Reviewerly/exHarmony huggingface dataset:
Lane-Level Map Matching Based on HMM

Lane-level map matching is essential for autonomous driving. In this paper, we propose a Hidden Markov Model (HMM) for matching a trajectory of noisy GPS measurements to the road lanes in which the vehicle records its positions. To our knowledge, this is the first time that HMM is used for lane-level map matching. Apart from GPS values, the model is further assisted by yaw rate data (converted to a lane change indicator signal) and visual cues in the form of the left and right lane marking types (dashed, solid, etc.). Having defined expressions for the HMM emission and transition probabilities, we evaluate our model to demonstrate that it achieves 95.1% recall and 3.3% median path length error for motorway trajectories.
High-fidelity composite quantum gates for Raman qubits

We present a general systematic approach to design robust and high-fidelity quantum logic gates with Raman qubits using the technique of composite pulses. We use two mathematical tools—the Morris-Shore and Majorana decompositions—to reduce the three-state Raman system to an equivalent two-state system. They allow us to exploit the numerous composite pulses designed for two-state systems by extending them to Raman qubits. We construct the not, Hadamard, and rotation gates by means of the Morris-Shore transformation with the same uniform approach: sequences of pulses with the same phases for each gate but different ratios of Raman couplings. The phase gate is constructed by using the Majorana decomposition. All composite Raman gates feature very high fidelity, well beyond the quantum computation benchmark values, and significant robustness to experimental errors. All composite phases and pulse areas are given by analytical formulas, which makes the method scalable to any desired accuracy and robustness to errors.Received 4 May 2020Accepted 6 October 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043194Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCoherent controlLight-matter interactionQuantum computationQuantum gatesQuantum information with atoms & lightAtomic, Molecular & OpticalQuantum Information
Indirect Local Attacks for Context-Aware Semantic Segmentation Networks

Recently, deep networks have achieved impressive semantic segmentation performance, in particular thanks to their use of larger contextual information. In this paper, we show that the resulting networks are sensitive not only to global adversarial attacks, where perturbations affect the entire input image, but also to indirect local attacks, where the perturbations are confined to a small image region that does not overlap with the area that the attacker aims to fool. To this end, we introduce an indirect attack strategy, namely adaptive local attacks, aiming to find the best image location to perturb, while preserving the labels at this location and producing a realistic-looking segmentation map. Furthermore, we propose attack detection techniques both at the global image level and to obtain a pixel-wise localization of the fooled regions. Our results are unsettling: Because they exploit a larger context, more accurate semantic segmentation networks are more sensitive to indirect local attacks. We believe that our comprehensive analysis will motivate the community to design architectures with contextual dependencies that do not trade off robustness for accuracy.
Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection

Designing activity detection systems that can be successfully deployed in daily-living environments requires datasets that pose the challenges typical of real-world scenarios. In this paper, we introduce a new untrimmed daily-living dataset that features several real-world challenges: Toyota Smarthome Untrimmed (TSU). TSU contains a wide variety of activities performed in a spontaneous manner. The dataset contains dense annotations including elementary, composite activities and activities involving interactions with objects. We provide an analysis of the real-world challenges featured by our dataset, highlighting the open issues for detection algorithms. We show that current state-of-the-art methods fail to achieve satisfactory performance on the TSU dataset. Therefore, we propose a new baseline method for activity detection to tackle the novel challenges provided by our dataset. This method leverages one modality (i.e. optic flow) to generate the attention weights to guide another modality (i.e RGB) to better detect the activity boundaries. This is particularly beneficial to detect activities characterized by high temporal variance. We show that the method we propose outperforms state-of-the-art methods on TSU and on another popular challenging dataset, Charades.
A Case Study on the Decentralisation of Lifelong Learning Using Blockchain Technology

Blockchain technology provides a decentralised peer-to-peer infrastructure, supporting openness, transparency, accountability, identity management and trust. As such, the Blockchain has the potential to revolutionise education in a number of ways. Blockchain technology offers opportunities to thoroughly rethink how we find educational content and training services online, how we register and pay for them, as well as how we get accredited for what we have learned and how this accreditation affects our career trajectory. This paper explores a case study on the decentralisation of lifelong learning using Blockchain technology. In particular, we investigate the different scenarios and requirements for making online education and lifelong learning more open and decentralised, while placing lifelong learners in control of their learning process and its associated data. Additionally, we discuss various approaches to the Semantic Blockchain and the applications of these approaches on education.
Facial UV map completion for pose-invariant face recognition: a novel adversarial approach based on coupled attention residual UNets

Abstract Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due to the large variation of pose, illumination and facial expression. A promising approach to deal with pose variation is to fulfill incomplete UV maps extracted from in-the-wild faces, then attach the completed UV map to a fitted 3D mesh and finally generate different 2D faces of arbitrary poses. The synthesized faces increase the pose variation for training deep face recognition models and reduce the pose discrepancy during the testing phase. In this paper, we propose a novel generative model called Attention ResCUNet-GAN to improve the UV map completion. We enhance the original UV-GAN by using a couple of U-Nets. Particularly, the skip connections within each U-Net are boosted by attention gates. Meanwhile, the features from two U-Nets are fused with trainable scalar weights. The experiments on the popular benchmarks, including Multi-PIE, LFW, CPLWF and CFP datasets, show that the proposed method yields superior performance compared to other existing methods.
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder

Deep probabilistic generative models enable modeling the likelihoods of very high dimensional data. An important application of generative modeling should be the ability to detect out-of-distribution (OOD) samples by setting a threshold on the likelihood. However, some recent studies show that probabilistic generative models can, in some cases, assign higher likelihoods on certain types of OOD samples, making the OOD detection rules based on likelihood threshold problematic. To address this issue, several OOD detection methods have been proposed for deep generative models. In this paper, we make the observation that many of these methods fail when applied to generative models based on Variational Auto-encoders (VAE). As an alternative, we propose Likelihood Regret, an efficient OOD score for VAEs. We benchmark our proposed method over existing approaches, and empirical results suggest that our method obtains the best overall OOD detection performances when applied to VAEs.
Quantum Reading Capacity: General Definition and Bounds

Quantum reading refers to the task of reading out classical information stored in a read-only memory device. In any such protocol, the transmitter and receiver are in the same physical location, and the goal of such a protocol is to use these devices (modeled by independent quantum channels), coupled with a quantum strategy, to read out as much information as possible from a memory device, such as a CD or DVD. As a consequence of the physical setup of quantum reading, the most natural and general definition for quantum reading capacity should allow for an adaptive operation after each call to the channel, and this is how we define quantum reading capacity in this paper. We also establish several bounds on quantum reading capacity, and we introduce an environment-parameterized memory cell with associated environment states, delivering second-order and strong converse bounds for its quantum reading capacity. We calculate the quantum reading capacities for some exemplary memory cells, including a thermal memory cell, a qudit erasure memory cell, and a qudit depolarizing memory cell. We finally provide an explicit example to illustrate the advantage of using an adaptive strategy in the context of zero-error quantum reading capacity.
Integrating Neural Networks Into the Blind Deblurring Framework to Compete With the End-to-End Learning-Based Methods

Recently, end-to-end learning-based methods based on deep neural network (DNN) have been proven effective for blind deblurring. Without human-made assumptions and numerical algorithms, they are able to restore images with fewer artifacts and better perceptual quality. However, in practice, we also find some of their drawbacks. Without the theoretical guidance, these methods can not perform well when the motion is complex and sometimes generate unreasonable results. In this paper, for overcoming these drawbacks, we integrate deep convolution neural networks into conventional deblurring framework. Specifically, we build Stacked Estimation Residual Net (SEN) to estimate the motion flow map and Recurrent Prior Generative and Adversarial Net (RP-GAN) to learn the implicit image prior in the optimization model. Comparing with state-of-the-art end-to-end learning-based methods, our method restores reasonable details and shows better generalization ability.
Parallel Intelligence: Belief and Prescription for Edge Emergence and Cloud Convergence in CPSS

ON AFFECTIVE COMPUTING.I am sure that IEEE TCSS will march to a new level of excellence under Prof. Bin Hu's leadership.Congratulations to him and TCSS for the beginning of a new chapter!I am grateful to report that, as of September 7, 2020, the Citescore of TCSS has leapfrogged back to 5.0, a new high.Many thanks to all of you for your great effort and support.After scanning the issue, I will present a brief history of my involvement in parallel intelligence and complete the recounting of my personal journey of getting into computational social systems research and applications.Thanks for the interesting messages and encouraging words I have received after my editorials on parallel emergency, parallel healthcare, and parallel economics.Hope they are useful for young researchers who are interested in multidisciplinary and interdisciplinary research.
MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering

We present MMFT-BERT (MultiModal Fusion Transformer with BERT encodings), to solve Visual Question Answering (VQA) ensuring individual and combined processing of multiple input modalities.Our approach benefits from processing multimodal data (video and text) adopting the BERT encodings individually and using a novel transformerbased fusion method to fuse them together.Our method decomposes the different sources of modalities, into different BERT instances with similar architectures, but variable weights.This achieves SOTA results on the TVQA dataset.Additionally, we provide TVQA-Visual, an isolated diagnostic subset of TVQA, which strictly requires the knowledge of visual (V) modality based on a human annotator's judgment.This set of questions helps us to study the model's behavior and the challenges TVQA poses to prevent the achievement of super human performance.Extensive experiments show the effectiveness and superiority of our method 1 .
Autoregressive Knowledge Distillation through Imitation Learning

The performance of autoregressive models on natural language generation tasks has dramatically improved due to the adoption of deep, self-attentive architectures.However, these gains have come at the cost of hindering inference speed, making state-of-the-art models cumbersome to deploy in real-world, timesensitive settings.We develop a compression technique for autoregressive models that is driven by an imitation learning perspective on knowledge distillation.The algorithm is designed to address the exposure bias problem.On prototypical language generation tasks such as translation and summarization, our method consistently outperforms other distillation algorithms, such as sequence-level knowledge distillation.Student models trained with our method attain 1.4 to 4.8 BLEU/ROUGE points higher than those trained from scratch, while increasing inference speed by up to 14 times in comparison to the teacher model. 1
Named Entity Recognition Only from Word Embeddings

Deep neural network models have helped named entity recognition achieve amazing performance without handcrafting features.However, existing systems require large amounts of human annotated training data.Efforts have been made to replace human annotations with external knowledge (e.g., NE dictionary, partof-speech tags), while it is another challenge to obtain such effective resources.In this work, we propose a fully unsupervised NE recognition model which only needs to take informative clues from pre-trained word embeddings.We first apply Gaussian Hidden Markov Model and Deep Autoencoding Gaussian Mixture Model on word embeddings for entity span detection and type prediction, and then further design an instance selector based on reinforcement learning to distinguish positive sentences from noisy sentences and then refine these coarse-grained annotations through neural networks.Extensive experiments on two CoNLL benchmark NER datasets (CoNLL-2003 English dataset and CoNLL-2002 Spanish dataset) demonstrate that our proposed light NE recognition model achieves remarkable performance without using any annotated lexicon or corpus.
Two Stage Classification with CNN for Colorectal Cancer Detection

In this paper, we address a current problem in medical image processing, the detection of colorectal cancer from colonoscopy videos.According to worldwide cancer statistics, colorectal cancer is one of the most common cancers.The process of screening and the removal of pre-cancerous cells from the large intestine is a crucial task to date.The traditional manual process is dependent on the expertise of the medical practitioner.In this paper, a two-stage classification is proposed to detect colorectal cancer.In the first stage, frames of colonoscopy video are extracted and are rated as significant if it contains a polyp, and these results are then aggregated in a second stage to come to an overall decision concerning the final classification of that frame to be neoplastic and non-neoplastic.In doing so, a comparative study is being made by considering the applicability of deep learning to perform this two-stage classification.The CNN models namely VGG16, VGG19, Inception V3, Xception, GoogLeNet, ResNet50, ResNet100, DenseNet, NASNetMobile, MobilenetV2, InceptionResNetV2 and fine-tuned version of each model is evaluated.It is observed that the VGG19 model is the best deep learning method for colonoscopy image diagnosis.
New confocal hyperbola-based ellipse fitting with applications to estimating parameters of mechanical pipes from point clouds

This manuscript presents a new method for fitting ellipses to two-dimensional data using the confocal hyperbola approximation to the geometric distance of points to ellipses. The proposed method was evaluated and compared to established methods on simulated and real-world datasets. First, it was revealed that the confocal hyperbola distance considerably outperforms other distance approximations such as algebraic and Sampson. Next, the proposed ellipse fitting method was compared with five reliable and established methods proposed by Halir, Taubin, Kanatani, Ahn and Szpak. The performance of each method as a function of rotation, aspect ratio, noise, and arc-length were examined. It was observed that the proposed ellipse fitting method achieved almost identical results (and in some cases better) than the gold standard geometric method of Ahn and outperformed the remaining methods in all simulation experiments. Finally, the proposed method outperformed the considered ellipse fitting methods in estimating the geometric parameters of cylindrical mechanical pipes from point clouds. The results of the experiments show that the confocal hyperbola is an excellent approximation to the true geometric distance and produces reliable and accurate ellipse fitting in practical settings.
Pandemic induced remote learning increases need for mobile game-based learning to engage learners

The current global health crisis has reduced our mobility, consequently increasing the need to connect with others digitally. This brief provides a research perspective on Giannakas et al.’s (Educational Technology Research and Development, 66, 341–384, 2018) manuscript, “A critical review of 13 years of mobile game-based learning.” To serve as a resource to educators navigating the transition to remote learning, this perspective summarizes key ideas relative to mGBL (mobile game-based learning), value of the work, application to the educator context, and limitations. Future suggestions provide a clear roadmap of ideas to continue research and development of mGBL.
Multimode CV‐QKD with non‐Gaussian operations

Non-Gaussian operations have been studied intensively in recent years due to their ability to increase the secret key rate for certain CV-QKD protocols. However, most previous studies on such protocols are carried out in a single-mode setting, even though in reality any quantum state contains multimode components in frequency space. In this work, we investigate the use of non-Gaussian operations in a multimode CV-QKD system. Our main finding is that, contrary to single-mode CV-QKD systems, in generic multimode CV-QKD systems, it is possible to use non-Gaussian operations to increase the optimized secret key rate. More specifically, we find that at losses of order 30dB, which represents a distance of order 160km and the effective maximum distance for CV-QKD, the key rate for multimode non-Gaussian operations can be orders of magnitude higher than single-mode operations. Our results are important for real-world CV-QKD systems, especially those dependent on quantum error correction—a process that requires non-Gaussian effects.
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation

This work presents our ongoing research of unsupervised pretraining in neural machine translation (NMT). In our method, we initialize the weights of the encoder and decoder with two language models that are trained with monolingual data and then fine-tune the model on parallel data using Elastic Weight Consolidation (EWC) to avoid forgetting of the original language modeling tasks. We compare the regularization by EWC with the previous work that focuses on regularization by language modeling objectives. The positive result is that using EWC with the decoder achieves BLEU scores similar to the previous work. However, the model converges 2-3 times faster and does not require the original unlabeled training data during the fine-tuning stage. In contrast, the regularization using EWC is less effective if the original and new tasks are not closely related. We show that initializing the bidirectional NMT encoder with a left-to-right language model and forcing the model to remember the original left-to-right language modeling task limits the learning capacity of the encoder for the whole bidirectional context.
Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning

Automatic summarization research has traditionally focused on providing high quality general-purpose summaries of documents. However, there are many applications that require more specific summaries, such as supporting question answering or topic-based literature discovery. In this paper, we study the problem of conditional summarization in which content selection and surface realization are explicitly conditioned on an ad-hoc natural language question or topic description. Because of the difficulty in obtaining sufficient reference summaries to support arbitrary conditional summarization, we explore the use of multi-task fine-tuning (MTFT) on twenty-one natural language tasks to enable zero-shot conditional summarization on five tasks. We present four new summarization datasets, two novel "online" or adaptive task-mixing strategies, and report zero-shot performance using T5 and BART, demonstrating that MTFT can improve zero-shot summarization quality.
The Cloud we Share: Access Control on Symmetrically Encrypted Data in Untrusted Clouds

Along with the rapid growth of cloud environments, rises the problem of secure data storage-a problem that both businesses and end-users take into consideration before moving their data online. Recently, a lot of solutions have been proposed based either on Symmetric Searchable Encryption (SSE) or Attribute-Based Encryption (ABE). SSE is an encryption technique that offers security against both internal and external attacks. However, since in an SSE scheme, a single key is used to encrypt everything, revoking a user would imply downloading the entire encrypted database and re-encrypt it with a fresh key. On the other hand, in an ABE scheme, the problem of revocation can be addressed. Unfortunately, though, the proposed solutions are based on the properties of the underlying ABE scheme and hence, the revocation costs grow along with the complexity of the policies. To this end, we use these two cryptographic techniques that squarely fit cloud-based environments to design a hybrid encryption scheme based on ABE and SSE in such a way that we utilize the best out of both of them. Moreover, we exploit the functionalities offered by Intel's SGX to design a revocation mechanism and an access control one, that are agnostic to the cryptographic primitives used in our construction.
On Deep Set Learning and the Choice of Aggregations

Recently, it has been shown that many functions on sets can be represented by sum decompositions. These decompositons easily lend themselves to neural approximations, extending the applicability of neural nets to set-valued inputs—Deep Set learning. This work investigates a core component of Deep Set architecture: aggregation functions. We suggest and examine alternatives to commonly used aggregation functions, including learnable recurrent aggregation functions. Empirically, we show that the Deep Set networks are highly sensitive to the choice of aggregation functions: beyond improved performance, we find that learnable aggregations lower hyper-parameter sensitivity and generalize better to out-of-distribution input size.