Reviewerly/exHarmony huggingface dataset:
Verifying Learning-Based Robotic Navigation Systems

Abstract Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for tasks where complex policies are learned within reactive systems. Unfortunately, these policies are known to be susceptible to bugs. Despite significant progress in DNN verification, there has been little work demonstrating the use of modern verification tools on real-world, DRL-controlled systems. In this case study, we attempt to begin bridging this gap, and focus on the important task of mapless robotic navigation — a classic robotics problem, in which a robot, usually controlled by a DRL agent, needs to efficiently and safely navigate through an unknown arena towards a target. We demonstrate how modern verification engines can be used for effective model selection , i.e., selecting the best available policy for the robot in question from a pool of candidate policies. Specifically, we use verification to detect and rule out policies that may demonstrate suboptimal behavior, such as collisions and infinite loops. We also apply verification to identify models with overly conservative behavior, thus allowing users to choose superior policies, which might be better at finding shorter paths to a target. To validate our work, we conducted extensive experiments on an actual robot, and confirmed that the suboptimal policies detected by our method were indeed flawed. We also demonstrate the superiority of our verification-driven approach over state-of-the-art, gradient attacks. Our work is the first to establish the usefulness of DNN verification in identifying and filtering out suboptimal DRL policies in real-world robots, and we believe that the methods presented here are applicable to a wide range of systems that incorporate deep-learning-based agents.
Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA approved AI applications for breast imaging, but adoption and utilization are widely variable and overall low. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer and many of the most exciting research and available AI applications focus on cancer detection for mammography. There are however multiple additional potential applications for AI in breast imaging including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.
SGMA: a novel adversarial attack approach with improved transferability

Abstract Deep learning models are easily deceived by adversarial examples, and transferable attacks are crucial because of the inaccessibility of model information. Existing SOTA attack approaches tend to destroy important features of objects to generate adversarial examples. This paper proposes the split grid mask attack (SGMA), which reduces the intensity of model-specific features by split grid mask transformation, effectively highlighting the important features of the input image. Perturbing these important features can guide the development of adversarial examples in a more transferable direction. Specifically, we introduce the split grid mask transformation into the input image. Due to the vulnerability of model-specific features to image transformations, the intensity of model-specific features decreases after aggregation while the intensities of important features remain. The generated adversarial examples guided by destroying important features have excellent transferability. Extensive experimental results demonstrate the effectiveness of the proposed SGMA. Compared to the SOTA attack approaches, our method improves the black-box attack success rates by an average of 6.4% and 8.2% against the normally trained models and the defense ones respectively.
Does Organizational Performance Increase with Innovation and Strategic Planning?

In today's rapidly changing economic environment, the success of organizations is largely determined by their organizational efficiency.The impact of innovation and strategic planning on organizational performance is the focus of this study, which was conducted in 2022 among middle-level managers and employees of public and private sector hospitals.A total of 63 questionnaires were collected, resulting in a response rate of approximately 72.41%.Structural equation modeling with Smart PLS3 software was utilized to examine the relationships between the variables.The results indicate that organizational performance is positively impacted by innovation.Furthermore, the study found that the performance of organizations is positively influenced by strategic planning.These findings have significant implications for managers and decision-makers in the healthcare sector and can inform the development of effective strategies for improving organizational performance.
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks

Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the “pre-train, fine-tune” and “pre-train, prompt” paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt.
ChatGPT and Chat History: Challenges for the New Wave

AFTERSHOCK Is the family of large language model (LLM) interfaces something new under the sun?Yes.However, it merges two trajectories: conversational artificial intelligence (AI) and commercial conversational AI.Conversational AI began in 1965, with Eliza, the first agent to maintain a coherent conversation.Significant commercial systems appeared in 2011 with Apple's Siri, followed in 2015 by the task-focused chatbot explosion set in motion by Facebook's M announcement.We saw systems wax and wane, the pressures they came under, and why they had limited traction or failed.We can look for parallels and differences.It is for us, and the developers and marketers of these tools, to assess which of the forces that affected past efforts still apply and how they might be addressed.How will this time be different?The challenge will include anticipating how LLMs will be used by sophisticated, motivated bad actors.This article includes dozens of quotations and citations of past work.For early AI, the specific references are in my monograph From Tool to Partner: The Evolution of Human-Computer Interaction.References for the work on chatbots through
Multi-tier supply chain behavior with blockchain technology: evidence from a frozen fish supply chain

Abstract Relationships in multi-tier supply chains (MTSCs) are complex and require constant information sharing. For MTSCs involved in perishable product distribution, blockchain technology (BCT) is one of the most promising technologies that can ensure products’ traceability and safety. This study examines a Portuguese MTSC for frozen fish products piloting BCT adoption for its supply chain (SC) practices. The goal is therefore to explore the consequences of information availability inherent for BCT adoption pilots in the SC context. This study follows a mixed-method approach, with qualitative and quantitative data collection techniques. Three semi-structured interviews with players from a single frozen fish SC, including supplier, transporter, and retailer were conducted. MAXQDA Analytics Pro 2022 software was used for further coding and data analysis. An online survey among retail chain consumers was conducted resulting in 112 responses. Regression analysis and Pearson correlation test were further performed via IBM SPSS Statistics software. Data collection for both parts took place between December 2021 and July 2022. This study explores four layers in the SC– including supplier, transporter, retailer and final consumer. Findings revealed a current lack of extensive and detailed information from upper tiers regarding products’ characteristics. Despite being hyped as a “trust-enabling” technology, BCT was found not to eliminate the need for trustful relationships between players prior to adoption, as information input requires human intervention. Even in those cases when the traceability option is not of major interest to final consumers, findings revealed a higher probability of purchasing fish products that have traceable information available.
Grammatical Error Correction: A Survey of the State of the Art

Abstract Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject–verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors, respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems, which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarize the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgments, before concluding with an overview of recent progress and suggestions for future work and remaining challenges.We hope that this survey will serve as a comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.
A Review of Artificial Intelligence Adoption in Second-Language Learning

Professionals are implementing artificial intelligence (AI) technology in different fields owing to its diverse uses and benefits. Similarly, AI professionals are also beginning to implement AI technology in foreign-language education and second-language learning. Therefore, through a systematic literature review, this paper analyzes the role of AI in helping learners of a second language to master pronunciation. A detailed and in-depth search of different well-known databases was conducted, and of 116 articles, only 39 were selected for this paper. AI algorithms can advance language learning and acquisition in almost every dialect and could be significant for different parties in different ways. For example, organizations could utilize AI technology to develop their workers’ knowledge; individual learners could use AI technology to facilitate their studies anywhere and anytime; and traditional learning institutions could incorporate AI-powered methods of language learning to diversify learners’ opportunities. There are many benefits to employing AI in language learning, particularly in second-language learning.
Content Moderators’ Strategies for Coping with the Stress of Moderating Content Online

Commercial content moderators are exposed to a range of stressors at work, including analysing content that has been flagged as harmful. However, not much is known about their specific coping strategies. Depth interviews were conducted with 11 content moderators exposed to child sexual abuse material (CSAM) as part of their job, and thematically analysed to investigate both individual coping strategies and those deployed organisationally. Results highlighted the importance of social support and validation of the role, as well as creating boundaries between work and home life. Moderators expressed a preference for mandatory, individual therapy with professionals who had specific experience supporting those exposed to CSAM. How content moderators cope and can be further supported are discussed.
Curated Pacific Northwest AI-ready Seismic Dataset

The curation of seismic datasets is the cornerstone of seismological research and the starting point of machine-learning applications in seismology. We present a 21-year-long AI-ready dataset of diverse seismic event parameters, instrumentation metadata, and waveforms, as curated by the Pacific Northwest Seismic Network and ourselves. The dataset contains about 190,000 three-component (3C) waveform traces from more than 65,000 earthquake and explosion events, and about 9,200 waveforms from 5,600 exotic events. The magnitude of the events ranges from 0 to 6.4, while the biggest one is 20 December 2022 M6.4 Ferndale Earthquake. We include waveforms from high-gain (EH, BH, and HH channels) and strong-motion (EN channels) seismometers and resample to 100 Hz. We describe the earthquake catalog and the temporal evolution of the data attributes (e.g., event magnitude type, channel type, waveform polarity, and signal-tonoise ratio, phase picks) as the network earthquake monitoring system evolved through time. We propose this AI-ready dataset as a new open-source benchmark dataset.
Privacy-Preserving Computation for Peer-to-Peer Energy Trading on a Public Blockchain

To ensure the success of energy transition and achieve the target of reducing the carbon footprint of energy systems, the management of energy systems needs to be decentralized. Public blockchains offer favorable features to support energy sector democratization and reinforce citizens' trust, such as tamper-proof energy data registration and sharing, decentralization, transparency, and support for peer-to-peer (P2P) energy trading. However, in blockchain-based P2P energy markets, transactional data are public and accessible, which raises privacy concerns related to prosumers' energy profiles while lacking scalability and featuring high transactional costs. In this paper, we employ secure multi-party computation (MPC) to assure privacy on a P2P energy flexibility market implementation in Ethereum by combining the prosumers' flexibility orders data and storing it safely on the chain. We provide an encoding mechanism for orders on the energy market to obfuscate the amount of energy traded by creating groups of prosumers, by splitting the amount of energy from bids and offers, and by creating group-level orders. The solution wraps around the smart contracts-based implementation of an energy flexibility marketplace, assuring privacy features on all market operations such as order submission, matching bids and offers, and commitment in trading and settlement. The experimental results show that the proposed solution is effective in supporting P2P energy flexibility trading, reducing the number of transactions, and gas consumption with a limited computational time overhead.
Distributed recursive filtering for multi-rate uniform sampling systems with packet losses in sensor networks

The distributed filtering problem is studied for a multi-rate uniform sampling linear discrete system over sensor networks, where packet losses may occur during data exchange between nodes. The multi-rate system is transformed into a single-rate system by using a virtual measurement method. At each sensor node, the estimates received from neighbour nodes are fused by matrix or scalar weighting according to their different estimation accuracy. A distributed recursive filter with the Kalman consensus filter structure is devised based on the fused estimate, where the filtering gain and consensus gain are solved by locally minimising an upper bound of the filtering error covariance. The obtained gains and the upper bound of the covariance depend on a group of free positive scalar parameters. The scalar parameters are optimised by minimising the upper bound of the covariance. It is proved that the upper bound of the covariance of the filter with optimal gains and parameters is smaller than the covariance of the local filter based on sensor measurements. It avoids calculating cross-covariance matrices between nodes and has small computational burden. The exponential boundedness in the mean square of the filtering error is analysed. A target tracking system verifies the effectiveness of the algorithm.
Compact Post-quantum Signatures from Proofs of Knowledge Leveraging Structure for the $$\textsf{PKP}$$, $$\textsf{SD}$$ and $$\textsf{RSD}$$ Problems

The MPC-in-the-head introduced in [IKOS07] has established itself as an important paradigm to design efficient digital signatures. For instance, it has been leveraged in the Picnic scheme [CDG+20] that reached the third round of the NIST Post-Quantum Cryptography Standardization process. In addition, it has been used in [Beu20] to introduce the Proof of Knowledge (PoK) with Helper paradigm. This construction permits to design shorter signatures but induces a non negligible performance overhead as it uses cut-and-choose. In this paper, we introduce the PoK leveraging structure paradigm along with its associated challenge space amplification technique. Our new approach to design PoK brings some improvements over the PoK with Helper one. Indeed, we show how one can substitute the Helper in these constructions by leveraging the underlying structure of the considered problem. This new approach does not suffer from the performance overhead inherent to the PoK with Helper paradigm hence offers different trade-offs between security, signature sizes and performances. In addition, we also present four new post-quantum signature schemes. The first one is based on a new PoK with Helper for the Syndrome Decoding problem. It relies on ideas from [BGKM22] and [FJR21] and improve the latter using a new technique that can be seen as performing some cut-and-choose with a meet in the middle approach. The three other signatures are based on our new PoK leveraging structure approach and as such illustrate its versatility. Indeed, we provide new PoK related to the Permuted Kernel Problem ( $$\textsf{PKP}$$ ), Syndrome Decoding ( $$\textsf{SD}$$ ) problem and Rank Syndrome Decoding $$(\textsf{RSD})$$ problem. Considering (public key + signature), we get sizes below 9 kB for our signature related to the $$\textsf{PKP}$$ problem, below 15 kB for our signature related to the $$\textsf{SD}$$ problem and below 7 kB for our signature related to the $$\textsf{RSD}$$ problem. These new constructions are particularly interesting presently as the NIST has recently announced its plan to reopen the signature track of its Post-Quantum Cryptography Standardization process.
Cross-Modality Transformer With Modality Mining for Visible-Infrared Person Re-Identification

Visible-infrared cross-modality person re-identification is a challenging ReID task, which aims to retrieve and match the same identity's images between the heterogeneous visible and infrared modalities. Thus, the core of this task is to bridge the huge gap between these two modalities. The existing convolutional neural network-based methods mainly face the problem of insufficient perception of modalities' information, and can not learn good discriminative modality-invariant embeddings for identities, which limits their performance. To solve these problems, we propose a cross-modality transformer-based method (CMTR) for the visible-infrared person re-identification task, which can explicitly mine the information of each modality and generate better discriminative features based on it. Specifically, to capture modalities' characteristics, we design the novel modality embeddings, which are fused with token embeddings to encode modalities' information. Furthermore, to enhance representation of modality embeddings and adjust matching embeddings' distribution, we propose a modality-aware enhancement loss based on the learned modalities' information, reducing intra-class distance and enlarging inter-class distance. To our knowledge, this is the first work of applying transformer network to the cross-modality re-identification task. We implement extensive experiments on the public SYSU-MM01 and RegDB datasets, and our proposed CMTR model's performance significantly surpasses existing outstanding CNN-based methods.
QuOCS: The quantum optimal control suite

Quantum optimal control includes the family of pulse-shaping algorithms that aim to unlock the full potential of a variety of quantum technologies. Our Quantum Optimal Control Suite (QuOCS) unites experimental focus and model-based approaches in a unified framework. The easy usage and installation of QuOCS and the availability of various combinable optimization strategies is designed to improve the performance of many quantum technology platforms, such as color defects in diamond, superconducting qubits, atom- or ion-based quantum computers. It can also be applied to the study of more general phenomena in physics. In this paper, we describe the software and the main toolbox of gradient-free and gradient-based algorithms. We then show how the user can connect it to their experiment. In addition, we provide illustrative examples where our optimization suite solves typical quantum optimal control problems, in both open- and closed-loop settings. Integration into existing experimental control software is already provided for the experiment control software Qudi [J. M. Binder et al., SoftwareX, 6, 85-90, (2017)], and further extensions are investigated and highly encouraged. QuOCS is available from GitHub, under Apache License 2.0, and can be found on the PyPI repository.