Reviewerly/exHarmony huggingface dataset:
Classes of (0,1)-matrices Where the Bruhat Order and the Secondary Bruhat Order Coincide

Given two nonincreasing integral vectors R and S, with the same sum, we denote by $\mathcal {A}(R,S)$ the class of all (0,1)-matrices with row sum vector R, and column sum vector S. The Bruhat order and the Secondary Bruhat order on $\mathcal {A}(R,S)$ are both extensions of the classical Bruhat order on Sn, the symmetric group of degree n. These two partial orders on $\mathcal {A}(R,S)$ are, in general, different. In this paper we prove that if R = (2,2,…,2) or R = (1,1,…,1), then the Bruhat order and the Secondary Bruhat order on $\mathcal {A}(R,S)$ coincide.
Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning

Semi-Supervised Support Vector Machine (S3VM) is one of the most popular methods for semi-supervised learning. To avoid the trivial solution of classifying all the unlabeled examples to a same class, balancing constraint is often used with S3VM (denoted as BCS3VM). Recently, a novel incremental learning algorithm (IL-S3VM) based on the path following technique was proposed to significantly scale up S3VM. However, the dynamic relationship of balancing constraint with previous labeled and unlabeled samples impede their incremental method for handling BCS3VM. To fill this gap, in this paper, we propose a new incremental S3VM algorithm (IL-BCS3VM) based on IL-S3VM which can effectively handle the balancing constraint and directly update the solution of BCS3VM. Specifically, to handle the dynamic relationship of balancing constraint with previous labeled and unlabeled samples, we design two unique procedures which can respectively eliminate and add the balancing constraint into S3VM. More importantly, we provide the finite convergence analysis for our IL-BCS3VM algorithm. Experimental results on a variety of benchmark datasets not only confirm the finite convergence of IL-BCS3VM, but also show a huge reduction of computational time compared with existing batch and incremental learning algorithms, while retaining the similar generalization performance.
Graph kernels based on linear patterns: Theoretical and experimental comparisons

Graph kernels are powerful tools to bridge the gap between machine learning and data encoded as graphs. Most graph kernels are based on the decomposition of graphs into a set of patterns. The similarity between two graphs is then deduced to the similarity between corresponding patterns. Kernels based on linear patterns constitute a good trade-off between accuracy and computational complexity. In this work, we propose a thorough investigation and comparison of graph kernels based on different linear patterns, namely walks and paths. First, all these kernels are explored in detail, including their mathematical foundations, structures of patterns and computational complexity. After that, experiments are performed on various benchmark datasets exhibiting different types of graphs, including labeled and unlabeled graphs, graphs with different numbers of vertices, graphs with different average vertex degrees, linear and non-linear graphs. Finally, for regression and classification tasks, accuracy and computational complexity of these kernels are compared and analyzed, in the light of baseline kernels based on non-linear patterns. Suggestions are proposed to choose kernels according to the types of graph datasets. This work leads to a clear comparison of strengths and weaknesses of these kernels. An open-source Python library containing an implementation of all discussed kernels is publicly available on GitHub to the community, thus allowing to promote and facilitate the use of graph kernels in machine learning problems.
Deriving an Abstract Machine for Strong Call by Need

Strong call by need is a reduction strategy for computing strong normal forms in the lambda calculus, where terms are fully normalized inside the bodies of lambda abstractions and open terms are allowed. As typical for a call-by-need strategy, the arguments of a function call are evaluated at most once, only when they are needed. This strategy has been introduced recently by Balabonski et al., who proved it complete with respect to full beta-reduction and conservative over weak call by need.We show a novel reduction semantics and the first abstract machine for the strong call-by-need strategy. The reduction semantics incorporates syntactic distinction between strict and non-strict let constructs and is geared towards an efficient implementation. It has been defined within the framework of generalized refocusing, i.e., a generic method that allows to go from a reduction semantics instrumented with context kinds to the corresponding abstract machine; the machine is thus correct by construction. The format of the semantics that we use makes it explicit that strong call by need is an example of a hybrid strategy with an infinite number of substrategies.
Cybersickness Among Gamers: An Online Survey

In recent years a trend of head-mounted displays (HMDs) appears on the consumer market and it expands the entertainment dimension by adding a new segment to it called virtual reality (VR) gaming. However, VR games not only bring enjoyment to the players, but they also bring discomfort called cybersickness. In this study, an online survey among gamers was used to identify whether and which symptoms of cybersickness occur while playing VR games. The results showed that the most experienced symptoms during or after VR exposure regarding gaming are nausea, fatigue, and general discomfort. Additionally, the paper aims to give insights about what gamers do against cybersickness associated with VR games. The findings showed that the side effects of VR games are widespread among gamers and an appropriate solution has not been found yet. This paper is a starting point for more extended research on cybersickness induced by VR games.
Undergraduate mobile phone use in the Caribbean

Purpose The purpose of this paper is to present the results of a survey regarding undergraduate students’ mobile phone usage patterns and its implication for teaching and learning in the Caribbean higher education academic environment. Design/methodology/approach A total of 144 students participated in the survey. The survey method utilized a structured questionnaire design comprising 24 items which was completed by students. A quantitative research methodology was used to analyze the data on student mobile phone usage patterns in an educational setting. Findings The results indicate that students find mobile phones to be an indispensable tool inside and outside the classroom environment. More interestingly, it revealed that despite unique socio-economic factors, students’ mobile phone adoption, usage and perception patterns in a developing country mirror those of their counterparts in developed states. This has profound implications for education policy in the region. Originality/value Little research has been done on students’ mobile phone use in the developing world context. Even less work has been done exploring mobile phone usage patterns of university students in the Caribbean region. Given the similarities with developed states, this paper shares ideas with university management and administration how they can incorporate mobile phone technology into their teaching methods, to enhance the learning experience in the Caribbean and the wider developing world context.
Real-Time Crowd Detection to Prevent Stampede

With the expanding population and several problems arising due to crowded situations, the necessity of crowd detection is also at a raise. It includes assessing the number of individuals in the group and in addition the appropriation of the crowd density in different regions of the group. Estimation of such crowd density can be done from the image of the crowded scene. This paper proposes real-time approaches to solving problems related to dense crowds. It uses static images captured from a crowded area and attempts to estimate the crowd density of the area by applying two different techniques. The proposed techniques for estimating crowd are via image processing and another one is using Convolutional Neural Network. In image processing technique, erosion is used to count head regions from the images. Images considered here are grayscale images. In CNN approach, deep learning technique is used to estimate the crowd density. A network of Raspberry Pi is used in order to perform the crowd detection as well as circulating the crowd condition to responsible authorities. At that point, the authorities might be alarmed about the likelihood of a stampede and can find a way to keep away from it.
Physical Education Motion Correction System Based on Virtual Reality Technology

Compared with traditional physical education (PE), the computer virtual digital-based education system is convenient and efficient, which can effectively solve the shortcomings of traditional teaching mode, and correct the PE motions in real time. Thus, the athletes can master the motion technical skills as soon as possible and the training effect can be improved. For this, based on computer virtual reality technology, this paper constructs a PE motion correction system. Then, through the analysis for real-time motion data and human movement posture, it achieves the training objective of real-time PE teaching motion correction under the three-dimensional visualization. This shall provide a scientific basis for the evaluation of PE teaching and training methods.
SASRT: Semantic-Aware Super-Resolution Transmission for Adaptive Video Streaming over Wireless Multimedia Sensor Networks

There are few network resources in wireless multimedia sensor networks (WMSNs). Compressing media data can reduce the reliance of user's Quality of Experience (QoE) on network resources. Existing video coding software, such as H.264 and H.265, focuses only on spatial and short-term information redundancy. However, video usually contains redundancy over a long period of time. Therefore, compressing video information redundancy with a long period of time without compromising the user experience and adaptive delivery is a challenge in WMSNs. In this paper, a semantic-aware super-resolution transmission for adaptive video streaming system (SASRT) for WMSNs is presented. In the SASRT, some deep learning algorithms are used to extract video semantic information and enrich the video quality. On the multimedia sensor, different bit-rate semantic information and video data are encoded and uploaded to user. Semantic information can also be identified on the user side, further reducing the amount of data that needs to be transferred. However, identifying semantic information on the user side may increase the computational cost of the user side. On the user side, video quality is enriched with super-resolution technologies. The major challenges faced by SASRT include where the semantic information is identified, how to choose the bit rates of semantic and video information, and how network resources should be allocated to video and semantic information. The optimization problem is formulated as a complexity-constrained nonlinear NP-hard problem. Three adaptive strategies and a heuristic algorithm are proposed to solve the optimization problem. Simulation results demonstrate that SASRT can compress video information redundancy with a long period of time effectively and enrich the user experience with limited network resources while simultaneously improving the utilization of these network resources.
Learning Pixel-Level and Instance-Level Context-Aware Features for Pedestrian Detection in Crowds

Pedestrian detection in crowded scenes is an intractable problem in computer vision, in which occlusion often presents a great challenge. In this paper, we propose a novel context-aware feature learning method for detecting pedestrians in crowds, with the purpose of making better use of context information for dealing with occlusion. Unlike most current pedestrian detectors that only extract context information from a single and fixed region, a new pixel-level context embedding module is developed to integrate multi-cue context into a deep CNN feature hierarchy, which enables access to the context of various regions by multi-branch convolution layers with different receptive fields. In addition, to utilize the distinctive visual characteristics formed by pedestrians that appear in groups and occlude each other, we propose a novel instance-level context prediction module which is actually implemented by a two-person detector, to improve the one-person detection performance. Applying with these strategies, we achieve an efficient and lightweight detector that can be trained in an end-to-end fashion. We evaluate the proposed approach on two popular pedestrian detection datasets, i.e., Caltech and CityPersons. The extensive experimental results demonstrate the effectiveness of the proposed method, especially under heavy occlusion cases.
A postdictive bias associated with déjà vu

Recent research links reports of déjà vu – the feeling of having experienced something before despite knowing otherwise – with an illusory feeling of prediction. In the present study, a new finding is presented in which reports of déjà vu are associated not only with a predictive bias, but also with a postdictive bias, whereby people are more likely to feel that an event unfolded as expected after the event prompted déjà vu than after it did not. During a virtual tour, feelings of predicting the next turn were more likely during reported déjà vu, as in prior research. Then, after actually seeing the turn, participants exhibited a postdictive bias toward feeling that the scene unfolded as expected following déjà vu reports. This postdictive bias following déjà vu reports was associated with higher perceived scene familiarity intensity. A potential reason for this association may be that high familiarity intensity as an event outcome unfolds falsely signals confirmatory evidence of having sensed all along how it would unfold. Future research should further investigate this possibility.
A chaotic image encryption scheme with complex diffusion matrix for plain image sensitivity

A chaotic cipher is presented in this paper using 1-Dimensional and 2- Dimensional chaotic maps like logistic, Chebyshev and Arnold cat map. Permutation phase utilizes logistic map followed by Arnold cat map whereas in diffusion phase, Chebyshev’s map is used. Subsequently, another complex diffusion matrix is generated from the original image. This matrix is employed to enhance the diffusion effect further. Eventually, strong input image sensitivity is explored due to this diffusion. Simulation results exhibit that the recommended cipher ensures not only high key and entropy value but also less correspondence between nearby pixels along all directions. The key point of this cipher is the high Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) values. Due to this impact, the proposed cipher produces completely random encrypted images.
Do Neural Information Extraction Algorithms Generalize Across Institutions?

PURPOSE Natural language processing (NLP) techniques have been adopted to reduce the curation costs of electronic health records. However, studies have questioned whether such techniques can be applied to data from previously unseen institutions. We investigated the performance of a common neural NLP algorithm on data from both known and heldout (ie, institutions whose data were withheld from the training set and only used for testing) hospitals. We also explored how diversity in the training data affects the system’s generalization ability. METHODS We collected 24,881 breast pathology reports from seven hospitals and manually annotated them with nine key attributes that describe types of atypia and cancer. We trained a convolutional neural network (CNN) on annotations from either only one (CNN1), only two (CNN2), or only four (CNN4) hospitals. The trained systems were tested on data from five organizations, including both known and heldout ones. For every setting, we provide the accuracy scores as well as the learning curves that show how much data are necessary to achieve good performance and generalizability. RESULTS The system achieved a cross-institutional accuracy of 93.87% when trained on reports from only one hospital (CNN1). Performance improved to 95.7% and 96%, respectively, when the system was trained on reports from two (CNN2) and four (CNN4) hospitals. The introduction of diversity during training did not lead to improvements on the known institutions, but it boosted performance on the heldout institutions. When tested on reports from heldout hospitals, CNN4 outperformed CNN1 and CNN2 by 2.13% and 0.3%, respectively. CONCLUSION Real-world scenarios require that neural NLP approaches scale to data from previously unseen institutions. We show that a common neural NLP algorithm for information extraction can achieve this goal, especially when diverse data are used during training.
Mutual information-based dropout: Learning deep relevant feature representation architectures

We propose a new regularization strategy called DropMI, which is a generalization of Dropout for the regularization of networks that introduces mutual information (MI) dynamic analysis. The standard Dropout randomly drops a certain proportion of neural units, according to the Bernoulli distribution, thereby resulting in the loss of some important hidden feature information. In DropMI, we first evaluate the importance of each neural unit in the feature representation of the hidden layer based on the MI between it and the target. We then construct a new binary mask matrix based on the sorting distribution of MI, thus developing a dynamic DropMI strategy that highlights the important neural units that are beneficial to the feature representation. The results from the MNIST, NORB, CIFAR-10, CIFAR-100, SVHN, and Multi-PIE datasets indicate that, relative to other state-of-the-art regularization methods based on the benchmark autoencoder and convolutional neural networks, our method has better feature representation performance and effectively reduces the overfitting of the model.
On Learning Graphs with Edge-Detecting Queries

We consider the problem of learning a general graph $G=(V,E)$ using edge-detecting queries, where the number of vertices $|V|=n$ is given to the learner. The information theoretic lower bound gives $m\log n$ for the number of queries, where $m=|E|$ is the number of edges. In case the number of edges $m$ is also given to the learner, Angluin-Chen's Las Vegas algorithm \cite{AC08} runs in $4$ rounds and detects the edges in $O(m\log n)$ queries. In the other harder case where the number of edges $m$ is unknown, their algorithm runs in $5$ rounds and asks $O(m\log n+\sqrt{m}\log^2 n)$ queries. There have been two open problems: \emph{(i)} can the number of queries be reduced to $O(m\log n)$ in the second case, and, \emph{(ii)} can the number of rounds be reduced without substantially increasing the number of queries (in both cases). For the first open problem (when $m$ is unknown) we give two algorithms. The first is an $O(1)$-round Las Vegas algorithm that asks $m\log n+\sqrt{m}(\log^{[k]}n)\log n$ queries for any constant $k$ where $\log^{[k]}n=\log \stackrel{k}{\cdots} \log n$. The second is an $O(\log^*n)$-round Las Vegas algorithm that asks $O(m\log n)$ queries. This solves the first open problem for any practical $n$, for example, $n 2^m$. Finally, we give a $3$-round Monte Carlo algorithm that asks $O(m\log n)$ queries for any $n$ and $m$.
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams

In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time. The suggested method acquires features incrementally based on a context-aware feature-value function. We formulate the problem in the reinforcement learning paradigm, and introduce a reward function based on the utility of each feature. Specifically, MC dropout sampling is used to measure expected variations of the model uncertainty which is used as a feature-value function. Furthermore, we suggest sharing representations between the class predictor and value function estimator networks. The suggested approach is completely online and is readily applicable to stream learning setups. The solution is evaluated on three different datasets including the well-known MNIST dataset as a benchmark as well as two cost-sensitive datasets: Yahoo Learning to Rank and a dataset in the medical domain for diabetes classification. According to the results, the proposed method is able to efficiently acquire features and make accurate predictions.
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials

Dense conditional random fields (CRFs) have become a popular framework for modeling several problems in computer vision such as stereo correspondence and multiclass semantic segmentation. By modeling long-range interactions, dense CRFs provide a labeling that captures finer detail than their sparse counterparts. Currently, the state-of-the-art algorithm performs mean-field inference using a filter-based method but fails to provide a strong theoretical guarantee on the quality of the solution. A question naturally arises as to whether it is possible to obtain a maximum a posteriori (MAP) estimate of a dense CRF using a principled method. Within this paper, we show that this is indeed possible. Specifically, we will show that, by using a filter-based method, continuous relaxations of the MAP problem can be optimized efficiently using state-of-the-art algorithms. Specifically, we will solve a quadratic programming relaxation using the Frank--Wolfe algorithm and a linear programming relaxation by developing a proximal minimization framework. By exploiting labeling consistency in the higher-order potentials and utilizing the filter-based method, we are able to formulate the above algorithms such that each iteration has a complexity linear in the number of classes and random variables. The presented algorithms can be applied to any labeling problem using a dense CRF with sparse higher-order potentials. In this paper, we use semantic segmentation as an example application as it demonstrates the ability of the algorithm to scale to dense CRFs with large dimensions. We perform experiments on the Pascal dataset to indicate that the presented algorithms are able to attain lower energies than the mean-field inference method.
The Augmented Reality in Lisbon Tourism

Augmented reality (RA) in tourism has contributed to revolutionizing the way tourists and visitors access information, acquire knowledge and integrate into the physical spaces and destinations visited. The studies produced concerning this field are still limited, particularly in the perception of how users adopt technology and what use they make of it. On the other hand, even more limited are the studies that consider the role of stakeholders in the implementation of AR technology. Thus, this study proposes a new Augmented Reality Technology Adoption Model, based on the adaptation of the Technology Adoption Model [11, 12, 13] and also by the applying of tourist surveys in the city of Lisbon and structured interviews to public and private agents operating in the tourist context of the city of Lisbon, in order to study the use and effects of AR technology.