Reviewerly/exHarmony huggingface dataset:
Similarity-based Multi-Domain Dialogue State Tracking with Copy Mechanisms for Task-based Virtual Personal Assistants

Task-based Virtual Personal Assistants (VPAs) rely on multi-domain Dialogue State Tracking (DST) models to monitor goals throughout a conversation. Previously proposed models show promising results on established benchmarks, but they have difficulty adapting to unseen domains due to domain-specific parameters in their model architectures. We propose a new Similarity-based Multi-domain Dialogue State Tracking model (SM-DST) that uses retrieval-inspired and fine-grained contextual token-level similarity approaches to efficiently and effectively track dialogue state. The key difference with state-of-the-art DST models is that SM-DST has a single model with shared parameters across domains and slots. Because we base SM-DST on similarity it allows the transfer of tracking information between semantically related domains as well as to unseen domains without retraining. Furthermore, we leverage copy mechanisms that consider the system’s response and the dialogue state from previous turn predictions, allowing it to more effectively track dialogue state for complex conversations. We evaluate SM-DST on three variants of the MultiWOZ DST benchmark datasets. The results demonstrate that SM-DST significantly and consistently outperforms state-of-the-art models across all datasets by absolute 5-18% and 3-25% in the few- and zero-shot settings, respectively.
MTS-Stega: Linguistic Steganography Based on Multi-Time-Step

Generative linguistic steganography encodes candidate words with conditional probability when generating text by language model, and then, it selects the corresponding candidate words to output according to the confidential message to be embedded, thereby generating steganographic text. The encoding techniques currently used in generative text steganography fall into two categories: fixed-length coding and variable-length coding. Because of the simplicity of coding and decoding and the small computational overhead, fixed-length coding is more suitable for resource-constrained environments. However, the conventional text steganography mode selects and outputs a word at one time step, which is highly susceptible to the influence of confidential information and thus may select words that do not match the statistical distribution of the training text, reducing the quality and concealment of the generated text. In this paper, we inherit the decoding advantages of fixed-length coding, focus on solving the problems of existing steganography methods, and propose a multi-time-step-based steganography method, which integrates multiple time steps to select words that can carry secret information and fit the statistical distribution, thus effectively improving the text quality. In the experimental part, we choose the GPT-2 language model to generate the text, and both theoretical analysis and experiments prove the effectiveness of the proposed scheme.
Through the Lens of the Web Conference Series: A Look Into the History of the Web

During the last three decades, the Web has been growing considerably in terms of number of available resources, traffic, types of media, usages, etc. In parallel, with 30+ editions, the WebConf series (ex. WWW, soon-to-be ACM WebConf) has witnessed how academia has been dealing with the Web as an object of research. In this study, we focus on the small story within the great one of the Web. In particular, by analysing the WebConf’s accepted papers and yearly events, we review how the conference has evolved across these decades and “driven” the evolution of the Web.
Modeling and Optimizing the Scaling Performance in Distributed Deep Learning Training

Distributed Deep Learning (DDL) is widely used to accelerate deep neural network training for various Web applications. In each iteration of DDL training, each worker synchronizes neural network gradients with other workers. This introduces communication overhead and degrades the scaling performance. In this paper, we propose a recursive model, OSF (Scaling Factor considering Overlap), for estimating the scaling performance of DDL training of neural network models, given the settings of the DDL system. OSF captures two main characteristics of DDL training: the overlap between computation and communication, and the tensor fusion for batching updates. Measurements on a real-world DDL system show that OSF obtains a low estimation error (ranging from 0.5% to 8.4% for different models). Using OSF, we identify the factors that degrade the scaling performance, and propose solutions to effectively mitigate their impacts. Specifically, the proposed adaptive tensor fusion improves the scaling performance by 32.2%∼ 150% compared to the constant tensor fusion buffer size.
Left or Right: A Peek into the Political Biases in Email Spam Filtering Algorithms During US Election 2020

Email services use spam filtering algorithms (SFAs) to filter emails that are unwanted by the user. However, at times, the emails perceived by an SFA as unwanted may be important to the user. Such incorrect decisions can have significant implications if SFAs treat emails of user interest as spam on a large scale. This is particularly important during national elections. To study whether the SFAs of popular email services have any biases in treating the campaign emails, we conducted a large-scale study of the campaign emails of the US elections 2020 by subscribing to a large number of Presidential, Senate, and House candidates using over a hundred email accounts on Gmail, Outlook, and Yahoo. We analyzed the biases in the SFAs towards the left and the right candidates and further studied the impact of the interactions (such as reading or marking emails as spam) of email recipients on these biases. We observed that the SFAs of different email services indeed exhibit biases towards different political affiliations.
Cloud-Based Hierarchical Consortium Blockchain Networks for Timely Publication and Efficient Retrieval of Electronic Health Records

Blockchain technology is seeing a trend of popularity and adoption in many different application areas.One such area is healthcare, as there is a need to develop a system that can reliably store and share electronic health records (EHRs) among hospital-based health facilities.In this paper, we present a cloud-based hierarchical consortium blockchain framework for storing and sharing EHRs in a scalable, secure, and reliable manner.The framework enables data sharing between local hospital blockchain networks (HBNs) through high-level blockchain networks, namely, city blockchain networks (CBNs) and a state blockchain network (SBN).To support the timely publication of EHRs in HBNs, we adopt a temporary and permanent block scheme in hospital blockchains.In addition, we develop role-based access control (RBAC) policies for data authorization and procedures for concurrent search and retrieval of EHRs across cities and states.The experimental results show that our proposed approach is feasible and supports timely publication and efficient retrieval of EHRs in cloud-based hierarchical blockchain networks.
SoftEdge: Regularizing Graph Classification with Random Soft Edges

Augmented graphs play a vital role in regularizing Graph Neural Networks (GNNs), which leverage information exchange along edges in graphs, in the form of message passing, for learning. Due to their effectiveness, simple edge and node manipulations (e.g., addition and deletion) have been widely used in graph augmentation. Nevertheless, such common augmentation techniques can dramatically change the semantics of the original graph, causing overaggressive augmentation and thus under-fitting in the GNN learning. To address this problem arising from dropping or adding graph edges and nodes, we propose SoftEdge, which assigns random weights to a portion of the edges of a given graph for augmentation. The synthetic graph generated by SoftEdge maintains the same nodes and their connectivities as the original graph, thus mitigating the semantic changes of the original graph. We empirically show that this simple method obtains superior accuracy to popular node and edge manipulation approaches and notable resilience to the accuracy degradation with the GNN depth.
From space to biomedicine: Enabling biomarker data science in the cloud

NASA's Jet Propulsion Laboratory (JPL) is advancing research capabilities for data science with two of the National Cancer Institute's major research programs, the Early Detection Research Network (EDRN) and the Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), by enabling data-driven discovery for cancer biomarker research. The research team pioneered a national data science ecosystem for cancer biomarker research to capture, process, manage, share, and analyze data across multiple research centers. By collaborating on software and data-driven methods developed for space and earth science research, the biomarker research community is heavily leveraging similar capabilities to support the data and computational demands to analyze research data. This includes linking diverse data from clinical phenotypes to imaging to genomics. The data science infrastructure captures and links data from over 1600 annotations of cancer biomarkers to terabytes of analysis results on the cloud in a biomarker data commons known as "LabCAS". As the data increases in size, it is critical that automated approaches be developed to "plug" laboratories and instruments into a data science infrastructure to systematically capture and analyze data directly. This includes the application of artificial intelligence and machine learning to automate annotation and scale science analysis.
Dynamic AI based Email Voice Assistant for Web Services

In recent years, voice assistant has shown significant process and its potential is growing. And billions of devices that incorporates them in domestic nowadays. Then, to communicate with one another, Email is one of the most prevalent way. In this paper, we are targeting to establish an AI based email voice assistant system. The voice assistant listens to the user’s voice input and converts it as a text and then sends it as an email message to the recipient. Firstly, we have to provide user’s email id and its Gmail account’s password to authenticate and this can be executed using Python in PyCharm community IDE. SMTP is the most common and popular protocol of email which means Simple Mail Transfer Protocol. It is used for transmitting email from one account to other over the internet. Protocol means a list of instructions that validate and manage the transfer of email and also it pushes the message from client to the server. MTA ( Message Transfer Agents ) is the client which should be present in the system to send and receive mail.
Quantum Rényi Entropy Functionals for Bosonic Gaussian Systems

In this study, the quantum R\'{e}nyi entropy power inequality of order $p>1$ and power $\kappa$ is introduced as a quantum analog of the classical R\'{e}nyi-$p$ entropy power inequality. To derive this inequality, we first exploit the Wehrl-$p$ entropy power inequality on bosonic Gaussian systems via the mixing operation of quantum convolution, which is a generalized beam-splitter operation. This observation directly provides a quantum R\'{e}nyi-$p$ entropy power inequality over a quasi-probability distribution for $D$-mode bosonic Gaussian regimes. The proposed inequality is expected to be useful for the nontrivial computing of quantum channel capacities, particularly universal upper bounds on bosonic Gaussian quantum channels, and a Gaussian entanglement witness in the case of Gaussian amplifier via squeezing operations.
Cyber Security Risk Management Frameworks Implementation in Malaysian Higher Education Institutions

Implementation of a cybersecurity risk management framework is one of the security management requirements in Malaysian public universities.It is critical to understand an institution's overall security profile.Therefore, this study aims to identify the current practice of cybersecurity risk management in Malaysian institutions of higher learning to address the security defies.This research employs a qualitative approach using a semi-structured interview to evaluate the current frameworks.This study considers the literature review on the cybersecurity risk management framework in Malaysian higher education institutions for data analysis.Thus, NVivo 12 software and thematic analysis were used to analyze the interview transcription from the audio recording.This paper expects to find the list of current frameworks applied in higher education institutions.It allows covering a wide range of cybersecurity risk management problems within the universities operation system.It is hoped that this study will make significant contributions to the cybersecurity risk management lifecycle in Malaysian institutions of higher learning.
Development of fashion recommendation system using collaborative deep learning

Purpose The purpose of this study is to solve the cold start problem caused by the lack of evaluation information about the products. Design/methodology/approach A recommendation system has been developed by using the image data of the clothing products, assuming that the user considers the visual characteristics importantly when purchasing fashion products. In order to evaluate the performance of the model developed in this study, it was compared with Random, Itempop, Matrix Factorization and Generalized Matrix Factorization models. Findings The newly developed model was able to cope with the cold start problem better than other models. Social implications A hybrid recommendation system has been developed that combines the existing recommendation system with deep learning to effectively recommend fashion products considering the user's taste. Originality/value This is the first research to improve the performance of fashion recommendation system using the deep learning model trained by the images of fashion products.
deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks

A lot of Machine Learning (ML) and Deep Learning (DL) research is of an empirical nature. Nevertheless, statistical significance testing (SST) is still not widely used. This endangers true progress, as seeming improvements over a baseline might be statistical flukes, leading follow-up research astray while wasting human and computational resources. Here, we provide an easy-to-use package containing different significance tests and utility functions specifically tailored towards research needs and usability.
SnapMode: An Intelligent and Distributed Large-Scale Fashion Image Retrieval Platform Based On Big Data and Deep Generative Adversarial Network Technologies

Fashion is now among the largest industries worldwide, for it represents human history and helps tell the worlds story. As a result of the Fourth Industrial Revolution, the Internet has become an increasingly important source of fashion information. However, with a growing number of web pages and social data, it is nearly impossible for humans to manually catch up with the ongoing evolution and the continuously variable content in this domain. The proper management and exploitation of big data can pave the way for the substantial growth of the global economy as well as citizen satisfaction. Therefore, computer scientists have found it challenging to handle e-commerce fashion websites by using big data and machine learning technologies. This paper first proposes a scalable focused Web Crawler engine based on the distributed computing platforms to extract and process fashion data on e-commerce websites. The role of the proposed platform is then described in developing a disentangled feature extraction method by employing deep convolutional generative adversarial networks (DCGANs) for content-based image indexing and retrieval. Finally, the state-of-the-art solutions are compared, and the results of the proposed approach are analyzed on a standard dataset. For the real-life implementation of the proposed solution, a Web-based application is developed on Apache Storm, Kafka, Solr, and Milvus platforms to create a fashion search engine called SnapMode.
A Survey on Bias and Fairness in Natural Language Processing

As NLP models become more integrated with the everyday lives of people, it becomes important to examine the social effect that the usage of these systems has. While these models understand language and have increased accuracy on difficult downstream tasks, there is evidence that these models amplify gender, racial and cultural stereotypes and lead to a vicious cycle in many settings. In this survey, we analyze the origins of biases, the definitions of fairness, and how different subfields of NLP mitigate bias. We finally discuss how future studies can work towards eradicating pernicious biases from NLP algorithms.
Natural Language to Code Translation with Execution

Generative models of code, pretrained on large corpora of programs, have shown great success in translating natural language to code (Chen et al., 2021; Austin et al., 2021; Li et al., 2022, inter alia). While these models do not explicitly incorporate program semantics (i.e., execution results) during training, they are able to generate correct solutions for many problems. However, choosing a single correct program from a generated set for each problem remains challenging. In this work, we introduce execution result--based minimum Bayes risk decoding (MBR-EXEC) for program selection and show that it improves the few-shot performance of pretrained code models on natural-language-to-code tasks. We select output programs from a generated candidate set by marginalizing over program implementations that share the same semantics. Because exact equivalence is intractable, we execute each program on a small number of test inputs to approximate semantic equivalence. Across datasets, execution or simulated execution significantly outperforms the methods that do not involve program semantics. We find that MBR-EXEC consistently improves over all execution-unaware selection methods, suggesting it as an effective approach for natural language to code translation. We open-source our code at github.com/facebookresearch/mbr-exec and data at dl.fbaipublicfiles.com/mbr-exec/mbr-exec-release.zip
Faster Pattern Matching under Edit Distance

We consider the approximate pattern matching problem under the edit distance. Given a text $T$ of length $n$, a pattern $P$ of length $m$, and a threshold $k$, the task is to find the starting positions of all substrings of $T$ that can be transformed to $P$ with at most $k$ edits. More than 20 years ago, Cole and Hariharan [SODA'98, J. Comput.'02] gave an $\mathcal{O}(n+k^4 \cdot n/ m)$-time algorithm for this classic problem, and this runtime has not been improved since. Here, we present an algorithm that runs in time $\mathcal{O}(n+k^{3.5} \sqrt{\log m \log k} \cdot n/m)$, thus breaking through this long-standing barrier. In the case where $n^{1/4+\varepsilon} \leq k \leq n^{2/5-\varepsilon}$ for some arbitrarily small positive constant $\varepsilon$, our algorithm improves over the state-of-the-art by polynomial factors: it is polynomially faster than both the algorithm of Cole and Hariharan and the classic $\mathcal{O}(kn)$-time algorithm of Landau and Vishkin [STOC'86, J. Algorithms'89]. We observe that the bottleneck case of the alternative $\mathcal{O}(n+k^4 \cdot n/m)$-time algorithm of Charalampopoulos, Kociumaka, and Wellnitz [FOCS'20] is when the text and the pattern are (almost) periodic. Our new algorithm reduces this case to a new dynamic problem (Dynamic Puzzle Matching), which we solve by building on tools developed by Tiskin [SODA'10, Algorithmica'15] for the so-called seaweed monoid of permutation matrices. Our algorithm relies only on a small set of primitive operations on strings and thus also applies to the fully-compressed setting (where text and pattern are given as straight-line programs) and to the dynamic setting (where we maintain a collection of strings under creation, splitting, and concatenation), improving over the state of the art.
Optimal Security Hardening over a Probabilistic Attack Graph

CySecTool is a tool that finds a cost-optimal security controls portfolio in a given budget for a probabilistic attack graph. A portfolio is a set of counter-measures, or controls, against vulnerabilities adopted for a computer system, while an attack graph is a type of a threat scenario model. In an attack graph, nodes are privilege states of the attacker, edges are vulnerabilities escalating privileges, and controls reduce the probabilities of some vulnerabilities being exploited. The tool builds on an optimisation algorithm published by Khouzani et al., enabling a user to quickly create, edit, and incrementally improve models, analyse results for given portfolios and display the best solutions for all possible budgets in the form of a Pareto frontier. A case study was performed utilising a system graph and suspected attack paths prepared by industrial security engineers based on an industrial source with which they work. The goal of the case study is to model a supervisory control and data acquisition (SCADA) industrial system which, due to having a potential to harm people, necessitates strong protection while not allowing to use of typical penetration tools like vulnerability scanners. Results are analysed to show how a cyber-security analyst would use CySecTool to store cyber-security intelligence and draw further conclusions.
Leveraging the Wikipedia Graph for Evaluating Word Embeddings

Deep learning models for different NLP tasks often rely on pre-trained word embeddings, that is, vector representations of words. Therefore, it is crucial to evaluate pre-trained word embeddings independently of downstream tasks. Such evaluations try to assess whether the geometry induced by a word embedding captures connections made in natural language, such as, analogies, clustering of words, or word similarities. Here, traditionally, similarity is measured by comparison to human judgment. However, explicitly annotating word pairs with similarity scores by surveying humans is expensive. We tackle this problem by formulating a similarity measure that is based on an agent for routing the Wikipedia hyperlink graph. In this graph, word similarities are implicitly encoded by edges between articles. We show on the English Wikipedia that our measure correlates well with a large group of traditional similarity measures, while covering a much larger proportion of words and avoiding explicit human labeling. Moreover, since Wikipedia is available in more than 300 languages, our measure can easily be adapted to other languages, in contrast to traditional similarity measures.
Establishing trust in quantum computations

Real-world quantum computers have grown sufficiently complex that they can no longer be simulated by classical supercomputers, but their computational power remains limited by errors. These errors corrupt the results of quantum algorithms, and it is no longer always feasible to use classical simulations to directly check the correctness of quantum computations. Without practical methods for quantifying the accuracy with which a quantum algorithm has been executed, it is difficult to establish trust in the results of a quantum computation. Here we solve this problem, by introducing a simple and efficient technique for measuring the fidelity with which an as-built quantum computer can execute an algorithm. Our technique converts the algorithm's quantum circuits into a set of closely related circuits whose success rates can be efficiently measured. It enables measuring the fidelity of quantum algorithm executions both in the near-term, with algorithms run on hundreds or thousands of physical qubits, and into the future, with algorithms run on logical qubits protected by quantum error correction.