Reviewerly/exHarmony huggingface dataset:
Dilated kernel prediction network for single-image denoising

Deep convolutional neural networks (CNNs) have achieved considerable success in terms of image denoising. However, previous CNN denoisers have been restricted by rigid kernel convolution that applies equal spatial treatment across images. To fully utilize the local differences, we propose a kernel prediction network that examines each pixel region and predicts unique pixel-wise kernels. Several optimizations have been further designed to gather sufficient information for single-image denoising task. We adopt dilated residual blocks to view the local pixel region at varying receptive fields. Then, kernel fusion assembles the information from different scopes and generates accurate kernels for each pixel. Instead of applying the predicted kernels to the original image, we construct a compressed feature map as a substitution such that more relevant local features are collected. Experiments are used to demonstrate that our network achieves favorable results compared with state-of-the-art methods and is adequate for practical applications.
Qubit Sensing: A New Attack Model for Multi-programming Quantum Computing

Noisy quantum computers suffer from readout or measurement error. It is a classical bit-flip error due to which state "1" is read out as "0" and vice-versa. The probability of readout error shows a state dependence i.e., flipping probability of state "1" may differ from flipping probability of state "0". Moreover, the probability shows correlation across qubits. These state-dependent and correlated error probability introduces a signature of victim outputs on adversary output when two programs are run simultaneously on the same quantum computer. This can be exploited to sense victim output which may contain sensitive information. In this paper, we systematically show that such readout error-dependent signatures exist and that an adversary can use such signature to infer a user output. We experimentally demonstrate the attack (inference) on 3 public IBM quantum computers. Using Jensen-Shannon Distance (JSD) a measure for statistical inference, we show that our approach identifies victim output with an accuracy of 96% on real hardware. We also present randomized output flipping as a lightweight yet effective countermeasure to thwart such information leakage attacks. Our analysis shows the countermeasure incurs a minor penalty of 0.05% in terms of fidelity.
Security Challenges of Blockchain

Blockchain brings amazing benefits to the growth of innovation in the information. Moreover, it is important to think about security and privacy perspectives in order to properly use blockchain. This paper offers a summary of the different components of blockchain security. Specifically, we describe the common forms of security threats against blockchain and security updates. We also concentrate on emerging blockchain security solutions and recommendation for future study using zero information and group signature strategy. In a blockchain organization, the selection signature may be utilized to accept transactions, & zero information to ensure the transactions are accurate irrespective of how the precise transaction details remains elusive. The combination of signature collection and zero-information may provide potential responses to improve blockchain security. Similarly, this agreement could be submitted to IoT organizations.
Graphical model and diffusion equation based manifold learning for sensor data processing

Graphical model, diffusion equation network dynamics, and manifold learning are different subareas in machine learning and network science. In this paper, combining the ideas from these subareas, we propose and implement graphical model and diffusion equation based manifold learning techniques for sensor data processing. We show that the graphical model and diffusion equation combined manifold learning can be used to perform data processing for one, two, and three-dimensional sensors. Experiments show that this manifold learning approach can solve many sensor data processing problems including radio frequency signal processing, image processing (computer vision), and three-dimensional lighting detection and ranging (LIDAR) processing problems better than some traditional methods.
Support Vector Machine Predictive Analysis Implementation: Case Study of Tax Revenue in Government of South Lampung

Tax is potential revenue that is used by the government as a source of funding to run the government. One of the conditions needed to implement decentralization is the availability of sources of local revenue. Some local revenue sources are hotel and restaurant taxes. Issues raised in this research are that the number of hotel and restaurant visitors changes every year, resulting in fluctuations in the amount of tax. On the other hand, the Government of South Lampung had difficulty in predicting the target of hotel and restaurant tax revenue when arranging a revenue budget. This is due to the absence of formula to calculate the potential tax revenue accurately, resulting in a lack of strategic management for local revenue improvement. Now, Business Intelligence is becoming a trend. Many sectors use Business Intelligence to analyze and prepare new strategies and improve performance. Thus, it is necessary to use Business Intelligence to predict the potential of hotel and restaurant tax revenue so that the Government of South Lampung can develop appropriate strategies to improve local tax revenue and minimize tax reduction. The method used is a predictive analysis using the Support Vector Machine (SVM). The result of this study is expected to be taken into consideration for South Lampung Local Government Revenue Service, in particular for the determination of the target of the hotel and restaurant tax sector in the coming year.
MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

Model-based reinforcement learning is a compelling framework for data-efficient learning of agents that interact with the world. This family of algorithms has many subcomponents that need to be carefully selected and tuned. As a result the entry-bar for researchers to approach the field and to deploy it in real-world tasks can be daunting. In this paper, we present MBRL-Lib -- a machine learning library for model-based reinforcement learning in continuous state-action spaces based on PyTorch. MBRL-Lib is designed as a platform for both researchers, to easily develop, debug and compare new algorithms, and non-expert user, to lower the entry-bar of deploying state-of-the-art algorithms. MBRL-Lib is open-source at https://github.com/facebookresearch/mbrl-lib.
Quantitative Information Flow with Monads in Haskell

Monads are a popular feature of the programming language Haskell because they can model many different notions of computation in a uniform and purely functional way. Our particular interest here is the probability monad, which can be -- and has been -- used to synthesise models for probabilistic programming. Quantitative Information Flow, or QIF, arises when security is combined with probability, and concerns the measurement of the amount of information that 'leaks' from a probabilistic program's state to a (usually) hostile observer: that is, not 'whether' leaks occur but rather 'how much?' Recently it has been shown that QIF can be seen monadically, a 'lifting' of the probability monad so that programs become functions from distributions to distributions of distributions: the codomain is 'hyper distributions'. Haskell's support for monads therefore suggests a synthesis of an executable model for QIF. Here, we provide the first systematic and thorough account of doing that: using distributions of distributions to synthesise a model for Quantitative Information Flow in terms of monads in Haskell.
Convolutional Neural Networks

No AccessNeuronale Netze mit C# programmierenApr 2021Convolutional Neural NetworksDaniel BaslerDaniel BaslerSearch for more papers by this authorhttps://doi.org/10.3139/9783446464261.006SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2020Pages: 159-171Print ISBN: 978-3-446-46229-8eISBN: 978-3-446-46426-1 Copyright & Permissions© 2021 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
L2C: Describing Visual Differences Needs Semantic Understanding of Individuals

Recent advances in language and vision push forward the research of captioning a single image to describing visual differences between image pairs.Suppose there are two images, I 1 and I 2 , and the task is to generate a description W 1,2 comparing them, existing methods directly model ⟨I 1 , I 2 ⟩ → W 1,2 mapping without the semantic understanding of individuals.In this paper, we introduce a Learningto-Compare (L2C) model, which learns to understand the semantic structures of these two images and compare them while learning to describe each one.We demonstrate that L2C benefits from a comparison between explicit semantic representations and singleimage captions, and generalizes better on the new testing image pairs.It outperforms the baseline on both automatic evaluation and human evaluation for the Birds-to-Words dataset.
Membership Inference Attacks on Knowledge Graphs

Membership inference attacks (MIAs) infer whether a specific data record is used for target model training. MIAs have provoked many discussions in the information security community since they give rise to severe data privacy issues, especially for private and sensitive datasets. Knowledge Graphs (KGs), which describe domain-specific subjects and relationships among them, are valuable and sensitive, such as medical KGs constructed from electronic health records. However, the privacy threat to knowledge graphs is critical but rarely explored. In this paper, we conduct the first empirical evaluation of privacy threats to knowledge graphs triggered by knowledge graph embedding methods (KGEs). We propose three types of membership inference attacks: transfer attacks (TAs), prediction loss-based attacks (PLAs), and prediction correctness-based attacks (PCAs), according to attack difficulty levels. In the experiments, we conduct three inference attacks against four standard KGE methods over three benchmark datasets. In addition, we also propose the attacks against medical KG and financial KG. The results demonstrate that the proposed attack methods can easily explore the privacy leakage of knowledge graphs.
Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation

Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the PolyTransform + SegFix baseline, we reached 1st place on the Cityscapes leaderboard.
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification

Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP.Nonetheless, their development has happened in isolation, while the combination of both could potentially be effective for tackling task-specific labelled data shortage.To bridge this gap, we combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task.Compared to strong supervised learning baselines, our semi-supervised classification framework is highly competitive and outperforms the state-of-the-art counterparts in lowresource settings across several languages.
Covering strong separating hash families

In principle, detecting arrays provide test suites with few tests for complex engineered systems with many interacting factors, each taking on one of a small set of levels, provided that there are few significant interactions, each involving few factors. In practice, explicit construction of detecting arrays for real-world testing remains challenging. To address this, covering strong separating hash families, which are certain arrays whose entries are vectors over a finite field, are introduced. Covering strong separating hash families are shown to underlie a very compact representation for detecting arrays for a variety of testing scenarios. The consequences of this compact representation for asymptotic existence bounds, and prospects for constructive algorithms, are discussed.
Landing Experiment of a Cleaning Robot on a Slanted Solar Panel by Using a Robot Arm for a Drone (RAD)

In this paper, the landing experiment of the Solar Panel Cleaning Robot(Dr.SPC) by a drone is presented. The robot arm for a drone (RAD) is designed to have two degrees-of-freedom to carry the cleaning mobile robot. The end-effector of the arm is equipped with an electromagnet to hold and release the robot electrically. The end-effector of the robot arm also has two ultrasonic sensors to measure the slanted angle of the panel and a camera to see the panel closely. Measuring the slanted angle of a solar panel is used for the arm to tilt the cleaning robot in parallel with the panel to minimize the contact force measured by a force sensor mounted on the first link. A drone is replaced with an x-y table for the planar motion of the arm. Actual landing experiments of Dr. SPC on the panel are conducted to test the feasibility.
StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision

In this paper, we propose StereoPIFu, which integrates the geometric constraints of stereo vision with implicit function representation of PIFu, to recover the 3D shape of the clothed human from a pair of low-cost rectified images. First, we introduce the effective voxel-aligned features from a stereo vision-based network to enable depth-aware reconstruction. Moreover, the novel relative z-offset is employed to associate predicted high-fidelity human depth and occupancy inference, which helps restore fine-level surface details. Second, a network structure that fully utilizes the geometry information from the stereo images is designed to improve the human body reconstruction quality. Consequently, our StereoPIFu can naturally infer the human body's spatial location in camera space and maintain the correct relative position of different parts of the human body, which enables our method to capture human performance. Compared with previous works, our StereoPIFu significantly improves the robustness, completeness, and accuracy of the clothed human reconstruction, which is demonstrated by extensive experimental results.
ARFIMA Modelling for Tectonic Earthquakes in The Maluku Region

Maluku Province is one of the regions in Indonesia with a very active and very prone earthquake intensity because it is a meeting place for 3 (three) plates, namely the Eurasian, Pacific and Australian plates. In the last 100 years, the history of tectonic earthquakes with tsunamis that occurred in Indonesia was 25-30% occurring in the Maluku Sea and Banda Sea. Based on this fact, this study aims to analyze the incidence of tectonic earthquakes that occurred in the Maluku region and its surroundings using the Autoregressive Fractionally Integrated Moving Averages (ARFIMA) model which has the ability to explain long-term time series data (long memory). The results of the research data analysis show that the best model for predicting the number of tectonic earthquakes that occur in Maluku and its surroundings is ARFIMA (0; 0.712; 1) with an MSE value of 0.1156. Meanwhile, the best model for predicting the average magnitude of the number of tectonic earthquakes that occurred in Maluku and its surroundings is ARFIMA (0; -3,224 x 10-9; 1) with an MSE value of 0.01237. Based on the two best models, the prediction results obtained from the number of tectonic earthquakes and the average magnitude of the number of tectonic earthquakes that occurred in Maluku and its surroundings for the next three periods, namely the first period there were 31 tectonic earthquakes with an average magnitude of 4.38481 SR. the second period there were 32 tectonic earthquakes with an average magnitude of 4.38407, and the third period there were 32 tectonic earthquakes with an average magnitude of 4.38333.
MM-Rec: Multimodal News Recommendation

Accurate news representation is critical for news recommendation. Most of existing news representation methods learn news representations only from news texts while ignore the visual information in news like images. In fact, users may click news not only because of the interest in news titles but also due to the attraction of news images. Thus, images are useful for representing news and predicting user behaviors. In this paper, we propose a multimodal news recommendation method, which can incorporate both textual and visual information of news to learn multimodal news representations. We first extract region-of-interests (ROIs) from news images via object detection. Then we use a pre-trained visiolinguistic model to encode both news texts and news image ROIs and model their inherent relatedness using co-attentional Transformers. In addition, we propose a crossmodal candidate-aware attention network to select relevant historical clicked news for accurate user modeling by measuring the crossmodal relatedness between clicked news and candidate news. Experiments validate that incorporating multimodal news information can effectively improve news recommendation.
Addressing the Vulnerability of NMT in Input Perturbations

Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations. As real input noise is difficult to predict during training, robustness is a big issue for system deployment. In this paper, we improve the robustness of NMT models by reducing the effect of noisy words through a Context-Enhanced Reconstruction (CER) approach. CER trains the model to resist noise in two steps: (1) perturbation step that breaks the naturalness of input sequence with made-up words; (2) reconstruction step that defends the noise propagation by generating better and more robust contextual representation. Experimental results on Chinese-English (ZH-EN) and French-English (FR-EN) translation tasks demonstrate robustness improvement on both news and social media text. Further fine-tuning experiments on social media text show our approach can converge at a higher position and provide a better adaptation.
Gating for target tracking among islands and fjords

Established target tracking theory assumes that there are no obstacles, allowing the targets to follow a motion model (such as the constant velocity model). While working fine in the open sea, this assumption can lead to wrong associations in coastal waters. We propose three different gating algorithms for use in coastal areas. The methods are based on anisotropic fast marching, breadth-first search on a regular lattice and Dijkstra's algorithm on a variable-resolution graph. The proposed methods have shown to give better association results than the circular gattng method.
Coalition situational understanding via explainable neuro-symbolic reasoning and learning

Recent years have seen significant advances in artificial intelligence (AI) and machine learning (ML) technologies applicable to coalition situational understanding (CSU). However, state-of-the-art ML techniques based on deep neural networks require large volumes of training data; unfortunately, representative training examples of situations of interest in CSU are usually sparse. Moreover, to be useful, ML-based analytic services must be capable of explaining their outputs. We describe an integrated CSU architecture that combines neural networks with symbolic learning and reasoning to address the problem of sparse training data. We also demonstrate how explainability can be achieved for deep neural networks operating on multimodal sensor feeds. The work focuses on real-time decision making settings at the tactical edge, with both the symbolic and neural network parts of the system --- including the explainabilty approaches --- able to deal with temporal features.
ESG Materiality Factors in the Fourth Industrial Revolution - Measuring Stakeholder Externalities via Dynamic Materiality

Materiality is the notion in accounting that applies to information that is important for an investment decision and is central to the definition of fiduciary duty. Recently ESG (Environmental, Social and Governance) investors have coalesced around the concept of dynamic materiality, Dynamic Materiality™ is the concept that companies, industries, and sectors have unique materiality signatures that evolve over time, determined by factors such as shifts in business models, changing consumer preferences, emerging technologies and new regulations. Dynamic Materiality is driven by how stakeholders respond to events, behavior and externalities experienced in relation to a company or an industry. This stands in contrast to the view that materiality is relatively static and can be defined by a company. We expand on Dynamic Materiality to provide empirical evidence that materiality has distinct dimensions in addition to industry and sector: region, country, economic development, and company size. These dimensions underpin the Truvalue ESG Materiality Factors, the first holistic materiality approach. We examine how the Materiality Signatures evolves across these dimensions. Furthermore, we present evidence that conventional ESG research frameworks operating on a single global materiality model are no longer suitable in the age of the Fourth Industrial Revolution.
Pointly-Supervised Instance Segmentation

We propose an embarrassingly simple point annotation scheme to collect weak supervision for instance segmentation. In addition to bounding boxes, we collect binary labels for a set of points uniformly sampled inside each bounding box. We show that the existing instance segmentation models developed for full mask supervision can be seamlessly trained with point-based supervision collected via our scheme. Remarkably, Mask R-CNN trained on COCO, PASCAL VOC, Cityscapes, and LVIS with only 10 annotated random points per object achieves 94%--98% of its fully-supervised performance, setting a strong baseline for weakly-supervised instance segmentation. The new point annotation scheme is approximately 5 times faster than annotating full object masks, making high-quality instance segmentation more accessible in practice. Inspired by the point-based annotation form, we propose a modification to PointRend instance segmentation module. For each object, the new architecture, called Implicit PointRend, generates parameters for a function that makes the final point-level mask prediction. Implicit PointRend is more straightforward and uses a single point-level mask loss. Our experiments show that the new module is more suitable for the point-based supervision.