Reviewerly/exHarmony huggingface dataset:
Data structures meet cryptography: 3SUM with preprocessing

This paper shows several connections between data structure problems and cryptography against preprocessing attacks. Our results span data structure upper bounds, cryptographic applications, and data structure lower bounds, as summarized next.
A framework for step-wise explaining how to solve constraint satisfaction problems

We explore the problem of step-wise explaining how to solve constraint satisfaction problems, with a use case on logic grid puzzles. More specifically, we study the problem of explaining the inference steps that one can take during propagation, in a way that is easy to interpret for a person. Thereby, we aim to give the constraint solver explainable agency, which can help in building trust in the solver by being able to understand and even learn from the explanations. The main challenge is that of finding a sequence of simple explanations, where each explanation should aim to be as cognitively easy as possible for a human to verify and understand. This contrasts with the arbitrary combination of facts and constraints that the solver may use when propagating. We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP. Our approach is agnostic of the underlying constraint propagation mechanisms, and can provide explanations even for inference steps resulting from combinations of constraints. In case multiple constraints are involved, we also develop a mechanism that allows to break the most difficult steps up and thus gives the user the ability to zoom in on specific parts of the explanation. Our proposed algorithm iteratively constructs the explanation sequence by using an optimistic estimate of the cost function to guide the search for the best explanation at each step. Our experiments on logic grid puzzles show the feasibility of the approach in terms of the quality of the individual explanations and the resulting explanation sequences obtained.
The impact of information security management practices on organisational agility

Purpose This study aims to determine the extent to which information security management (ISM) practices impact the organisational agility by examining the relationship between both concepts. Design/methodology/approach A quantitative method research design has been used in this study. This study was conducted throughout Malaysia with a total of 250 valid questionnaires obtained from managers and executives from the Multimedia Super Corridor (MSC)-status companies. Structural equation modelling (SEM) using partial least square was used to analyse the data and to test all nine hypotheses developed in this study. Findings Findings from this study indicate that operational agility (OA) is significantly related to ISM practices in MSC-status companies. The validation of the structural model of nine hypotheses developed for this study has demonstrated satisfactory results, exhibited six significant direct relationships and three insignificant relationships. Research limitations/implications This study has addressed the needs for a comprehensive, coherent and empirically tested ISM practices and organisational agility framework. The current theoretical framework used in this study emphasised on the ISM–organisational agility dimensions that are predominantly important to ascertain high level of ISM practices and perceived agility level among the information technology (IT) business companies in Malaysia. With the application of SEM for powerful analysis, the empirical-based framework established in this study was validated by the empirical findings, thus contributing significantly to the field of information security (InfoSec). Originality/value This study has filled the research gap between different constructs of ISM practices and OA. The model put forth in this study contributes in several ways to the InfoSec research community. The recognition of InfoSec practices that could facilitate organisational agility in the IT industry in Malaysia is vital and contributes to more value creation for the organisations.
SnapNav: Learning Mapless Visual Navigation with Sparse Directional Guidance and Visual Reference

Learning-based visual navigation still remains a challenging problem in robotics, with two overarching issues: how to transfer the learnt policy to unseen scenarios, and how to deploy the system on real robots. In this paper, we propose a deep neural network based visual navigation system, SnapNav. Unlike map-based navigation or Visual-Teach-and-Repeat (VT&R), SnapNav only receives a few snapshots of the environment combined with directional guidance to allow it to execute the navigation task. Additionally, SnapNav can be easily deployed on real robots due to a two-level hierarchy: a high level commander that provides directional commands and a low level controller that provides real-time control and obstacle avoidance. This also allows us to effectively use simulated and real data to train the different layers of the hierarchy, facilitating robust control. Extensive experimental results show that SnapNav achieves a highly autonomous navigation ability compared to baseline models, enabling sparse, map-less navigation in previously unseen environments.
Wish You Were Here: Context-Aware Human Generation

We present a novel method for inserting objects, specifically humans, into existing images, such that they blend in a photorealistic manner, while respecting the semantic context of the scene. Our method involves three subnetworks: the first generates the semantic map of the new person, given the pose of the other persons in the scene and an optional bounding box specification. The second network renders the pixels of the novel person and its blending mask, based on specifications in the form of multiple appearance components. A third network refines the generated face in order to match those of the target person. Our experiments present convincing high-resolution outputs in this novel and challenging application domain. In addition, the three networks are evaluated individually, demonstrating for example, state of the art results in pose transfer benchmarks.
S, R, and data science

Data science is increasingly important and challenging. It requires computational tools and programming environments that handle big data and difficult computations, while supporting creative, high-quality analysis. The R language and related software play a major role in computing for data science. R is featured in most programs for training in the field. R packages provide tools for a wide range of purposes and users. The description of a new technique, particularly from research in statistics, is frequently accompanied by an R package, greatly increasing the usefulness of the description. The history of R makes clear its connection to data science. R was consciously designed to replicate in open-source software the contents of the S software. S in turn was written by data analysis researchers at Bell Labs as part of the computing environment for research in data analysis and collaborations to apply that research, rather than as a separate project to create a programming language. The features of S and the design decisions made for it need to be understood in this broader context of supporting effective data analysis (which would now be called data science). These characteristics were all transferred to R and remain central to its effectiveness. Thus, R can be viewed as based historically on a domain-specific language for the domain of data science.
End-to-End Spatial Transform Face Detection and Recognition

Plenty of face detection and recognition methods have been proposed and got excellent results in decades. Common face recognition pipeline consists of: 1) face detection, 2) face alignment, 3) feature extraction, 4) similarity calculation, which are separated and independent from each other. The separated face analyzing stages lead the model redundant calculation and are hard for end-to-end training. In this paper, we proposed a novel end-to-end trainable convolutional network framework for face detection and recognition, in which a geometric transformation matrix was directly learned to align the faces, instead of predicting the facial landmarks. In training stage, our single CNN model is supervised only by face bounding boxes and personal identities, which are publicly available from WIDER FACE [52] dataset and CASIA-WebFace [53] dataset. Tested on Face Detection Dataset and Benchmark (FDDB) [21] dataset and Labeled Face in the Wild (LFW) [19] dataset, we have achieved 89.24% recall for face detection task and 98.63% verification accuracy for face recognition task simultaneously, which are comparable to state-of-the-art results.
Staleness Control for Edge Data Analytics

A new generation of cyber-physical systems has emerged with a large number of devices that continuously generate and consume massive amounts of data in a distributed and mobile manner. Accurate and near real-time decisions based on such streaming data are in high demand in many areas of optimization for such systems. Edge data analytics bring processing power in the proximity of data sources, reduce the network delay for data transmission, allow large-scale distributed training, and consequently help meeting real-time requirements. Nevertheless, the multiplicity of data sources leads to multiple distributed machine learning models that may suffer from sub-optimal performance due to the inconsistency in their states. In this work, we tackle the insularity, concept drift, and connectivity issues in edge data analytics to minimize its accuracy handicap without losing its timeliness benefits. Thus, we propose an efficient model synchronization mechanism for distributed and stateful data analytics. Staleness Control for Edge Data Analytics (SCEDA) ensures the high adaptability of synchronization frequency in the face of an unpredictable environment by addressing the trade-off between the generality and timeliness of the model.
Copula Guided Neural Topic Modelling for Short Texts

Extracting the topical information from documents is important for public opinion analysis, text classification, and information retrieval tasks. Compared with identifying a wide variety of topics from long documents, it is challenging to generate a concentrated topic distribution for each short message. Although this problem can be tackled by adjusting the hyper-parameters in traditional topic models such as Latent Dirichlet Allocation, it remains an open problem in neural topic modelling. In this paper, we focus on adapting the popular Auto-Encoding Variational Bayes based neural topic models to short texts, by exploring the Archimedean copulas to guide the estimated topic distributions derived from linear projected samples of re-parameterized posterior distributions. Experimental results show the superiority of our method when compared with existing neural topic models in terms of perplexity, topic coherence, and classification accuracy.
Deep learning from a statistical perspective

As one of the most rapidly developing artificial intelligence techniques, deep learning has been applied in various machine learning tasks and has received great attention in data science and statistics. Regardless of the complex model structure, deep neural networks can be viewed as a nonlinear and nonparametric generalization of existing statistical models. In this review, we introduce several popular deep learning models including convolutional neural networks, generative adversarial networks, recurrent neural networks, and autoencoders, with their applications in image data, sequential data and recommender systems. We review the architecture of each model and highlight their connections and differences compared with conventional statistical models. In particular, we provide a brief survey of the recent works on the unique overparameterization phenomenon, which explains the strengths and advantages of using an extremely large number of parameters in deep learning. In addition, we provide a practical guidance on optimization algorithms, hyperparameter tuning, and computing resources.
Robust image hashing with compressed sensing and ordinal measures

Abstract Image hashing is an efficient technology for processing digital images and has been successfully used in image copy detection, image retrieval, image authentication, image quality assessment, and so on. In this paper, we design a new image hashing with compressed sensing (CS) and ordinal measures. This hashing method uses a visual attention model called Itti model and Canny operator to construct an image representation, and exploits CS to extract compact features from the representation. Finally, the CS-based compact features are quantized via ordinal measures. L2 norm is used to judge similarity of hashes produced by the proposed hashing method. Experiments about robustness validation, discrimination test, block size discussion, selection of visual attention model, selection of quantization scheme, and effectiveness of the use of ordinal measures are conducted to verify performances of the proposed hashing method. Comparisons with some state-of-the-art algorithms are also carried out. The results illustrate that the proposed hashing method outperforms some compared algorithms in classification according to ROC (receiver operating characteristic) graph.
A Discriminative Point Matching Algorithm Based on Local Structure Consensus Constraint

Due to the existence of repetitive patterns, ambiguous features, and similar local structures in remote sensing images, it is inevitable that the outliers with local pseudoisomorphic structures are preserved as inliers, which makes point matching still a challenging problem. To improve the accuracy of feature matching, a discriminative point matching algorithm named local structure consensus constraint is proposed to remove the outliers from putative correspondences and find two local structure consensus graphs composed of inliers. First, a local structure descriptor is proposed to evaluate the corresponding structure similarity of the K-nearest neighbors. Then, a cost function is defined to evaluate the local structure consistency. With a two-stage outlier removing strategy, the feature points with different local structures are eliminated, and two local structure consensus graphs are obtained. To evaluate the performance of the proposed algorithm, 45 aerial image pairs taken around the Shandong Peninsula with repetitive local patterns and ambiguous features are used. Compared with five state-of-the-art point matching methods, the proposed algorithm is proven to be more accurate and efficient.
Attentional Bottleneck: Towards an Interpretable Deep Driving Network

Deep neural networks are a key component of behavior prediction and motion generation for self-driving cars. One of their main drawbacks is a lack of transparency: they should provide easy to interpret rationales for what triggers certain behaviors. We propose an architecture called Attentional Bottleneck with the goal of improving transparency. Our key idea is to combine visual attention, which identifies what aspects of the input the model is using, with an information bottleneck that enables the model to only use aspects of the input which are important. This not only provides sparse and interpretable attention maps (e.g. focusing only on specific vehicles in the scene), but it adds this transparency at no cost to model accuracy. In fact, we find slight improvements in accuracy when applying Attentional Bottleneck to the ChauffeurNet model in comparison to a traditional visual attention model that degrades accuracy.
Abnormal Event Detection in Surveillance Videos Using Two-Stream Decoder

Abnormal event detection in surveillance videos refers to the identification of events that deviate from the normal pattern. An autoencoder can be used to learn the normal patterns from the videos, and its reconstruction errors can be used to detect the abnormalities. Surveillance videos consist of two components: dynamic objects and a static background. Because of the nature of the static background, we can assume that the source of abnormality is from the objects. In this work, we propose the use of a two-stream decoder model to tackle the abnormal event detection problem in surveillance videos. The two-stream decoder comprised a background stream that models the static background and a foreground stream that models the dynamic objects. We also utilized a two-stream encoder to learn from optical flow, which contains motion information, and skip connections used to improve the details in the output frames. Several experiments on publicly available datasets were used to validate the effectiveness of the proposed model.
Covariance matrix entanglement criterion for an arbitrary set of operators

Abstract A criterion for entanglement detection based on covariance matrices for an arbitrary set of observables is formulated. The method generalizes the covariance matrix entanglement criterion by Simon (2000 Phys. Rev. Lett. 84 2726) to a more general set of operators using the positive partial transpose test for the covariance matrix. The relation is found by starting from the generalized uncertainty relation for multiple operators, and taking the partial transpose on the bipartition. The method is highly efficient and versatile in the sense that the set of measurement operators can be freely chosen, and there is no constraint on the commutation relations. The main restriction on the chosen set of measurement operators is that the correlators and expectation values of the partially transposed observable operators can be evaluated. The method is particularly suited for systems with higher dimensionality since the computations do not scale with the dimension of the Hilbert space—rather they scale with the number of chosen observables. We illustrate the approach by examining the entanglement between two spin ensembles, and show that it detects entanglement in a basis independent way.
Trends in performance factors of large photovoltaic solar plants

This short communication discusses the two parameters recently emerged as a Key Performance Indicator of solar energy facilities, the mean capacity factor over a year, and the standard deviation of the capacity factor computed with high-frequency sampling. Both parameters impact directly and indirectly on the Levelised Cost Of Electricity (LCOE), permitting to quantify the energy production by the specific facility, and the attribution of the grid energy storage costs to the specific facility. The annual average capacity factors of the latest, largest, 53 photovoltaic (PV) solar energy facilities in the US, vary between 10% and 36%, with a mean value of 27% and a standard deviation of 5%. There are large differences also in between plants located in areas of a similar solar resource. Inference of long term performance degradation or O&M costs is difficult. We know from Australia that solar PV facilities work with high-frequency capacity factors' standard deviations larger than the mean, for coefficients of variability above unity, and significant variability also at the grid level. This variability necessitates of energy storage. High-frequency data to assess the standard deviation of the individual US facility contribution to the different grids, as well as the energy storage needed for every grid, is unavailable. Construction cost data are less reliable than energy production data and mostly missing. Based on data for 15 plants, completed between 2014 and 2017, the construction cost dramatically varies between different facilities. The specific cost varies between 1,719 [US$/kW] and 7,143 [US$/kW], an average of 3,983 [US$/kW]. Considering the actual generating power vs. the nominal generating power, the specific cost varies between 6,374 and 22,806, an average 14,006 [US$/kW]. Hence, an accurate prediction of the LCOE of large PV facilities in the US is presently difficult.
Research on the Problems and Countermeasures of the Cultivation of College Students’ Innovation and Entrepreneurship Quality

How to cultivate college students' innovative undertaking is the key to innovative talent training.How to play a role in the creative talents in colleges and universities is becoming an important content of higher education personnel training mode perfect.This article from the university students' innovative entrepreneurial education problems existing in the process puts forward some countermeasures to improve the innovative entrepreneurial talent quality training.
Imaging-Correlated Intra Prediction for Plenoptic 2.0 Video Coding

The plenoptic 2.0 video records time-varying light fields varying with much higher spatial resolution for each subaperture image. However, the huge data volume becomes a significant obstacle of the plenoptic 2.0 video application. Therefore, this paper proposes an efficient compression method designed for plenoptic 2.0 video coding. First, the matching blocks relative to the current block are determined by imaging principles. The matching blocks and current block are imaged by the same object under neighboring microlens, which are highly correlated with each other. Then, weighted prediction by using the matching blocks as reference blocks is adopted for the intra prediction of current block. Compared with HEVC, experimental results show that the proposed method can save 9.63% and 4.73% bitrate under the All Intra and Random Access configuration, respectively.
Near-Infrared Image Guided Reflection Removal

Removing reflections from a single RGB image is a highly ill-posed problem. Unlike RGB images, near-infrared (NIR) images obtained through an active NIR camera are less likely to be affected by reflections when glass and camera planes form certain angles, while textures on objects could “vanish” under certain circumstances. Based on this observation, we propose a two-stream neural network to remove undesired reflections in an RGB image with the guidance of an NIR image. To tackle the insufficiency of training data, we propose a synthetic data generation pipeline that simulates the reflection-suppressing nature of the active NIR imaging and build a dataset mixed with synthetic and real data. Experimental results show that the proposed method outperforms state-of-the-art reflection removal methods in both quantitative metrics and visual quality.
KINMIX: A Data Augmentation Approach for Kinship Verification

In this paper, we propose a KinMix method to generate positive samples in the feature space for facial kinship verification. Unlike most existing data augmentation methods, we generate samples at the feature level instead of the original image level for data augmentation. Specifically, we use a pair of features with a kin relationship to construct a feature space instead of a single image, which aims to maintain the clustering results of features. We assume that a pair of kinship features lie in the same feature space, so that the generated feature remains the same kin relationship if it is sampled from the feature space. We use a linear sampling method to generate positive samples with the kin relationship. We evaluate our method on two widely used kinship datasets: KinFaceW-I and KinFaceW-II, and our experimental results are presented to show the effectiveness of the proposed approach.