Reviewerly/exHarmony huggingface dataset:
Active Vision in Immersive, 360° Real-World Environments

Abstract Vision is an active process. Humans actively sample their sensory environment via saccades, head turns, and body movements. Yet, little is known about active visual processing in real-world environments. Here, we exploited recent advances in immersive virtual reality (VR) and in-headset eye-tracking to show that active viewing conditions impact how humans process complex, real-world scenes. Specifically, we used quantitative, model-based analyses to compare which visual features participants prioritize over others while encoding a novel environment in two experimental conditions: active and passive. In the active condition, participants used head-mounted VR displays to explore 360º scenes from a first-person perspective via self-directed motion (saccades and head turns). In the passive condition, 360º scenes were passively displayed to participants within the VR headset while they were head-restricted. Our results show that signatures of top-down attentional guidance increase in active viewing conditions: active viewers disproportionately allocate their attention to semantically relevant scene features, as compared with passive viewers. We also observed increased signatures of exploratory behavior in eye movements, such as quicker, more entropic fixations during active as compared with passive viewing conditions. These results have broad implications for studies of visual cognition, suggesting that active viewing influences every aspect of gaze behavior – from the way we move our eyes to what we choose to attend to – as we construct a sense of place in a real-world environment. Significance Statement Eye-tracking in immersive virtual reality offers an unprecedented opportunity to study human gaze behavior under naturalistic viewing conditions without sacrificing experimental control. Here, we advanced this new technique to show how humans deploy attention as they encode a diverse set of 360º, real-world scenes, actively explored from a first-person perspective using head turns and saccades. Our results build on classic studies in psychology, showing that active, as compared with passive, viewing conditions fundamentally alter perceptual processing. Specifically, active viewing conditions increase information-seeking behavior in humans, producing faster, more entropic fixations, which are disproportionately deployed to scene areas that are rich in semantic meaning. In addition, our results offer key benchmark measurements of gaze behavior in 360°, naturalistic environments.
A Compiler Assisted Scheduler for Detecting and Mitigating Cache-Based Side Channel Attacks

Side channel attacks steal secret keys by cleverly leveraging information leakages and can, therefore, break encryption. Thus, detection and mitigation of side channel attacks is a very important problem, but the solutions proposed in the literature have limitations in that they do not work in a real-world multi-tenancy setting on servers, have high false positives, or have high overheads. In this work, we demonstrate a compiler guided scheduler, Biscuit, that detects cache-based side channel attacks for processes scheduled on multi-tenancy server farms. A key element of this solution involves the use of a cache-miss model which is inserted by the compiler at the entrances of loop nests to predict the cache misses of the corresponding loop. Such inserted library calls, or beacons, convey the cache miss information to the scheduler at run time, which uses it to co-schedule processes such that their combined cache footprint does not exceed the maximum capacity of the last level cache. The scheduled processes are then monitored for actual vs predicted cache misses, and when an anomaly is detected, the scheduler performs a search to isolate the attacker. We show that Biscuit is able to detect and mitigate Prime+Probe, Flush+Reload, and Flush+Flush attacks on OpenSSL cryptography algorithms with an F-score of 1, and also to detect and mitigate degradation of service on a vision application suite with an F-score of 0.9375. Under a no-attack scenario, the scheme poses low overheads (up to a maximum of 6 percent). In the case of an attack, the scheme ends up with less than 11 percent overhead and is able to reduce the degradation of service in some cases by 40 percent. With these many desirable features such as an ability to deal with multi-tenancy, its ability to detect attacks early, its ability to mitigate those attacks, and low runtime overheads, Biscuit is a practical solution.
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference

Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise. In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. Specifically, we extract various phrases from the hypotheses (artificial patterns) in the training sets, and show that they have been strong indicators to the specific labels. We then figure out `hard' and `easy' instances from the original test sets whose labels are opposite to or consistent with those indications. We also set up baselines including both pretrained models (BERT, RoBERTa, XLNet) and competitive non-pretrained models (InferSent, DAM, ESIM). Apart from the benchmark and baselines, we also investigate two debiasing approaches which exploit the artificial pattern modeling to mitigate such hypothesis-only bias: down-sampling and adversarial training. We believe those methods can be treated as competitive baselines in NLI debiasing tasks.
Financial and Management Tools to Identify Realistic Factors Affecting Production Output

Abstract Correlation and regression analysis can be used to establish casual relationships between variables. In practice, these tools help to find out the variable value and determine its impact on the change of the dependent variable. The purpose of the article is to study the factors affecting the change in the production output, in particular, stock inventories, their turnover as well as contractors and buyers of finished products. Even if the values are expressed in different units of measurement, i.e. the production output and inventories - in rubles and inventory turnover - in turns, contractors - in the number of men, the economic and mathematical models presented enable us to make a comparison. Therefore, the article demonstrates a practical approach to find the dependency between specific and above factors (variables) and their cause-and-effect associations. In practice, it is necessary to find out the values of the variable and determine its impact on the change of the dependent variable. In this particular case, the production output is taken as a dependent value. Its value is influenced by variables (stocks of raw materials and supplies, their turnover, number of consumers formed for a certain type of finished product of a given production), and therefore they change proportionally. The calculation of values for one variable on the basis of another one will be shown using regression equation with known initial numbers. Consequently, a practical approach presented demonstrates how correlation analysis, mathematical function, regression equation and trend line can be applied allowing practitioners to identify not only cause-and-effect but also regression relationships between variables. The proposed regression analysis allows to identify changes in the microeconomic production policy and to trace their impact on the economic activity of the entity over time.
Lip Reading to Text using Artificial Intelligence

An application that uses the camera of a smartphone to detect the lip movements of the person and convert the movements into text that can be understood by the hearingimpaired person.This application uses LRW dataset to visualise every movement of the lips.After the visualisation is completed, the captured movements are then converted into the form the person can understand easily.
Pemetaan Curah Hujan Dalam Upaya Mengurangi Resiko Bencana Hidrometeorologi Dengan Sistem Informasi Geografis (SIG) Untuk Wilayah Kalimantan Utara

North Kalimantan is one of the regions in Indonesia which is prone to hydrometeorological disasters. The purpose of this study was to determine the rainfall analysis of the North Kalimantan Region as a basis in finding rainfall levels in areas that do not have climatological stations, knowing the rainfall maps of the North Kalimantan Region and knowing the conditions of high rainfall causing flood prone in the North Kalimantan region. Rainfall data collection locations are climatology stations Tanjung Harapan, Juwata, Tuvai Semaring, Kalimarau and R.A. Bessing. Rainfall data is taken from OGIMET for 10 years. The result of rainfall analysis is the average monthly rainfall, which is from January to December. The method used to determine the monthly rainfall value in the North Kalimantan region is the Spatial Analyst Interpolation -Kriging method. The results are in the form of monthly rainfall maps, from January to December. Monthly rainfall map is a source of information on the value of rainfall in the region in North Kalimantan. Maximum rainfall is found in the areas of Tanjung Selor and Tarakan, namely January 313,368 mm, May 366,238 mm, July 358,868 mm and December 324,513 mm. Map of monthly rainfall in January, May, July and December is a parameter to see the condition of high rainfall causing hydrometeorological disasters. Through this research it is expected to anticipate the risk of disasters caused by the weather.
A Geometric Perspective on Visual Imitation Learning

We consider the problem of visual imitation learning without human supervision (e.g. kinesthetic teaching or teleoperation), nor access to an interactive reinforcement learning (RL) training environment. We present a geometric perspective to derive solutions to this problem. Specifically, we propose VGS-IL (Visual Geometric Skill Imitation Learning), an end-to-end geometry-parameterized task concept inference method, to infer globally consistent geometric feature association rules from human demonstration video frames. We show that, instead of learning actions from image pixels, learning a geometry-parameterized task concept provides an explainable and invariant representation across demonstrator to imitator under various environmental settings. Moreover, such a task concept representation provides a direct link with geometric vision based controllers (e.g. visual servoing), allowing for efficient mapping of high-level task concepts to low-level robot actions.
Time Scales for Rounding of Rocks through Stochastic Chipping

For 3D geometries, we consider stones (modeled as convex polyhedra) subject to weathering with planar slices of random orientation and depth successively removing material, ultimately yielding smooth and round (i.e. spherical) shapes. An exponentially decaying acceptance probability in the area exposed by a prospective slice provides a stochastically driven physical basis for the removal of material in fracture events. With a variety of quantitative measures, in steady state we find a power law decay of deviations in a toughness parameter $γ$ from a perfect spherical shape. We examine the time evolution of shapes for stones initially in the form of cubes as well as irregular fragments created by cleaving a regular solid many times along random fracture planes. In the case of the former, we find two sets of second order structural phase transitions with the usual hallmarks of critical behavior. The first involves the simultaneous loss of facets original to the parent solid, while the second of these involves a shift to a spherical profile. Nevertheless, for mono-dispersed irregular solids, the loss of primordial facets is not simultaneous but occurs in stages. In the case of initially irregular stones, strong disorder obscures individual structural transitions, and relevant observables are smooth with respect to time. More broadly, we find that salient times scale quadratically in $γ$. We use the universal dependence of variables on the volume remaining to calculate time dependent variables for a variety of erosion scenarios with results from a single weathering scheme such as the case in which the fracture acceptance probability depends on the relative area of the prospective new face. We calculate time scales for the attainment of structural milestones, obtaining a closed form approximate expression which bounds direct simulation results from above.
Fall Detection Using the Histogram of Oriented Gradients and Decision-Based Fusion

As the number of fall incidents among elderly people and patients are continuously growing, researches boosted their researches to propose efficient automatic fall detection systems.In particular, they formulated the fall detection problem as a supervised learning task where some visual features are extracted from the video frames and used to automatically identify the position of a human as "Fall" or "Non-Fall" based on a model learned using labeled training frames.Despite the promising reported results, existing fall detection systems exhibit noticeable room for improvement.Learner fusion which builds multiple models and aggregates their respective decisions is an alternative that would improve the fall detection performance.In this paper, an image-based fall detection system that captures the visual property and the spatial position of the human body using the Histogram of Oriented Gradient from the video frames is proposed.Then, the extracted features are used to train three classification models.Namely, the Naïve Bayes, the K-Nearest Neighbors and the Support Vector Machine algorithms are adopted.Next, the majority vote is used to aggregate the decisions of the individual learners.The proposed system was assessed using a standard dataset and yielded promising results.Standard performance measures along with the statistical significance t-test were used to prove that the fall detection system based on majority vote fusion outperforms the individual classifier based approaches.
Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty

Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, hit, or push, and solves the resulting smooth trajectory optimization. The expressive power of logic allows LGP for handling complex, large-scale sequential manipulation and tool-use planning problems. In this paper, we extend the LGP formulation to stochastic domains. Based on the control-inference duality, we interpret LGP in a stochastic domain as fitting a mixture of Gaussians to the posterior path distribution, where each logic profile defines a single Gaussian path distribution. The proposed framework enables a robot to prioritize various interaction modes and to acquire interesting behaviors such as contact exploitation for uncertainty reduction, eventually providing a composite control scheme that is reactive to disturbance. The supplementary video can be found at this https URL
Scientific VS Non-Scientific Citation Annotational Complexity Analysis using Machine Learning Classifiers

This paper evaluates the citation sentences' annotation complexity of both scientific as well as non-scientific text related articles to find out major complexity reasons by performing sentiment analysis of scientific and non-scientific domain articles using our own developed corpora of these domains separately.For this research, we selected different data sources to prepare our corpora in order to perform sentimental analysis.After that, we have performed a manual annotation procedure to assign polarities using our defined annotation guidelines.We developed a classification system to check the quality of annotation work for both domains.From results, we have found that the scientific domain gave us more accurate results than the non-scientific domain.We have also explored the reasons for less accurate results and concluded that non-scientific text especially linguistics is of complex nature that leads to poor understanding and incorrect annotation.
Revisiting Simple Domain Adaptation Methods in Unsupervised Neural Machine Translation

Domain adaptation has been well-studied in supervised neural machine translation (SNMT). However, it has not been well-studied for unsupervised neural machine translation (UNMT), although UNMT has recently achieved remarkable results in several domain-specific language pairs. Besides the inconsistent domains between training data and test data for SNMT, there sometimes exists an inconsistent domain between two monolingual training data for UNMT. In this work, we empirically show different scenarios for unsupervised neural machine translation. Based on these scenarios, we revisit the effect of the existing domain adaptation methods including batch weighting and fine tuning methods in UNMT. Finally, we propose modified methods to improve the performances of domain-specific UNMT systems.
Ellipsis and information structure: Evidence from Romanian gapping

The main goal of this paper is to investigate the information structure of gapping, a linguistic level which has been much less studied than syntax or semantics.Romanian is a good test case for such a study, since it displays a specific contrastive conjunction iar, which occurs frequently with gapping and which severely restricts the information structure ordering.We explain its high frequency with ellipsis by the fact that it shares several crucial constraints with gapping.Our data show that the contrastive pairs with iar do not have the same contribution: one pair contains contrastive topics, and the other foci.However, the order of the correlates in the source clause does not necessarily match the order of the remnants in the gapped clause.We use a QUD model, based on question-answer congruence, which is very useful for a careful investigation of the discourse and information structure of ellipsis.
Real-time Quadrilateral Object Corner Detection Algorithm Based on Deep Learning

In the field of computer vision, there are many quadrilateral objects such as slide, screen, book, etc, which usually has the characteristics of rotation, perspective, and beveling. Therefore, it becomes a key task to detect the corner points of the quadrilateral object to restore the content. This paper proposes an algorithm to solve the problem of quadrilateral object corner detection. Different from the previous methods, our method does not make any assumptions about the background, distance, content and other attributes of the quadrilateral object, which can be applied to a wider range of quadrilateral objects. We named the proposed method as Corner Detect Network (CDN). Simulation results show that the proposed algorithm can quickly and accurately extract the corner point of quadrilateral object.
CRYPTOCURRENCIES: HISTORY AND TYPES

The history of the development of electronic payment systems as a prerequisite for&#x0D; the emergence of cryptocurrencies is studied. The most popular cryptocurrencies are considered, their&#x0D; advantages and disadvantages are highlighted.
HEDGES, POLITENESS STRATEGIES, AND POWER: A CASE STUDY OF WOMEN COMMUNITY LEADERS IN MALANG REGENCY

Gender and power related speech patterns have been a focus of communication studies. Women and power used to be in opposite side of each other. On this ground, we were intrigued to investigate whether being a female leader has something to do with defying certain assumed ideals of women speech. This case study was conducted to women leaders engaged in a social community called Pelangi Nusantara. Concerning this study, we focused on the women leaders’ speech to examine the stereotyped-speech-pattern related to the use of hedges and politeness strategies in female speakers albeit holding a high position. A descriptive qualitative approach was employed along with the purposive technique to select the interviewees. Based on the analysis, it is revealed that despite holding a high position in the community, the female leaders mostly showed the stereotyped-speech-pattern through the use of hedges, convergence, and politeness strategies as influenced by their cultural background.&#x0D;
A Neuro-AI Interface for Evaluating Generative Adversarial Networks

Generative adversarial networks (GANs) are increasingly attracting attention in the computer vision, natural language processing, speech synthesis and similar domains. However, evaluating the performance of GANs is still an open and challenging problem. Existing evaluation metrics primarily measure the dissimilarity between real and generated images using automated statistical methods. They often require large sample sizes for evaluation and do not directly reflect human perception of image quality. In this work, we introduce an evaluation metric called Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals. Our results show that Neuroscore has superior performance to the current evaluation metrics in that: (1) It is more consistent with human judgment; (2) The evaluation process needs much smaller numbers of samples; and (3) It is able to rank the quality of images on a per GAN basis. A convolutional neural network (CNN) based neuro-AI interface is proposed to predict Neuroscore from GAN-generated images directly without the need for neural responses. Importantly, we show that including neural responses during the training phase of the network can significantly improve the prediction capability of the proposed model. Codes and data can be referred at this link: https://github.com/villawang/Neuro-AI-Interface.
Personalized Web Search

World Wide Web (WWW) serves as a platform for retrieving a variety of information. This high volume of information leads to the information overload on the Internet. When a query is made, most search engines return same results to all users without considering the context of the user’s requests. For example, ‘virus’ in the context of one user can be malicious software, but for a biologist, it is a microorganism. This gives rise to the need for personalized web search. It is one of the growing concepts in web technologies. It is all about giving the right content to the right person at the right time. With the use of the user’s browsing history, we can personalize the results and improve the user’s visit experience. Through this paper, we emphasize on techniques to rank the pages in the order of relevance to the context of the user to achieve better results.