Reviewerly/exHarmony huggingface dataset:
Operational analysis: A method for observing and analyzing digital media operations

Interventional digital media applications such as robotic surgery, remote-controlled vehicles or wearable tracking devices pose a challenge to media research methodologically as well as conceptually. How do we go about analyzing operational media, where human and non-human agencies intertwine in seemingly inscrutable ways? This article introduces the method of o perational analysis to systematically observe and critically analyze such situated, interventional and multilayered entanglements. Against the background of ongoing efforts to develop operational models for understanding digital media, the method of operational analysis conceptually ascribes to media technologies a real efficacy by approaching them as adaptive mediators. As an operational middle-range approach, it allows to integrate theoretical discussions with considerations of the situatedness, directedness, and task-orientation of operational media. The article presents an analytical toolbox for observing and analyzing digital media operations while simultaneously testing it on a particular application in robotic radiosurgery.
Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions

Many areas of educational research require the analysis of data that have an inherent sequential or temporal ordering. In certain cases, researchers are specifically interested in the transitions between different states—or events—in these sequences, with the goal being to understand the significance of these transitions; one notable example is the study of affect dynamics, which aims to identify important transitions between affective states. Unfortunately, a recent study has revealed a statistical bias with several metrics used to measure and compare these transitions, possibly causing these metrics to return unexpected and inflated values. This issue then causes extra difficulties when interpreting the results of these transition metrics. Building on this previous work, in this study we look in more detail at the specific mechanisms that are responsible for the bias with these metrics. After giving a theoretical explanation for the issue, we present an alternative procedure that attempts to address the problem with the use of marginal models. We then analyze the effectiveness of this procedure, both by running simulations and by applying it to actual student data. The results indicate that the marginal model procedure seemingly compensates for the bias observed in other transition metrics, thus resulting in more accurate estimates of the significance of transitions between states.
Logical-Combinatorial Approaches in Dynamic Recognition Problems

A pattern recognition scenario, where instead of object classification into the classes by the learning set, the algorithm aims to allocate all objects to the same, the so-called "normal" class, is the research objective. Given the learning set L; the class K0 is called “normal”, and the reminder l classes K1, K2, ... , Kl from the environment K are “deviated”. The classification algorithm is for a recurrent use in a "classification, action" format. Actions Ai are defined for each “deviated” class Ki. Applied to an object x ∈ Ki, the action delivers update Ai(x) of the object. The goal is in constructing a classification algorithm A that applied repeatedly (small number of times) to the objects of L, moves the objects (correspondingly, the elements of K) to the “normal” class. In this way, the static recognition action is transferred to a dynamic domain. This paper is continuing the discussion on the “normal” class classification problem, its theoretical postulations, possible use cases, and advantages of using logical-combinatorial approaches in solving these dynamic recognition problems. Some light relation to the topics like reinforcement learning, and recurrent neural networks are also provided.
Agricultural Soil Data Analysis Using Spatial Clustering Data Mining Techniques

As an unsupervised learning method, spatial clustering has emerged to be one of the most important techniques in the field of agriculture for soil data analysis. Soil data analysis is usually related to practice in agricultural production management or discovery in agro-ecosystem process, so it is not easy to obtain labeled data that requires human intervention, and it is also not realistic to set specified pattern in advance. It is desirable to review the research work on soil data analysis using spatial clustering techniques in context of agricultural applications, which is the object of this survey. Soil properties (including physical, chemical, and biological properties) and the characteristics of the spatial soil data are first introduced. Spatial clustering techniques are then summarized in five different categories. Soil data analysis using spatial clustering is reviewed in four categories of agricultural applications: agricultural production management zoning, comprehensive assessment of soil and land, soil and land classification, and correlation study for agro-ecosystem. The traditional clustering algorithms generally work well, and prototype-based clustering methods are more preferred in practice. Some machine learning models can be further introduced into the spatial clustering algorithms for better accommodation to various characteristics of soil dataset.
A Hyperintensional Theory of Intelligent Question Answering in TIL

The paper deals with natural language processing and question answering over large corpora of formalised natural language texts. Our background theory is the system of Transparent Intensional Logic (TIL) which is a partial, hyperintensional, typed $$\uplambda $$ -calculus. Having a fine-grained analysis of natural language sentences in the form of TIL constructions, we apply Gentzen’s system of natural deduction adjusted for TIL to answer questions in an ‘intelligent’ way. It means that our system derives logical consequences entailed by the input sentences rather than merely searching answers by keywords. The theory of question answering must involve special rules rooted in the rich semantics of a natural language, and the TIL system makes it possible to formalise all the semantically salient features of natural languages in a fine-grained way. In particular, since TIL is a logic of partial functions, it is apt for dealing with non-referring terms and sentences with truth-value gaps. It is important because sentences often come attached with a presupposition that must be true so that a given sentence had any truth-value. And since answering is no less important than raising questions, we also propose a method of adequate unambiguous answering questions with presuppositions. In case the presupposition of a question is not true (because either false or ‘gappy’), there is no unambiguous direct answer, and an adequate complete answer is instead a negated presupposition. There are two novelties; one is the analysis and answering of Wh-questions that transform into $$\uplambda $$ -terms referring to $$\upalpha $$ -objects where $$\upalpha $$ is not the type of a truth-value. The second is integration of special rules rooted in the semantics of natural language into Gentzen’s system of natural deduction, together with a heuristic method of searching relevant sentences in the labyrinth of input text data that is driven by constituents of a given question.
ProSeCo: Visual analysis of class separation measures and dataset characteristics

Class separation is an important concept in machine learning and visual analytics. We address the visual analysis of class separation measures for both high-dimensional data and its corresponding projections into 2D through dimensionality reduction (DR) methods. Although a plethora of separation measures have been proposed, it is difficult to compare class separation between multiple datasets with different characteristics, multiple separation measures, and multiple DR methods. We present ProSeCo, an interactive visualization approach to support comparison between up to 20 class separation measures and up to 4 DR methods, with respect to any of 7 dataset characteristics: dataset size, dataset dimensions, class counts, class size variability, class size skewness, outlieriness, and real-world vs. synthetically generated data. ProSeCo supports (1) comparing across measures, (2) comparing high-dimensional to dimensionally-reduced 2D data across measures, (3) comparing between different DR methods across measures, (4) partitioning with respect to a dataset characteristic, (5) comparing partitions for a selected characteristic across measures, and (6) inspecting individual datasets in detail. We demonstrate the utility of ProSeCo in two usage scenarios, using datasets [1] posted at https://osf.io/epcf9/.
Personalized POI Recommendation: Spatio-Temporal Representation Learning with Social Tie

Recommending a limited number of Point-of-Interests (POIs) a user will visit next has become increasingly important to both users and POI holders for Location-Based Social Networks (LBSNs). However, POI recommendation is a challenging task since complex sequential patterns and rich contexts are contained in extremely sparse user check-in data. Recent studies show that embedding techniques effectively incorporate POI contextual information to alleviate the data sparsity issue, and Recurrent Neural Network (RNN) has been successfully employed for sequential prediction. Nevertheless, existing POI recommendation approaches are still limited in capturing user personalized preference due to separate embedding learning or network modeling. To this end, we propose a novel unified spatio-temporal neural network framework, named PPR, which leverages users’ check-in records and social ties to recommend personalized POIs for querying users by joint embedding and sequential modeling. Specifically, PPR first learns user and POI representations by joint modeling User-POI relation, sequential patterns, geographical influence, and social ties in a heterogeneous graph, and then models user personalized sequential patterns using the designed spatio-temporal neural network based on LSTM model for the personalized POI recommendation. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms state-of-the-art baselines for successive POI recommendation in terms of Accuracy, Precision, Recall and NDCG. The source code is available at: https://github.com/dsj96/PPR-master .
Generating Discorrelated States for Quantum Information Protocols by Coherent Multimode Photon Addition

Abstract It is demonstrated that the recently developed technique of delocalized single photon addition may generate discorrelation, a new joint statistical property of multimode quantum light states, whereby the number of photons in each mode can take any value individually, but two modes together never exhibit the same. By coherently adding a single photon to two identical coherent states of light in different temporal modes, the first experimental observation of discorrelation is provided. The capability of manipulating this statistical property has applications in scenarios involving the secure distribution of information among untrusted parties, like in the so‐called “mental poker” games.
Optimization-based multilevel threshold image segmentation for identifying ischemic stroke lesion in brain MR images

Brain stroke, a noncommunicable disease, is the third prime cause of death and disability worldwide. According to World Health Organization, 17 million people worldwide suffer from stroke. Every year 6.2 million die and 5 million remain permanently disabled. In India, every year the stroke population varies from 116 to 163 per 100,000 persons. Around 87% of all strokes are ischemic strokes (IS). Stroke is also termed brain ischemia or cerebral ischemia. The interior structural brain regions are investigated to identify the presence of IS. Magnetic resonance (MR) imaging is useful to diagnose IS and to determine the treatment strategy in the acute phase. In this work, diffusion-weighted (DW) MR images have been acquired from the Ischemic Stroke Lesion Segmentation Challenge 2015 dataset. Though MR imaging has good image quality, the low contrast between tissues could make it very difficult to analyze the abnormalities in the image; this also affects the accuracy of clinical diagnosis. The quality of the image is improved by using preprocessing techniques such as filtering. An anisotropic diffusion filter (ADF) provides better visualization, which aids diagnostics. The edges of the MR images are preserved in spite of noise being removed by ADF. Performance of the preprocessing technique is analyzed by using statistical-based histogram features. An ischemic stroke lesion (ISL) in an MR image is identified by a multilevel thresholding-based harmony search optimization algorithm and an electromagnetism-like optimization (EMO) algorithm for Otsu and Kapur object functions. The segmented outcomes acquired from these two algorithms are compared with expert's ground truth images and their performance is analyzed using a statistical measure of similarity indices. It is observed that the multilevel thresholding EMO-based Kapur method performs better in identifying ISL in the DW modality of brain MR images.
Acceptance Criteria of Bearings-only Passive Target Tracking Solution

In passive target tracking, target motion parameters (TMP) i.e. range, course, and speed are estimated using bearing measurements. In the simulation mode, the accuracy of the estimated solution can be calculated, as true values are available. In the actual scenario, the true values of TMP are not readily obtainable. In this research, the focus is laid on the evolution of the acceptance criterion of the TMP in the actual scenario. Detailed mathematical modeling is carried out and an unscented Kalman filter (UKF) is used to estimate TMP. The state vector covariance matrix of UKF is utilized to express the errors in estimated TMP and an acceptance criterion is derived in terms of the standard deviation of errors in estimated TMP. Submarine to submarine tracking is carried out and scenarios are chosen accordingly. Several low angle on target bow (ATB) scenarios are used in the Monte-Carlo simulation and the results are presented. A similar procedure can be carried out for medium and high ATB scenarios and a submarine to ship or vice-versa passive target tracking.
Verification of phase measurement error sources in phase-shifting interferometry with four step phase-shifting algorithms

The phase-shifting technique is one of the most important techniques in interferometry. The measurement accuracy of this technique is affected by several kinds of systematic and random errors like miscalibration, nonlinear response and tilt shift of the phase shifter, nonlinear recording, speckles, fringe fluctuation, etc. In this paper, a method for verification of all of these phase measurement error sources is proposed by introducing a grayscale image. This image is composed from the lattice-site representation determined by a four step phase-shifting algorithm, which represents the distribution of phase-shift angle values. The shape of the grayscale image is definitely more expressive and demonstrative for the kinds of measurement error sources than the black dot distribution in lattice-site representation. So, with this technique, we can recognize the existence of various measurement error sources and roughly estimate their magnitudes before proceeding with experiments. Our method is to be rather qualitative than quantitative. The availability of this method is discussed in a phase-shifting Fizeau interferometer and a fringe projection profilometry. This method is useful in all kinds of interferometric measurements based on the phase-shifting technique.
Quantum Key Search for Ternary LWE

Ternary LWE, i.e., LWE with coefficients of the secret and the error vectors taken from $$\{-1, 0, 1\}$$ , is a popular choice among NTRU-type cryptosystems and some signatures schemes like BLISS and GLP. In this work we consider quantum combinatorial attacks on ternary LWE. Our algorithms are based on the quantum walk framework of Magniez-Nayak-Roland-Santha. At the heart of our algorithms is a combinatorial tool called the representation technique that appears in algorithms for the subset sum problem. This technique can also be applied to ternary LWE resulting in faster attacks. The focus of this work is quantum speed-ups for such representation-based attacks on LWE. When expressed in terms of the search space $$\mathcal {S}$$ for LWE keys, the asymptotic complexity of the representation attack drops from $$\mathcal {S}^{0.24}$$ (classical) down to $$\mathcal {S}^{0.19}$$ (quantum). This translates into noticeable attack’s speed-ups for concrete NTRU instantiations like NTRU-HRSS [CHES’17] and NTRU Prime [SAC’17]. Our algorithms do not undermine current security claims for NTRU or other ternary LWE based schemes, yet they can lay ground for improvements of the combinatorial subroutines inside hybrid attacks on LWE.
Bilingual autoencoder-based efficient harmonization of multi-source private data for accurate predictive modeling

Sharing electronic health record data is essential for advanced analysis, but may put sensitive information at risk. Several studies have attempted to address this risk using contextual embedding, but with many hospitals involved, they are often inefficient and inflexible. Thus, we propose a bilingual autoencoder-based model to harmonize local embeddings in different spaces. Cross-hospital reconstruction of embeddings makes encoders map embeddings from hospitals to a shared space and align them spontaneously. We also suggest two-phase training to prevent distortion of embeddings during harmonization with hospitals that have biased information. In experiments, we used medical event sequences from the Medical Information Mart for Intensive Care-III dataset and simulated the situation of multiple hospitals. For evaluation, we measured the alignment of events from different hospitals and the prediction accuracy of a patient’s diagnosis in the next admission in three scenarios in which local embeddings do not work. The proposed method efficiently harmonizes embeddings in different spaces, increases prediction accuracy, and gives flexibility to include new hospitals, so is superior to previous methods in most cases. It will be useful in predictive tasks to utilize distributed data while preserving private information.
Semi-Supervised end-to-end Speech Recognition via Local Prior Matching

For sequence transduction tasks like speech recognition, a strong structured prior model encodes rich information about the target space, implicitly ruling out invalid sequences by assigning them low probability. In this work, we propose local prior matching (LPM), a semi-supervised objective that distills knowledge from a strong prior (e.g. a language model) to provide learning signal to an end-to-end model trained on unlabeled speech. We demonstrate that LPM is simple to implement and superior to existing knowledge distillation techniques under comparable settings. Starting from a baseline trained on 100 hours of labeled speech, with an additional 360 hours of unlabeled data, LPM recovers 54%/82% and 73%/91% of the word error rate on clean and noisy test sets with/without language model rescoring relative to a fully supervised model on the same data.
Underwater Imaging System Based on Light Field Technology

The image quality might be degraded severely due to water scatterings, especially near-shore water scatterings. In addition, the degraded image reduces the accuracy of underwater 3D reconstruction. We propose an underwater imaging system based on light field camera array to obtain enhanced high-quality images and accurate three-dimensional reconstruction from harsh underwater environments. Both 2D-imaging and 3D-reconstruction using light-field imaging were studied by experiments. By refocusing and analyzing the light field images of the target through turbid water, we obtained accurate three-dimensional estimations in the underwater scene. These experiments confirm the reliability of our method, showing that the SSIM (structural similarity) value of the system in turbid water is 0.378, compared to 0.197, the SSIM value of conventional single-camera imaging in the experiments. Therefore, we verify that the light field imaging system is less affected by forward scattering of water and has a larger detective range than conventional single-camera imaging with any specific resolution.
“Video Unavailable”: Analysis and Prediction of Deleted and Moderated YouTube Videos

YouTube strives to moderate its content by censoring, demonetizing or removing videos that allegedly violate their community guidelines. Such strategies, especially if seen as unjust by the affected users, could be met with resentment, anger, and in some cases, violence. In addition to YouTube removing videos, uploaders sometimes delete their videos for a variety of reasons such as paraphrasing or preserving online self-image. In this paper, we provide a detailed analysis of deleted/removed videos on YouTube. To do this, we tracked over 73,000 recent YouTube videos for one week and identified those that got deleted or removed. We have then conducted a large-scale analysis of this data and reported on the most informative features that distinguish deleted/removed videos from the ones that remain available. Based on our analysis, we have developed machine learning prediction models that predict videos that will get deleted/removed at different stages of a video's lifetime, viz., at the time of posting, and after up to seven days have elapsed. Our findings indicate that we can predict video deletion/removal with high accuracy even at the time of posting-a strategy that could help users perceive the removal of their videos as fair as well as reduce public and moderators exposure to problematic videos.
Adversarial Attacks on Featureless Deep Learning Malicious URLs Detection

Detecting malicious Uniform Resource Locators (URLs) is crucially important to prevent attackers from committing cybercrimes.Recent researches have investigated the role of machine learning (ML) models to detect malicious URLs.By using ML algorithms, rst, the features of URLs are extracted, and then different ML models are trained.The limitation of this approach is that it requires manual feature engineering and it does not consider the sequential patterns in the URL.Therefore, deep learning (DL) models are used to solve these issues since they are able to perform featureless detection.Furthermore, DL models give better accuracy and generalization to newly designed URLs; however, the results of our study show that these models, such as any other DL models, can be susceptible to adversarial attacks.In this paper, we examine the robustness of these models and demonstrate the importance of considering this susceptibility before applying such detection systems in real-world solutions.We propose and demonstrate a black-box attack based on scoring functions with greedy search for the minimum number of perturbations leading to a misclassi cation.The attack is examined against different types of convolutional neural networks (CNN)-based URL classi ers and it causes a tangible decrease in the accuracy with more than 56% reduction in the accuracy of the best classi er (among the selected classi ers for this work).Moreover, adversarial training shows promising results in reducing the in uence of the attack on the robustness of the model to less than 7% on average.