Reviewerly/exHarmony huggingface dataset:
The impact of transparent money flows: Effects of stablecoin transfers on the returns and trading volume of Bitcoin

• Stablecoins are fully transparent: every transfer is recorded on a public blockchain. • We analyze how large stablecoin transfers affect Bitcoin returns and trading volume. • We identify significant positive abnormal trading volume and abnormal returns. • Effect differ based on sender/receiver addresses: unknown, exchanges, treasuries. • Transparent money flows can increase market efficiency. Stablecoins are digital currencies that peg to non-volatile values, most commonly a fiat currency. Unlike fiat currency, stablecoins are fully transparent—every transfer is recorded on a public blockchain. In this regard, they can serve as a valuable case study of the disruptive effect which transparent money flows could have on financial markets. We analyze how 1,587 stablecoin transfers of $1 million or more between April 2019 and March 2020 affected Bitcoin returns and trading volume. We find highly significant positive abnormal trading volume and significant abnormal returns in the hours around stablecoin transfers. The sender and receiver of each transfer are categorized as (1) unknown, (2) cryptocurrency exchange or (3) stablecoin treasury. The effects on trading volume and returns differ across the nine resulting subsamples, suggesting that market participants presume different transfer motives and varying degrees of information asymmetry for each sender-receiver combination. The findings illustrate the feedback effects between cryptocurrency markets and stablecoin usage and suggest that transparent money flows can increase market efficiency.
Detecting Signal Spoofing Attack in UAVs Using Machine Learning Models

Due to the tremendous advancement in interactive multimedia systems and technologies, security has become a major aspect. Advanced technology can be utilized for hacking autonomous systems like Unmanned Aerial vehicles (UAVs) in different ways such as spoofing and jamming. It can be spoofed by the injection of fake signals into the sensors. For the protection of the UAVs from the Global Positioning System (GPS) signal spoofing attack, we propose a new methodology by incorporating a machine learning (ML) algorithm such as Support Vector Machine (SVM). A detailed analysis of several learning algorithms is also carried out to choose the suitable learning algorithm for the proposed work. Once the suitable ML algorithm is selected, we perform K-fold analyses to develop other learning models by choosing different values of K-folds thus we called them K-learning models. The purpose of developing K-learning models is to apply voting techniques to the developed K-learning models. Moreover, the signal features used in the proposed work are jitter, jitter (absolute), jitter (local), jitter (RAP), jitter (ppq5), shimmer, shimmer (local), shimmer (dB), shimmer (apq3), shimmer (apq5) and frequency modulation. Based on these features of the signal, we train our proposed model for the detection of counterfeit GPS signals. To gauge the performance of the proposed model, we perform different experimentation analyses such as accuracy, precision, recall, and F1-score. The results and analysis show the effectiveness of the proposed work over existing techniques.
Information security in the post quantum era for 5G and beyond networks: Threats to existing cryptography, and post-quantum cryptography

Quantum computing is an emerging field that uses the concepts of quantum mechanics to outperform classical computers. Quantum computing finds plethora of applications in the 5G and Beyond networks. It can process data at an exponential rate, which can address numerous business and scientific challenges. In this paper, we provide a detailed review of the field, starting from the most important applications of quantum computers and then diving into the future of cryptography. The major applications of quantum computing include unstructured search, quantum simulation, and optimization. It can also provide improvements in terms of speed and accuracy for some existing technologies, such as machine learning. These technologies have numerous applications in 5G and beyond networks. However, due to such abilities of quantum computing, it can also pose a serious risk to many existing security systems, especially the asymmetric key cryptography schemes. The risk of quantum computing has also influenced the mobile broadband standards to move from a symmetric key cryptography techniques to PKI-based trust model. We also discuss in detail various alternate cryptosystems based on mathematical problems that are believed to be hard even for a quantum computer to solve. In parallel, we discuss the developments in the field of quantum key distribution that makes use of quantum phenomenon to develop a quantum-resistant crypto systems. Such quantum-resistant systems have a great potential in provisioning secure 5G and beyond networks.
Federated Learning for Automatic Modulation Classification Under Class Imbalance and Varying Noise Condition

Automatic modulation classification (AMC) is a promising technology for identifying modulation types, and deep learning (DL)-based AMC is one of its main research directions. Conventional DL-based AMC methods are centralized solutions (i.e., CentAMC), which are trained on abundant data collected from local clients and stored in the server and generally have advanced performance, but their major problem is the risk of data leakage. Besides, if DL-based AMC is only trained with the data from their corresponding clients, it may exhibit weak performance. Thus, a federated learning (FL)-based AMC (FedeAMC) is proposed under the condition of class imbalance and noise varying. Its advantage is low risk of data leakage without severe performance loss, because data and training are in each local client, while only knowledge (i.e., gradient or model weight), rather than data, is shared with the server. In addition, there is generally class imbalance problem in each local client, and balanced cross entropy is introduced as loss function for solving this problem. Simulation results demonstrated that average accuracy gap between FedeAMC and CentAMC is less than 2&#x0025;.
Recognition of multivariate geochemical anomalies associated with mineralization using an improved generative adversarial network

Deep learning (DL) algorithms have a strong ability to recognize high-level features in geochemical exploration data and have been widely employed for the recognition of multivariate geochemical anomalies linked to mineralization. In this study, the adversarially learned anomaly detection (ALAD) algorithm, an improved generative adversarial network (GAN), was employed to detect multivariate geochemical anomalies related to mineralization. Compared with other unsupervised deep learning algorithms, ALAD significantly improves anomaly detection performance by combining the advantages of deep variational autoencoder and generative adversarial network. Various experiments were performed to construct a well-designed network structure to process high-dimensional geochemical data to identify geochemical anomalies linked to tungsten (W) polymetallic mineralization in the south of Jiangxi Province of China. The extracted geochemical anomalies have a high spatial distribution correction with the locations of the discovered W polymetallic mineralization. Both the area under the receiver operating characteristic curve and the prediction-area plot indicate a good performance of the ALAD model. Furthermore, the extracted geochemical anomalies are spatially correlated with granites which control the spatial distribution of W polymetallic mineralization. These observations indicate that, as an unsupervised deep learning algorithm, ALAD is useful for detecting geochemical anomalies associated with mineralization.
MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

In this paper, we propose MonoRec, a semi-supervised monocular dense reconstruction architecture that predicts depth maps from a single moving camera in dynamic environments. MonoRec is based on a multi-view stereo setting which encodes the information of multiple consecutive images in a cost volume. To deal with dynamic objects in the scene, we introduce a MaskModule that predicts moving object masks by leveraging the photometric inconsistencies encoded in the cost volumes. Unlike other multi-view stereo methods, MonoRec is able to reconstruct both static and moving objects by leveraging the predicted masks. Furthermore, we present a novel multi-stage training scheme with a semi-supervised loss formulation that does not require LiDAR depth values. We carefully evaluate MonoRec on the KITTI dataset and show that it achieves state-of-theart performance compared to both multi-view and singleview methods. With the model trained on KITTI, we furthermore demonstrate that MonoRec is able to generalize well to both the Oxford RobotCar dataset and the more challenging TUM-Mono dataset recorded by a handheld camera. Code and related materials are available at https://vision.in.tum.de/research/monorec.
Visualizing Adapted Knowledge in Domain Transfer

A source model trained on source data and a target model learned through unsupervised domain adaptation (UDA) usually encode different knowledge. To understand the adaptation process, we portray their knowledge difference with image translation. Specifically, we feed a translated image and its original version to the two models respectively, formulating two branches. Through updating the translated image, we force similar outputs from the two branches. When such requirements are met, differences between the two images can compensate for and hence represent the knowledge difference between models. To enforce similar outputs from the two branches and depict the adapted knowledge, we propose a source-free image translation method that generates source-style images using only target images and the two models. We visualize the adapted knowledge on several datasets with different UDA methods and find that generated images successfully capture the style difference between the two domains. For application, we show that generated images enable further tuning of the target model without accessing source data. Code available at https://github.com/houyz/DA_visualization.
Platform is not destiny: Embodied learning effects comparing <scp>2D</scp> desktop to <scp>3D</scp> virtual reality <scp>STEM</scp> experiences

Abstract Experiences in immersive 3D virtual reality (VR) are more presence‐inducing, and so it may be tempting to claim that content will be learned better in VR. This randomized control trial study on natural selection challenges that assumption. This study answers the question of whether learning STEM in an immersive 3D VR environment is always superior to learning via a 2D monitor (PC). This is a 2 × 2 × 3 design. The first factor is platform immersivity (low = 2D PC, or high = 3D VR), the second factor is level of embodiment (lower = watching playback video, or higher = using mouse/controller to agentically manipulate content), and the third factor is test time (pretest, posttest, and follow‐up). There was a significant main effect for embodiment, the high embodied and agentic groups learned the most. There was not a main effect for platform, because the participants in the low embodied VR group performed significantly worse than the three other groups. Although, the one high embodied, VR group learned and retained the most knowledge. A path‐analysis revealed that the effect of platform was significantly mediated by presence, agency, and engagement. The smaller learning gain in the low embodied VR condition suggests that participants come to the immersive VR experience with expectations about agency and control of the virtual content, and when those expectations were not met, the disconnect was deleterious for learning. More agentic and interactive control of manipulable virtual content is encouraged. Design is critical, and platform is not destiny.
Who needs to know what, when?: Broadening the Explainable AI (XAI) Design Space by Looking at Explanations Across the AI Lifecycle

The interpretability or explainability of AI systems (XAI) has been a topic gaining renewed attention in recent years across AI and HCI communities. Recent work has drawn attention to the emergent explainability requirements of in situ, applied projects, yet further exploratory work is needed to more fully understand this space. This paper investigates applied AI projects and reports on a qualitative interview study of individuals working on AI projects at a large technology and consulting company. Presenting an empirical understanding of the range of stakeholders in industrial AI projects, this paper also draws out the emergent explainability practices that arise as these projects unfold, highlighting the range of explanation audiences (who), as well as how their explainability needs evolve across the AI project lifecycle (when). We discuss the importance of adopting a sociotechnical lens in designing AI systems, noting how the “AI lifecycle” can serve as a design metaphor to further the XAI design field.
Meepo: Sharded Consortium Blockchain

Blockchain performance cannot meet the requirement nowadays. One of the crucial ways to improve performance is sharding. However, most blockchain sharding research focuses on public blockchain. As for consortium blockchain, previous studies cannot support high cross-shard efficiency, cross-contract flexibility, shard availability, and strict transaction atomicity, which are the essential requirements but also the challenges in consortium blockchain systems. Facing these challenges, we propose Meepo, a systematic study on sharded consortium blockchain. Meepo enhances cross-shard efficiency via the cross-epoch and cross-call. Moreover, a partial cross-call merging strategy is designed to handle the multi-state dependency in contract calls, achieving cross-contract flexibility. Meepo employs a replay-epoch to ensure strict transaction atomicity, and it also uses a backup algorithm called shadow shard based recovery to improve the shard robustness. We implement Meepo on the AliCloud, using 32 shards in maximum, achieving more than 120,000 cross-shard TPS under the workload of 100,000,000 asset transactions.
DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning

We present a system for inductive program synthesis called DreamCoder, which inputs a corpus of synthesis problems each specified by one or a few examples, and automatically derives a library of program components and a neural search policy that can be used to efficiently solve other similar synthesis problems. The library and search policy bootstrap each other iteratively through a variant of "wake-sleep" approximate Bayesian learning. A new refactoring algorithm based on E-graph matching identifies common sub-components across synthesized programs, building a progressively deepening library of abstractions capturing the structure of the input domain. We evaluate on eight domains including classic program synthesis areas and AI tasks such as planning, inverse graphics, and equation discovery. We show that jointly learning the library and neural search policy leads to solving more problems, and solving them more quickly.
Dual Attention Guided Gaze Target Detection in the Wild

Gaze target detection aims to infer where each person in a scene is looking. Existing works focus on 2D gaze and 2D saliency, but fail to exploit 3D contexts. In this work, we propose a three-stage method to simulate the human gaze inference behavior in 3D space. In the first stage, we introduce a coarse-to-fine strategy to robustly estimate a 3D gaze orientation from the head. The predicted gaze is decomposed into a planar gaze on the image plane and a depth-channel gaze. In the second stage, we develop a Dual Attention Module (DAM), which takes the planar gaze to produce the filed of view and masks interfering objects regulated by depth information according to the depth-channel gaze. In the third stage, we use the generated dual attention as guidance to perform two sub-tasks: (1) identifying whether the gaze target is inside or out of the image; (2) locating the target if inside. Extensive experiments demonstrate that our approach performs favorably against state-of-the-art methods on GazeFollow and VideoAttentionTarget datasets.
Emotion Detection of Textual Data: An Interdisciplinary Survey

Emotion is a primary aspect of communication and can be expressed in many modalities. Text-Based Emotion Detection (TBED), one of the fastest growing branches of Natural Language Processing (NLP), is the process of classifying syntactic or semantic units of a corpus into a given set of emotion classes proposed by a psychological model. Automatic TBED mechanisms use machine learning approaches to create computational platforms automating the process of extracting emotions. TBED has a wide variety of applications in the area of artificial intelligence: Semantic analysis of documents and public messages related to terrorist attacks (to mitigate risks), automated analysis of historical corpora, and study of product reviews (to assess customer satisfaction). This work reviews the current literature of TBED and the psychological models associated with them.
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer

Artistic style transfer aims at migrating the style from an example image to a content image. Currently, optimization-based methods have achieved great stylization quality, but expensive time cost restricts their practical applications. Meanwhile, feed-forward methods still fail to synthesize complex style, especially when holistic global and local patterns exist. Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle). LapStyle first transfers global style patterns in low-resolution via a Drafting Network. It then revises the local details in high-resolution via a Revision Network, which hallucinates a residual image according to the draft and the image textures extracted by Laplacian filtering. Higher resolution details can be easily generated by stacking Revision Networks with multiple Laplacian pyramid levels. The final stylized image is obtained by aggregating outputs of all pyramid levels. Experiments demonstrate that our method can synthesize high quality stylized images in real time, where holistic style patterns are properly transferred.
On Learning the Geodesic Path for Incremental Learning

Neural networks notoriously suffer from the problem of catastrophic forgetting, the phenomenon of forgetting the past knowledge when acquiring new knowledge. Overcoming catastrophic forgetting is of significant importance to emulate the process of "incremental learning", where the model is capable of learning from sequential experience in an efficient and robust way. State-of-the-art techniques for incremental learning make use of knowledge distillation towards preventing catastrophic forgetting. Therein, one updates the network while ensuring that the network’s responses to previously seen concepts remain stable throughout updates. This in practice is done by minimizing the dissimilarity between current and previous responses of the network one way or another. Our work contributes a novel method to the arsenal of distillation techniques. In contrast to the previous state of the art, we propose to firstly construct low-dimensional manifolds for previous and current responses and minimize the dissimilarity between the responses along the geodesic connecting the manifolds. This induces a more formidable knowledge distillation with smooth properties which preserves the past knowledge more efficiently as observed by our comprehensive empirical study. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback

We tackle the task of image retrieval with text feedback, where a reference image and modifier text are combined to identify the desired target image. We focus on designing an image-text compositor, i.e., integrating multi-modal inputs to produce a representation similar to that of the target image. In our algorithm, Content-Style Modulation (CoSMo), we approach this challenge by introducing two modules based on deep neural networks: the content and style modulators. The content modulator performs local updates to the reference image feature after normalizing the style of the image, where a disentangled multi-modal non-local block is employed to achieve the desired content modifications. Then, the style modulator reintroduces global style information to the updated feature. We provide an in-depth view of our algorithm and its design choices, and show that it accomplishes outstanding performance on multiple image-text retrieval benchmarks. Our code can be found at: https://github.com/postBG/CosMo.pytorch
Why Do My Blockchain Transactions Fail?

Permissioned blockchain systems promise to provide both decentralized trust and privacy. Hyperledger Fabric is currently one of the most wide-spread permissioned blockchain systems and is heavily promoted both in industry and academia. Due to its optimistic concurrency model, the transaction failure rates in Fabric can become a bottleneck. While there is active research to reduce failures, there is a lack of understanding on their root cause and, consequently, a lack of guidelines on how to configure Fabric optimally for different scenarios. To close this gap, in this paper, we first introduce a formal definition of the different types of transaction failures in Fabric. Then, we develop a comprehensive testbed and benchmarking system, HyperLedgerLab, along with four different chaincodes that represent realistic use cases and a chaincode/workload generator. Using HyperLedgerLab, we conduct exhaustive experiments to analyze the impact of different parameters of Fabric such as block size, endorsement policies, and others, on transaction failures. We further analyze three recently proposed optimizations from the literature, Fabric++, Streamchain and FabricSharp, and evaluate under which conditions they reduce the failure rates. Finally, based on our results, we provide recommendations for Fabric practitioners on how to configure the system and also propose new research directions.
Open-book Video Captioning with Retrieve-Copy-Generate Network

In this paper, we convert traditional video captioning task into a new paradigm, i.e., Open-book Video Captioning, which generates natural language under the prompts of video-content-relevant sentences, not limited to the video itself. To address the open-book video captioning problem, we propose a novel Retrieve-Copy-Generate network, where a pluggable video-to-text retriever is constructed to retrieve sentences as hints from the training corpus effectively, and a copy-mechanism generator is introduced to extract expressions from multi-retrieved sentences dynamically. The two modules can be trained end-to-end or separately, which is flexible and extensible. Our framework co-ordinates the conventional retrieval-based methods with orthodox encoder-decoder methods, which can not only draw on the diverse expressions in the retrieved sentences but also generate natural and accurate content of the video. Extensive experiments on several benchmark datasets show that our proposed approach surpasses the state-of-the-art performance, indicating the effectiveness and promising of the proposed paradigm in the task of video captioning.