Reviewerly/exHarmony huggingface dataset:
Learning Stereo from Single Images

Supervised deep networks are among the best methods for finding correspondences in stereo image pairs. Like all supervised approaches, these networks require ground truth data during training. However, collecting large quantities of accurate dense correspondence data is very challenging. We propose that it is unnecessary to have such a high reliance on ground truth depths or even corresponding stereo pairs. Inspired by recent progress in monocular depth estimation, we generate plausible disparity maps from single images. In turn, we use those flawed disparity maps in a carefully designed pipeline to generate stereo training pairs. Training in this manner makes it possible to convert any collection of single RGB images into stereo training data. This results in a significant reduction in human effort, with no need to collect real depths or to hand-design synthetic data. We can consequently train a stereo matching network from scratch on datasets like COCO, which were previously hard to exploit for stereo. Through extensive experiments we show that our approach outperforms stereo networks trained with standard synthetic datasets, when evaluated on KITTI, ETH3D, and Middlebury. Code to reproduce our results is available at https://github.com/nianticlabs/stereo-from-mono/ .
An Efficient and Lightweight Deep Learning Model for Human Activity Recognition Using Smartphones

Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified by specialized smartphones into well known movements that can be done with human activity recognition. With the high success and wide adaptation of deep learning approaches for the recognition of human activities, these techniques are widely used in wearable devices and smartphones to recognize the human activities. In this paper, convolutional layers are combined with long short-term memory (LSTM), along with the deep learning neural network for human activities recognition (HAR). The proposed model extracts the features in an automated way and categorizes them with some model attributes. In general, LSTM is alternative form of recurrent neural network (RNN) which is famous for temporal sequences’ processing. In the proposed architecture, a dataset of UCI-HAR for Samsung Galaxy S2 is used for various human activities. The CNN classifier, which should be taken single, and LSTM models should be taken in series and take the feed data. For each input, the CNN model is applied, and each input image’s output is transferred to the LSTM classifier as a time step. The number of filter maps for mapping of the various portions of image is the most important hyperparameter used. Transformation on the basis of observations takes place by using Gaussian standardization. CNN-LSTM, a proposed model, is an efficient and lightweight model that has shown high robustness and better activity detection capability than traditional algorithms by providing the accuracy of 97.89%.
Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement

Recently, most of the state-of-the-art human pose estimation methods are based on heatmap regression. The final coordinates of keypoints are obtained by decoding heatmap directly. In this paper, we aim to find a better approach to get more accurate localization results. We mainly put forward two suggestions for improvement: 1) different features and methods should be applied for rough and accurate localization, 2) relationship between keypoints should be considered. Specifically, we propose a two-stage graph-based and model-agnostic framework, called Graph-PCNN, with a localization subnet and a graph pose refinement module added onto the original heatmap regression network. In the first stage, heatmap regression network is applied to obtain a rough localization result, and a set of proposal keypoints, called guided points, are sampled. In the second stage, for each guided point, different visual feature is extracted by the localization subnet. The relationship between guided points is explored by the graph pose refinement module to get more accurate localization results. Experiments show that Graph-PCNN can be used in various backbones to boost the performance by a large margin. Without bells and whistles, our best model can achieve a new state-of-the-art 76.8% AP on COCO test-dev split.
Evaluation of energy extraction of PV systems affected by environmental factors under real outdoor conditions

Abstract The global agenda to increase the renewable energy share has driven many countries and entities to harness solar energy from solar photovoltaic (PV) systems. However, the power generation of PV systems is strongly affected by climate conditions. Therefore, the main objective of this study is to analyze and predict the power generation of different PV technologies under arid desert climate conditions on an hourly basis. Two areas have been considered as case studies: Adrar in Algeria and Alice Springs in Australia. A total of nine physical models and input parameter combinations from six different power plants have been used and tested for the suitability of the proposed models for predicting the power yield of PV power plants depending on solar irradiance and other meteorological variables. Then, an ensemble learning technique is applied to improve the performance capabilities of the best-fit input combinations. The results reveal that the global irradiance, ambient air temperature, and relative humidity combination are the most related to the PV power output of all technologies under all-sky conditions and provide effective and efficient performance with the proposed ensemble learning, with an estimated accuracy of over 99%.
Blockchain and the Internet of Value

Don Tapscott believes blockchain will provide the foundation for an Internet of Value that will fundamentally reshape both business and society.Don Tapscott is the author of 16 books and has been a...
Peer-to-peer electricity trading system: smart contracts based proof-of-benefit consensus protocol

Abstract Nowadays, people trade electricity through centralized companies or organizations which is vulnerable to cyber attacks and incapable of coping with increasing demands from stakeholders. In this paper, we propose a new Peer-to-Peer Electricity Blockchain Trading (P2PEBT) system based on the current charging and discharging schemes for electric vehicles (EV) in the smart grid to enable users to participate in the trading process. In order to cope with the current situation of the high volume of EV integration, the proof-of-Benefit (PoB) consensus primitives are proposed for P2PEBT to achieve demand response by providing incentives to balance local electricity demand in the novel blockchain system. PoB is implemented by executing the smart contracts on the Ethereum platform, and the process of achieving the maximal benefits is completed by submitting the transaction in the decentralized network. Security analysis shows that the P2PEBT system is able to manage a potential protection against up to a number of attacks. We demonstrate that the proposed system using the PoB consensus mechanism can achieve lower power fluctuation without requiring a third-party intermediary.
Visual Exploration of Neural Document Embedding in Information Retrieval: Semantics and Feature Selection

Neural embeddings are widely used in language modeling and feature generation with superior computational power. Particularly, neural document embedding - converting texts of variable-length to semantic vector representations - has shown to benefit widespread downstream applications, e.g., information retrieval (IR). However, the black-box nature makes it difficult to understand how the semantics are encoded and employed. We propose visual exploration of neural document embedding to gain insights into the underlying embedding space, and promote the utilization in prevalent IR applications. In this study, we take an IR application-driven view, which is further motivated by biomedical IR in healthcare decision-making, and collaborate with domain experts to design and develop a visual analytics system. This system visualizes neural document embeddings as a configurable document map and enables guidance and reasoning; facilitates to explore the neural embedding space and identify salient neural dimensions (semantic features) per task and domain interest; and supports advisable feature selection (semantic analysis) along with instant visual feedback to promote IR performance. We demonstrate the usefulness and effectiveness of this system and present inspiring findings in use cases. This work will help designers/developers of downstream applications gain insights and confidence in neural document embedding, and exploit that to achieve more favorable performance in application domains.
User Rating Classification via Deep Belief Network Learning and Sentiment Analysis

Deep learning is a methodology applied across many fields. User comments are important for recommender systems because they include various types of emotional information that may influence the correctness or precision of the recommendation. Improving the accuracy of user ratings from obtained feasible recommendations is essential. In this paper, we propose a deep learning model to process user comments and to generate a possible user rating for user recommendations. First, the system uses sentiment analysis to create a feature vector as the input nodes. Next, the system implements noise reduction in the data set to improve the classification of user ratings. Finally, a deep belief network and sentiment analysis (DBNSA) achieves data learning for the recommendations. The experimental results indicated that this system has better accuracy than traditional methods.
Is This Really You? An Empirical Study on Risk-Based Authentication Applied in the Wild

Risk-based authentication (RBA) is an adaptive security measure to strengthen password-based authentication. RBA monitors additional implicit features during password entry such as device or geolocation information, and requests additional authentication factors if a certain risk level is detected. RBA is recommended by the NIST digital identity guidelines, is used by several large online services, and offers protection against security risks such as password database leaks, credential stuffing, insecure passwords and large-scale guessing attacks. Despite its relevance, the procedures used by RBA-instrumented online services are currently not disclosed. Consequently, there is little scientific research about RBA, slowing down progress and deeper understanding, making it harder for end users to understand the security provided by the services they use and trust, and hindering the widespread adoption of RBA. In this paper, with a series of studies on eight popular online services, we (i) analyze which features and combinations/classifiers are used and are useful in practical instances, (ii) develop a framework and a methodology to measure RBA in the wild, and (iii) survey and discuss the differences in the user interface for RBA. Following this, our work provides a first deeper understanding of practical RBA deployments and helps fostering further research in this direction.
Scheduled Sampling for Transformers

Scheduled sampling is a technique for avoiding one of the known problems in sequenceto-sequence generation: exposure bias.It consists of feeding the model a mix of the teacher forced embeddings and the model predictions from the previous step in training time.The technique has been used for improving the model performance with recurrent neural networks (RNN).In the Transformer model, unlike the RNN, the generation of a new word attends to the full sentence generated so far, not only to the last word, and it is not straightforward to apply the scheduled sampling technique.We propose some structural changes to allow scheduled sampling to be applied to Transformer architecture, via a two-pass decoding strategy.Experiments on two language pairs achieve performance close to a teacherforcing baseline and show that this technique is promising for further exploration.
Sentence-Level Emotion Detection from Text Based on Semantic Rules

Detecting emotion from text has become an interesting topic in the field of natural language processing. Emotion detection aims to detect and recognize types of emotion from various sources such as text, facial expression and gestures, and speech. This paper proposes an efficient emotion detection technique by searching emotional words from a pre-defined emotional keyword database. The method analyzes the emotion words and phrasal verbs, also considers negation words and exhibits better performance than recent approaches.
How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature

We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths. This is due to a non-linear pattern between ROUGE F1 and summary length. To alleviate the effect of length during evaluation, we have proposed a new method which normalizes the ROUGE F1 scores of a system by that of a random system with same average output length. A pilot human evaluation has shown that humans prefer short summaries in terms of the verbosity of a summary but overall consider longer summaries to be of higher quality. While human evaluations are more expensive in time and resources, it is clear that normalization, such as the one we proposed for automatic evaluation, will make human evaluations more meaningful.
Biased Nonce Sense: Lattice Attacks Against Weak ECDSA Signatures in Cryptocurrencies

In this paper, we compute hundreds of Bitcoin private keys and dozens of Ethereum, Ripple, SSH, and HTTPS private keys by carrying out cryptanalytic attacks against digital signatures contained in public blockchains and Internet-wide scans. The ECDSA signature algorithm requires the generation of a per-message secret nonce. If this nonce is not generated uniformly at random, an attacker can potentially exploit this bias to compute the long-term signing key. We use a lattice-based algorithm for solving the hidden number problem to efficiently compute private ECDSA keys that were used with biased signature nonces due to multiple apparent implementation vulnerabilities.
Federated Learning of N-Gram Language Models

We propose algorithms to train productionquality n-gram language models using federated learning.Federated learning is a distributed computation platform that can be used to train global models for portable devices such as smart phones.Federated learning is especially relevant for applications handling privacy-sensitive data, such as virtual keyboards, because training is performed without the users' data ever leaving their devices.While the principles of federated learning are fairly generic, its methodology assumes that the underlying models are neural networks.However, virtual keyboards are typically powered by n-gram language models for latency reasons.
Environmental drivers of systematicity and generalization in a situated agent.

The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we consider tests of out-of-sample generalisation that require an agent to respond to never-seen-before instructions by manipulating and positioning objects in a 3D Unity simulated room. We first describe a comparatively generic agent architecture that exhibits strong performance on these tests. We then identify three aspects of the training regime and environment that make a significant difference to its performance: (a) the number of object/word experiences in the training set; (b) the visual invariances afforded by the agent's perspective, or frame of reference; and (c) the variety of visual input inherent in the perceptual aspect of the agent's perception. Our findings indicate that the degree of generalisation that networks exhibit can depend critically on particulars of the environment in which a given task is instantiated. They further suggest that the propensity for neural networks to generalise in systematic ways may increase if, like human children, those networks have access to many frames of richly varying, multi-modal observations as they learn.
The Internet as Cognitive Enhancement

The Internet has been identified in human enhancement scholarship as a powerful cognitive enhancement technology. It offers instant access to almost any type of information, along with the ability to share that information with others. The aim of this paper is to critically assess the enhancement potential of the Internet. We argue that unconditional access to information does not lead to cognitive enhancement. The Internet is not a simple, uniform technology, either in its composition, or in its use. We will look into why the Internet as an informational resource currently fails to enhance cognition. We analyze some of the phenomena that emerge from vast, continual fluxes of information-information overload, misinformation and persuasive design-and show how they could negatively impact users' cognition. Methods for mitigating these negative impacts are then advanced: individual empowerment, better collaborative systems for sorting and categorizing information, and the use of artificial intelligence assistants that could guide users through the informational space of today's Internet.
Predicted Adoption Rates of Contact Tracing App Configurations - Insights from a Choice-Based Conjoint Study with a Representative Sample of the UK population

Widespread adoption of a contact tracing app by the UK public is an important part of safely easing or lifting the lockdown. In this context, it is essential to understand how adoption rates are influenced by different configurations of a proposed contact tracing app. There are many implementation options that can impact app adoption. For example, which institution should be responsible for and have oversight of the app? What type of data is collected? Does it matter how long it is stored? This whitepaper provides data-driven insights into these and other questions to guide app implementation choices.
Enhanced Lightning Network (off-chain)-based micropayment in IoT ecosystems

Information is being seen as the new “oil” for companies. Trading and negotiating personal data, which includes data generated by owned smart devices, is gaining attention and acceptance in the Internet of Things (IoT) era. There is a global trend to move towards open innovation ecosystems that allow data owners to have better control over their data and privacy, choosing if/what and with whom to share/trade specific data streams. Nonetheless, this requires the design of IoT ecosystems that integrate automatic enforcing mechanisms to guarantee the delivery of the negotiated data, or still the capability of making near-instantaneous payments for the data (in the form of micro-units). This paper discusses the requirements that need to be fulfilled to properly support (micro)-payment in IoT, and further the extent to which different blockchain technologies can fulfill those requirements. Based on this analysis, our paper progresses the current state-of-the-art in three-respect: (i) by carrying out a benchmark performance analysis between LN and other-like solutions; (ii) by integrating the Lightning Network (LN) off-chain technology within an existing IoT ecosystem, developed as part of the bIoTope H2020 project, and (iii) by designing a novel algorithm for payment channel fee reduction. Experiments carried out in this paper show that LN outperforms traditional blockchain solutions under IoT-specific constraints and objectives, and that an optimal parameter setting of the proposed algorithm can be identified.
Hyperspectral Image Mixed Noise Removal Based on Multidirectional Low-Rank Modeling and Spatial–Spectral Total Variation

Conventional low-rank (LR)-based hyperspectral image (HSI) denoising models generally convert high-dimensional data into 2-D matrices or just treat this type of data as 3-D tensors. However, these pure LR or tensor low-rank (TLR)-based methods lack flexibility for considering different correlation information from different HSI directions, which leads to the loss of comprehensive structure information and inherent spatial-spectral relationship. To overcome these shortcomings, we propose a novel multidirectional LR modeling and spatial-spectral total variation (MLR-SSTV) model for removing HSI mixed noise. By incorporating the weighted nuclear norm, we obtain the weighted sum of weighted nuclear norm minimization (WSWNNM) and the weighted sum of weighted tensor nuclear norm minimization (WSWTNNM) to estimate the more accurate LR tensor, especially, to remove the dead-line noise better. Gaussian noise is further denoised and the local spatial-spectral smoothness is preserved effectively by SSTV regularization. We develop an efficient algorithm for solving the derived optimization based on the alternating direction method of multipliers (ADMM). Extensive experiments on both synthetic data and real data demonstrate the superior performance of the proposed MLR-SSTV model for HSI mixed noise removal.
Densely Self-guided Wavelet Network for Image Denoising

During the past years, deep convolutional neural networks have achieved impressive success in image denoising. In this paper, we propose a densely self-guided wavelet network (DSWN) for real-world image denoising. The basic structure of DSWN is a top-down self-guidance architecture which is able to efficiently incorporate multi- scale information and extract good local features to recover clean images. Moreover, such a structure requires a smaller number of parameters and enables us to achieve better effectiveness than Unet structure. To avoid information loss and achieve a better receptive field size, we embed wavelet transform into DSWN. In addition, we apply densely residual learning to convolution blocks to enhance the feature extraction capability of the proposed network. At the full resolution level of DSWN, we adopt a double branch structure to generate the final output. One branch of them tends to pay attention to dark areas and the other performs better on bright areas. Such a double branch strategy is able to handle the noise at different exposures. The proposed network is validated by BSD68, Kodak24 and SIDD+ benchmark. Additional experimental results show that the proposed network outperforms most state-of-the-art image denoising solutions.
Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning

Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information. Current methods for 3D volume reconstruction from freehand US scans require external tracking devices to provide spatial position for every frame. In this paper, we propose a deep contextual learning network (DCL-Net), which can efficiently exploit the image feature relationship between US frames and reconstruct 3D US volumes without any tracking device. The proposed DCL-Net utilizes 3D convolutions over a US video segment for feature extraction. An embedded self-attention module makes the network focus on the speckle-rich areas for better spatial movement prediction. We also propose a novel case-wise correlation loss to stabilize the training process for improved accuracy. Highly promising results have been obtained by using the developed method. The experiments with ablation studies demonstrate superior performance of the proposed method by comparing against other state-of-the-art methods. Source code of this work is publicly available at https://github.com/DIAL-RPI/FreehandUSRecon.