Reviewerly/exHarmony huggingface dataset:
Modularizing while Training: A New Paradigm for Modularizing DNN Models

Deep neural network (DNN) models have become increasingly crucial components in intelligent software systems. However, training a DNN model is typically expensive in terms of both time and money. To address this issue, researchers have recently focused on reusing existing DNN models - borrowing the idea of code reuse in software engineering. However, reusing an entire model could cause extra overhead or inherits the weakness from the undesired functionalities. Hence, existing work proposes to decompose an already trained model into modules, i.e., modularizing-after-training, and enable module reuse. Since trained models are not built for modularization, modularizing-after-training incurs huge overhead and model accuracy loss. In this paper, we propose a novel approach that incorporates modularization into the model training process, i.e., modularizing-while-training (MwT). We train a model to be structurally modular through two loss functions that optimize intra-module cohesion and inter-module coupling. We have implemented the proposed approach for modularizing Convolutional Neural Network (CNN) models in this work. The evaluation results on representative models demonstrate that MwT outperforms the state-of-the-art approach. Specifically, the accuracy loss caused by MwT is only 1.13 percentage points, which is 1.76 percentage points less than that of the baseline. The kernel retention rate of the modules generated by MwT is only 14.58%, with a reduction of 74.31% over the state-of-the-art approach. Furthermore, the total time cost required for training and modularizing is only 108 minutes, half of the baseline.
Quantum State Tomography for Matrix Product Density Operators

The reconstruction of quantum states from experimental measurements, often achieved using quantum state tomography (QST), is crucial for the verification and benchmarking of quantum devices. However, performing QST for a generic unstructured quantum state requires an enormous number of state copies that grows \emph{exponentially} with the number of individual quanta in the system, even for the most optimal measurement settings. Fortunately, many physical quantum states, such as states generated by noisy, intermediate-scale quantum computers, are usually structured. In one dimension, such states are expected to be well approximated by matrix product operators (MPOs) with a finite matrix/bond dimension independent of the number of qubits, therefore enabling efficient state representation. Nevertheless, it is still unclear whether efficient QST can be performed for these states in general. In this paper, we attempt to bridge this gap and establish theoretical guarantees for the stable recovery of MPOs using tools from compressive sensing and the theory of empirical processes. We begin by studying two types of random measurement settings: Gaussian measurements and Haar random rank-one Positive Operator Valued Measures (POVMs). We show that the information contained in an MPO with a finite bond dimension can be preserved using a number of random measurements that depends only \emph{linearly} on the number of qubits, assuming no statistical error of the measurements. We then study MPO-based QST with physical quantum measurements through Haar random rank-one POVMs that can be implemented on quantum computers. We prove that only a \emph{polynomial} number of state copies in the number of qubits is required to guarantee bounded recovery error of an MPO state.
A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems

The ability to monitor and interpret of hardware system events and behaviors are crucial to improving the robustness and reliability of these systems, especially in a supercomputing facility. The growing complexity and scale of these systems demand an increase in monitoring data collected at multiple fidelity levels and varying temporal resolutions. In this work, we aim to build a holistic analytical system that helps make sense of such massive data, mainly the hardware logs, job logs, and environment logs collected from disparate subsystems and components of a supercomputer system. This end-to-end log analysis system, coupled with visual analytics support, allows users to glean and promptly extract supercomputer usage and error patterns at varying temporal and spatial resolutions. We use multiresolution dynamic mode decomposition (mrDMD), a technique that depicts high-dimensional data as correlated spatial-temporal variations patterns or modes, to extract variation patterns isolated at specified frequencies. Our improvements to the mrDMD algorithm help promptly reveal useful information in the massive environment log dataset, which is then associated with the processed hardware and job log datasets using our visual analytics system. Furthermore, our system can identify the usage and error patterns filtered at user, project, and subcomponent levels. We exemplify the effectiveness of our approach with two use scenarios with the Cray XC40 supercomputer.
FedMultimodal: A Benchmark For Multimodal Federated Learning

Over the past few years, Federated Learning (FL) has become an emerging machine learning technique to tackle data privacy challenges through collaborative training. In the Federated Learning algorithm, the clients submit a locally trained model, and the server aggregates these parameters until convergence. Despite significant efforts that have been made to FL in fields like computer vision, audio, and natural language processing, the FL applications utilizing multimodal data streams remain largely unexplored. It is known that multimodal learning has broad real-world applications in emotion recognition, healthcare, multimedia, and social media, while user privacy persists as a critical concern. Specifically, there are no existing FL benchmarks targeting multimodal applications or related tasks. In order to facilitate the research in multimodal FL, we introduce FedMultimodal, the first FL benchmark for multimodal learning covering five representative multimodal applications from ten commonly used datasets with a total of eight unique modalities. FedMultimodal offers a systematic FL pipeline, enabling end-to-end modeling framework ranging from data partition and feature extraction to FL benchmark algorithms and model evaluation. Unlike existing FL benchmarks, FedMultimodal provides a standardized approach to assess the robustness of FL against three common data corruptions in real-life multimodal applications: missing modalities, missing labels, and erroneous labels. We hope that FedMultimodal can accelerate numerous future research directions, including designing multimodal FL algorithms toward extreme data heterogeneity, robustness multimodal FL, and efficient multimodal FL. The datasets and benchmark results can be accessed at: https://github.com/usc-sail/fed-multimodal.
QFT based quantum arithmetic logic unit on IBM quantum computer

In this study, we constructed a primitive quantum arithmetic logic unit (qALU) based on the quantum Fourier transform. The qALU is capable of performing arithmetic ADD (addition) and logic NAND gate operations. We presented two versions of the arithmetic logic unit, with two-input and four-input capabilities. By comparing the required number of quantum gates for serial and parallel architectures in executing arithmetic addition, we evaluated the performance. We executed our quantum Fourier transform based qALU design on real quantum computer hardware provided by IBM. The results demonstrated that the proposed circuit can perform arithmetic and logic operations with a high success rate. Furthermore, we discussed the potential implementation of qALU circuit in the field of computer science, highlighting the possibility of constructing soft-core processor on quantum processing unit.
b-more-incomplete and b-more positive: Insights on A Robust Estimator of Magnitude Distribution

The $b$-value in earthquake magnitude-frequency distribution quantifies the relative frequency of large versus small earthquakes. Monitoring its evolution could provide fundamental insights into temporal variations of stress on different fault patches. However, genuine $b$-value changes are often difficult to distinguish from artificial ones induced by temporal variations of the detection threshold. A highly innovative and effective solution to this issue has recently been proposed by van der Elst (2021) through the b-positive method, which is based on analyzing only the positive differences in magnitude between successive earthquakes. Here, we provide support to the robustness of the method, largely unaffected by detection issues due to the properties of conditional probability. However, we show that the b-positive method becomes less efficient when earthquakes below the threshold are reported, leading to the paradoxical behavior that it is more efficient when the catalog is more incomplete. Thus, we propose the b-more-incomplete method, where the b-method is applied only after artificially filtering the instrumental catalog to be more incomplete. We also present other modifications of the b-method, such as the b-more-positive method, and demonstrate when these approaches can be efficient in managing time-independent incompleteness present when the seismic network is sparse. We provide analytical and numerical results and apply the methods to fore-mainshock sequences investigated by van der Elst (2021) for validation. The results support the observed small changes in $b$-value as genuine foreshock features.
Pengembangan Domain Specific Language Untuk Pengelolaan Data Warehouse

Efforts to improve the performance of services on the transaction at a bank can be done by performing data retention, reduce the volume of data in the database production by cutting the historical data in accordance with the rules in a bank to a data warehouse. Design and implementation of applications Domain Specific Language (DSL) for handling the data transfer on the data warehouse is divided into lexical analysis, syntax analysis, semantic analysis and code generation. Each part has different characteristics to produce an executable command. Has been developed an application with the DSL method, which is beneficial to reduce the error of writing a command for a normal (non-technical) way to transfer data. From the test result in a decision Oracle transfer method according to the size scale of a particular data.
Cybersecurity Career Requirements: A Literature Review

This study employs a systematic literature review approach to identify the requirements of a career as a cybersecurity professional. It aims to raise public awareness regarding opportunities in the Information Security (IS) profession. A total of 1,520 articles were identified from four academic databases by searching using the terms "cybersecurity" and "skills". After rigorous screening according to various criteria, 31 papers remained. The findings of these studies were thematically analyzed to describe the knowledge and skills an IS professional should possess. The research found that a considerable investment in time is necessary for cybersecurity professionals to reach the required technical proficiency. It also identified female gender barriers to cybersecurity careers due to the unique requirements of the field and suggests that females may successfully enter at lower levels and progress up the tiers as circumstances dictate.
A general quantum matrix exponential dimensionality reduction framework based on block-encoding

As a general framework, Matrix Exponential Dimensionality Reduction (MEDR) deals with the small-sample-size problem that appears in linear Dimensionality Reduction (DR) algorithms. High complexity is the bottleneck in this type of DR algorithm because one has to solve a large-scale matrix exponential eigenproblem. To address it, here we design a general quantum algorithm framework for MEDR based on the block-encoding technique. This framework is configurable, that is, by selecting suitable methods to design the block-encodings of the data matrices, a series of new efficient quantum algorithms can be derived from this framework. Specifically, by constructing the block-encodings of the data matrix exponentials, we solve the eigenproblem and then obtain the digital-encoded quantum state corresponding to the compressed low-dimensional dataset, which can be directly utilized as input state for other quantum machine learning tasks to overcome the curse of dimensionality. As applications, we apply this framework to four linear DR algorithms and design their quantum algorithms, which all achieve a polynomial speedup in the dimension of the sample over their classical counterparts.
Power-law Dynamic arising from machine learning

We study a kind of new SDE that was arisen from the research on optimization in machine learning, we call it power-law dynamic because its stationary distribution cannot have sub-Gaussian tail and obeys power-law. We prove that the power-law dynamic is ergodic with unique stationary distribution, provided the learning rate is small enough. We investigate its first exist time. In particular, we compare the exit times of the (continuous) power-law dynamic and its discretization. The comparison can help guide machine learning algorithm.
UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation

Multiple Object Tracking (MOT) aims to find bounding boxes and identities of targeted objects in consecutive video frames. While fully-supervised MOT methods have achieved high accuracy on existing datasets, they cannot generalize well on a newly obtained dataset or a new unseen domain. In this work, we first address the MOT problem from the cross-domain point of view, imitating the process of new data acquisition in practice. Then, a new cross-domain MOT adaptation from existing datasets is proposed without any pre-defined human knowledge in understanding and modeling objects. It can also learn and update itself from the target data feedback. The intensive experiments are designed on four challenging settings, including MOTSynth to MOT17, MOT17 to MOT20, MOT17 to VisDrone, and MOT17 to DanceTrack. We then prove the adaptability of the proposed self-supervised learning strategy. The experiments also show superior performance on tracking metrics MOTA and IDF1, compared to fully supervised, unsupervised, and self-supervised state-of-the-art methods.
Modelling, identification and geometric control of autonomous quadcopters for agile maneuvering

This paper presents a multi-step procedure to construct the dynamic motion model of an autonomous quadcopter, identify the model parameters, and design a model-based nonlinear trajectory tracking controller. The aim of the proposed method is to speed up the commissioning of a new quadcopter design, i.e., to enable the drone to perform agile maneuvers with high precision in the shortest time possible. After a brief introduction to the theoretical background of the modelling and control design, the steps of the proposed method are presented using the example of a self-developed quadcopter platform. The performance of the method is tested and evaluated by real flight experiments.
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets

High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design. Existing works contribute heavy human efforts to manually labeling or designing and extending recommender dialogue templates. However, they suffer from (i) the limited number of human annotators results in that datasets can hardly capture rich and large-scale cases in the real world, (ii) the limited experience and knowledge of annotators account for the uninformative corpus and inappropriate recommendations. In this paper, we propose a novel automatic dataset synthesis approach that can generate both large-scale and high-quality recommendation dialogues through a data2text generation process, where unstructured recommendation conversations are generated from structured graphs based on user-item information from the real world. In doing so, we comprehensively exploit: (i) rich personalized user profiles from traditional recommendation datasets, (ii) rich external knowledge from knowledge graphs, and (iii) the conversation ability contained in human-to-human conversational recommendation datasets. Extensive experiments validate the benefit brought by the automatically synthesized data under low-resource scenarios and demonstrate the promising potential to facilitate the development of a more effective conversational recommendation system.
Temporal Difference Learning with Experience Replay

Temporal-difference (TD) learning is widely regarded as one of the most popular algorithms in reinforcement learning (RL). Despite its widespread use, it has only been recently that researchers have begun to actively study its finite time behavior, including the finite time bound on mean squared error and sample complexity. On the empirical side, experience replay has been a key ingredient in the success of deep RL algorithms, but its theoretical effects on RL have yet to be fully understood. In this paper, we present a simple decomposition of the Markovian noise terms and provide finite-time error bounds for TD-learning with experience replay. Specifically, under the Markovian observation model, we demonstrate that for both the averaged iterate and final iterate cases, the error term induced by a constant step-size can be effectively controlled by the size of the replay buffer and the mini-batch sampled from the experience replay buffer.
PIEChain -- A Practical Blockchain Interoperability Framework

A plethora of different blockchain platforms have emerged in recent years, but many of them operate in silos. As such, there is a need for reliable cross-chain communication to enable blockchain interoperability. Blockchain interoperability is challenging because transactions can typically not be reverted - as such, if one transaction is committed then the protocol must ensure that all related transactions are committed as well. Existing interoperability approaches, e.g., Cosmos and Polkadot, are limited in the sense that they only support interoperability between their own subchains, or require intrusive changes to existing blockchains. To overcome this limitation, we propose PIEChain, a general, Kafka-based cross-chain communication framework. We utilize PIEChain for a practical case study: a cross-chain auction in which users who hold tokens on multiple chains bid for a ticket sold on another chain. PIEChain is the first publicly available, practical implementation of a general framework for cross-chain communication.
Politeness Stereotypes and Attack Vectors: Gender Stereotypes in Japanese and Korean Language Models

In efforts to keep up with the rapid progress and use of large language models, gender bias research is becoming more prevalent in NLP. Non-English bias research, however, is still in its infancy with most work focusing on English. In our work, we study how grammatical gender bias relating to politeness levels manifests in Japanese and Korean language models. Linguistic studies in these languages have identified a connection between gender bias and politeness levels, however it is not yet known if language models reproduce these biases. We analyze relative prediction probabilities of the male and female grammatical genders using templates and find that informal polite speech is most indicative of the female grammatical gender, while rude and formal speech is most indicative of the male grammatical gender. Further, we find politeness levels to be an attack vector for allocational gender bias in cyberbullying detection models. Cyberbullies can evade detection through simple techniques abusing politeness levels. We introduce an attack dataset to (i) identify representational gender bias across politeness levels, (ii) demonstrate how gender biases can be abused to bypass cyberbullying detection models and (iii) show that allocational biases can be mitigated via training on our proposed dataset. Through our findings we highlight the importance of bias research moving beyond its current English-centrism.
Union-find quantum decoding without union-find

The union-find decoder is a leading algorithmic approach to the correction of quantum errors on the surface code, achieving code thresholds comparable to minimum-weight perfect matching (MWPM) with amortised computational time scaling near-linearly in the number of physical qubits. This complexity is achieved via optimisations provided by the disjoint-set data structure. We demonstrate, however, that the behaviour of the decoder at scale underutilises this data structure for twofold analytic and algorithmic reasons, and that improvements and simplifications can be made to architectural designs to reduce resource overhead in practice. To reinforce this, we model the behaviour of erasure clusters formed by the decoder and show that there does not exist a percolation threshold within the data structure for any mode of operation. This yields a linear-time worst-case complexity for the decoder at scale, even with a naive implementation omitting popular optimisations.
"Recreating the Image of Chan Master Huineng: An Interpersonal Approach to Translations of the Platform Sutra"

The study is a comparison of four English translations of a Chinese Chan Buddhist text, the Platform Sutra. It investigates translators’ choices on the level of lexico-grammar, the semantic consequences of such choices as the image of Huineng and the underlying contextual factors contributing to the variations among translations. Interpersonal system of SysFan, a computational tool specialized in systemic functional analysis, is used and choices in terms of SUBJECT PERSON, MODALITY and MOOD TYPE are analyzed and compared. It is demonstrated that in translation, lexico-grammatical choices will lead to variation in meanings, and there tend to be contextual motivations for the seemingly unconscious choices.
CroCoDai: A Stablecoin for Cross-Chain Commerce

Decentralized Finance (DeFi), in which digital assets are exchanged without trusted intermediaries, has grown rapidly in value in recent years. The global DeFi ecosystem is fragmented into multiple blockchains, fueling the demand for cross-chain commerce. Existing approaches for cross-chain transactions, e.g., bridges and cross-chain deals, achieve atomicity by locking assets in escrow. However, locking up assets increases the financial risks for the participants, especially due to price fluctuations and the long latency of cross-chain transactions. Stablecoins, which are pegged to a non-volatile asset such as the US dollar, help mitigate the risk associated with price fluctuations. However, existing stablecoin designs are tied to individual blockchain platforms, and trusted parties or complex protocols are needed to exchange stablecoin tokens between blockchains. Our goal is to design a practical stablecoin for cross-chain commerce. Realizing this goal requires addressing two challenges. The first challenge is to support a large and growing number of blockchains efficiently. The second challenge is to be resilient to price fluctuations and blockchain platform failures. We present CroCoDai to address these challenges. We also present three prototype implementations of our stablecoin system, and show that it incurs small execution overhead.