Reviewerly/exHarmony huggingface dataset:
Compressed-Domain Highway Vehicle Counting by Spatial and Temporal Regression

Counting on-road vehicles in the highway is fundamental for intelligent transportation management. This paper presents the first highway vehicle counting method in compressed domain, aiming at achieving comparable estimation performance with the pixel-domain methods. Counting in compressed domain is rather challenging due to limited information about vehicles and large variance in vehicle numbers. To address this problem, we develop new low-level features to mitigate the challenge from insufficient information in compressed videos. The new proposed features can be easily extracted from the coding-related metadata. Then, we propose a hierarchical classification-based regression (HCR) model to estimate the number of vehicles from the compressed-domain low-level features for individual frame. HCR hierarchically divides the traffic scenes into different cases according to the density of vehicles such that the large variance of traffic scenes can be effectively captured. Beside the spatial regression in each frame, we propose a locally temporal regression model to further refine the counting results, which exploits the continuous variation characteristics of the traffic flow. We extensively evaluate the proposed method on real highway surveillance videos. The experimental results consistently show that the proposed method is very competitive compared with the pixel-domain methods, which can reach similar performance with much lower computational cost.
Robust Fitting in Computer Vision: Easy or Hard?

Robust model fitting plays a vital role in computer vision, and research into algorithms for robust fitting continues to be active. Arguably the most popular paradigm for robust fitting in computer vision is consensus maximisation, which strives to find the model parameters that maximise the number of inliers. Despite the significant developments in algorithms for consensus maximisation, there has been a lack of fundamental analysis of the problem in the computer vision literature. In particular, whether consensus maximisation is “tractable” remains a question that has not been rigorously dealt with, thus making it difficult to assess and compare the performance of proposed algorithms, relative to what is theoretically achievable. To shed light on these issues, we present several computational hardness results for consensus maximisation. Our results underline the fundamental intractability of the problem, and resolve several ambiguities existing in the literature.
Secure and efficient outsourcing differential privacy data release scheme in Cyber–physical system

A cyber–physical system is a mechanism controlled or monitored by computer-based algorithms, tightly integrated with the internet and its users. Cyber–physical systems such as smart grid, autonomous automobile systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics will use physical sensors to produce and collect data. Most of the data contains personal information, which is so called privacy, should be carefully protected. How to protect privacy is now a hot-topic not only in academia but also in industry. Differential privacy has been accepted as the privacy concept due to its concise definition and its simple implementation. However, the interactive model cannot achieve differential privacy without data provider’s timely answers, which means data provider should always be attachable. It is unrealistic to keep data provider online due to the risk of data provider be broken will grow rapidly as time goes by. With today’s differential privacy technology, a non-interactive model remains an open problem. To find an alternative, we consider implant whole dataset into a cloud server to provide all the functions instead of data provider. Nonetheless, once the server is compromised, the privacy of the data cannot be guaranteed. It appears that there should be a strong definition, the cloud server is completely trustworthy, before differential privacy can actually be implemented. An intuitive thought to improve this situation is to only upload encrypted datasets. Then, the server could be semi-honest or even fully malicious. Homomorphic encryption can make the encrypted dataset operable, but it requires considerable storage space and bandwidth, which are impractical. We realized that order-preserving encryption is a tradeoff between data utility and practicability. Thus, we propose a novel outsourcing differential privacy data release scheme in cyber–physical system. The proposed scheme allows data providers to outsource their datasets to a cloud service provider with low communication cost. Let the cloud service provider be the host that answers the queries from the data evaluator with noisy results. The data providers can go offline after uploading their encrypted datasets, which is one of the critical requirements for a practical system. In this paper, we present a detailed theoretical analysis, including proofs of differential privacy and security. We also report an experimental evaluation on real datasets.
Deep Learning-Based Language Identification in English-Hindi-Bengali Code-Mixed Social Media Corpora

Abstract This article addresses language identification at the word level in Indian social media corpora taken from Facebook, Twitter and WhatsApp posts that exhibit code-mixing between English-Hindi, English-Bengali, as well as a blend of both language pairs. Code-mixing is a fusion of multiple languages previously mainly associated with spoken language, but which social media users also deploy when communicating in ways that tend to be rather casual. The coarse nature of code-mixed social media text makes language identification challenging. Here, the performance of deep learning on this task is compared to feature-based learning, with two Recursive Neural Network techniques, Long Short Term Memory (LSTM) and bidirectional LSTM, being contrasted to a Conditional Random Fields (CRF) classifier. The results show the deep learners outscoring the CRF, with the bidirectional LSTM demonstrating the best language identification performance.
Practical Long-Distance Side-Channel-Free Quantum Key Distribution

Quantum key distribution (QKD) can provide secure communication even when an eavesdropper (the villainous ``Eve'') completely controls the channel. In practice, though, side-channel effects exist due to device imperfections, and Eve can still intercept information via a side channel. Thus the authors devise a QKD scheme that is both free of side channels in the source state and measurement-device-independent. While some other protocols can also achieve side-channel-free security, this one is based on mature technology without any demand for local detection efficiency, and works for distances greater than 200 km, even though the misalignment error rate may be as large as 20%.
Bayesian Fairness

We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of Bayesian fairness as a suitable candidate for fair decision rules. Using balance, a definition of fairness introduced in (Kleinberg, Mullainathan, and Raghavan 2016), we show how a Bayesian perspective can lead to well-performing and fair decision rules even under high uncertainty.
Using response time distributions and race models to characterize primacy and recency effects in free recall initiation.

Primacy and recency effects are common benchmarks for models of free recall and episodic memory. In this work, we show that RT distributions carry diagnostic information about how items enter into competition for recall, and how that competition impacts on the dynamics of recall and leads to novel conclusions about the forms of primacy and recency effects. We jointly fit RT distributions and serial position functions for free recall initiation with both a racing diffusion model and the linear ballistic accumulator (LBA: Brown & Heathcote, 2008). The models were fit in a hierarchical Bayesian framework, factorially varying different assumptions of how primacy and recency are generated. Recency functions were either exponential or power law in shape. Primacy was treated either as a strength boost to the early list items so that both primacy and recency items jointly compete to be retrieved; a mixture of primacy and recency gradients reflecting the usage of different retrieval cues; or a primacy-as-recency account in which primacy items are functionally recent due to the contribution of rehearsal. Although serial position curves do not distinguish between these accounts, they make distinct predictions about how RT distributions vary across serial positions. Results from a number of data sets strongly favor an exponential recency function along with a mixture model of primacy and recency gradients. These results suggest that complete RT distributions can provide informative constraints on models of free recall. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
nsROC: An R package for Non-Standard ROC Curve Analysis

The authors acknowledge support by the Grants MTM2015-63971-P and MTM2014-55966-P from the Spanish Ministerio of Economia y Competitividad and by FC-15-GRUPIN14-101 from the Principado de Asturias
Multi-Source Neural Variational Inference

Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference methods that take into account the complementarity and redundancy of various information sources. In this paper we formulate a variational autoencoder based multi-source learning framework in which each encoder is conditioned on a different information source. This allows us to relate the sources via the shared latent variables by computing divergence measures between individual source’s posterior approximations. We explore a variety of options to learn these encoders and to integrate the beliefs they compute into a consistent posterior approximation. We visualise learned beliefs on a toy dataset and evaluate our methods for learning shared representations and structured output prediction, showing trade-offs of learning separate encoders for each information source. Furthermore, we demonstrate how conflict detection and redundancy can increase robustness of inference in a multi-source setting.
A robust and effective phase-shift fringe projection profilometry method for the extreme intensity

The structured-light with phase-shift fringe projection profilometry (PFPP) plays a significant role in non-contact 3D surface measurement. The multi-frequency heterodyne (MFH) phase unwrapping algorithm is widely used in PFPP because of its high precision and noise-isolation features. However, measuring the over-dark and over-bright surfaces is a headache of the MFH algorithm due to its limit intensity tolerance in decoding phases. We provide a new method named as Moire Fringe Patterns Sequence (MFPS) algorithm to avoid the phase error enlargement, so as to increase the robustness of the decoding algorithm. In the MFPS algorithm, we project the synthetic patterns to the object directly, which doubles the tolerance of phase error. The MFPS algorithm has the same accuracy as the MFH. It can successfully decode the phase of the extreme pixel intensity (as low as 10 or saturated at 255), but require no more hardware or additional work. The theoretical, simulation and experimental studies verify that the MFPS algorithm is robust and effective in measuring the over-dark and over-bright object.
From free text to clusters of content in health records: an unsupervised graph partitioning approach

Electronic healthcare records contain large volumes of unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of richly detailed information often remains under-used in practice because of a lack of suitable methodologies to extract interpretable content in a timely manner. Here we apply network-theoretical tools to the analysis of free text in Hospital Patient Incident reports in the English National Health Service, to find clusters of reports in an unsupervised manner and at different levels of resolution based directly on the free text descriptions contained within them. To do so, we combine recently developed deep neural network text-embedding methodologies based on paragraph vectors with multi-scale Markov Stability community detection applied to a similarity graph of documents obtained from sparsified text vector similarities. We showcase the approach with the analysis of incident reports submitted in Imperial College Healthcare NHS Trust, London. The multiscale community structure reveals levels of meaning with different resolution in the topics of the dataset, as shown by relevant descriptive terms extracted from the groups of records, as well as by comparing a posteriori against hand-coded categories assigned by healthcare personnel. Our content communities exhibit good correspondence with well-defined hand-coded categories, yet our results also provide further medical detail in certain areas as well as revealing complementary descriptors of incidents beyond the external classification. We also discuss how the method can be used to monitor reports over time and across different healthcare providers, and to detect emerging trends that fall outside of pre-existing categories.
GAS: A genetic atlas selection strategy in multi-atlas segmentation framework

Multi-Atlas based Segmentation (MAS) algorithms have been successfully applied to many medical image segmentation tasks, but their success relies on a large number of atlases and good image registration performance. Choosing well-registered atlases for label fusion is vital for an accurate segmentation. This choice becomes even more crucial when the segmentation involves organs characterized by a high anatomical and pathological variability. In this paper, we propose a new genetic atlas selection strategy (GAS) that automatically chooses the best subset of atlases to be used for segmenting the target image, on the basis of both image similarity and segmentation overlap. More precisely, the key idea of GAS is that if two images are similar, the performances of an atlas for segmenting each image are similar. Since the ground truth of each atlas is known, GAS first selects a predefined number of similar images to the target, then, for each one of them, finds a near-optimal subset of atlases by means of a genetic algorithm. All these near-optimal subsets are then combined and used to segment the target image. GAS was tested on single-label and multi-label segmentation problems. In the first case, we considered the segmentation of both the whole prostate and of the left ventricle of the heart from magnetic resonance images. Regarding multi-label problems, the zonal segmentation of the prostate into peripheral and transition zone was considered. The results showed that the performance of MAS algorithms statistically improved when GAS is used.
FPGA implementation of color image encryption using a new chaotic map

&lt;p&gt;In this paper, an FPGA implementation of efficient image encryption algorithm using a chaotic map has been proposed. The proposed system consists of two phases image encryption technique. First phase consists of scrambling of pixel position and second phase consist of diffusion of bit value. In the first phase, original pixel values remain unchanged. In second phase, pixel values are modified. These modifications are done by using chaotic behavior of a recently developed chaotic map called Nahrain. A color image encryption using Nahrain chaotic map is simulated in software via Matlab, Altera Quartus Prime 17.0 Lite EditionI and ModelSim software tools then implemented in hardware via Cyclone V GX Starter Kit FPGA platform. The results show the feasibility and effectiveness of the cryptosystem. As a typical application, the image encryption/decryption is used to demonstrate and verify the operation of the cryptosystem hardware. Complete analysis on robustness of the method is investigated. Correlation, Encryption time, Decryption time and key sensitivity show that the proposed crypto processor offers high security and reliable encryption speed for real-time image encryption and transmission. To evaluate the performance, histogram, correlation, information entropy, number of pixel change rate (NPCR), and unified average changing intensity (UACI) measures are used for security analysis. The simulation results and security analysis have demonstrated that the proposed encryption system is robust and flexible. For example the amount of entropy obtained by the proposed algorithm is 7.9964, which is very close to its ideal amount: 8, and NPCR is 99.76 %, which is the excellent value to obtain. The hardware simulation results show that the number of pins that used of the proposed system reaches to 6% of total pins and Logic utilization (in ALMs) is 1%.&lt;/p&gt;
Multi-View Anomaly Detection: Neighborhood in Locality Matters

Identifying anomalies in multi-view data is a difficult task due to the complicated data characteristics of anomalies. Specifically, there are two types of anomalies in multi-view data–anomalies that have inconsistent features across multiple views and anomalies that are consistently anomalous in each view. Existing multi-view anomaly detection approaches have some issues, e.g., they assume multiple views of a normal instance share consistent and normal clustering structures while anomaly exhibits anomalous clustering characteristics across multiple views. When there are no clusters in data, it is difficult for existing approaches to detect anomalies. Besides, existing approaches construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. The objective is formulated to profile normal instances, but not to estimate the set of normal instances, which results in sub-optimal detectors. In addition, the model trained to profile normal instances uses the entire dataset including anomalies. However, anomalies could undermine the model, i.e., the model is not robust to anomalies. To address these issues, we propose the nearest neighborbased MUlti-View Anomaly Detection (MUVAD) approach. Specifically, we first propose an anomaly measurement criterion and utilize this criterion to formulate the objective of MUVAD to estimate the set of normal instances explicitly. We further develop two concrete relaxations for implementing the MUVAD as MUVAD-QPR and MUVAD-FSR. Experimental results validate the superiority of the proposed MUVAD approaches.
Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse

We study the problem of sketch image recognition. This problem is plagued with two major challenges: 1) sketch images are often scarce in contrast to the abundance of natural images, rendering the training task difficult, and 2) the significant domain gap between sketch image and its natural image counterpart makes the task of bridging the two domains challenging. In order to overcome these challenges, in this paper we propose to transfer the knowledge of a network learned from natural images to a sketch network - a new deep net architecture which we term as cousin network. This network guides a sketch-recognition network to extract more relevant features that are close to those of natural images, via adversarial training. Moreover, to enhance the transfer ability of the classification model, a sketch-to-image attribute warehouse is constructed to approximate the transformation between the sketch domain and the real image domain. Extensive experiments conducted on the TU-Berlin dataset show that the proposed model is able to efficiently distill knowledge from natural images and achieves superior performance than the current state of the art.
Bringing Order to Chaos – A Compact Representation of Partial Order in SAT-Based HTN Planning

HTN planning provides an expressive formalism to model complex application domains. It has been widely used in realworld applications. However, the development of domainindependent planning techniques for such models is still lacking behind. The need to be informed about both statetransitions and the task hierarchy makes the realisation of search-based approaches difficult, especially with unrestricted partial ordering of tasks in HTN domains. Recently, a translation of HTN planning problems into propositional logic has shown promising empirical results. Such planners benefit from a unified representation of state and hierarchy, but until now require very large formulae to represent partial order. In this paper, we introduce a novel encoding of HTN Planning as SAT. In contrast to related work, most of the reasoning on ordering relations is not left to the SAT solver, but done beforehand. This results in much smaller formulae and, as shown in our evaluation, in a planner that outperforms previous SAT-based approaches as well as the state-of-the-art in search-based HTN planning.
Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization

Multi-view Multi-instance Multi-label Learning (M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance.\&#x0D; In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instances, and labels, to encode different types of relations via multiple relational data matrices. To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices. An aggregation scheme is further introduced to aggregate the instance-level labels into bag-level and to guide the factorization. An empirical study on benchmark datasets show that M3Lcmf outperforms other related competitive solutions both in the instance-level and bag-level prediction.
Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)

Selecting appropriate tutoring help actions that account for both a student’s content mastery and engagement level is essential for effective human tutors, indicating the critical need for these skills in autonomous tutors. In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable Markov decision process (AT-POMDP). We designed the AT-POMDP and derived its parameters based on data from a prior robot-student tutoring study. The policy that results from solving the AT-POMDP allows a robot tutor to decide upon the optimal tutoring help action to give a student, while maintaining a belief of the student’s mastery of the material and engagement with the task. This approach is validated through a between-subjects field study, which involved 4th grade students (n=28) interacting with a social robot solving long division problems over five sessions. Students who received help from a robot using the AT-POMDP policy demonstrated significantly greater learning gains than students who received help from a robot with a fixed help action selection policy. Our results demonstrate that this robust computational framework can be used effectively to deliver diverse and personalized tutoring support over time for students.