Reviewerly/exHarmony huggingface dataset:
What Do Language Representations Really Represent?

A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just as it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, whereas genetic relationships—a convenient benchmark used for evaluation in previous work—appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.
A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping

Quantification of a mineral prospectivity mapping (MPM) heavily relies on geological, geophysical and geochemical analysis, which combines various evidence layers into a single map. However, MPM is subject to considerable uncertainty due to lack of understanding of the metallogenesis and limited spatial data samples. In this paper, we provide a framework that addresses how uncertainty in the evidence layers can be quantified and how such uncertainty is propagated to the prediction of mineral potential. More specifically, we use Monte Carlo simulation to jointly quantify uncertainties on all uncertain evidence variables, categorized into geological, geochemical and geophysical. On stochastically simulated sets of the multiple input layers, logistic regression is employed to produce different quantifications of the mineral potential in terms of probability. Uncertainties we address lie in the downscaling of magnetic data to a scale that makes such data comparable with known mineral deposits. Additionally, we deal with the limited spatial sampling of geochemistry that leads to spatial uncertainty. Next, we deal with the conceptual geological uncertainty related to how the spatial extent of the influence of evidential geological features such as faults, granite intrusions and sedimentary formations. Finally, we provide a novel way to interpret the established uncertainty in a risk-return analysis to decide areas with high potential but at the same time low uncertainty on that potential. Our methods are illustrated and compared with traditional deterministic MPM on a real case study of prospecting skarn Fe deposition in southwestern Fujian, China.
In Neural Machine Translation, What Does Transfer Learning Transfer?

Transfer learning improves quality for lowresource machine translation, but it is unclear what exactly it transfers.We perform several ablation studies that limit information transfer, then measure the quality impact across three language pairs to gain a black-box understanding of transfer learning.Word embeddings play an important role in transfer learning, particularly if they are properly aligned.Although transfer learning can be performed without embeddings, results are sub-optimal.In contrast, transferring only the embeddings but nothing else yields catastrophic results.We then investigate diagonal alignments with auto-encoders over real languages and randomly generated sequences, finding even randomly generated sequences as parents yield noticeable but smaller gains.Finally, transfer learning can eliminate the need for a warmup phase when training transformer models in high resource language pairs.
Edge Computing to Secure IoT Data Ownership and Trade with the Ethereum Blockchain

With an increasing penetration of ubiquitous connectivity, the amount of data describing the actions of end-users has been increasing dramatically, both within the domain of the Internet of Things (IoT) and other smart devices. This has led to more awareness of users in terms of protecting personal data. Within the IoT, there is a growing number of peer-to-peer (P2P) transactions, increasing the exposure to security vulnerabilities, and the risk of cyberattacks. Blockchain technology has been explored as middleware in P2P transactions, but existing solutions have mainly focused on providing a safe environment for data trade without considering potential changes in interaction topologies. we present EdgeBoT, a proof-of-concept smart contracts based platform for the IoT built on top of the ethereum blockchain. With the Blockchain of Things (BoT) at the edge of the network, EdgeBoT enables a wider variety of interaction topologies between nodes in the network and external services while guaranteeing ownership of data and end users’ privacy. in EdgeBoT, edge devices trade their data directly with third parties and without the need of intermediaries. This opens the door to new interaction modalities, in which data producers at the edge grant access to batches of their data to different third parties. Leveraging the immutability properties of blockchains, together with the distributed nature of smart contracts, data owners can audit and are aware of all transactions that have occurred with their data. we report initial results demonstrating the potential of EdgeBoT within the IoT. we show that integrating our solutions on top of existing IoT systems has a relatively small footprint in terms of computational resource usage, but a significant impact on the protection of data ownership and management of data trade.
Multimodal cyberbullying detection using capsule network with dynamic routing and deep convolutional neural network

Cyberbullying is the use of information technology networks by individuals’ to humiliate, tease, embarrass, taunt, defame and disparage a target without any face-to-face contact. Social media is the 'virtual playground' used by bullies with the upsurge of social networking sites such as Facebook, Instagram, YouTube and Twitter. It is critical to implement models and systems for automatic detection and resolution of bullying content available online as the ramifications can lead to a societal epidemic. This paper presents a deep neural model for cyberbullying detection in three different modalities of social data, namely textual, visual and info-graphic (text embedded along with an image). The all-in-one architecture, CapsNet–ConvNet, consists of a capsule network (CapsNet) deep neural network with dynamic routing for predicting the textual bullying content and a convolution neural network (ConvNet) for predicting the visual bullying content. The info-graphic content is discretized by separating text from the image using Google Lens of Google Photos app. The perceptron-based decision-level late fusion strategy for multimodal learning is used to dynamically combine the predictions of discrete modalities and output the final category as bullying or non-bullying type. Experimental evaluation is done on a mix-modal dataset which contains 10,000 comments and posts scrapped from YouTube, Instagram and Twitter. The proposed model achieves a superlative performance with the AUC–ROC of 0.98.
High-rate quantum key distribution exceeding 110 Mb s–1

Quantum key distribution (QKD) can provide fundamentally proven secure communication. Towards application, the secret key rate (SKR) is a key figure of merit for any QKD system. The SKR has so far been limited to about a few megabits per second. Here we report a QKD system that is able to generate keys at a record high SKR of 115.8 Mb s–1 over a 10 km standard optical fibre, and distribute keys over up to 328 km of ultralow-loss fibre. Such abilities are attributed to a multipixel superconducting nanowire single-photon detector with an ultrahigh counting rate, an integrated transmitter that can stably encode polarization states with low error, a fast post-processing algorithm for generating keys in real time and the high system clock rate operation. The results demonstrate the feasibility of practical high-rate QKD with photonic techniques, thus opening its possibility for widespread applications. A quantum key distribution with a key rate of 115.8 Mb s–1 is demonstrated over 10 km standard optical fibre. To this end, a high-speed and stable system, an integrated transmitter for low error modulation and multipixel superconducting nanowire single-photon detectors are developed.
3D CAD data extraction and conversion for application of augmented/virtual reality to the construction of ships and offshore structures

Due to the global economic downturn and low oil prices, orders for offshore plants and vessels have declined. Moreover, the range of regulations has increased and customer requirements have diversified. As a result, a new paradigm for the manufacturing environment is needed to improve competitiveness. As IT technology develops, we are making efforts to improve product design and production efficiency by combining various technologies such as digital twin, virtual reality and augmented reality. To use these technologies, 3D models of products are important. A product model exists in a 3D CAD system, which is required to utilize the model. The focus of previous research was on shape transformation using neutral formats such as igs and step files. However, this was also incomplete, and would be better visualized in virtual/augmented reality. In this study, we aimed to develop a framework to extract and visualize 3D models that could be used in augmented/virtual reality in real time and we conducted a test to verify the framework’s validity and efficiency. As a result of the test, it is expected that this framework will be a valuable variety field in the lifecycles of a ship, an offshore structure, and an industrial plant.
Exploring Diverse Expressions for Paraphrase Generation

Paraphrasing plays an important role in various natural language processing (NLP) tasks, such as question answering, information retrieval and sentence simplification.Recently, neural generative models have shown promising results in paraphrase generation.However, prior work mainly focused on single paraphrase generation, while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications.Few works have been done to solve diverse paraphrase generation.In this paper, we propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases.A reinforcement learning algorithm is applied to train our model.Our experiments on two realworld datasets demonstrate that our model not only gains a significant increase in diversity but also improves generation quality over several state-of-the-art baselines.
Succinct Arguments in the Quantum Random Oracle Model

Succinct non-interactive arguments (SNARGs) are highly efficient certificates of membership in non-deterministic languages. Constructions of SNARGs in the random oracle model are widely believed to be post-quantum secure, provided the oracle is instantiated with a suitable post-quantum hash function. No formal evidence, however, supports this belief. In this work we provide the first such evidence by proving that the SNARG construction of Micali is unconditionally secure in the quantum random oracle model. We also prove that, analogously to the classical case, the SNARG inherits the zero knowledge and proof of knowledge properties of the PCP underlying the Micali construction. We thus obtain the first zero knowledge SNARG of knowledge (zkSNARK) that is secure in the quantum random oracle model. Our main tool is a new lifting lemma that shows how, for a rich class of oracle games, we can generically deduce security against quantum attackers by bounding a natural classical property of these games. This means that in order to prove our theorem we only need to establish classical properties about the Micali construction. This approach not only lets us prove post-quantum security but also enables us to prove explicit bounds that are tight up to small factors. We additionally use our techniques to prove that SNARGs based on interactive oracle proofs (IOPs) with round-by-round soundness are unconditionally secure in the quantum random oracle model. This result establishes the post-quantum security of many SNARGs of practical interest.
Gamified, Automated Virtual Reality Exposure Therapy for Fear of Spiders: A Single-Subject Trial Under Simulated Real-World Conditions

Virtual Reality exposure therapy (VRET) is an evidence-based treatment of phobias and recent research suggests that this applies also to self-contained, automated interventions requiring no therapist guidance. With the advent and growing adoption of consumer VR technology, automated VR intervention have the potential to close the considerable treatment gap for specific phobias through dissemination as consumer applications, self-help at clinics, or as blended treatment. There is however a lack of translational effectiveness studies on VRET treatment effects under real-world conditions.We conducted a single-arm (n = 25), single-subject study of automated, gamified VRET for fear of spiders, under simulated real-world conditions. After setup and reading instructions, participants completed the automated, single-session treatment by themselves. Self-rated fear of spiders and quality of life served as outcome measures, measured twice before, and one and two weeks after treatment, and at a six-month follow-up. Session characteristics and user experience measures were collected at the end of the session.Mixed-effects modeling revealed a significant and large (d = 1.26) effect of treatment-onset on phobia symptoms (p < .001), and a small (d = 0.49) effect on quality of life (p = .025). Results were maintained at a six-month follow-up (p > .053). The intervention was tolerable and practical. There were no significant correlations between any user experience measure and decrease in phobia symptoms (p > .209).An automated VRET intervention for fear of spiders showed equivalent effects on phobia symptoms under effectiveness conditions as previously reported under efficacy conditions. These results suggest that automated VRET applications are promising self-help treatments also when provided under real-world conditions.Open Science Foundation, https://doi.org/10.17605/OSF.IO/78GUB.
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI

We demonstrate a unified approach to rigorous design of safety-critical autonomous systems using the VerifAI toolkit for formal analysis of AI-based systems. VerifAI provides an integrated toolchain for tasks spanning the design process, including modeling, falsification, debugging, and ML component retraining. We evaluate all of these applications in an industrial case study on an experimental autonomous aircraft taxiing system developed by Boeing, which uses a neural network to track the centerline of a runway. We define runway scenarios using the Scenic probabilistic programming language, and use them to drive tests in the X-Plane flight simulator. We first perform falsification, automatically finding environment conditions causing the system to violate its specification by deviating significantly from the centerline (or even leaving the runway entirely). Next, we use counterexample analysis to identify distinct failure cases, and confirm their root causes with specialized testing. Finally, we use the results of falsification and debugging to retrain the network, eliminating several failure cases and improving the overall performance of the closed-loop system.
Learning Where to Focus for Efficient Video Object Detection

Transferring existing image-based detectors to the video is non-trivial since the quality of frames is always deteriorated by part occlusion, rare pose, and motion blur. Previous approaches exploit to propagate and aggregate features across video frames by using optical flow-warping. However, directly applying image-level optical flow onto the high-level features might not establish accurate spatial correspondences. Therefore, a novel module called Learnable Spatio-Temporal Sampling (LSTS) has been proposed to learn semantic-level correspondences among adjacent frame features accurately. The sampled locations are first randomly initialized, then updated iteratively to find better spatial correspondences guided by detection supervision progressively. Besides, Sparsely Recursive Feature Updating (SRFU) module and Dense Feature Aggregation (DFA) module are also introduced to model temporal relations and enhance per-frame features, respectively. Without bells and whistles, the proposed method achieves state-of-the-art performance on the ImageNet VID dataset with less computational complexity and real-time speed. Code will be made available at LSTS.
Event Classification with Quantum Machine Learning in High-Energy Physics

We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics. We focus on variational quantum approach to learn the properties of input data and evaluate the performance of the event classification using both simulators and quantum computing devices. Comparison of the performance with standard multi-variate classification techniques based on a boosted-decision tree and a deep neural network using classical computers shows that the quantum algorithm has comparable performance with the standard techniques at the considered ranges of the number of input variables and the size of training samples. The variational quantum algorithm is tested with quantum computers, demonstrating that the discrimination of interesting events from background is feasible. Characteristic behaviors observed during a learning process using quantum circuits with extended gate structures are discussed, as well as the implications of the current performance to the application in high-energy physics experiments.
AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds

There have been two streams in the 3D detection from point clouds: single-stage methods and two-stage methods. While the former is more computationally efficient, the latter usually provides better detection accuracy. By carefully examining the two-stage approaches, we have found that if appropriately designed, the first stage can produce accurate box regression. In this scenario, the second stage mainly rescores the boxes such that the boxes with better localization get selected. From this observation, we have devised a single-stage anchor-free network that can fulfill these requirements. This network, named AFDetV2, extends the previous work by incorporating a self-calibrated convolution block in the backbone, a keypoint auxiliary supervision, and an IoU prediction branch in the multi-task head. We take a simple product of the predicted IoU score with the classification heatmap to form the final classification confidence. The enhanced backbone strengthens the box localization capability, and the rescoring approach effectively joins the object presence confidence and the box regression accuracy. As a result, the detection accuracy is drastically boosted in the single-stage. To evaluate our approach, we have conducted extensive experiments on the Waymo Open Dataset and the nuScenes Dataset. We have observed that our AFDetV2 achieves the state-of-the-art results on these two datasets, superior to all the prior arts, including both the single-stage and the two-stage 3D detectors. AFDetV2 won the 1st place in the Real-Time 3D Detection of the Waymo Open Dataset Challenge 2021. In addition, a variant of our model AFDetV2-Base was entitled the "Most Efficient Model" by the Challenge Sponsor, showing a superior computational efficiency. To demonstrate the generality of this single-stage method, we have also applied it to the first stage of the two-stage networks. Without exception, the results show that with the strengthened backbone and the rescoring approach, the second stage refinement is no longer needed.
Integrated photonic metasystem for image classifications at telecommunication wavelength

Miniaturized image classifiers are potential for revolutionizing their applications in optical communication, autonomous vehicles, and healthcare. With subwavelength structure enabled directional diffraction and dispersion engineering, the light propagation through multi-layer metasurfaces achieves wavelength-selective image recognitions on a silicon photonic platform at telecommunication wavelength. The metasystems implement high-throughput vector-by-matrix multiplications, enabled by near 103 nanoscale phase shifters as weight elements within 0.135 mm2 footprints. The diffraction manifested computing capability incorporates the fabrication and measurement related phase fluctuations, and thus the pre-trained metasystem can handle uncertainties in inputs without post-tuning. Here we demonstrate three functional metasystems: a 15-pixel spatial pattern classifier that reaches near 90% accuracy with femtosecond inputs, a multi-channel wavelength demultiplexer, and a hyperspectral image classifier. The diffractive metasystem provides an alternative machine learning architecture for photonic integrated circuits, with densely integrated phase shifters, spatially multiplexed throughput, and data processing capabilities.
Mutual consistency learning for semi-supervised medical image segmentation

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions in the ambiguous regions (e.g., adhesive edges or thin branches) for medical image segmentation. Leveraging these challenging samples can make the semi-supervised segmentation model training more effective. Therefore, our proposed MC-Net+ model consists of two new designs. First, the model contains one shared encoder and multiple slightly different decoders (i.e., using different up-sampling strategies). The statistical discrepancy of multiple decoders’ outputs is computed to denote the model’s uncertainty, which indicates the unlabeled hard regions. Second, we apply a novel mutual consistency constraint between one decoder’s probability output and other decoders’ soft pseudo labels. In this way, we minimize the discrepancy of multiple outputs (i.e., the model uncertainty) during training and force the model to generate invariant results in such challenging regions, aiming at regularizing the model training. We compared the segmentation results of our MC-Net+ model with five state-of-the-art semi-supervised approaches on three public medical datasets. Extension experiments with two standard semi-supervised settings demonstrate the superior performance of our model over other methods, which sets a new state of the art for semi-supervised medical image segmentation. Our code is released publicly at https://github.com/ycwu1997/MC-Net.