Reviewerly/exHarmony huggingface dataset:
Camel: Managing Data for Efficient Stream Learning

Many real-world applications rely on predictive models that are incrementally learned online. Specifically, models are updated with a single pass over continuously arriving data batches in a typical stream learning framework. However, this framework has three shortcomings: high training cost, low data effectiveness, and catastrophic forgetting. We describe Camel, a system that addresses the above issues. Camel includes two independent data management components: coreset selection and buffer update. To accelerate model training, Camel selects a coreset from each streaming data batch for model update. Selecting a coreset with worst-case guarantees is NP-hard. To solve this problem, we reformulate coreset selection as a submodular maximization problem by deriving an upper bound on the objective function. To mitigate catastrophic forgetting, Camel maintains a buffer of past representative samples as new data arrive. Moreover, Camel quantizes numerical data in buffer via a quantile sketch to reduce the memory footprint. Finally, extensive experiments validate the effectiveness and efficiency of Camel. In particular, our coreset selection algorithm can achieve a linear speedup with a marginal accuracy loss on redundant datasets. Furthermore, our buffer update algorithms can outperform the state-of-the-art methods for anti-forgetting on various data distributions.
A flexible type system for fearless concurrency

This paper proposes a new type system for concurrent programs, allowing threads to exchange complex object graphs without risking destructive data races. While this goal is shared by a rich history of past work, existing solutions either rely on strictly enforced heap invariants that prohibit natural programming patterns or demand pervasive annotations even for simple programming tasks. As a result, past systems cannot express intuitively simple code without unnatural rewrites or substantial annotation burdens. Our work avoids these pitfalls through a novel type system that provides sound reasoning about separation in the heap while remaining flexible enough to support a wide range of desirable heap manipulations. This new sweet spot is attained by enforcing a heap domination invariant similarly to prior work, but tempering it by allowing complex exceptions that add little annotation burden. Our results include: (1) code examples showing that common data structure manipulations which are difficult or impossible to express in prior work are natural and direct in our system, (2) a formal proof of correctness demonstrating that well-typed programs cannot encounter destructive data races at run time, and (3) an efficient type checker implemented in Gallina and OCaml.
Co-Designing AI Agents to Support Social Connectedness Among Online Learners: Functionalities, Social Characteristics, and Ethical Challenges

Due to the lack of face-to-face interactions, online learners frequently experience social isolation that negatively impacts students' well-being and learning experiences. Many text-based AI agents have been equipped with different social characteristics and functionalities to support people who are socially isolated. However, the design of agent's functionalities, social characteristics, and ethical challenges in promoting social connectedness among online learners are underexplored. Taking a co-design approach, we included 23 online learners enrolled in an online for-degree graduate program as active participants in two virtual co-design workshop studies. Through four different co-design activities, we identified online learners' preferences for AI agent's functionalities and social characteristics in promoting their social connectedness as well as potential ethical concerns. Based on our findings, we establish the role of AI agent as a facilitator to continuously scaffold online learners' social connection process. We further discuss the unique ethical challenges regarding agent-mediated social interaction in online learning.
Underwater object detection algorithm based on channel attention and feature fusion

Due to the color deviation, low contrast and fuzzy object in underwater optical images, there are some problems in underwater object detection, such as missed detection and false detection. In order to solve the above-mentioned problems, an underwater object detection algorithm is proposed based on the channel attention and feature fusion for underwater optical images. The excitation residual module is designed based on the channel attention, and the forward propagation feature information is adaptively allocated weights to highlight the salience of different channel feature maps, which improves the network ability to extract high-frequency information from the underwater images. The multi-scale feature fusion module is designed to add a large scale feature map for object detection, which improves the detection performance of the network for small size objects by using its corresponding small size receptive field, and further improves the detection accuracy of the network for different size objects in the underwater environment. To improve the generalization performance of the network to the underwater environment, the data augmentation method based on the stitching and fusion is designed to simulate the overlap, occlusion and blurring of underwater objects, which improves the adaptability of the network to the underwater environment. Through experiments on the public dataset URPC, the algorithm in this paper improves the mean average precision by 5.42%, 3.20% and 0.9% compared with YOLOv3, YOLOv4 and YOLOv5, respectively, effectively improving the missed and false detection of objects of different sizes in complex underwater environments.
Post-Quantum Cryptography in Use: Empirical Analysis of the TLS Handshake Performance

With the increasing speed in the development of quantum computers, secure communication is at risk. A promising candidate to replace existing cryptographic patterns with quantum-secure variants is Post-Quantum Cryptography (PQC). This paper presents an empirical analysis of how Post-Quantum Cryptography affects real-world performance compared to classical cryptography. Therefore we analyze the Transport Layer Security (TLS) handshake performance showing that Post-Quantum Cryptography can be as fast or even faster than classical cryptography, depending on the specific encryption and signature algorithms used.
Adaptive estimation of external fields in reproducing kernel Hilbert spaces

Summary This article studies the distributed parameter system that governs adaptive estimation by mobile sensor networks of external fields in a reproducing kernel Hilbert space (RKHS). The article begins with the derivation of conditions that guarantee the well‐posedness of the ideal, infinite dimensional governing equations of evolution for the centralized estimation scheme. Subsequently, convergence of finite dimensional approximations is studied. Rates of convergence in all formulations are established using history‐dependent bases defined from translates of the RKHS kernel that are centered at sample points along the agent trajectories. Sufficient conditions are derived that ensure that the finite dimensional approximations of the ideal estimator equations converge at a rate that is bounded by the fill distance of samples in the agents' assigned subdomains. The article concludes with examples of simulations and experiments that illustrate the qualitative performance of the introduced algorithms.
XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework

Predictive Process Monitoring (PPM) has been integrated into process mining use cases as a value-adding task. PPM provides useful predictions on the future of the running business processes with respect to different perspectives, such as the upcoming activities to be executed next, the final execution outcome, and performance indicators. In the context of PPM, Machine Learning (ML) techniques are widely employed. In order to gain trust of stakeholders regarding the reliability of PPM predictions, eXplainable Artificial Intelligence (XAI) methods have been increasingly used to compensate for the lack of transparency of most of predictive models. Multiple XAI methods exist providing explanations for almost all types of ML models. However, for the same data, as well as, under the same preprocessing settings or same ML models, generated explanations often vary significantly. Corresponding variations might jeopardize the consistency and robustness of the explanations and, subsequently, the utility of the corresponding model and pipeline settings. This paper introduces a framework that enables the analysis of the impact PPM-related settings and ML-model-related choices may have on the characteristics and expressiveness of the generated explanations. Our framework provides a means to examine explanations generated either for the whole reasoning process of an ML model, or for the predictions made on the future of a certain business process instance. Using well-defined experiments with different settings, we uncover how choices made through a PPM workflow affect and can be reflected through explanations. This framework further provides the means to compare how different characteristics of explainability methods can shape the resulting explanations and reflect on the underlying model reasoning process.
current and potential role of blockchain-based technology in managing medical records in Africa

Accurate and well-managed medical records play a major role in high-quality health care. They are a valuable asset that allows hospitals to treat patients in an effective and efficient way. They also play an important role in health care governance, providing evidence in court cases between patients and hospitals. However, in Africa, medical records are not given much academic attention. Good record-keeping has been hampered by a poor management, as well as a lack of trained personnel who understand how to record and keep these records. This study is aimed at exploring the utilisation of blockchain technology in improving medial records at African hospitals. The reserach is qualitative, comprising desktop research of secondary material and its analysis. It finds that poor medical records in hospitals contribute to the prevalence of disease in African countries, which results in funds that could be invested in development projects being diverted to health care instead. It also shows that 4IR, specifically blockchain-based technology, could play a significant role in the management of medical records in Africa. It could help staff to capture and maintain more accurate medial records, and ensure their security and longevity. Moreover, it could improve access to medical records for all health practitioners. Patients could also access their records, thus helping them to evaluate and manage their own health.
Survey of Consensus Algorithms for Proof of Stake in Blockchain

As the core of blockchain technology, the consensus algorithm directly affects the security, stability, and decentralisation of the blockchain and numerous other important characteristics. Choosing an appropriate consensus algorithm for different scenarios is currently a challenge in the implementation of blockchain applications. This paper classifies the improvement schemes of proof of stake (PoS) into three categories: PoS-based consensus algorithms, PoS- and PoW-based consensus algorithms, and PoS- and BFT-based consensus algorithms. First, the study introduces the PoS and PoS consensus algorithm variants and then summarises the core ideas, effects, advantages, and disadvantages of these algorithms. Subsequently, the performances of the improved algorithms are compared. Finally, the main improved methods are summarised, and the most common network security attacks are discussed. The study lays a foundation for the main improvement directions of PoS in the future, hoping to provide a reference for researchers to help them select and design consensus algorithms in different application scenarios while also helping the evolution of consensus algorithms and the implementation of blockchain applications.
A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization

Parameter quantization in convolutional neural networks (CNNs) can help generate efficient models with lower memory footprint and computational complexity. But, homogeneous quantization can result in significant degradation of CNN model accuracy. In contrast, heterogeneous quantization represents a promising approach to realize compact, quantized models with higher inference accuracies. In this paper, we propose HQNNA, a CNN accelerator based on non-coherent silicon photonics that can accelerate both homogeneously quantized and heterogeneously quantized CNN models. Our analyses show that HQNNA achieves up to 73.8x better energy-per-bit and 159.5x better throughput-energy efficiency than state-of-the-art photonic CNN accelerators
Slide2Remember: an Interactive Wall Frame Enriching Reminiscence Experiences by Providing Re-encounters of Taken Photos and Heard Music in a Similar Period

The abundance of easily captured digital media and storing everything "just in case" has made it difficult for people to revisit them and have a rich reminiscence experience. To reinstate this value, we developed Slide2Remember, a wall photo frame enabling people to re-encounter their histories of digital photos and music. By physically sliding the device's front cover, users can hear songs that they had listened to in the same period when the photo was taken. From our four-week in-field study with 7 participants, we showed how Slide2Remember supported narrative remembering experiences of past moments and found detailed roles of each medium in the recall of life events. Moreover, the sliding interaction encouraged participants to begin their retrospection with anticipation and curiosity. Our findings imply considerations for providing a new recalling experience by overlaying one digital possession type over another and ways to encounter past data in everyday living spaces.
A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy

Currently, the federated graph neural network (GNN) has attracted a lot of attention due to its wide applications in reality without violating the privacy regulations. Among all the privacy-preserving technologies, the differential privacy (DP) is the most promising one due to its effectiveness and light computational overhead. However, the DP-based federated GNN has not been well investigated, especially in the sub-graph-level setting, such as the scenario of recommendation system. The biggest challenge is how to guarantee the privacy and solve the non independent and identically distributed (non-IID) data in federated GNN simultaneously. In this paper, we propose DP-FedRec, a DP-based federated GNN to fill the gap. Private Set Intersection (PSI) is leveraged to extend the local graph for each client, and thus solve the non-IID problem. Most importantly, DP is applied not only on the weights but also on the edges of the intersection graph from PSI to fully protect the privacy of clients. The evaluation demonstrates DP-FedRec achieves better performance with the graph extension and DP only introduces little computations overhead.
Semantic soundness for language interoperability

Programs are rarely implemented in a single language, and thus questions of type soundness should address not only the semantics of a single language, but how it interacts with others. Even between type-safe languages, disparate features can frustrate interoperability, as invariants from one language can easily be violated in the other. In their seminal 2007 paper, Matthews and Findler proposed a multi-language construction that augments the interoperating languages with a pair of boundaries that allow code from one language to be embedded in the other. While this technique has been widely applied, their syntactic source-level interoperability doesn't reflect practical implementations, where the behavior of interaction is only defined after compilation to a common target, and any safety must be ensured by target invariants or inserted target-level "glue code."
Slanted Speculations:

Over the past few years, AI bias has become a central concern within design and computing fields. But as the concept of bias has grown in visibility, its meaning and form have become harder to grasp. To help designers realize bias, we take inspiration from textile bias (the skew of woven material) and examine the topic across its myriad forms: visual, textual, and tactile. By introducing a slanted experience of material and therefore of reality, we explore the translation of fraught machine learning algorithms into personal and probing artifacts. In this pictorial, we present nine pieces that materialize complex relationships with machine learning; ground these relationships in the present and the personal; and point to generative ways of engaging with biased systems around us.
Contextual drivers of employees' phishing susceptibility: Insights from a field study

Phishing attacks rate as one of the most prevalent security threats to contemporary organizations. Hence, managers strive heavily to apply security measures that keep their employees safe from these risks, thereby relying on insights from security researchers who have predominantly focused on recipient characteristics, message attributes, and interventions to explicate the phishing susceptibility of individuals. A theoretical lens yet to be explored is the discrete context in which individuals encounter phishing attacks. This paper presents a multi-dimensional model – comprising the three contextual components social, task, and physical – that explains why an employee is likely to fall for phishing emails or not. To empirically validate our model, we conducted a field study among 2302 employees of an internationally operating pharmaceutical company in the United States. By combining employees' behavioral responses to a phishing email, training data, and contextual data, like help desk reliance, job status or workspace, we find that context is key to a more thorough understanding of phishing susceptibility. Moreover, our study provides practical insights on how organizations can identify and support employees prone to phishing as well as tailor training programs to prevent their workforce from falling prey to cybercriminals.
The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

Artificial intelligence (AI) models and procedures hold remarkable predictive efficiency in the medical domain through their ability to discover hidden, non-obvious clinical patterns in data. However, due to the sparsity, noise, and time-dependency of medical data, AI procedures are raising unprecedented issues related to the mismatch between doctors' mentalreasoning and the statistical answers provided by algorithms. Electronic systems can reproduce or even amplify noise hidden in the data, especially when the diagnosis of the subjects in the training data set is inaccurate or incomplete. In this paper we describe the conditions that need to be met for AI instruments to be truly useful in the orthodontic domain. We report some examples of computational procedures that are capable of extracting orthodontic knowledge through ever deeper patient representation. To have confidence in these procedures, orthodontic practitioners should recognize the benefits, shortcomings, and unintended consequences of AI models, as algorithms that learn from human decisions likewise learn mistakes and biases.
A Multimodal Model with Twitter Finbert Embeddings for Extreme Price Movement Prediction of Bitcoin

Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword `Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over a `hold' or moving average strategy.
Optimal entanglement enhancing via conditional measurements

Enhancing quantum entanglement is important for many quantum information processing applications. In this paper, we consider a protocol for entanglement enhancing in a two-mode squeezed vacuum state (TMSVS), attained based on photon subtraction, photon catalysis, and photon addition. Central to such an operation is the task of mixing and detecting number states with each mode of TMSVS. We analyze various settings and find an optimal setup for improving the entanglement of the state.
Detecting Extreme Traffic Events Via a Context Augmented Graph Autoencoder

Accurate and timely detection of large events on urban transportation networks enables informed mobility management. This work tackles the problem of extreme event detection on large-scale transportation networks using origin-destination mobility data, which is now widely available. Such data is highly structured in time and space, but high dimensional and sparse. Current multivariate time series anomaly detection methods cannot fully address these challenges. To exploit the structure of mobility data, we formulate the event detection problem in a novel way, as detecting anomalies in a set of time-dependent directed weighted graphs. We further propose a Context augmented Graph Autoencoder (Con-GAE) model to solve the problem, which leverages graph embedding and context embedding techniques to capture the spatial and temporal patterns. Con-GAE adopts an autoencoder framework and detects anomalies via semi-supervised learning. The performance of the method is assessed on several city-scale travel-time datasets from Uber Movement, New York taxis, and Chicago taxis and compared to state-of-the-art approaches. The proposed Con-GAE can achieve an improvement in the area under the curve score as large as 0.15 over the second best method. We also discuss real-world traffic anomalies detected by Con-GAE.