-Down Feedback

Top-down feedback is an important yet poorly understood aspect of visual processing, due to an ambiguity in the involvement of bottom-up and top-down information in the processing of complex stimuli. For example, an apparent categorical or conceptual difference may be derivable from a feed-forward process based on simple visual statistics, particularly in familiar stimuli. Convolutional neural networks have evidenced a striking ability to successfully perform such tasks in the absence of any top-down feedback. Here, we present the first steps in a series of studies aimed at disentangling top-down and bottom-up effects by taking advantage of several unique aspects of visual word recognition. Words afford excellent control of their low-level characteristics, unambiguous involvement of non-visual information, and a high degree of localization of their cortical processing. To begin, we examined the word superiority effect (WSE) wherein letters are more quickly and accurately identified in words than pseudowords, taken to be indicative of top-down feedback. The current experiment is the first in a series of studies that will investigate whether WSE is modulated by semantics and direct physiological intervention. We hypothesized that frequency should modulate the strength of connections between every high-level system and the ventral temporal locus of letter recognition. As predicted, we found a strong correlation between word frequency and the strength of the WSE in both accuracy and reaction time. Further, the location within the US where a person lives was found to modulate the WSE by defining their familiarity with regionally specific words. Other manipulations, such as the addition of cognitive load, as well as priming of both orthographic and semantic neighbors, yielded modulations. These results demonstrate the potential utility of studying top-down feedback in the context of visual word recognition.
CHARACTERISTICS OF SELF-ORGANIZING TEAMS IN AGILE PROJECT MANAGEMENT: A CASE STUDY

Self-organizing teams, especially in the context of Agile Project Management are subject of interest in business and academia. Agile approach has roots in the IT industry and expending to other fields. One of the most dominant attributes of Agile approach is self-organizing teams. In the literature, self-organizing teams have been studied sine 50s of XX century and can be described by six dimensions: autonomy, communication, learning, team orientation, shared leadership and redundancy. Also, self-organizing agile teams can be characterized by existing informal roles like a mentor, coordinator, translator, promoter, champion, terminator. In the article, the author used a case study method for examining the above characteristics of self-organizing teams. As a result, the existing theoretical models can be useful in diagnosing the state of a self-organizing team. Further studies with more strict research methods are recommended to extend knowledge about the self-organizing team in Agile Project Management.
Exploiting Partial Knowledge in Declarative Domain-Specific Heuristics for ASP

Domain-specific heuristics are an important technique for solving combinatorial problems efficiently.We propose a novel semantics for declarative specifications of domain-specific heuristics in Answer Set Programming (ASP).Decision procedures that are based on a partial solution are a frequent ingredient of existing domain-specific heuristics, e.g., for placing an item that has not been placed yet in bin packing.Therefore, in our novel semantics negation as failure and aggregates in heuristic conditions are evaluated on a partial solver state.State-of-the-art solvers do not allow such a declarative specification.Our implementation in the lazy-grounding ASP system Alpha supports heuristic directives under this semantics.By that, we also provide the first implementation for incorporating declaratively specified domain-specific heuristics in a lazy-grounding setting.Experiments confirm that the combination of ASP solving with lazy grounding and our novel heuristics can be a vital ingredient for solving industrial-size problems.
Finding Reasons for Vaccination Hesitancy: Evaluating Semi-Automatic Coding of Internet Discussion Forums.

Computer-assisted text coding can facilitate the analysis of large text collections. To evaluate the functionality of providing an analyst with a ranked list of suggestions for suitable text codes, we used a data set of discussion posts, which had been manually coded for reasons given for taking a stance on the topic of vaccination. We trained a logistic regression classifier to rank these reasons according to the probability that they would be present in the post. The approach was evaluated for its ability to include the expected reasons among the n top-ranked reasons, using an n between 1 and 6. The logistic regression-based ranking was more effective than the baseline, which ranked reasons according to their frequency in the training data. Providing such a list of possible codes, ranked by logistic regression, could therefore be a useful feature in a tool for text coding.
When intercepting an approaching ball in flight, only some individuals compensate for its acceleration through head-centered spherical space.

An attempt to catch a ball must be preceded by accurate visual tracking while, as a result of the ball’s changing depth, it accelerates through the head-centered, spherical frame of reference (described in degrees azimuth/elevation). However, studies of smooth pursuit of 2D fronto-parallel motion demonstrate a perceptual insensitivity to acceleration. This predicts that gaze will lag behind an accelerating target. In the natural context, one might couple rotation of the gaze-vector to a visual source of information that covaries with changes in depth. This study tests the potential role of information related to angular size. Subjects were immersed in a virtual reality ball catching simulation, and gaze was monitored using an integrated eye tracker. The ball travelled to one of three lateral distances from the subject’s head. Arrival height was randomized. A gain was applied to the ball’s natural looming rate resulting in inflation/deflation of the approaching virtual ball. Subjects performed 10 repetitions at gain values of 0.5, 0.75, 1 (normal looming rate), 1.25, and 1.5. Ten repetitions of each gain/distance combination yielded 150 trials per subject. Although the preliminary analysis presented at VSS2018 supported the role of looming rate in compensation for the ball’s acceleration, a new, more sensitive analysis casts doubt on these findings. On each trial, tracking behavior was compared to ball movement during a windowed portion of the ball’s trajectory through head-centered spherical space, just prior to the attempted catch, during which the trajectory was linear and subject to acceleration. Comparison between the location of gaze at the end of the window to 1) the actual ball location at the end of the window and 2) the location predicted by a constant-velocity model of ball movement revealed two distinct classes of participant – those that partially compensated for acceleration, and those who did not.
Functional Load, Perception, and the Learning of Phonological Alternations

Author(s): Lin, Isabelle | Advisor(s): Zuraw, Kie; Sundara, Megha | Abstract: Languages use combinations of sounds to form words, and some words are only distinguished by a one sound difference (e.g. [d]ank “thank” and [t]ank “tank” in German). Sounds that can induce meaning differences are said to contrast. However, some contrasting sounds can be neutralized in specific positions in the word (in German /d/ becomes [t] at the end of a word, e.g. ra/t/ and ra/d/ both become ra[t]). Within a language, the functional load hypothesis (Jakobson, 1931; Martinet, 1952) states that sounds distinguishing more words (high functional load) are less likely to disappear or merge with other sounds during sound change.The aim of this dissertation was to examine whether this tendency to preserve highly informative contrasts carries over to the learning of new sound patterns. Additionally, we tested whether learning alternations involving a specific sound pair impacts the perceptual distinctiveness of its members. We used artificial language learning experiments to assess the learnability of alternations involving contrasts with different functional loads in English, and a perceptual discrimination task to assess the perceptual distinctiveness of said contrasts pre- and post- learning.There are three main results. First, functional load in English predicts the perceptual distinctiveness of contrasts before learning an artificial language. Second, alternations involving high functional load contrasts are learned better, regardless of whether the contrast is neutralized in the artificial language. Finally, learning an artificial language where a contrast is neutralized reduces the perceptual distinctiveness of that contrast post-learning. In summary, we show that phonological contrastiveness in the native language, as well as learning an artificial language, alters perception.
A New Method for Geometric Interpretation of Elliptic Curve Discrete Logarithm Problem

In this paper, we intend to study the geometric meaning of the discrete logarithm problem defined over an Elliptic Curve. The key idea is to reduce the Elliptic Curve Discrete Logarithm Problem (EC-DLP) into a system of equations. These equations arise from the interesection of quadric hypersurfaces in an affine space of lower dimension. In cryptography, this interpretation can be used to design attacks on EC-DLP. Presently, the best known attack algorithm having a sub-exponential time complexity is through the implementation of Summation Polynomials and Weil Descent. It is expected that the proposed geometric interpretation can result in faster reduction of the problem into a system of equations. These overdetermined system of equations are hard to solve. We have used F4 (Faugere) algorithms and got results for primes less than 500,000. Quantum Algorithms can expedite the process of solving these over-determined system of equations. In the absence of fast algorithms for computing summation polynomials, we expect that this could be an alternative. We do not claim that the proposed algorithm would be faster than Shor's algorithm for breaking EC-DLP but this interpretation could be a candidate as an alternative to the 'summation polynomial attack' in the post-quantum era.
Fault-Diagnosing SLAM for Varying Scale Change Detection

In this paper, we present a new fault diagnosis (FD) -based approach for detection of imagery changes that can detect significant changes as inconsistencies between different sub-modules (e.g., self-localizaiton) of visual SLAM. Unlike classical change detection approaches such as pairwise image comparison (PC) and anomaly detection (AD), neither the memorization of each map image nor the maintenance of up-to-date place-specific anomaly detectors are required in this FD approach. A significant challenge that is encountered when incorporating different SLAM sub-modules into FD involves dealing with the varying scales of objects that have changed (e.g., the appearance of small dangerous obstacles on the floor). To address this issue, we reconsider the bag-of-words (BoW) image representation, by exploiting its recent advances in terms of self-localization and change detection. As a key advantage, BoW image representation can be reorganized into any different scaling by simply cropping the original BoW image. Furthermore, we propose to combine different self-localization modules with strong and weak BoW features with different discriminativity, and to treat inconsistency between strong and weak self-localization as an indicator of change. The efficacy of the proposed approach for FD with/without AD and/or PC was experimentally validated.
The Effectiveness of Using Short Stories On Students‟ Vocabulary Mastery” (An Experimental Research at tenth Grade Students‟ of MAN 2 CILEGON),

Hayati Oktaviani, 2018, “The Effectiveness of Using Short Stories On Students‟ Vocabulary Mastery” (An Experimental Research at tenth Grade Students‟ of MAN 2 CILEGON),” (Undergraduate Research Paper, English Education Department, Faculty of Education and Teacher Training, The State Islamic University Sultan Maulana Hasanuddin Banten. Adviser: Dr. H.Subhan, M. Ed. And Moh.Nur Arifin, SAg, M.Pd. ABSTRACT The Research investigates the Effectiveness of Using Short Stories on Students Vocabulary Mastery. This research conducted based on the research question 1) How is the students‟ vocabulary mastery at the tenth grade of MAN 2 CILEGON?. 2)How is short stories applied in teaching vocabulary?. 3) How is the effect of short stories on students‟ vocabulary mastery this research is. 1)To know the students vocabulary mastery at the tenth grade of MAN 2 CILEGON. 2)To know the application of short stories in teaching vocabulary.3) To know the effect of short stories on student‟s vocabulary mastery. To answer question, the writer collect data from 30 students at tenth grade of MAN 2 CILEGON with population is 90 students. The result of the research shows that use short stories has significant effect on students vocabulary mastery. It can be seen from the result that researcher has got the mean of pre-test 1860,and post test score 2400 from the experimental class \and the pre-test score is 1700 and post test score is 2040 from control class. The writer got to 6,8 df = 70, ttable 5% = 2,00.It mean Ha (alternative hypothesis) of the research is accepted that there is significant improvement of effect using short stories on students vocabulary mastery. The conclusion of the research is that there is the effectiveness of using short stories on students‟ Vocabulary Mastery. It can be seen from the result that researcher has gotten the average score of toobservation= 6,8 df=70 and ttable 5% and 2,00 with the level is significant of 5% or 1%. So to is bigger than ttable , it shows the technique is effective. keywords: short stories, experimental research, vocabulary mastery
Comparing visual object representational similarity in convolutional neural networks and the human ventral visual regions

Convolutional neural networks (CNNs) have achieved amazing successes in visual object categorization tasks in recent years. Some have considered them to be good working models of the primate visual system, with different CNN layers corresponding to different levels of visual processing in the primate brain. However, much remains unknown about how visual information is processed within a CNN, making it more like a blackbox than a well understood system. Using methods developed in human neuroscience, here we examined information processing in several CNNs using two approaches. In our first approach, we compared the representational similarities of visual object categories from the different CNN layers to those obtained in retino-topically and functionally defined human occipito-temporal regions through fMRI studies. Regardless of the specific CNN examined, when natural object categories were used, the representational similarities of the early CNN layers aligned well with those obtained from early visual areas, presumably because these layers were modeled directly after these brain regions. However, representational similarities of the later CNN layers diverged from those of the higher ventral visual object processing regions. When artificial shape categories were used, both early and late CNN layers diverged from the corresponding human visual regions. In our second approach, we compared visual representations in CNNs and human visual processing regions in terms of their tolerance to changes in image format, position, size, and the spatial frequency content of an image. Again, we observed several differences between CNNs and the human visual regions. Thus despite CNNs’ ability to successfully perform visual object categorization, they process visual information somewhat differently from the human brain. As such current CNNs may not be directly applicable to model the details of the human visual system. Nevertheless, such brain and CNN comparisons can be useful to guide the training of more human brain-like CNNs.
Teaching Reforming Practice for Non-chemistry Major Students in GDUT

Guangdong University of Technology has been carrying out teaching reforming for more than 10 years. It is still challenging to make non-chemistry major students highly qualified in general chemistry and chemical education within very limited period. We are making attempts to improve the attractiveness of the teaching materials and continuously optimize the teaching process.
The formation of successful physics students : Part of round table discussion: Social justice in science, technology, engineering and mathematics education: Establishing a platform for conversation

The formation of successful physics students : Part of round table discussion: Social justice in science, technology, engineering and mathematics education: Establishing a platform for conversation
Compositional uncertainty in deep Gaussian processes

Gaussian processes (GPs) are nonparametric priors over functions. Fitting a GP implies computing a posterior distribution of functions consistent with the observed data. Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations. However, exact Bayesian inference is intractable for DGPs, motivating the use of various approximations. We show that the application of simplifying mean-field assumptions across the hierarchy leads to the layers of a DGP collapsing to near-deterministic transformations. We argue that such an inference scheme is suboptimal, not taking advantage of the potential of the model to discover the compositional structure in the data. To address this issue, we examine alternative variational inference schemes allowing for dependencies across different layers and discuss their advantages and limitations.
The Role of Awareness in Figure-ground Segregation in Human Visual System

An essential part of visual perception is segmenting images into figures and background. Previous studies have shown that this segmenting process relies on two complementary processes: boundary detection and subsequent region-filling. However, the neuronal mechanisms for these processes and whether they depend on awareness remain unclear. Here we performed psychophysical and fMRI experiments to examine these issues. In our experiments, each figure-ground stimulus had a regular Manhattan grid of 33 × 45 bars, presented on a dark screen. All bars were identically oriented except for a square figure of bars with another orientation in either the upper left or the upper right quadrant. There were three possible figures: the first and second consisted of 2 × 2 (Small Figure, SF) and 10 × 10 (Large Figure, LF) bars, respectively, and the third was a combination of the two (Combined Figure, CF). Low- and high-contrast masks, which had the same grid as the figure-ground stimuli, rendered the whole stimulus visible (Experiment 1) and invisible (Experiment 2) to subjects, respectively. In the psychophysical experiment, the Posner cueing paradigm was adopted to measure the spatial cueing effect of each figure on an orientation discrimination task with Small or Large Gratings, which had the same size as the SF and LF, respectively. For the Small Grating, there was no significant difference between the LF and CF, and both were significantly lower than the SF in Experiment 1; there was no significant difference between the SF and CF, and both were significantly higher than the LF in Experiment 2. No significant difference was found for the Large Grating in either Experiments 1 or 2. A further fMRI experiment confirmed these findings and suggested that boundary detection is an early, automatic process that is independent of awareness, whereas subsequent region filling is strongly modulated by awareness.
METHODICAL PRINCIPLES OF THE IMPLEMENTATION OF THE COMPETENCE APPROACH IN THE TRAINING OF PHYSICAL AND TECHNICAL DISCIPLINES OF FUTURE SPECIALISTS OF COMPUTER TECHNOLOGIES IN THE CONDITIONS OF THE INFORMATION SOCIETY

The article deals with the issues related to the outline of the methodical principles of the implementation of the competence approach in the teaching of physics and technical disciplines.In the conditions of the information society, attention was focused on the development of information and digital competence as a key.Formulation of the problem.The rapid development of the technogenic-information society at the turn of the XX -XXI centuries.has led to a revision of the requirements for the organization of educational process in higher education institutions.There was a problem of its improvement.This poses the conditions for organizing the educational process in physics and technical disciplines in the training of future computer technology specialists.It is these educational subjects that are the basis of scientific and technological progress.It requires the development of future specialists in information and digital competence.In the era of mass digitalization, she gets the key status.Materials and methods.The study used the following methods: analysis and synthesis of content of normative documents and dissertation researches; modeling of educational process in physical and technical disciplines; structural and logical analysis; system prism method.Results.On the basis of analysis of normative documents, research results and own experience, structures have been identified: curriculum of competency training, actions of the subject of training on the formation of the ability of the subject of learning before gaining competence, the designing of the main objectives of the curriculum and the scheme of forming competence for solving the tasks of the contents of the curriculum.Particular attention is paid to the components and indicators of key competences, the general content lines of teaching physics and technical disciplines.To ensure the implementation of requirements for the training of future specialists in computer technology and the development of their competencies, in particular, information and digital.We have proposed the principles of methodology for teaching physics and technical disciplines in a higher education institution.Conclusions.With the development of an information society, information and digital competence from a number of professional (for students of the specialty: "Professional Education (Computer Technologies)") goes to key competencies.Its development should take place while studying all subjects without exclusion.In this regard, we have proposed methodological principles for teaching physics and technical disciplines of future computer technology specialists on the basis of a competent approach.