Reviewerly/exHarmony huggingface dataset:
Quantum advantage for computations with limited space

Quantum computations promise the ability to solve problems intractable in the classical setting. Restricting the types of computations considered often allows to establish a provable theoretical advantage by quantum computations, and later demonstrate it experimentally. In this paper, we consider space-restricted computations, where input is a read-only memory and only one (qu)bit can be computed on. We show that $n$-bit symmetric Boolean functions can be implemented exactly through the use of quantum signal processing as restricted space quantum computations using $O(n^2)$ gates, but some of them may only be evaluated with probability $1/2 + O(n/\sqrt{2}^n)$ by analogously defined classical computations. We experimentally demonstrate computations of $3$-, $4$-, $5$-, and $6$-bit symmetric Boolean functions by quantum circuits, leveraging custom two-qubit gates, with algorithmic success probability exceeding the best possible classically. This establishes and experimentally verifies a different kind of quantum advantage -- one where quantum scrap space is more valuable than analogous classical space -- and calls for an in-depth exploration of space-time tradeoffs in quantum circuits.
Achieving Graph Clustering Privacy Preservation Based on Structure Entropy in Social IoT

Decoding the real structure from the Social Internet-of-Things (SIoT) network with a large-scale noise structure plays a fundamental role in data mining. Protecting private information from leakage in the mining process and obtaining accurate mining results is a significant challenge. To tackle this issue, we present a graph clustering privacy-preserving method based on structure entropy, which combines data mining with the structural information theory. Specially, user private information in SIoT is encrypted by Brakerski–Gentry–Vaikuntanathan (BGV) homomorphism to generate a graph structure in the ciphertext state, the ciphertext graph structure is then divided into different modules by applying a 2-D structural information solution algorithm and a entropy reduction principle node module partition algorithm, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -dimensional structural information solution algorithm is utilized to further cluster the internal nodes of the partition module. Moreover, normalized structural information and network node partition similarity are introduced to analyze the correctness and similarity degree of clustering results. Finally, security analysis and theoretical analysis indicate that this scheme not only guarantees the correctness of the clustering results but also improves the security of private information in SIoT. Experimental evaluation and analysis shows that the clustering results of this scheme have higher efficiency and reliability.
The rise and fall of cryptocurrency coins and tokens

Since Bitcoin's introduction in 2009, interest in cryptocurrencies has soared. One manifestation of this interest has been the explosion of newly created coins and tokens. In this paper, we analyze the dynamics of this burgeoning industry. We consider both cryptocurrency coins and tokens. The paper examines the dynamics of coin and token creation, competition and destruction in the cryptocurrency industry. In order to conduct the analysis, we develop a methodology to identify peaks in prices and trade volume, as well as when coins and tokens are abandoned and subsequently "resurrected". We also study trading activity. Our data spans more than 4 years: there are 1082 coins and 725 tokens in the data. While there are some similarities between coins and tokens regarding dynamics, there are some striking differences as well. Overall, we find that 44% of publicly-traded coins are abandoned, at least temporarily. 71% of abandoned coins are later resurrected, leaving 18% of coins to fail permanently. Tokens experience abandonment less frequently, with only 7% abandonment and 5% permanent token abandonment at the end of the data. Using linear regressions, we find that market variables such as the bitcoin price are not associated with the rate of introducing new coins, though they are positively associated with issuing new tokens. We find that for both coins and tokens, market variables are positively associated with resurrection. We then examine the effect that the bursting of the Bitcoin bubble in December 2017 had on the dynamics in the industry. Unlike the end of the 2013 bubble, some alternative cryptocurrencies continue to flourish after the bursting of this bubble.
Hierarchical Multi-View Graph Pooling with Structure Learning

Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in numerous graph related tasks. However, existing GNN models mainly focus on designing graph convolution operations. The graph pooling (or downsampling) operations, that play an important role in learning hierarchical representations, are usually overlooked. In this paper, we proposed a novel multi-view graph pooling operator dubbed as MVPool, which ranks nodes across different views with different contextual graph information. Meanwhile, attention mechanism is utilized to promote the collaboration of different views for generating robust node rankings. Then the pooling operation adaptively selects a subset of nodes to form an induced subgraph based on the ranking list. To preserve the underlying graph topological information, we further introduce a structure learning mechanism to learn a refined graph structure for the pooled graph at each layer. The proposed MVPool operator is a general strategy that can be integrated into various graph neural network architectures. By combining MVPool operator with graph neural networks, we perform hierarchical representation learning for both node and graph level classification as well as clustering tasks. Experimental results on nine widely used benchmarks demonstrate the effectiveness of our proposed model.
GIS-based and Naïve Bayes for nitrogen soil mapping in Lendah, Indonesia

Rice or Oryza sativa L. is the staple food of Indonesian society. Currently, the demand for rice in Indonesia is increasing, while the level of rice production is decreasing. Therefore, alternative technologies are needed to assist the community in increasing rice production. One of the important factors in the growth of rice is the quality of the soil that contains high levels of nitrogen for photosynthesis, protein formation, acid formation, and accelerating plant growth. This research was applied for soil nitrogen mapping with a smart prototype using the TCS3200 sensor combined with Naive Bayes algorithm and GIS (Geographical information systems). This system is carried through the Wemos D1 Mini microcontroller of TCS3200 sensor for reading data and sending the data to the web ceerduad.com. Geographical information systems (GIS) was developed to mapping area and to obtain training data for the Naïve Bayes Algorithm which the researcher took 20 soil samples in Lendah sub-district. From the experiment results, a prototype of soil nitrogen content using the TCS3200 sensor can measure soil nitrogen levels with an accuracy of 87.5% and sending the sensor data to the web server. Implementation of GIS in this research was successfully mapping low, medium, and high levels of nitrogen.
AW3M: An auto-weighting and recovery framework for breast cancer diagnosis using multi-modal ultrasound

Recently, more clinicians have realized the diagnostic value of multi-modal ultrasound in breast cancer identification and began to incorporate Doppler imaging and Elastography in the routine examination. However, accurately recognizing patterns of malignancy in different types of sonography requires expertise. Furthermore, an accurate and robust diagnosis requires proper weights of multi-modal information as well as the ability to process missing data in practice. These two aspects are often overlooked by existing computer-aided diagnosis (CAD) approaches. To overcome these challenges, we propose a novel framework (called AW3M) that utilizes four types of sonography (i.e. B-mode, Doppler, Shear-wave Elastography, and Strain Elastography) jointly to assist breast cancer diagnosis. It can extract both modality-specific and modality-invariant features using a multi-stream CNN model equipped with self-supervised consistency loss. Instead of assigning the weights of different streams empirically, AW3M automatically learns the optimal weights using reinforcement learning techniques. Furthermore, we design a light-weight recovery block that can be inserted to a trained model to handle different modality-missing scenarios. Experimental results on a large multi-modal dataset demonstrate that our method can achieve promising performance compared with state-of-the-art methods. The AW3M framework is also tested on another independent B-mode dataset to prove its efficacy in general settings. Results show that the proposed recovery block can learn from the joint distribution of multi-modal features to further boost the classification accuracy given single modality input during the test.
An Algebraic Framework for Universal and Updatable SNARKs

We introduce Checkable Subspace Sampling Arguments, a new information theoretic interactive proof system in which the prover shows that a vector has been sampled in a subspace according to the verifier's coins. We show that this primitive provides a unifying view that explains the technical core of most of the constructions of universal and updatable pairing-based (zk)SNARKs. This characterization is extended to a fully algebraic framework for designing such SNARKs in a modular way. We propose new constructions of CSS arguments that lead to SNARKs with different performance trade-offs.
Detection of Morphed Face Images Using Discriminative Wavelet Sub-bands

This work investigates the well-known problem of morphing attacks, which has drawn considerable attention in the biometrics community. Morphed images have exposed face recognition systems' susceptibility to false acceptance, resulting in dire consequences, especially for national security applications. To detect morphing attacks, we propose a method which is based on a discriminative 2D Discrete Wavelet Transform (2D-DWT). A discriminative wavelet sub-band can highlight inconsistencies between a real and a morphed image. We observe that there is a salient discrepancy between the entropy of a given subband in a bona fide image, and the same sub-band's entropy in a morphed sample. Considering this dissimilarity between these two entropy values, we find the Kullback-Leibler divergence between the two distributions, namely the entropy of the bona fide and the corresponding morphed images. The most discriminative wavelet sub-bands are those with the highest corresponding KL-divergence values. Accordingly, 22 sub-bands are selected as the most discriminative ones in terms of morph detection. We show that a Deep Neural Network (DNN) trained on the 22 discriminative sub-bands can detect morphed samples precisely. Most importantly, the effectiveness of our algorithm is validated through experiments on three datasets: VISAPP17, LMA, and MorGAN. We also performed an ablation study on the sub-band selection.
Unveiling the power behind cryptocurrency mining in Venezuela: A fragile energy infrastructure and precarious labor

In the aftermath of the global financial crisis (2008–2010), peer-to-peer online payment mechanisms emerged as a way to avoid financial intermediaries and untrusted centralized state authorities. Cryptocurrencies proliferated among like-minded individuals, generally depicted by scholars as anarcho-capitalists. Much social science literature has focused on governance and technical puzzles that these new market tools bring about as well as on the risks associated with the problems of trust, security, money laundering and illegal financing. In this article, I shift gaze to center on the material underpinnings of cryptocurrencies, such as energy systems and infrastructure, as well as how cryptocurrencies can expand in contexts of crisis in peripheral societies. I focus on the case of Venezuela to explain some of these processes in the rise and expansion of cryptocurrency markets that have remained at the margins of energy and political economy discussions. A deep dive into Venezuela allows us to de-virtualize our understandings of cryptocurrencies. It redirects our attention to infrastructure, energy, rent, and precarious conditions of living in a crumbling rentier society, shedding light on the use and expansion of cryptocurrencies in a nominally socialist country.
P <sup>2</sup> B-Trace

The eruption of a pandemic, such as COVID-19, can cause an unprecedented global crisis. Contact tracing, as a pillar of communicable disease control in public health for decades, has shown its effectiveness on pandemic control. Despite intensive research on contact tracing, existing schemes are vulnerable to attacks and can hardly simultaneously meet the requirements of data integrity and user privacy. The design of a privacy-preserving contact tracing framework to ensure the integrity of the tracing procedure has not been sufficiently studied and remains a challenge. In this paper, we propose P2B-Trace, a privacy-preserving contact tracing initiative based on blockchain. First, we design a decentralized architecture with blockchain to record an authenticated data structure of the user's contact records, which prevents the user from intentionally modifying his local records afterward. Second, we develop a zero-knowledge proximity verification scheme to further verify the user's proximity claim while protecting user privacy. We implement P2B-Trace and conduct experiments to evaluate the cost of privacy-preserving tracing integrity verification. The evaluation results demonstrate the effectiveness of our proposed system.
Class-Incremental Experience Replay for Continual Learning under Concept Drift

Modern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning and data stream mining. Continual learning focuses on accumulating knowledge and avoiding forgetting, assuming information once learned should be stored. Data stream mining focuses on adaptation to concept drift and discarding outdated information, assuming that only the most recent data is relevant. While these two areas are mainly being developed in separation, they offer complementary views on the problem of learning from dynamic data. There is a need for unifying them, by offering architectures capable of both learning and storing new information, as well as revisiting and adapting to changes in previously seen concepts. We propose a novel continual learning approach that can handle both tasks. Our experience replay method is fueled by a centroid-driven memory storing diverse instances of incrementally arriving classes. This is enhanced with a reactive subspace buffer that tracks concept drift occurrences in previously seen classes and adapts clusters accordingly. The proposed architecture is thus capable of both remembering valid and forgetting outdated information, offering a holistic framework for continual learning under concept drift.
Connecting What to Say With Where to Look by Modeling Human Attention Traces

We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation frame-work [31], where each word of a given caption is paired with a mouse trace segment. We propose two novel tasks: (1) predict a trace given an image and caption (i.e., visual grounding), and (2) predict a caption and a trace given only an image. Learning the grounding of each word is challenging, due to noise in the human-provided traces and the presence of words that cannot be meaningfully visually grounded. We present a novel model architecture that is jointly trained on dual tasks (controlled trace generation and controlled caption generation). To evaluate the quality of the generated traces, we propose a local bipartite matching (LBM) distance metric which allows the comparison of two traces of different lengths. Extensive experiments show our model is robust to the imperfect training data and outperforms the baselines by a clear margin. More-over, we demonstrate that our model pre-trained on the pro-posed tasks can be also beneficial to the downstream task of COCO’s guided image captioning. Our code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and project page <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> are publicly available.
Face Mask Detection using Convolutional Neural Network (CNN) to reduce the spread of Covid-19

The COVID-19 coronavirus pandemic is wreaking havoc on the world's health. The healthcare sector is in a state of disaster. Many precautionary steps have been taken to prevent the spread of this disease, including the usage of a mask, which is strongly recommended by the World Health Organization (WHO). In this paper, we used three deep learning methods for face mask detection, including Max pooling, Average pooling, and MobileNetV2 architecture, and showed the methods detection accuracy. A dataset containing 1845 images from various sources and 120 co-author pictures taken with a webcam and a mobile phone camera is used to train a deep learning architecture. The Max pooling achieved 96.49% training accuracy and validation accuracy is 98.67%. Besides, the Average pooling achieved 95.190/0 training accuracy and validation accuracy is 96.23%. MobileNetV2 architecture gained the highest accuracy 99.72% for training and 99.82 % for validation.
NeuNAC: A novel fragile watermarking algorithm for integrity protection of neural networks

The last decade has witnessed a massive deployment of Machine Learning tools in everyday life automated tasks. Neural Networks are nowadays in use in a growing number of application areas because of their excellent performances. Unfortunately, it has been shown by many researchers that they can be attacked and fooled in several different ways, and this can dangerously impair their ability to correctly perform their tasks. In this paper we describe a watermarking algorithm that can protect and verify the integrity of (Deep) Neural Networks when deployed in safety critical systems, such as autonomous driving systems or monitoring and surveillance systems.
A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification

We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Yet their performance pales in comparison to a transfer learning baseline, an alternative approach for learning from a few examples. Furthermore, in the transfer setting, while existing SSL methods provide improvements, the presence of out-of-class is often detrimental. In this setting, standard fine-tuning followed by distillation-based self-training is the most robust. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.
VF <sup>2</sup> Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning

With the ever-evolving concerns on privacy protection, vertical federated learning (FL), where participants own non-overlapping features for the same set of instances, is becoming a heated topic since it enables multiple enterprises to strengthen the machine learning models collaboratively with privacy guarantees. Nevertheless, to achieve privacy preservation, vertical FL algorithms involve complicated training routines and time-consuming cryptography operations, leading to slow training speed.
Improving Accuracy of Binary Neural Networks using Unbalanced Activation Distribution

Binarization of neural network models is considered as one of the promising methods to deploy deep neural network models on resource-constrained environments such as mobile devices. However, Binary Neural Networks (BNNs) tend to suffer from severe accuracy degradation compared to the full-precision counterpart model. Several techniques were proposed to improve the accuracy of BNNs. One of the approaches is to balance the distribution of binary activations so that the amount of information in the binary activations becomes maximum. Based on extensive analysis, in stark contrast to previous work, we argue that unbalanced activation distribution can actually improve the accuracy of BNNs. We also show that adjusting the threshold values of binary activation functions results in the unbalanced distribution of the binary activation, which increases the accuracy of BNN models. Experimental results show that the accuracy of previous BNN models (e.g. XNOR-Net and Bi-Real-Net) can be improved by simply shifting the threshold values of binary activation functions without requiring any other modification.
IQMA Network: Image Quality Multi-scale Assessment Network

Image Quality Assessment (IQA), which aims to provide computational models for automatically predicting perceptual image quality, is an important computer vision task with many applications. In recent years, a variety of IQA methods have been proposed based on different metric de-signs, which measure the quality of images affected by various types of distortion. However, with the rapid development of Generative Adversarial Networks (GAN), a new challenge has been brought to the IQA community. Especially, the GAN-based Image Reconstruction (IR) methods overfit the traditional PSNR-based IQA methods by generating images with sharper edges and texture-like noises, leading the outputs to be similar to the reference image in appearance but with loss of details. In this paper, we propose a bilateral-branch multi-scale image quality estimation network, named IQMA network. The two branches are designed with Feature Pyramid Network (FPN)-like architecture, extracting multi-scale features for patches of the reference image and corresponding patches of the distorted image separately. Then features of the same scale from both branches are sent into several scale-specific feature fusion modules. Each module performs feature fusion and a novelly designed pooling operation for corresponding features. Then several score regression modules are used to learn a quality score for each scale. Finally, image scores for different scales are fused as the quality score of the image. IQMA network has achieved 1st place on the NTIRE 21 IQA public leaderboard and 2nd place on the NTIRE 21 IQA private leaderboard, and consistently outperforms existing state-of-the-art (SOTA) methods on LIVE and TID2013.