Reviewerly/exHarmony huggingface dataset:
On the Fundamental Limits of Coded Data Shuffling for Distributed Machine Learning

We consider the data shuffling problem in a distributed learning system, in which a master node is connected to a set of worker nodes, via a shared link, in order to communicate a set of files to the worker nodes. The master node has access to a database of files. In every shuffling iteration, each worker node processes a new subset of files, and has excess storage to partially cache the remaining files, assuming the cached files are uncoded. The caches of the worker nodes are updated every iteration, and they should be designed to satisfy any possible unknown permutation of the files in subsequent iterations. For this problem, we characterize the exact load-memory trade-off for worst-case shuffling by deriving the minimum communication load for a given storage capacity per worker node. As a byproduct, the exact load-memory trade-off for any shuffling is characterized when the number of files is equal to the number of worker nodes. We propose a novel deterministic coded shuffling scheme, which improves the state of the art, by exploiting the cache memories to create coded functions that can be decoded by several worker nodes. Then, we prove the optimality of our proposed scheme by deriving a matching lower bound and showing that the placement phase of the proposed coded shuffling scheme is optimal over all shuffles.
A Cryptoeconomic Traffic Analysis of Bitcoin’s Lightning Network

Lightning Network (LN) is designed to amend the scalability and privacy issues of Bitcoin.It is a payment channel network where Bitcoin transactions are issued off the blockchain and onion routed through a private payment path with the aim to settle transactions in a faster, cheaper, and more private manner, as they are not recorded in a costly-to-maintain, slow, and public ledger.In this work, we design a traffic simulator to empirically study LN's transaction fees and privacy provisions.The simulator relies only on publicly-available data of the network structure and capacities, and generates transactions under assumptions that we attempt to validate based on information spread by certain blog posts of LN node owners.Our findings on the estimated revenue from transaction fees are in line with the widespread opinion that participation is economically irrational for the majority of the large routing nodes who currently hold the network together.Either traffic or transaction fees must increase by orders of magnitude to make payment routing economically viable.We give worst-case estimates for the potential fee increase by assuming strong price competition among the routers.We also estimate how current channel structures and pricing policies respond to a potential increase in traffic, how reduction in locked funds on channels would affect the network, and show examples of nodes who are estimated to operate with economically feasible revenue.Our second set of findings considers privacy.Even if transactions are onion routed, strong statistical evidence on payment source and destination can be inferred, as many transaction paths only consist of a single intermediary by the side effect of LN's small-world nature.Based on our simulation experiments, we (1) quantitatively characterize the privacy shortcomings of current LN operation; and(2) propose a method to inject additional hops in routing paths to demonstrate how privacy can be strengthened with very little additional transactional cost.
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

Convex relaxations have emerged as a promising approach for verifying desirable properties of neural networks like robustness to adversarial perturbations. Widely used Linear Programming (LP) relaxations only work well when networks are trained to facilitate verification. This precludes applications that involve verification-agnostic networks, i.e., networks not specially trained for verification. On the other hand, semidefinite programming (SDP) relaxations have successfully be applied to verification-agnostic networks, but do not currently scale beyond small networks due to poor time and space asymptotics. In this work, we propose a first-order dual SDP algorithm that (1) requires memory only linear in the total number of network activations, (2) only requires a fixed number of forward/backward passes through the network per iteration. By exploiting iterative eigenvector methods, we express all solver operations in terms of forward and backward passes through the network, enabling efficient use of hardware like GPUs/TPUs. For two verification-agnostic networks on MNIST and CIFAR-10, we significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively. We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
Exploiting Vulnerabilities of Deep Neural Networks for Privacy Protection

Adversarial perturbations can be added to images to protect their content from unwanted inferences. These perturbations may, however, be ineffective against classifiers that were not {seen} during the generation of the perturbation, or against defenses {based on re-quantization, median filtering or JPEG compression. To address these limitations, we present an adversarial attack {that is} specifically designed to protect visual content against { unseen} classifiers and known defenses. We craft perturbations using an iterative process that is based on the Fast Gradient Signed Method and {that} randomly selects a classifier and a defense, at each iteration}. This randomization prevents an undesirable overfitting to a specific classifier or defense. We validate the proposed attack in both targeted and untargeted settings on the private classes of the Places365-Standard dataset. Using ResNet18, ResNet50, AlexNet and DenseNet161 {as classifiers}, the performance of the proposed attack exceeds that of eleven state-of-the-art attacks. The implementation is available at https://github.com/smartcameras/RP-FGSM/.
Sentiment Analysis on Bangla Text Using Long Short-Term Memory (LSTM) Recurrent Neural Network

Sentiment analysis has become an important source in people’s trust. Search engine statistics and the local survey show 92% of consumers trust online reviews as much as personal recommendations. In this paper, classification of different emotions within the text using advanced text analysis techniques has been proposed. This work intends to deploy long short-term memory (LSTM) deep recurrent network for sentiment analysis on Bangla text as it is developed to avoid long-term dependency. A small dataset of Bangla sentences has been developed and stratified. This work is going to show the effects of hyperparameter tuning and the ways it can be helpful for sentiment analysis on the dataset. The addressed approach is useful for analyzing common expressions, like positive or negative. The goal of this study is to establish a sentiment classification framework to analyze the performance of different deep learning models with a variety of parameter calibration combinations. The proposed LSTM model with advanced layers achieves better performance on resolving sentiment polarity for aimed entities with an accuracy of 94%. This result intends to help psychologists and researchers to identify customer sentiment toward products, detect emotions of individuals from their social activities in the virtual world, and help them to take necessary steps to prevent undesirable doings.
Experimental Evaluation of Deep Learning Models for Marathi Text Classification

The Marathi language is one of the prominent languages used in India. It is predominantly spoken by the people of Maharashtra. Over the past decade, the usage of language on online platforms has tremendously increased. However, research on Natural Language Processing (NLP) approaches for Marathi text has not received much attention. Marathi is a morphologically rich language and uses a variant of the Devanagari script in the written form. This works aims to provide a comprehensive overview of available resources and models for Marathi text classification. We evaluate CNN, LSTM, ULMFiT, and BERT based models on two publicly available Marathi text classification datasets and present a comparative analysis. The pre-trained Marathi fast text word embeddings by Facebook and IndicNLP are used in conjunction with word-based models. We show that basic single layer models based on CNN and LSTM coupled with FastText embeddings perform on par with the BERT based models on the available datasets. We hope our paper aids focused research and experiments in the area of Marathi NLP.
Babylonian-style Programming: Design and Implementation of an Integration of Live Examples into General-purpose Source Code

When working on a program, developers traditionally have to simulate the behavior of the abstract code in their heads until they can execute the application. Live programming aims to support the development and comprehension of programs by providing more immediate feedback on program behavior, but the divide between code and behavior often remains. The goal of example-based live programming is to remove this gap by allowing programmers to explore the actual behavior of their code during development. This is achieved by defining live examples for parts of the application. The idea of live examples has been already addressed in other tools and environments. However, most of those solutions are limited to specific domains and are suitable only for small programs. Thus, we aim to extend the application of example-based live programming to more complex programs potentially spanning multiple modules. We investigate existing solutions to derive a set of requirements for an integration of live examples into source code. Based on these requirements we propose a new approach to live examples and present a prototype in its support. We reproduce, discuss, and extend scenarios from related work to show the practicality of our approach in the context of larger, more complicated, and with that also more realistic scenarios. Also, we measure and evaluate the system response time of our prototypical implementation. Our first results show that example-based live programming can provide more insights into the run-time behavior of parameterized code for non-trivial programs. They also reveal unsolved and new challenges affecting example-based live programming environments. In presenting this more general approach to example-based live programming, we hope to motivate further research into this area and to make practical solutions available.
A Blockchain-based Iterative Double Auction Protocol Using Multiparty State Channels

Although the iterative double auction has been widely used in many different applications, one of the major problems in its current implementations is that they rely on a trusted third party to handle the auction process. This imposes the risk of single point of failures, monopoly, and bribery. In this article, we aim to tackle this problem by proposing a novel decentralized and trustless framework for iterative double auction based on blockchain. Our design adopts the smart contract and state channel technologies to enable a double auction process among parties that do not need to trust each other, while minimizing the blockchain transactions. In specific, we propose an extension to the original concept of state channels that can support multiparty computation. Then, we provide a formal development of the proposed framework and prove the security of our design against adversaries. Finally, we develop a proof-of-concept implementation of our framework using Elixir and Solidity, on which we conduct various experiments to demonstrate its feasibility and practicality.
Classification of Histopathological Images for Early Detection of Breast Cancer Using Deep Learning

ABSTRACTBreast cancer is one of the most common and deadly types of cancer that develops in the breast tissue of women worldwide. This is why researchers and experts are interested in developing a computer-aided diagnostic system (CAD) for diagnosing histopathological images of breast cancer. CAD has contributed to increasing the diagnostic accuracy of the biopsy tissue using eosin stained and hematoxylin images. Most CAD systems have used traditional methods to extract handcrafted features, which are imprecise in diagnosis and time-consuming. The diagnostics by both CAD and the calculations are used to reduce the pathologist’s workload and improve accuracy. In this study, the proposed convolutional neural network (AlexNet) approach to extract the deepest features from the BreaKHis dataset to diagnose breast cancer as either benign or malignant. In the current proposal, the study performed four experiments according to a magnification factor (40X, 100X, 200X and 400X). Each experiment contains 1407 images. The network was trained and validated on 80 % tissue images and 20 % for testing. The proposed system obtained accuracy, sensitivity, specificity, and AUC, 95 %, 97 %, 90 % and 99.36 % respectively.
Skeletal semantics and their interpretations

The development of mechanised language specification based on structured operational semantics, with applications to verified compilers and sound program analysis, requires huge effort. General theory and frameworks have been proposed to help with this effort. However, none of this work provides a systematic way of developing concrete and abstract semantics, connected together by a general consistency result. We introduce a skeletal semantics of a language, where each skeleton describes the complete semantic behaviour of a language construct. We define a general notion of interpretation , which provides a systematic and language-independent way of deriving semantic judgements from the skeletal semantics. We explore four generic interpretations: a simple well-formedness interpretation; a concrete interpretation; an abstract interpretation; and a constraint generator for flow-sensitive analysis. We prove general consistency results between interpretations, depending only on simple language-dependent lemmas. We illustrate our ideas using a simple While language.
Website Fingerprinting in the Age of QUIC

Abstract With the meteoric rise of the QUIC protocol, the supremacy of TCP as the de facto transport protocol underlying web traffic will soon cease. HTTP/3, the next version of the HTTP protocol, will not support TCP. Current website-fingerprinting literature has ignored the introduction of this new protocol to all modern browsers. In this work, we investigate whether classifiers trained in the TCP setting generalise to QUIC traces, whether QUIC is inherently more difficult to fingerprint than TCP, how feature importance changes between these protocols, and how to jointly classify QUIC and TCP traces. Experiments using four state-of-theart website-fingerprinting classifiers and our combined QUIC-TCP dataset of ~117,000 traces show that while QUIC is not inherently more difficult to fingerprint than TCP, TCP-trained classifiers may fail to detect up to 96% of QUIC visits to monitored URLs. Furthermore, classifiers that take advantage of the common information between QUIC and TCP traces for the same URL may outperform ensembles of protocol-specific classifiers in limited data settings.
Competitive Interactive Video Retrieval in Virtual Reality with vitrivr-VR

Virtual Reality (VR) has emerged and developed as a new modality to interact with multimedia data. In this paper, we present vitrivr-VR, a prototype of an interactive multimedia retrieval system in VR based on the open source full-stack multimedia retrieval system vitrivr. We have implemented query formulation tailored to VR: Users can use speech-to-text to search collections via text for concepts, OCR and ASR data as well as entire scene descriptions through a video-text co-embedding feature that embeds sentences and video sequences into the same feature space. Result presentation and relevance feedback in vitrivr-VR leverages the capabilities of virtual spaces.
Unitary-coupled restricted Boltzmann machine ansatz for quantum simulations

Abstract Neural-network quantum state (NQS) has attracted significant interests as a powerful wave-function ansatz to model quantum phenomena. In particular, a variant of NQS based on the restricted Boltzmann machine (RBM) has been adapted to model the ground state of spin lattices and the electronic structures of small molecules in quantum devices. Despite these progresses, significant challenges remain with the RBM-NQS-based quantum simulations. In this work, we present a state-preparation protocol to generate a specific set of complex-valued RBM-NQS, which we name the unitary-coupled RBM-NQS, in quantum circuits. Our proposal expands the applicability of NQS as prior works deal exclusively with real-valued RBM-NQS for quantum algorithms. With this scheme, we achieve (1) modeling complex-valued wave functions, (2) using as few as one ancilla qubit to simulate M hidden spins in an RBM architecture, and (3) avoiding post-selections to improve scalability.
Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement

We propose the Recursive Non-autoregressive Graph-to-Graph Transformer architecture (RNGTr) for the iterative refinement of arbitrary graphs through the recursive application of a non-autoregressive Graph-to-Graph Transformer and apply it to syntactic dependency parsing. We demonstrate the power and effectiveness of RNGTr on several dependency corpora, using a refinement model pre-trained with BERT. We also introduce Syntactic Transformer (SynTr), a non-recursive parser similar to our refinement model. RNGTr can improve the accuracy of a variety of initial parsers on 13 languages from the Universal Dependencies Treebanks, English and Chinese Penn Treebanks, and the German CoNLL2009 corpus, even improving over the new state-of-the-art results achieved by SynTr, significantly improving the state-of-the-art for all corpora tested.
An Automated Microscopic Malaria Parasite Detection System Using Digital Image Analysis

Rapid diagnosis and parasitemia measurement is crucial for management of malaria. Microscopic examination of peripheral blood (PB) smears is the gold standard for malaria detection. However, this method is labor-intensive. Here, we aimed to develop a completely automated microscopic system for malaria detection and parasitemia measurement. The automated system comprises a microscope, plastic chip, fluorescent dye, and an image analysis program. Analytical performance was evaluated regarding linearity, precision, and limit of detection and was compared with that of conventional microscopic PB smear examination and flow cytometry. The automated microscopic malaria parasite detection system showed a high degree of linearity for Plasmodium falciparum culture (R2 = 0.958, p = 0.005) and Plasmodium vivax infected samples (R2 = 0.931, p = 0.008). Precision was defined as the %CV of the assay results at each level of parasitemia and the %CV value for our system was lower than that for microscopic examination for all densities of parasitemia. The limit of detection analysis showed 95% probability for parasite detection was 0.00066112%, and a high correlation was observed among all three methods. The sensitivity and specificity of the system was both 100% (n = 21/21) and 100% (n = 50/50), respectively, and the system correctly identified all P. vivax and P. falciparum samples. The automated microscopic malaria parasite detection system offers several advantages over conventional microscopy for rapid diagnosis and parasite density monitoring of malaria.
Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering

Question answering over knowledge bases (KBQA) usually involves three sub-tasks, namely topic entity detection, entity linking and relation detection.Due to the large number of entities and relations inside knowledge bases (KB), previous work usually utilized sophisticated rules to narrow down the search space and managed only a subset of KBs in memory.In this work, we leverage a retrieveand-rerank framework to access KBs via traditional information retrieval (IR) method, and re-rank retrieved candidates with more powerful neural networks such as the pre-trained BERT model.Considering the fact that directly assigning a different BERT model for each sub-task may incur prohibitive costs, we propose to share a BERT encoder across all three sub-tasks and define task-specific layers on top of the shared layer.The unified model is then trained under a multi-task learning framework.Experiments show that: (1) Our IRbased retrieval method is able to collect highquality candidates efficiently, thus enables our method adapt to large-scale KBs easily; (2) the BERT model improves the accuracy across all three sub-tasks; and (3) benefiting from multitask learning, the unified model obtains further improvements with only 1/3 of the original parameters.Our final model achieves competitive results on the SimpleQuestions dataset and superior performance on the FreebaseQA dataset.
Prospects for the introduction of artificial intelligence technologies in higher education

At present, comprehensive research, from various points of view (social, economic, ethical, legal), devoted to the implementation of artificial intelligence technologies in various spheres of society, is of particular relevance. It is important to identify opportunities and threats, to determine the limits of the use of artificial intelligence in practice. The article is devoted to the implementation of artificial intelligence technologies in the field of higher education. The perspective directions of using artificial intelligence in the field of higher education are considered and analyzed. The authors come to the conclusion that artificial intelligence is not at all a competitor to the teaching staff, either in teaching or in evaluating students knowledge. Artificial intelligence is an auxiliary, but valuable tool that can perform and improve a large number of different operations carried out at the university, help in organizing an effective educational process and building the necessary communications. The effective use of AI technologies in higher education will allow the selection of the most optimal learning strategy, adapted to the individual abilities and needs of students and the needs of the labor market.
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic data usually consists of outliers and regular instances with clear characteristics and thus allows for a more meaningful evaluation of detection methods in principle. Nonetheless, there have only been few attempts to include synthetic data in benchmarks for outlier detection. This might be due to the imprecise notion of outliers or to the difficulty to arrive at a good coverage of different domains with synthetic data. In this work, we propose a generic process for the generation of datasets for such benchmarking. The core idea is to reconstruct regular instances from existing real-world benchmark data while generating outliers so that they exhibit insightful characteristics. We propose and describe a generic process for the benchmarking of unsupervised outlier detection, as sketched so far. We then describe three instantiations of this generic process that generate outliers with specific characteristics, like local outliers. To validate our process, we perform a benchmark with state-of-the-art detection methods and carry out experiments to study the quality of data reconstructed in this way. Next to showcasing the workflow, this confirms the usefulness of our proposed process. In particular, our process yields regular instances close to the ones from real data. Summing up, we propose and validate a new and practical process for the benchmarking of unsupervised outlier detection.