Reviewerly/exHarmony huggingface dataset:
Multi-Modal Sentiment Analysis Using Deep Canonical Correlation Analysis

This paper learns multi-modal embeddings from text, audio, and video views/modes of data in order to improve upon down-stream sentiment classification. The experimental framework also allows investigation of the relative contributions of the individual views in the final multi-modal embedding. Individual features derived from the three views are combined into a multi-modal embedding using Deep Canonical Correlation Analysis (DCCA) in two ways i) One-Step DCCA and ii) Two-Step DCCA. This paper learns text embeddings using BERT, the current state-of-the-art in text encoders. We posit that this highly optimized algorithm dominates over the contribution of other views, though each view does contribute to the final result. Classification tasks are carried out on two benchmark datasets and on a new Debate Emotion data set, and together these demonstrate that the one-Step DCCA outperforms the current state-of-the-art in learning multi-modal embeddings.
Adaptive Binocular Fringe Dynamic Projection Method for High Dynamic Range Measurement

Three-dimensional measurement with fringe projection sensor has been commonly researched. However, the measurement accuracy and efficiency of most fringe projection sensors are still seriously affected by image saturation and the non-linear effects of the projector. In order to solve the challenge, in conjunction with the advantages of stereo vision technology and fringe projection technology, an adaptive binocular fringe dynamic projection method is proposed. The proposed method can avoid image saturation by adaptively adjusting the projection intensity. Firstly, the flowchart of the proposed method is explained. Then, an adaptive optimal projection intensity method based on multi-threshold segmentation is introduced to adjust the projection illumination. Finally, the mapping relationship of binocular saturation point and projection point is established by binocular transformation and left camera–projector mapping. Experiments demonstrate that the proposed method can achieve higher accuracy for high dynamic range measurement.
Computationally Efficient Light Field Image Compression Using a Multiview HEVC Framework

The acquisition of the spatial and angular information of a scene using light field (LF) technologies supplement a wide range of post-processing applications, such as scene reconstruction, refocusing, virtual view synthesis, and so forth.The additional angular information possessed by LF data increases the size of the overall data captured while offering the same spatial resolution.The main contributor to the size of captured data (i.e., angular information) contains a high correlation that is exploited by state-of-the-art video encoders by treating the LF as a pseudo video sequence (PVS).The interpretation of LF as a single PVS restricts the encoding scheme to only utilize a single-dimensional angular correlation present in the LF data.In this paper, we present an LF compression framework that efficiently exploits the spatial and angular correlation using a multiview extension of high-efficiency video coding (MV-HEVC).The input LF views are converted into multiple PVSs and are organized hierarchically.The rate-allocation scheme takes into account the assigned organization of frames and distributes quality/bits among them accordingly.Subsequently, the reference picture selection scheme prioritizes the reference frames based on the assigned quality.The proposed compression scheme is evaluated by following the common test conditions set by JPEG Pleno.The proposed scheme performs 0.75 dB better compared to state-of-the-art compression schemes and 2.5 dB better compared to the x265-based JPEG Pleno anchor scheme.Moreover, an optimized motionsearch scheme is proposed in the framework that reduces the computational complexity (in terms of the sum of absolute difference [SAD] computations) of motion estimation by up to 87% with a negligible loss in visual quality (approximately 0.05 dB).
Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace

Applying computer vision to mobile robot navigation has been studied more than twodecades. The most challenging problems for a vision-based AGV running in a complex workspaceinvolve the non-uniform illumination, sight-line occlusion or stripe damage, which inevitably resultin incomplete or deformed path images as well as many fake artifacts. Neither the fixed thresholdmethods nor the iterative optimal threshold methods can obtain a suitable threshold for the pathimages acquired on all conditions. It is still an open question to estimate the model parameters ofguide paths accurately by distinguishing the actual path pixels from the under- or oversegmentationerror points. Hence, an intelligent path recognition approach based on KPCA–BPNNand IPSO–BTGWP is proposed here, in order to resist the interferences from the complexworkspace. Firstly, curvilinear paths were recognized from their straight counterparts by means of apath classifier based on KPCA–BPNN. Secondly, an approximation method based on BTGWP wasdeveloped for replacing the curve with a series of piecewise lines (a polyline path). Thirdly, a robustpath estimation method based on IPSO was proposed to figure out the path parameters from a set ofpath pixels surrounded by noise points. Experimental results showed that our approach caneffectively improve the accuracy and reliability of a low-cost vision-guidance system for AGVs in acomplex workspace.
Extending the YAGO2 Knowledge Graph with Precise Geospatial Knowledge

We extend YAGO2 with geospatial information represented by geometries (e.g., lines, polygons, multipolygons, etc.) encoded by Open Geospatial Consortium standards. The new geospatial information comes from official sources such as the administrative divisions of countries but also from volunteered open data of OpenStreetMap. The resulting knowledge graph is currently the richest in terms of geospatial information publicly available, open source, knowledge graph.
Open Universities: Innovative Past, Challenging Present, and Prospective Future

This article examines the innovative past of the large-scale, single-mode open universities that follow the model of the UK Open University (UKOU), analyzes the main challenges which they are currently facing in the digital era, and concludes with highlighting leading prospects for their future operation.&#x0D; The establishment of the UKOU in 1969 marked a new era in distance higher education. It gave distance education a new legitimacy and opened up new prospects for populations that for a variety of reasons were unable to attend a campus-based university. Many of the new open universities were heralded as a conspicuous development in higher education, with innovative features such as: open access, reaching out to part-time adult students, providing academic faculty the opportunity to work in teams to prepare study materials, modular credit accumulation, teaching huge numbers of students, and harnessing innovative technologies into their teaching/learning processes.&#x0D; In the last three decades, many of these innovative characteristics pioneered by open universities have been adopted by campus universities. This has eroded the unique status of open universities in many national jurisdictions. Furthermore, the emergence of digital technologies has challenged the underlying premises of the industrial model of many open universities, as well as their logistic operation. Present challenges facing open universities emerge from: blurred boundaries between distance and campus universities; the changing of initial target populations; the need to restructure the technological and logistic infrastructure of open universities; the changing roles of the academic faculty; and the growing competition for both students and funds. In order to find success and keep being relevant in the future, open universities should take into consideration: future target populations; the use of MOOCs and OER; support systems for both students and professors; collaboration with other higher education institutions; collaboration with the corporate and work worlds; and enhancing the academic status of open universities.
Chain-Free String Constraints

We address the satisfiability problem for string constraints that combine relational constraints represented by transducers, word equations, and string length constraints. This problem is undecidable in general. Therefore, we propose a new decidable fragment of string constraints, called weakly chaining string constraints, for which we show that the satisfiability problem is decidable. This fragment pushes the borders of decidability of string constraints by generalising the existing straight-line as well as the acyclic fragment of the string logic. We have developed a prototype implementation of our new decision procedure, and integrated it into in an existing framework that uses CEGAR with under-approximation of string constraints based on flattening. Our experimental results show the competitiveness and accuracy of the new framework.
Context-Aware Adaptive Remote Access for IoT Applications

The rapid growth of communication networking, ubiquitous sensing, and signal processing has spurred the emergence of the Internet of Things (IoT) era. As a novel cutting-edge technology, the IoT enables a plethora of smart-devices equipped with diverse computing, sensing, and actuation capabilities to be connected to the Internet. Thus, it promises to provide a revolutionary and fully connected “smart” world while greatly developing economies and enhancing the quality of life. IoT is indeed an emergent global phenomenon, where real-time remote access to data and applications opens new unprecedented opportunities for ubiquitous monitoring and managing. In such dynamic, interconnected, and heterogeneous environment where the context conditions (location, time, situation sensitivity, etc.) are continuously and frequently changing, context-aware and adaptive solutions for data access are required to respond to the applications' needs. Nevertheless, until now, no schemes provide concrete context-aware access control mechanisms in IoT. In this article, we design a novel context-aware attribute-based access control (CAABAC) that considers the dynamic context changes. The proposed approach incorporates the contextual information with the ciphertext-policy attribute-based encryption (CP-ABE) to guarantee adaptive contextual access to data. The extensive analysis and simulations prove both the effectiveness and efficiency of the proposed scheme. Specifically, context-aware and adaptive remote access is enabled while outperforming other benchmarked schemes in terms of storage, communication, and computational cost.
End-to-End Handwritten Text Detection and Transcription in Full Pages

When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately.
Learning Robust Representations via Multi-View Information Bottleneck

The information bottleneck method provides an information-theoretic view of representation learning. The original formulation, however, can only be applied in the supervised setting where task-specific labels are available at learning time. We extend this method to the unsupervised setting, by taking advantage of multi-view data, which provides two views of the same underlying entity. A theoretical analysis leads to the definition of a new multi-view model which produces state-of-the-art results on two standard multi-view datasets, Sketchy and MIR-Flickr. We also extend our theory to the single-view setting by taking advantage of standard data augmentation techniques, empirically showing better generalization capabilities when compared to traditional unsupervised approaches.
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning

Q-learning suffers from overestimation bias, because it approximates the maximum action value using the maximum estimated action value. Algorithms have been proposed to reduce overestimation bias, but we lack an understanding of how bias interacts with performance, and the extent to which existing algorithms mitigate bias. In this paper, we 1) highlight that the effect of overestimation bias on learning efficiency is environment-dependent; 2) propose a generalization of Q-learning, called \emph{Maxmin Q-learning}, which provides a parameter to flexibly control bias; 3) show theoretically that there exists a parameter choice for Maxmin Q-learning that leads to unbiased estimation with a lower approximation variance than Q-learning; and 4) prove the convergence of our algorithm in the tabular case, as well as convergence of several previous Q-learning variants, using a novel Generalized Q-learning framework. We empirically verify that our algorithm better controls estimation bias in toy environments, and that it achieves superior performance on several benchmark problems.
The Signature-Based Model for Early Detection of Sepsis From Electronic Health Records in the Intensive Care Unit

Optimal feature selection leads to enhanced efficiency and accuracy when developing both supervised and unsupervised machine-learning models. In this work, a new signature-based regression model is proposed to automatically identify a patient's risk of sepsis based on physiological data streams and to make a positive or negative prediction ofsepsis for every time interval since admission to the intensive care unit. The gradient boosting machine algorithm that uses the features at the current time-points and the signature features extracted from the time-series to model the longitudinal effects ofsepsis yields the utility function score of 0.360 (officially ranked 1st, team name: ‘Can I get your Signature?’) on the full test set. The signature method shows a systematic and competitive approach to model sepsis by learning from health data streams.
Integrated moving target defense and control reconfiguration for securing Cyber-Physical systems

With the increasingly connected nature of Cyber-Physical Systems (CPS), new attack vectors are emerging that were previously not considered in the design process. Specifically, autonomous vehicles are one of the most at risk CPS applications, including challenges such as a large amount of legacy software, non-trusted third party applications, and remote communication interfaces. With zero day vulnerabilities constantly being discovered, an attacker can exploit such vulnerabilities to inject malicious code or even leverage existing legitimate code to take over the cyber part of a CPS. Due to the tightly coupled nature of CPS, this can lead to altering physical behavior in an undesirable or devastating manner. Therefore, it is no longer effective to reactively harden systems, but a more proactive approach must be taken. Moving target defense (MTD) techniques such as instruction set randomization (ISR), and address space randomization (ASR) have been shown to be effective against code injection and code reuse attacks. However, these MTD techniques can result in control system crashing which is unacceptable in CPS applications since such crashing may cause catastrophic consequences. Therefore, it is crucial for MTD techniques to be complemented by control reconfiguration to maintain system availability in the event of a cyber-attack. This paper addresses the problem of maintaining system and security properties of a CPS under attack by integrating moving target defense techniques, as well as detection, and recovery mechanisms to ensure safe, reliable, and predictable system operation. Specifically, we consider the problem of detecting code injection as well as code reuse attacks, and reconfiguring fast enough to ensure the safety and stability of autonomous vehicle controllers are maintained. By using MTD such as ISR, and ASR, our approach provides the advantage of preventing attackers from obtaining the reconnaissance knowledge necessary to perform code injection and code reuse attacks, making sure attackers can’t find vulnerabilities in the first place. Our system implementation includes a combination of runtime MTD utilizing AES 256 ISR and fine grained ASR, as well as control management that utilizes attack detection, and reconfiguration capabilities. We evaluate the developed security architecture in an autonomous vehicle case study, utilizing a custom developed hardware-in-the-loop testbed.
Neural Network Branching for Neural Network Verification

Formal verification of neural networks is essential for their deployment in safety-critical areas. Many available formal verification methods have been shown to be instances of a unified Branch and Bound (BaB) formulation. We propose a novel framework for designing an effective branching strategy for BaB. Specifically, we learn a graph neural network (GNN) to imitate the strong branching heuristic behaviour. Our framework differs from previous methods for learning to branch in two main aspects. Firstly, our framework directly treats the neural network we want to verify as a graph input for the GNN. Secondly, we develop an intuitive forward and backward embedding update schedule. Empirically, our framework achieves roughly $50\%$ reduction in both the number of branches and the time required for verification on various convolutional networks when compared to the best available hand-designed branching strategy. In addition, we show that our GNN model enjoys both horizontal and vertical transferability. Horizontally, the model trained on easy properties performs well on properties of increased difficulty levels. Vertically, the model trained on small neural networks achieves similar performance on large neural networks.
BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations

Binary Neural Network (BNN) has been gaining interest thanks to its computing cost reduction and memory saving. However, BNN suffers from performance degradation mainly due to the gradient mismatch caused by binarizing activations. Previous works tried to address the gradient mismatch by reducing the discrepancy between activation functions used at forward and backward passes, which is an indirect measure. In this work, we introduce coordinate discrete gradient (CDG) to better estimate the gradient mismatch. Analysis using the CDG indicates that using higher precision for activation is more effective than modifying the backward pass of binary activation function. Based on the observation, we propose a new training scheme for binary activation network called BinaryDuo in which two binary activations are coupled into a ternary activation during training. Experimental results show that BinaryDuo outperforms state-of-the-art BNNs on various benchmarks with the same amount of parameters and computing cost.
Si microring resonator crossbar arrays for deep learning accelerator

We propose Si microring resonator (MRR) crossbar arrays as a programmable nanophotonoic processor (PNP) for a deep learning accelerator. The proposed MRR crossbar array can perform multiply-accumulate (MAC) operation in an optical domain. We numerically reveal that an optical neural network (ONN) based on the proposed MRR crossbar arrays can be used for the pattern recognition task with a similar performance to that of an ONN based on cascaded Mach–Zehnder interferometers. We predict that the power consumption can be reduced by approximately tenfold. The small area of the MRR also contributes to the reduction in its chip size by a factor of 36. The fabricated test devices consisting of Si MRRs with phase shifters exhibited no significant crosstalk between neighboring MRRs and showed the feasibility of MAC operation. Photonic integrated circuit using the proposed MRR crossbar arrays is promising for large-scale and low-power PNP for deep learning.
Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying <i>c</i>-Diversity

Privacy preserving data publishing (PPDP) refers to the releasing of anonymized data for the purpose of research and analysis. A considerable amount of research work exists for the publication of data, having a single sensitive attribute. The practical scenarios in PPDP with multiple sensitive attributes (MSAs) have not yet attracted much attention of researchers. Although a recently proposed technique ( p , k )-Angelization provided a novel solution, in this regard, where one-to-one correspondence between the buckets in the generalized table (GT) and the sensitive table (ST) has been used. However, we have investigated a possibility of privacy leakage through MSA correlation among linkable sensitive buckets and named it as “fingerprint correlation <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mtext>fcorr</mml:mtext></mml:mrow></mml:mfenced></mml:mrow></mml:math> attack.” Mitigating that in this paper, we propose an improved solution “<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>c</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:math>-anonymization” algorithm. The proposed solution thwarts the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mtext>fcorr</mml:mtext></mml:mrow></mml:math> attack using some privacy measures and improves the one-to-one correspondence to one-to-many correspondence between the buckets in GT and ST which further reduces the privacy risk with increased utility in GT. We have formally modelled and analysed the attack and the proposed solution. Experiments on the real-world datasets prove the outperformance of the proposed solution as compared to its counterpart.