Reviewerly/exHarmony huggingface dataset:
Aspect-Oriented Programming based building block platform to construct Domain-Specific Language for HPC application

The world of HPC systems is changing to a more complicated system because the performance improvement of processors has been slowed down. One of the promising approaches is Domain-Specific Language (DSL), which provides a productive environment to create a high-efficient program without pain. However, existing DSL platforms themselves often lack portability and cost DSL developers great effort. To solve this issue, we propose an Aspect-Oriented Programming (AOP) based DSL constructing platform, enabling developers to build a DSL platform by combining Aspect modules. Aspect modules manage abstracted application flow, data structure, and memory access on our platform. Therefore, developers can create any DSL platform whose target application has the attributes which HPC applications usually have, the abstraction assumes. This study implemented a prototype platform that can handle MPI and OpenMP layers. The prototype supports three types of applications (Structured-Grid, Unstructured-Grid, and Particle Simulation). Then, we evaluated the overheads caused by achieving flexibility and productivity of the platform.
CoAvoid: Secure, Privacy-Preserved Tracing of Contacts for Infectious Diseases

To fight against infectious diseases (e.g., SARS, COVID-19, Ebola, etc.), government agencies, technology companies and health institutes have launched various contact tracing approaches to identify and notify the people exposed to infection sources. However, existing tracing approaches can lead to severe privacy and security concerns, thereby preventing their secure and widespread use among communities. To tackle these problems, this paper proposes CoAvoid, a decentralized, privacy-preserved contact tracing system that features good dependability and usability. CoAvoid leverages the Google/Apple Exposure Notification (GAEN) API to achieve decent device compatibility and operating efficiency. It utilizes GPS along with Bluetooth Low Energy (BLE) to dependably verify user information. In addition, to enhance privacy protection, CoAvoid applies fuzzification and obfuscation measures to shelter sensitive data, making both servers and users agnostic to information of both low and high-risk populations. The evaluation demonstrates good efficacy and security of CoAvoid. Compared with four state-of-art contact tracing applications, CoAvoid can reduce upload data by at least 90% and simultaneously resist wormhole and replay attacks in various scenarios.
Document-Level Event Extraction via Human-Like Reading Process

Document-level Event Extraction (DEE) is particularly tricky due to the two challenges it poses: scattering-arguments and multi-events. The first challenge means that arguments of one event record could reside in different sentences in the document, while the second one reflects that one document may simultaneously contain multiple such event records. Motivated by humans’ reading cognitive to extract information of interests, in this paper, we propose a method called HRE (Human Reading inspired Extractor for Document Events), where DEE is decomposed into these two iterative stages, rough reading and elaborate reading. Specifically, the first stage browses the document to detect the occurrence of events, and the second stage serves to extract specific event arguments. For each concrete event role, elaborate reading hierarchically works from sentences to characters to locate arguments across sentences, thus the scattering-arguments problem is tackled. Meanwhile, rough reading is explored in a multi-round manner to discover undetected events, thus the multi-events problem is handled. Experiment results show the superiority of HRE over prior competitive methods.
Effect of relativity and vacuum fluctuations on quantum measurement

Vacuum fluctuations can obscure the detection signal of the measurement of the smallest quantum objects like single particles seemingly implying a fundamental limit to measurement accuracy. However, as we show relativistic invariance implies the disappearance of fluctuations for the space-like spectrum of an observable at zero temperature. This complete absence of noise can be harnessed to perform noiseless measurement of single particles, as we illustrate for electrons or photons. We outline a general scheme to illustrate the noiseless measurement involving the space-like spectrum of observables based on the self-interference of counter-propagating paths of a single particle in a triangular Sagnac interferometer.
Enumeration of Extended Irreducible Binary Goppa Codes

The family of Goppa codes is one of the most interesting subclasses of linear codes. As the McEliece cryptosystem often chooses a random Goppa code as its key, knowledge of the number of inequivalent Goppa codes for fixed parameters may facilitate in the evaluation of the security of such a cryptosystem. In this paper we present a new approach to give an upper bound on the number of inequivalent extended irreducible binary Goppa codes. To be more specific, let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n&gt;3$ </tex-math></inline-formula> be an odd prime number and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q=2^{n}$ </tex-math></inline-formula> ; let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r\geq 3$ </tex-math></inline-formula> be a positive integer satisfying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gcd (r,n)=1$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gcd \big (r,q(q^{2}-1)\big)=1$ </tex-math></inline-formula> . We obtain an upper bound for the number of inequivalent extended irreducible binary Goppa codes of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q+1$ </tex-math></inline-formula> and degree <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> .
Expert training: Enhancing AI resilience to image coding artifacts

In the Machine-to-Machine (M2M) transmission context, there is a great need to reduce the amount of transmitted information using lossy compression.However, commonly used image compression methods are designed for human perception, not for Artificial Intelligence (AI) algorithms performances.It is known that these compression distortions affect many deep learning based architectures on several computer vision tasks.In this paper, we focus on the classification task and propose a new approach, named expert training, to enhance Convolutional Neural Networks (CNNs) resilience to compression distortions.We validated our approach using MnasNet and ResNet50 architectures, against image compression distortions introduced by three commonly used methods (JPEG, J2K and BPG), on the ImageNet dataset.The results showed a better robustness of these two architectures against the tested coding artifacts using the proposed expert training approach.Our code is publicly available at https://github.com/albmarie/expert_training.
Fabchain: Managing Audit-able 3D Print Job over Blockchain

Improvements in fabrication devices such as 3D printers are becoming possible for personal fabrication to freely fabricate any products. To clarify who is liable for the product, the fabricator should keep the fabrication history in an immutable and sustainably accessible manner. In this paper, we propose a new scheme, "Fabchain," that can record the fabrication history in such a manner. By utilizing a scheme that employs a blockchain as an auditable communication channel, Fabchain manages print jobs for the fabricator’s 3D printer over the blockchain, while maintaining a history of a print job. We implemented Fabchain on Ethereum and evaluated the performance for recording a print job. Our results demonstrate that Fabchain can complete communication of a print job sequence in less than 1 minute on the Ethereum test network. We conclude that Fabchain can manage a print job in a reasonable duration for 3D printing, while satisfying the requirements for immutability and sustainability.
Fldp: Flexible Strategy For Local Differential Privacy

Local differential privacy (LDP), a technique applying unbiased statistical estimations instead of real data, is often adopted in data collection. In particular, this technique is used in frequency oracles (FO) because it can protect each user’s privacy and prevent leakage of sensitive information. However, the definition of LDP is so conservative that it requires all inputs to be indistinguishable after perturbation. Indeed, LDP protects each value; however, it is rarely used in practical scenarios owing to its cost in terms of accuracy. In this paper, we address the challenge of providing weakened but flexible protection where each value only needs to be indistinguishable from part of the domain after perturbation. First, we present this weakened but flexible LDP (FLDP) notion which splits the domain. We then prove the association with LDP. Second, we design a Flexible Hadamard Response (FHR) approach for the common FO issue while satisfying FLDP. The proposed approach balances communication cost, computational complexity, and estimation accuracy. Finally, experimental results using practical and synthetic datasets verify the effectiveness and efficiency of our approach.
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing

Mobile Crowdsensing systems are vulnerable to various attacks as they build on non-dedicated and ubiquitous properties. Machine learning (ML)-based approaches are widely investigated to build attack detection systems and ensure MCS systems security. However, adversaries that aim to clog the sensing front-end and MCS back-end leverage intelligent techniques, which are challenging for MCS platform and service providers to develop appropriate detection frameworks against these attacks. Generative Adversarial Networks (GANs) have been applied to generate synthetic samples, that are extremely similar to the real ones, deceiving classifiers such that the synthetic samples are indistinguishable from the originals. Previous works suggest that GAN-based attacks exhibit more crucial devastation than empirically designed attack samples, and result in low detection rate at the MCS platform. With this in mind, this paper aims to detect intelligently designed illegitimate sensing service requests by integrating a GAN-based model. To this end, we propose a two-level cascading classifier that combines the GAN discriminator with a binary classifier to prevent adversarial fake tasks. Through simulations, we compare our results to a single-level binary classifier, and the numeric results show that proposed approach raises Adversarial Attack Detection Rate (AADR), from 0% to 97.5% by KNN/NB, from 45.9% to 100% by Decision Tree. Meanwhile, with two-levels classifiers, Original Attack Detection Rate (OADR) improves for the three binary classifiers, with comparison, such as NB from 26.1% to 61.5%.
Grounding Commands for Autonomous Vehicles via Layer Fusion with Region-specific Dynamic Layer Attention

Grounding a command to the visual environment is an essential ingredient for interactions between autonomous vehicles and humans. In this work, we study the problem of language grounding for autonomous vehicles, which aims to localize a region in a visual scene according to a natural language command from a passenger. Prior work only employs the top layer representations of a vision-and-language pretrained model to predict the region referred to by the command. However, such a method omits the useful features encoded in other layers, and thus results in inadequate understanding of the input scene and command. To tackle this limitation, we present the first layer fusion approach for this task. Since different visual regions may require distinct types of features to disambiguate them from each other, we further propose the region-specific dynamic (RSD) layer attention to adaptively fuse the multimodal information across layers for each region. Extensive experiments on the Talk2Car benchmark demonstrate that our approach helps predict more accurate regions and outperforms state-of-the-art methods.
K-VQG: Knowledge-aware Visual Question Generation for Common-sense Acquisition

Visual Question Generation (VQG) is a task to generate questions from images. When humans ask questions about an image, their goal is often to acquire some new knowledge. However, existing studies on VQG have mainly addressed question generation from answers or question categories, overlooking the objectives of knowledge acquisition. To introduce a knowledge acquisition perspective into VQG, we constructed a novel knowledge-aware VQG dataset called K-VQG. This is the first large, humanly annotated dataset in which questions regarding images are tied to structured knowledge. We also developed a new VQG model that can encode and use knowledge as the target for a question. The experiment results show that our model outperforms existing models on the K-VQG dataset. Our dataset is publicly available at https://uehara-mech.github.io/kvqg.
Integrated semi‐quantum layered communication

In recent times, secure quantum communication in layered networks has emerged as an important area of study. In this paper, we harness the potential offered by multidimensional states in secure quantum communication with only one quantum participant and all the other classical participants. We propose three protocols for (i) entanglement-based layered semi--quantum key distribution, (ii) layered semi-quantum secret sharing, and, (iii) integrated layered semi-quantum key distribution and secret sharing to share secret information in an arbitrarily layered network. These protocols integrate the features of semi quantum communication in layered networks. All three protocols allow for the simultaneous distribution of secure information in all the layers of a network, thanks to the employment of multidimensional states. We present these protocols for a small network of at most five participants and three layers and show the robustness of the same against various eavesdropping strategies. Finally, we provide a detailed procedure for the generalization of the proposed protocols to distribute keys/secrets in any arbitrarily structured quantum network.
Learning to Minimize the Remainder in Supervised Learning

The learning process of deep learning methods usually updates the model’s parameters in multiple iterations. Each iteration can be viewed as the first-order approximation of Taylor’s series expansion. The remainder, which consists of higher-order terms, is usually ignored in the learning process for simplicity. This learning scheme empowers various multimedia-based applications, such as image retrieval, recommendation system, and video search. Generally, multimedia data ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> images) are semantics-rich and high-dimensional, hence the remainders of approximations are possibly non-zero. In this work, we consider that the remainder is informative and study how it affects the learning process. To this end, we propose a new learning approach, namely gradient adjustment learning (GAL), to leverage the knowledge learned from the past training iterations to adjust vanilla gradients, such that the remainders are minimized and the approximations are improved. The proposed GAL is model- and optimizer-agnostic, and is easy to adapt to the standard learning framework. It is evaluated on three tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> image classification, object detection, and regression, with state-of-the-art models and optimizers. The experiments show that the proposed GAL consistently enhances the evaluated models, whereas the ablation studies validate various aspects of the proposed GAL. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/luoyan407/gradient_adjustment.git</uri> .
Modular Adaptive Policy Selection for Multi- Task Imitation Learning through Task Division

Deep imitation learning requires many expert demonstrations, which can be hard to obtain, especially when many tasks are involved. However, different tasks often share similarities, so learning them jointly can greatly benefit them and alleviate the need for many demonstrations. But, joint multi-task learning often suffers from negative transfer, sharing information that should be task-specific. In this work, we introduce a method to perform multi-task imitation while allowing for task-specific features. This is done by using proto-policies as modules to divide the tasks into simple sub-behaviours that can be shared. The proto-policies operate in parallel and are adaptively chosen by a selector mechanism that is jointly trained with the modules. Experiments on different sets of tasks show that our method improves upon the accuracy of single agents, task-conditioned and multi-headed multi-task agents, as well as state-of-the-art meta learning agents. We also demonstrate its ability to autonomously divide the tasks into both shared and task-specific sub-behaviours.
Netrca: An Effective Network Fault Cause Localization Algorithm

Localizing the root cause of network faults is crucial to network operation and maintenance. However, due to the complicated network architectures and wireless environments, as well as limited labeled data, accurately localizing the true root cause is challenging. In this paper, we propose a novel algorithm named NetRCA to deal with this problem. Firstly, we extract effective derived features from the original raw data by considering temporal, directional, attribution, and interaction characteristics. Secondly, we adopt multivariate time series similarity and label propagation to generate new training data from both labeled and unlabeled data to overcome the lack of labeled samples. Thirdly, we design an ensemble model which combines XGBoost, rule set learning, attribution model, and graph algorithm, to fully utilize all data information and enhance performance. Finally, experiments and analysis are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge to demonstrate the superiority and effectiveness of our approach.
Passive verification protocol for thermal graph states

Graph states are entangled resource states for universal measurement-based quantum computation. Although matter qubits such as superconducting circuits and trapped ions are promising candidates to generate graph states, it is technologically hard to entangle a large number of them due to several types of noise. Since they must be sufficiently cooled to maintain their quantum properties, thermal noise is one of the major ones. In this paper, we show that, for any temperature $T$, the fidelity $\ensuremath{\langle}G|{\ensuremath{\rho}}_{T}|G\ensuremath{\rangle}$ between an ideal graph state $|G\ensuremath{\rangle}$ at zero temperature and a thermal graph state ${\ensuremath{\rho}}_{T}$, which is a graph state at temperature $T$, can be efficiently estimated by using only one measurement setting. A remarkable property of our protocol is that it is passive, while existing protocols are active, namely, they switch between at least two measurement settings. Since thermal noise is equivalent to an independent phase-flip error, our estimation protocol also works for that error. By generalizing our protocol to hypergraph states, we apply our protocol to the quantum-computational-supremacy demonstration with instantaneous quantum polynomial time circuits. Our results should make the characterization of entangled matter qubits extremely feasible under thermal noise.
Reinforced Imitative Graph Learning for Mobile User Profiling

Mobile user profiling refers to the efforts of extracting users’ characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user's profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users’ mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach.
SGL: Symbolic Goal Learning in a Hybrid, Modular Framework for Human Instruction Following

This paper investigates human instruction following for robotic manipulation via a hybrid, modular system with symbolic and connectionist elements. Symbolic methods build modular systems with semantic parsing and task planning modules for producing sequences of actions from natural language requests. Modern connectionist methods employ deep neural networks that learn visual and linguistic features for mapping inputs to a sequence of low-level actions, in an end-to-end fashion. The hybrid, modular system blends these two approaches to create a modular framework: it formulates instruction following as symbolic goal learning via deep neural networks followed by task planning via symbolic planners. Connectionist and symbolic modules are bridged with Planning Domain Definition Language. The vision-and-language learning network predicts its goal representation, which is sent to a planner for producing a task-completing action sequence. For improving the flexibility of natural language, we further incorporate implicit human intents with explicit human instructions. To learn generic features for vision and language, we propose to separately pretrain vision and language encoders on scene graph parsing and semantic textual similarity tasks. Benchmarking evaluates the impacts of different components of, or options for, the vision-and-language learning model and shows the effectiveness of pretraining strategies. Manipulation experiments conducted in the simulator AI2THOR show the robustness of the framework to novel scenarios.
Short Paper: Device- and Locality-Specific Fingerprinting of Shared NISQ Quantum Computers

Fingerprinting of quantum computer devices is a new threat that poses a challenge to shared, cloud-based quantum computers. Fingerprinting can allow adversaries to map quantum computer infrastructures, uniquely identify cloud-based devices which otherwise have no public identifiers, and it can assist other adversarial attacks. This work shows idle tomography-based fingerprinting method based on crosstalk-induced errors in NISQ quantum computers. The device- and locality-specific fingerprinting results show prediction accuracy values of 99.1% and 95.3%, respectively.