Reviewerly/exHarmony huggingface dataset:
Cryptocurrency research: future directions

Since Bitcoin was first proposed in late 2008 and went live in 2009, hundreds of research papers have been published trying to understand the behaviour of cryptocurrencies and their impact on financial markets. Their size and importance to the financial sector has increased substantially also has the number of challenges they face and the negative externalities they have caused. This article reviews the related cryptocurrency literature and introduces articles included in this special issue on this theme which were presented at the 2020 Cryptocurrency Research Conference. We conclude by offering possible future research directions.
Path Planning of a Mobile Robot for a Dynamic Indoor Environment Based on an SAC-LSTM Algorithm

This paper proposes an improved Soft Actor-Critic Long Short-Term Memory (SAC-LSTM) algorithm for fast path planning of mobile robots in dynamic environments. To achieve continuous motion and better decision making by incorporating historical and current states, a long short-term memory network (LSTM) with memory was integrated into the SAC algorithm. To mitigate the memory depreciation issue caused by resetting the LSTM's hidden states to zero during training, a burn-in training method was adopted to boost the performance. Moreover, a prioritized experience replay mechanism was implemented to enhance sampling efficiency and speed up convergence. Based on the SAC-LSTM framework, a motion model for the Turtlebot3 mobile robot was established by designing the state space, action space, reward function, and overall planning process. Three simulation experiments were conducted in obstacle-free, static obstacle, and dynamic obstacle environments using the ROS platform and Gazebo9 software. The results were compared with the SAC algorithm. In all scenarios, the SAC-LSTM algorithm demonstrated a faster convergence rate and a higher path planning success rate, registering a significant 10.5 percentage point improvement in the success rate of reaching the target point in the dynamic obstacle environment. Additionally, the time taken for path planning was shorter, and the planned paths were more concise.
Evolving Towards a Trustworthy AIEd Model to Predict at Risk Students in Introductory Programming Courses

Artificial intelligence in education has the potential to transform the educational landscape and influence the role of the involved stakeholders. With recent European Commission publications on the use of artificial intelligence and data in education and training, in addition to the Ethics Guidelines for Trustworthy AI and the forthcoming AI act, there is now a precedent that using AI, in and for education, needs to be more trustworthy (transparent and explainable) to be considered for adoption.
Application of Data Mining Techniques in Automobile Insurance Fraud Detection

The insurance industry is a fast-growing industry and handles substantial amounts of data. Fraudulent claims are the main problem in the industry. Auto insurance fraud is one of the most prominent types of insurance fraud. Numerous fraudulent claims affect not only the insurance company but also the sincere policy holders because of the increasing in premium amounts. Therefore, detection of insurance fraud is a challenging problem. Traditional approaches are hard to handle and inefficient. Data mining has recently offered significant contributions to insurance analysis. To overcome this, data mining techniques are used to predict fraudulent claims. This work would help in a screening process to investigate claims, thus minimizing human resources and monetary losses. Three sets of features are obtained by logistic regression models: one with forward selection, one with backward elimination, and one without variable selection. Three algorithms including Naïve Bayes, random forest and adaptive boosting are employed as classifiers. K-fold cross validation is used to evaluate the algorithm performance. The results suggest that the smaller number of features, the better performance. The random forest performs the best with highest accuracy (85.28%), sensitivity (93.85%), and precision (97.91%) whereas the adaptive boosting provides the highest specificity (70.41%) and F-score (89.86%).
Auto-Encoding Generative Adversarial Networks towards Mode Collapse Reduction and Feature Representation Enhancement

Generative Adversarial Nets (GANs) are a kind of transformative deep learning framework that has been frequently applied to a large variety of applications related to the processing of images, video, speech, and text. However, GANs still suffer from drawbacks such as mode collapse and training instability. To address these challenges, this paper proposes an Auto-Encoding GAN, which is composed of a set of generators, a discriminator, an encoder, and a decoder. The set of generators is responsible for learning diverse modes, and the discriminator is used to distinguish between real samples and generated ones. The encoder maps generated and real samples to the embedding space to encode distinguishable features, and the decoder determines from which generator the generated samples come and from which mode the real samples come. They are jointly optimized in training to enhance the feature representation. Moreover, a clustering algorithm is employed to perceive the distribution of real and generated samples, and an algorithm for cluster center matching is accordingly constructed to maintain the consistency of the distribution, thus preventing multiple generators from covering a certain mode. Extensive experiments are conducted on two classes of datasets, and the results visually and quantitatively demonstrate the preferable capability of the proposed model for reducing mode collapse and enhancing feature representation.
Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device's signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.
Making TSM better: Preserving foundational philosophy for efficient action recognition

In this study, we present the Discriminative Temporal Shift Module (D-TSM), an enhancement of the Temporal Shift Module (TSM) for action recognition. TSM has limitations in capturing intricate temporal dynamics due to its simplistic feature shifting. D-TSM addresses this by introducing a subtraction operation before the shifting. This enables the extraction of discriminative features between adjacent frames, thereby allowing for effective action recognition where subtle motions serve as crucial cues. It preserves TSM’s foundational philosophy, prioritizing minimal computational overhead and no additional parameters. Our experiments demonstrate that D-TSM significantly improves performance of TSM and outperforms other leading 2D CNN-based methods.
Novel Low-Pass Two-Dimensional Mittag–Leffler Filter and Its Application in Image Processing

This paper presents an innovative Mittag–Leffler two-dimensional filter and its application in image processing. The proposed filter leverages the utilization of a Mittag–Leffler function within the probability density function. It introduces three adjustable filter parameters that enable the manipulation of the curve shape and the filter’s forgetting factor. Moreover, a two-dimensional Mittag–Leffler distribution was defined and used for the first time in an image filter. By conducting a comparative analysis against conventional filtering techniques, the paper showcases the distinct advantages of the proposed filter through illustrative examples. Additionally, the paper provides detailed implementation explanations and presents the Matlab function corresponding to the proposed two-dimensional filter.
Quality analysis of machine learning methods applied to the geothermal potential assessment: a case study

Accurately determining the favorable areas of geothermal resources and selecting the target positions of exploration wells are extremely important for exploration and efficient development. This study used the Pearson correlation coefficient and Gini gain to analyze five influencing factors related to the presence of economically viable geothermal potential. The evaluation model of the favorable areas was constructed by using different Machine Learning (ML) methods: Bayesian classifier (Bayes), Support Vector Machine, Bootstrap Aggregating (Bagging), BP neural network, Decision Tree and Logistic Regression classification. The quality of each model was verified by statistical evaluation indicators: Accuracy (ACC), F1 score (F1) and Receiver Operating Characteristic curve (ROC curve). The methodology was applied to the case study of Xinjiang Uygur Autonomous Region, China. Due to the results obtained, all ML models showed strong prediction and classification performance on the target area selection of geothermal exploration, as evidenced by each model’s metrics: the ACC was above 80%, the F1 was above 0.8, and the Area Under the ROC Curve (AUC) was greater than 0.85. The metrics obtained by the Bagging method were the highest. Finally, the results of the six ML models were combined to classify the study area’s geothermal potential, which was consistent with the available information. This study provides a specific basis and technical support for applying the method in further surveys and campaigns.
What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety-Critical Systems

Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges. Among the challenges, it is known that a rigorous, yet practical, way of achieving safety guarantees is one of the most prominent. In this paper, we first discuss the engineering and research challenges associated with the design and verification of such systems. Then, based on the observation that existing works cannot actually achieve provable guarantees, we promote a two-step verification method for the ultimate achievement of provable statistical guarantees.
Welcome Remarks from AISoLA 2023/Track C2 Chairs

We are happy to present the proceedings of AISoLA’s Track C2: Verification meets Learning and Statistics.
Formal XAI via Syntax-Guided Synthesis

In this paper, we propose a novel application of syntax-guided synthesis to find symbolic representations of a model’s decision-making process, designed for easy comprehension and validation by humans. Our approach takes input-output samples from complex machine learning models, such as deep neural networks, and automatically derives interpretable mimic programs. A mimic program precisely imitates the behavior of an opaque model over the provided data. We discuss various types of grammars that are well-suited for computing mimic programs for tabular and image input data. Our experiments demonstrate the potential of the proposed method: we successfully synthesized mimic programs for neural networks trained on the MNIST and the Pima Indians diabetes data sets. All experiments were performed using the SMT-based $$\texttt{cvc5}\;$$ synthesis tool.
Optimal Flight-Gate Assignment on a Digital Quantum Computer

We investigate the performance of the variational quantum eigensolver (VQE) for the problem of optimal flight-gate assignment. This is a combinatorial-optimization problem that aims at finding an optimal assignment of flights to the gates of an airport, in order to minimize the passenger travel time. To study the problem, we adopt a qubit-efficient binary encoding with a cyclic mapping, which is suitable for a digital quantum computer. Using this encoding in conjunction with the conditional value at risk (CVaR) as an aggregation function, we systematically explore the performance of the approach by classically simulating the CVaR VQE. Our results indicate that the method allows for finding a good solution with high probability and that it significantly outperforms the naive VQE approach. We examine the role of entanglement for the performance and find that ans\"atze with entangling gates allow for better results than pure product states. Studying the problem for various sizes, our numerical data show that the scaling of the number of cost-function calls for obtaining a good solution is not exponential for the regimes that we investigate in this work.
Deep Neural Networks, Explanations, and Rationality

Abstract “Rationality” is the principle that humans make decisions on the basis of step-by-step (algorithmic) reasoning using systematic rules of logic. An ideal “explanation” for a decision is a chronicle of the steps used to arrive at the decision. Herb Simon’s “bounded rationality” is the observation that the ability of a human brain to handle algorithmic complexity and data is limited. As a consequence, human decision-making in complex cases mixes some rationality with a great deal of intuition, relying more on Daniel Kahneman’s “System 1” than “System 2.” A DNN-based AI, similarly, does not arrive at a decision through a rational process in this sense. An understanding of the mechanisms of the DNN yields little or no insight into any rational explanation for its decisions. The DNN is also operating in a manner more like System 1 than System 2. Humans, however, are quite good at constructing post hoc rationalizations of their intuitive decisions. If we demand rational explanations for AI decisions, engineers will inevitably develop AIs that are very effective at constructing such post hoc rationalizations. With their ability to handle vast amounts of data, the AIs will learn to build rationalizations using many more precedents than any human could, thereby constructing rationalizations for any decision that will become very hard to refute. The demand for explanations, therefore, could backfire, resulting in effectively ceding to the AIs much more power.
Reduced-order modeling of two-dimensional turbulent Rayleigh-Bénard flow by hybrid quantum-classical reservoir computing

Two hybrid quantum-classical reservoir computing models are presented to reproduce the low-order statistical properties of a two-dimensional turbulent Rayleigh-B\'enard convection flow at a Rayleigh number $Ra={10}^{5}$ and Prandtl number $Pr=10$. These properties comprise the mean vertical profiles of the root mean square velocity and temperature and the turbulent convective heat flux. The latter is composed of vertical velocity and temperature and measures the global turbulent heat transfer across the convection layer; it manifests locally in coherent hot and cold thermal plumes that rise from the bottom and fall from the top boundaries. Both quantum algorithms differ by the arrangement of the circuit layers of the quantum reservoir, in particular the entanglement layers. The second of the two quantum circuit architectures, denoted H2, enables a complete execution of the reservoir update inside the quantum circuit without the usage of external memory. Their performance is compared with that of a classical reservoir computing model. Therefore, all three models have to learn the nonlinear and chaotic dynamics of the turbulent flow at hand in a lower-dimensional latent data space which is spanned by the time-dependent expansion coefficients of the 16 most energetic proper orthogonal decomposition (POD) modes. These training data are generated by a POD snapshot analysis from direct numerical simulations of the original turbulent flow. All reservoir computing models are operated in the reconstruction or open-loop mode, i.e., they receive three POD modes as an input at each step and reconstruct the 13 missing modes. We analyze different measures of the reconstruction error in dependence on the hyperparameters which are specific for the quantum cases or shared with the classical counterpart, such as the reservoir size and the leaking rate. We show that both quantum algorithms are able to reconstruct the essential statistical properties of the turbulent convection flow successfully with similar performance compared with the classical reservoir network. Most importantly, the quantum reservoirs are by a factor of four to eight smaller in comparison with the classical case.
Teachers’ Perspectives on Using Augmented-Reality-Enhanced Analytics as a Measure of Student Disengagement

There are various ways that teachers manage student disengagement levels during their class lessons, and managing disengagement can be both stressful and challenging, especially since each student is unique. Methods and techniques utilised are specific to teachers’ own experience level, subject knowledge, and teaching styles. We report on the techniques and methods teachers utilise to identify, mitigate, and measure student disengagement during class lessons; the paper presents the results of a mixed-methods, multisession study design comprising gathered qualitative and quantitative data to enable a greater understanding. Eight educators who were full-time educators with varying years of experience from three different schools, who taught or had taught English, maths, and science subjects at the primary school level, participated in this study. The study also observed that teachers used three AR applications and collected valuable feedback on their perspectives by using analytics generated by AR applications to help manage student disengagement. A postsession survey tool was used to gather the perceived importance and ranking of the techniques and methods discussed by the teachers during the previous sessions. The results showed that the majority of teachers deemed spending “Time on Tasks” and giving “Feedback/Reflections” most suited for measuring disengagement, and encouraging “Movement” and use of “Technology” emerged as the most favoured for mitigating disengagement. For utilising AR enhanced analytics in mitigating and measuring student disengagement, the data suggested a difference in perspectives based on teachers’ teaching levels, especially concerning conversations and the use of technology devices. The study did not find conclusive evidence of differences based on teachers’ teaching subjects and there was a notable distinction in building positive relationships among English teachers. This leads to the suggestion that subject-specific pedagogy might influence the perceived effectiveness of using AR-generated analytics in mitigating and measuring student disengagement.
Quantum sensor network metrology with bright solitons

We consider a multiparameter quantum metrology problem with bright soliton networks in the presence of weak losses. We introduce the general Heisenberg limit (GHL) ${\ensuremath{\sigma}}_{\mathbit{\ensuremath{\chi}}}=1/{N}^{k}$ that characterizes fundamental limitations for unknown parameter measurement and estimation accuracy ${\ensuremath{\sigma}}_{\mathbit{\ensuremath{\chi}}}$ within linear ($k=1$) and nonlinear ($k=3$) quantum metrology approaches to solitons. We examine multipartite NOON states specially prepared for the improvement of multiparameter estimation protocols. As a particular example of producing such states, we propose a three-mode soliton Josephson-junction (TMSJJ) model as a three-mode extension for the soliton Josephson-junction bosonic model, which we previously proposed. The energy spectrum of the TMSJJ exhibits sharp phase transition peculiarities for the TMSJJ ground state. The transition occurs from a Gaussian-like (coherent) state to the superposition of entangled Fock states, which rapidly approach the three-mode NOON state. We show that in the presence of weak losses the TMSJJ enables saturation scaling relevant to the optimal state limit close to the GHL. Our findings open prospects for quantum network sensorics with atomtronic circuits.
Track C1: Safety Verification of Deep Neural Networks (DNNs)

Formal verification of neural networks and broader machine learning models is an emerging field that has gained significant attention due to the growing use and impact of these data-driven methods. This track explores techniques for formally verifying neural networks and other machine learning models across various application domains. It includes papers and presentations discussing new methodologies, software frameworks, technical approaches, and case studies. Benchmarks play a crucial role in evaluating the effectiveness and scalability of these methods. Currently, available benchmarks mainly focus on computer vision problems, such as local robustness to adversarial perturbations of image classifiers. To address this limitation, this track compiles and publishes benchmarks comprising machine learning models and their specifications across domains such as computer vision, finance, security, and others. These benchmarks will help assess the suitability and applicability of formal verification methods in diverse domains.
Reducing circuit depth with qubitwise diagonalization

A variety of quantum algorithms employ Pauli operators as a convenient basis for studying the spectrum or evolution of Hamiltonians or measuring multibody observables. One strategy to reduce circuit depth in such algorithms involves simultaneous diagonalization of Pauli operators generating unitary evolution operators or observables of interest. We propose an algorithm yielding quantum circuits with depths $O(nlogr)$ diagonalizing $n$-qubit operators generated by $r$ Pauli operators. Moreover, as our algorithm iteratively diagonalizes all operators on at least one qubit per step, it is well suited to maintain low circuit depth even on hardware with limited qubit connectivity. We observe that our algorithm performs favorably in producing quantum circuits diagonalizing randomly generated Hamiltonians as well as molecular Hamiltonians with short depths and low two-qubit gate counts.
Quantum partial information decomposition

Partial information decomposition (PID) takes one step beyond Shannon's theory in decomposing the information two variables, $A$ and $B$, possess about a third variable, $\mathcal{T}$, into distinct parts: unique, shared (or redundant), and synergistic information. Here we show how these concepts can be defined in a quantum setting. We apply a quantum PID to scrambling in quantum many-body systems, for which a quantum-theoretic description has been proven to be productive. Unique information in particular provides a finer description of scrambling than does the so-called tri-information.
Information propagation in long-range quantum many-body systems

The authors study the information transportation capabilities of bosonic quantum many-body systems with long-range interactions. They rigorously prove that initially localized quantum states exhibit a linear Lieb-Robinson light cone for information propagation and they derive consequences of this result, e.g., bounds on quantum messaging and quantum state control.