Reviewerly/exHarmony huggingface dataset:
Qraft: reverse your Quantum circuit and know the correct program output

Current Noisy Intermediate-Scale Quantum (NISQ) computers are useful in developing the quantum computing stack, test quantum algorithms, and establish the feasibility of quantum computing. However, different statistically significant errors permeate NISQ computers. To reduce the effect of these errors, recent research has focused on effective mapping of a quantum algorithm to a quantum computer in an error-and-constraints-aware manner. We propose the first work, QRAFT, to leverage the reversibility property of quantum algorithms to considerably reduce the error beyond the reduction achieved by effective circuit mapping.
Explaining Deep Learning-Based Driver Models

Different systems based on Artificial Intelligence (AI) techniques are currently used in relevant areas such as healthcare, cybersecurity, natural language processing, and self-driving cars. However, many of these systems are developed with “black box” AI, which makes it difficult to explain how they work. For this reason, explainability and interpretability are key factors that need to be taken into consideration in the development of AI systems in critical areas. In addition, different contexts produce different explainability needs which must be met. Against this background, Explainable Artificial Intelligence (XAI) appears to be able to address and solve this situation. In the field of automated driving, XAI is particularly needed because the level of automation is constantly increasing according to the development of AI techniques. For this reason, the field of XAI in the context of automated driving is of particular interest. In this paper, we propose the use of an explainable intelligence technique in the understanding of some of the tasks involved in the development of advanced driver-assistance systems (ADAS). Since ADAS assist drivers in driving functions, it is essential to know the reason for the decisions taken. In addition, trusted AI is the cornerstone of the confidence needed in this research area. Thus, due to the complexity and the different variables that are part of the decision-making process, this paper focuses on two specific tasks in this area: the detection of emotions and the distractions of drivers. The results obtained are promising and show the capacity of the explainable artificial techniques in the different tasks of the proposed environments.
Hulls of Generalized Reed-Solomon Codes via Goppa Codes and Their Applications to Quantum Codes

A Goppa code over \Bbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q<sup>m</sup></sub> is a well-known subclass of algebraic error-correcting code. If m=1, then it is a generalized Reed-Solomon(GRS) code and its dual code is called a GRS code via a Goppa code. In this paper, we give a necessary and sufficient condition that the dual codes of GRS codes via (expurgated) Goppa codes are also GRS codes via Goppa codes. Under the above condition, we show that the hulls of GRS codes via Goppa codes are still GRS codes via Goppa codes. As an application, we characterize LCD GRS codes and self-dual GRS codes under the above condition. Some numerical examples are also presented to illustrate our main results. Moreover, we also apply our result to entanglement-assisted quantum error correcting codes (EAQECCs) and obtain two new families of MDS EAQECCs with arbitrary parameters.
“Short is the Road that Leads from Fear to Hate”: Fear Speech in Indian WhatsApp Groups

WhatsApp is the most popular messaging app in the world. Due to its popularity, WhatsApp has become a powerful and cheap tool for political campaigning being widely used during the 2019 Indian general election, where it was used to connect to the voters on a large scale. Along with the campaigning, there have been reports that WhatsApp has also become a breeding ground for harmful speech against various protected groups and religious minorities. Many such messages attempt to instil fear among the population about a specific (minority) community. According to research on inter-group conflict, such ‘fear speech’ messages could have a lasting impact and might lead to real offline violence. In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. We curate a new dataset and try to characterize fear speech from this dataset. We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community. We build models to classify fear speech and observe that current state-of-the-art NLP models do not perform well at this task. Fear speech messages tend to spread faster and could potentially go undetected by classifiers built to detect traditional toxic speech due to their low toxic nature. Finally, using a novel methodology to target users with Facebook ads, we conduct a survey among the users of these WhatsApp groups to understand the types of users who consume and share fear speech. We believe that this work opens up new research questions that are very different from tackling hate speech which the research community has been traditionally involved in. We have made our code and dataset public for other researchers.
A Graph-Enhanced Click Model for Web Search

To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback. Most traditional click models are based on the probabilistic graphical model (PGM) framework, which requires manually designed dependencies and may oversimplify user behaviors. Recently, methods based on neural networks are proposed to improve the prediction accuracy of user behaviors by enhancing the expressive ability and allowing flexible dependencies. However, they still suffer from the data sparsity and cold-start problems. In this paper, we propose a novel graph-enhanced click model (GraphCM) for web search. Firstly, we regard each query or document as a vertex, and propose novel homogeneous graph construction methods for queries and documents respectively, to fully exploit both intra-session and inter-session information for the sparsity and cold-start problems. Secondly, following the examination hypothesis, we separately model the attractiveness estimator and examination predictor to output the attractiveness scores and examination probabilities, where graph neural networks and neighbor interaction techniques are applied to extract the auxiliary information encoded in the pre-constructed homogeneous graphs. Finally, we apply combination functions to integrate examination probabilities and attractiveness scores into click predictions. Extensive experiments conducted on three real-world session datasets show that GraphCM not only outperforms the state-of-art models, but also achieves superior performance in addressing the data sparsity and cold-start problems.
A Radio Anomaly Detection Algorithm Based on Modified Generative Adversarial Network

Detecting ever increasing anomalous signals is critical to effective spectrum management. In this letter, we present a radio anomaly detection algorithm based on modified generative adversarial network (GAN). Firstly, short time fourier transform (STFT) is applied to obtain the spectrogram image from the received signal. Then, a novel encoder-GAN (E-GAN) structure is proposed by incorporating an encoder network into the original GAN to reconstruct the spectrogram. As a result, the existence of anomalies can be detected based on the reconstruction error and discriminator loss. In addition, the reconstruction error can also be exploited to locate the anomalies in time-frequency domain. Simulation results show that the proposed algorithm brings a performance improvement of up to 10 dB compared with the spectrum anomaly detector with interpretable features (SAIFE).
Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback

We study the task of conversational fashion image retrieval via multiturn natural language feedback. Most previous studies are based on single-turn settings. Existing models on multiturn conversational fashion image retrieval have limitations, such as employing traditional models, and leading to ineffective performance. We propose a novel framework that can effectively handle conversational fashion image retrieval with multiturn natural language feedback texts. One characteristic of the framework is that it searches for candidate images based on exploitation of the encoded reference image and feedback text information together with the conversation history. Furthermore, the image fashion attribute information is leveraged via a mutual attention strategy. Since there is no existing fashion dataset suitable for the multiturn setting of our task, we derive a large-scale multiturn fashion dataset via additional manual annotation efforts on an existing single-turn dataset. The experiments show that our proposed model significantly outperforms existing state-of-the-art methods.
Research on parallelization of Apriori algorithm in association rule mining

Aiming at the performance bottleneck of traditional Apriori algorithm when the data set is slightly large, this paper adopts the idea of parallelization and improves the Apriori algorithm based on MapReduce model. Firstly, the local frequent itemsets on each sub node in the cluster are calculated, then all the local frequent itemsets are merged into the global candidate itemsets, and finally, the frequent itemsets that meet the conditions are filtered according to the minimum support threshold. The advantage of the improved algorithm is that it only needs to scan the transaction database twice and calculate the frequent item set in parallel, which improves the efficiency of the algorithm.
Modeling Intent Graph for Search Result Diversification

Search result diversification aims to offer diverse documents that cover as many intents as possible. Most existing implicit diversification approaches model diversity through the similarity of document representation, which is indirect and unnatural. To handle the diversity more precisely, we measure the similarity of documents by their similarity of the intent coverage. Specifically, we build a classifier to judge whether two different documents contain the same intent based on the document's content. Then we construct an intent graph to present the complicated relationship of documents and the query. On the intent graph, documents are connected if they are similar, while the query and the document are gradually connected based on the document selection result. Then we employ graph convolutional networks (GCNs) to update the representation of the query and each document by aggregating its neighbors. By this means, we can obtain the context-aware query representation and the intent-aware document representations through the dynamic intent graph during the document selection process. Furthermore, these representations and intent graph features are fused into diversity features. Combined with the traditional relevance features, we obtain the final ranking score that balances the relevance and the diversity. Experimental results show that this implicit diversification model significantly outperforms all existing implicit diversification methods, and it can even beat the state-of-the-art explicit models.
Incentive Mechanism Design for Federated Learning: Challenges and Opportunities

Federated learning is a new distributed machine learning paradigm that many clients (e.g., mobile devices or organizations) collaboratively train a model under the orchestration of a parameter server (e.g., service provider), while keeping the training data locally. One of the main challenges in federated learning is the data island, that is, each client maintains its local data and has no incentive for contributing data to model training if no reward is granted. Thus, we must motivate a large number of clients to participate in federated learning to break the limitation of data in the form of isolated islands. We discuss the fundamental research challenges in the incentive mechanism design for federated learning, and present a general framework with potential solutions to the challenges. Experiments are conducted to verify the effectiveness of the proposed framework. With several future research directions identified in incentive mechanism design for federated learning, we expect that more research interest will be stimulated in this novel area.
Construction of an S-Box Based on Chaotic and Bent Functions

An S-box is the most important part of a symmetric encryption algorithm. Various schemes are put forward by using chaos theory. In this paper, a construction method of S-boxes with good cryptographic properties is proposed. The output of an S-box can be regarded as a group of Boolean functions. Therefore, we can use the different properties of chaos and Bent functions to generate a random Bent function with a high nonlinearity. By constructing a set of Bent functions as the output of an S-box, we can create an S-box with good cryptological properties. The nonlinearity, differential uniformity, strict avalanche criterion and the independence criterion of output bits are then analyzed and tested. A security analysis shows that the proposed S-box has excellent cryptographic properties.
User Responses to a Humanoid Robot Observed in Real Life, Virtual Reality, 3D and 2D

Humanoid robots (i.e., robots with a human-like body) are projected to be mass marketed in the future in several fields of application. Today, however, user evaluations of humanoid robots are often based on mediated depictions rather than actual observations or interactions with a robot, which holds true not least for scientific user studies. People can be confronted with robots in various modes of presentation, among them (1) 2D videos, (2) 3D, i.e., stereoscopic videos, (3) immersive Virtual Reality (VR), or (4) live on site. A systematic investigation into how such differential modes of presentation influence user perceptions of a robot is still lacking. Thus, the current study systematically compares the effects of different presentation modes with varying immersive potential on user evaluations of a humanoid service robot. Participants ( N = 120) observed an interaction between a humanoid service robot and an actor either on 2D or 3D video, via a virtual reality headset (VR) or live. We found support for the expected effect of the presentation mode on perceived immediacy. Effects regarding the degree of human likeness that was attributed to the robot were mixed. The presentation mode had no influence on evaluations in terms of eeriness, likability, and purchase intentions. Implications for empirical research on humanoid robots and practice are discussed.
Outlier-Resilient Web Service QoS Prediction

The proliferation of Web services makes it difficult for users to select the most appropriate one among numerous functionally identical or similar service candidates. Quality-of-Service (QoS) describes the non-functional characteristics of Web services, and it has become the key differentiator for service selection. However, users cannot invoke all Web services to obtain the corresponding QoS values due to high time cost and huge resource overhead. Thus, it is essential to predict unknown QoS values. Although various QoS prediction methods have been proposed, few of them have taken outliers into consideration, which may dramatically degrade the prediction performance. To overcome this limitation, we propose an outlier-resilient QoS prediction method in this paper. Our method utilizes Cauchy loss to measure the discrepancy between the observed QoS values and the predicted ones. Owing to the robustness of Cauchy loss, our method is resilient to outliers. We further extend our method to provide time-aware QoS prediction results by taking the temporal information into consideration. Finally, we conduct extensive experiments on both static and dynamic datasets. The results demonstrate that our method is able to achieve better performance than state-of-the-art baseline methods.
Drawing Reproducible Conclusions from Observational Clinical Data with OHDSI

The current observational research literature shows extensive publication bias and contradiction. The Observational Health Data Sciences and Informatics (OHDSI) initiative seeks to improve research reproducibility through open science.OHDSI has created an international federated data source of electronic health records and administrative claims that covers nearly 10% of the world's population. Using a common data model with a practical schema and extensive vocabulary mappings, data from around the world follow the identical format. OHDSI's research methods emphasize reproducibility, with a large-scale approach to addressing confounding using propensity score adjustment with extensive diagnostics; negative and positive control hypotheses to test for residual systematic error; a variety of data sources to assess consistency and generalizability; a completely open approach including protocol, software, models, parameters, and raw results so that studies can be externally verified; and the study of many hypotheses in parallel so that the operating characteristics of the methods can be assessed.OHDSI has already produced findings in areas like hypertension treatment that are being incorporated into practice, and it has produced rigorous studies of COVID-19 that have aided government agencies in their treatment decisions, that have characterized the disease extensively, that have estimated the comparative effects of treatments, and that the predict likelihood of advancing to serious complications.OHDSI practices open science and incorporates a series of methods to address reproducibility. It has produced important results in several areas, including hypertension therapy and COVID-19 research.
SDFVAE: Static and Dynamic Factorized VAE for Anomaly Detection of Multivariate CDN KPIs

Content Delivery Networks (CDNs) are critical for providing good user experience of cloud services. CDN providers typically collect various multivariate Key Performance Indicators (KPIs) time series to monitor and diagnose system performance. State-of-the-art anomaly detection methods mostly use deep learning to extract the normal patterns of data, due to its superior performance. However, KPI data usually exhibit non-additive Gaussian noise, which makes it difficult for deep learning models to learn the normal patterns, resulting in degraded performance in anomaly detection. In this paper, we propose a robust and noise-resilient anomaly detection mechanism using multivariate KPIs. Our key insight is that different KPIs are constrained by certain time-invariant characteristics of the underlying system, and that explicitly modelling such invariance may help resist noise in the data. We thus propose a novel anomaly detection method called SDFVAE, short for Static and Dynamic Factorized VAE, that learns the representations of KPIs by explicitly factorizing the latent variables into dynamic and static parts. Extensive experiments using real-world data show that SDFVAE achieves a F1-score ranging from 0.92 to 0.99 on both regular and noisy dataset, outperforming state-of-the-art methods by a large margin.
BAT.jl: A Julia-Based Tool for Bayesian Inference

Abstract We describe the development of a multi-purpose software for Bayesian statistical inference, BAT.jl, written in the Julia language. The major design considerations and implemented algorithms are summarized here, together with a test suite that ensures the proper functioning of the algorithms. We also give an extended example from the realm of physics that demonstrates the functionalities of BAT.jl.
Relative Feature Importance

Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ in whether they consider features of interest in isolation, e.g., Permutation Feature Importance (PFI), or in relation to all remaining feature variables, e.g., Conditional Feature Importance (CFI). As such, the perturbation mechanisms inherent to PFI and CFI represent extreme reference points. We introduce Relative Feature Importance (RFI), a generalization of PFI and CFI that allows for a more nuanced feature importance computation beyond the PFI versus CFI dichotomy. With RFI, the importance of a feature relative to any other subset of features can be assessed, including variables that were not available at training time. We derive general interpretation rules for RFI based on a detailed theoretical analysis of the implications of relative feature relevance, and demonstrate the method's usefulness on simulated examples.
Blockchain-Based Cloud Data Integrity Verification Scheme with High Efficiency

With the large-scale application of cloud storage, how to ensure cloud data integrity has become an important issue. Although many methods have been proposed, they still have their limitations. This paper improves some defects of the previous methods and proposes an efficient cloud data integrity verification scheme based on blockchain. In this paper, we proposed a lattice signature algorithm to resist quantum computing and introduced cuckoo filter to simplify the computational overhead of the user verification phase. Finally, the decentralized blockchain network is introduced to replace traditional centralized audit to publicize and authenticate the verification results, which improves the transparency and the security of this scheme. Security analysis shows that our scheme can resist malicious attacks and experimental results show that our scheme has high efficiency, especially in the user verification phase.
Synthesis of 3D MRI Brain Images With Shape and Texture Generative Adversarial Deep Neural Networks

Generative Adversarial Networks (GAN) are emerging as an exciting training paradigm which promises a step improvement to the impressive feature learning capabilities of deep neural networks. Unlike supervised learning approaches, GAN learns generalizable features without requiring labeled images to achieve new capabilities like distinguishing previously unseen anomalies, creating novel instances of data and factorizing learned features into explainable dimensions in fully unsupervised fashion. The advanced feature learning property of GAN will enable the next generation of computational image understanding tasks. However, GAN models are difficult to train to converge towards good models, especially for high resolution and high dimensional datasets like image volumes. We develop a GAN approach to learn a generative model of T1-contrast 3D MRI image volumes of the healthy human brain by training on 1112 MRI images from the Human Connectome Project. Our method utilizes a first unconditional Super-Resolution GAN, dubbed the shape network, to learn the 3D shape variations in adult brains and a second conditional pix2pix GAN, dubbed the texture network, to upgrade image slices with realistic local contrast patterns. Novel 3D MRI images are synthesized by first applying the 3D voxel-wise deformation map which is generated from the shape network to deform the Montreal Neurological Institute (MNI) brain template and subsequently performing style transfer on axial-wise slices using the texture network. The Maximum Mean Discrepancy (MMD) and Multi-scale Structural Similarity Index Measure (MS-SSIM) scores of MRI image volumes synthesized using our GAN approach are competitive with state-of-art GAN methods. Our work establishes the feasibility of an alternative approach to high-dimensional GAN learning - splitting the type of information content learned among several GANs can be an effective form of regularization and complementary to latent code shaping or super-resolution approaches in state-of-the-art methods.