Reviewerly/exHarmony huggingface dataset:
Adaptive quantum error mitigation using pulse-based inverse evolutions

Abstract Quantum Error Mitigation (QEM) enables the extraction of high-quality results from the presently-available noisy quantum computers. In this approach, the effect of the noise on observables of interest can be mitigated using multiple measurements without additional hardware overhead. Unfortunately, current QEM techniques are limited to weak noise or lack scalability. In this work, we introduce a QEM method termed ‘Adaptive KIK’ that adapts to the noise level of the target device, and therefore, can handle moderate-to-strong noise. The implementation of the method is experimentally simple — it does not involve any tomographic information or machine-learning stage, and the number of different quantum circuits to be implemented is independent of the size of the system. Furthermore, we have shown that it can be successfully integrated with randomized compiling for handling both incoherent as well as coherent noise. Our method handles spatially correlated and time-dependent noise which enables us to run shots over the scale of days or more despite the fact that noise and calibrations change in time. Finally, we discuss and demonstrate why our results suggest that gate calibration protocols should be revised when using QEM. We demonstrate our findings in the IBM quantum computers and through numerical simulations.
Do technical trading rules outperform the simple buy-and-hold strategy in the cryptocurrency market?

The authors test the weak-form efficiency in cryptocurrency markets using the most recent and comprehensive data as of 2021. The authors apply various technical indicators to take a long or short position on 99 cryptocurrencies and compare the 10-day returns based on the technical trading strategies to the simple buy-and-hold returns. The authors find that the trading strategies based on single indicators or the combination of two indicators do not generate higher returns than buy-and-hold returns among cryptos. These findings suggest that cryptocurrency markets are weak-form efficient in general.
A visually secure image encryption method based on semi-tensor product compressed sensing and IWT-HD-SVD embedding

The conventional approach for images encryption entails transforming a regular image into an encrypted image that resembles noise. However, this noise-like encrypted image is susceptible to drawing the attention of an attacker when transmitted through a public channel. Hence, there has been a recent surge in the interest of academics towards visually secure image encryption techniques. In a broad sense, encryption methods that include visual significance should prioritize four key elements: the resemblance between the cypher picture and the carrier image, the capacity for embedding, the attainment of good recovery quality, and resilience against many forms of attacks. To address the issues pertaining to inadequate visual security, limited resistance against attacks, and subpar quality of reconstructed images observed in contemporary image encryption and compression methodologies. This paper proposes a visually secure image encryption method based on improved semi-tensor product compressed sensing, two-way cross zigzag obfuscation, and IWT-HD-SVD embedding. Firstly, the plain image is sparsely represented in the Discrete Wavelet Transform (DWT) domain, and a two-way cross zigzag mismatch strategy is proposed to disarrange the coefficient vectors. Then the plain image is encrypted as a secret image by the improved semi-tensor product compression sensing technique. After that, IWT-HD-SVD embedding technique is proposed to embed the secret image into the carrier image to generate the final meaningful cryptographic image. This dramatically improves the visual security of the cryptographic image. Simulation results show that the quality of the decrypted image is approximately 36 dB and up to 44 dB. In addition, the cryptographic image is highly robust against common noise attacks of 0.05 %.
A Comprehensive Survey On Efficient Transformers

In recent years, there has been substantial attention directed towards the development of proficient Transformers, which display considerable potential in effectively managing extensive sequences in various fields, including but not limited to natural language processing and computer vision. While researchers have conducted survey papers on this field, there remains a notable gap in papers that specifically present and discuss benchmark datasets tailored to evaluate the efficiency and performance of efficient transformers in specific challenges, notably hierarchical reasoning on long-range sequences, question answering on large documents, and key phrase extraction. Moreover, our paper aims to fill this void by offering a clear and comprehensive survey of efficient Transformers. We focus on three main approaches: sparse attention, linearized attention, and low rank attention, all geared towards mitigating the computational and memory challenges associated with traditional Transformers while ensuring consistently high-performance levels. By presenting and analyzing these critical aspects, our paper endeavors to contribute to the broader understanding and advancements in efficient Transformers, paving the way for further research and innovation in this dynamic field.
Game-Theoretic Trajectory Planning of Mobile Robots in Unstructured Intersection Scenarios

The motion of autonomous mobile robot platforms in a shared operation area can lead to intersection scenarios that cannot be resolved by individual and uncoordinated motion planning. A coordinated solution approach is needed to solve these kinds of scenarios for which game theory provides a suitable framework. Therefore, this paper presents a game-theoretic trajectory planner based on a nonlinear receding horizon control scheme. To handle complex collision avoidance in unstructured environments with no reference paths, the proposed method extends an existing sensitivity enhanced iterated best response algorithm. Our reformulation of the sensitivity cost term within the optimal control problem of each vehicle enables a cooperative and time-efficient collision avoidance behavior. Another benefit of our proposed approach is that no central coordination unit is required, and only minimal communication between the vehicles is necessary. We simulatively compare our decentralized approach in different intersection scenarios involving 2 to 4 mobile robots to a centralized multi-robot trajectory planner. The simulation results show that our algorithm resolves the intersection scenarios in most cases with only a minimum average duration extension until each vehicle reaches its goal state compared to the centralized planner.
Incorrectness Proofs for Object-Oriented Programs via Subclass Reflection

Inheritance and method overriding are crucial concepts in object-oriented programming (OOP) languages. These concepts support a hierarchy of classes that reuse common data and methods. Most existing works for OO verification focus on modular reasoning in which they could support dynamic dispatching and thus efficiently enforce the Liskov substitution principle on behavioural subtyping. They are based on $$superclass\ abstraction$$ to reason about the correctness of OO programs. However, techniques to reason about the incorrectness of OOP are yet to be investigated. In this paper, we present a mechanism that 1) specifies the normal and abnormal executions of OO programs by using specifications and specifications respectively; 2) verifies these specifications by a novel under-approximation proof system based on incorrectness logic that can support dynamic modularity. We introduce subclass reflection with dynamic views and an adapted subtyping relation for under-approximation. Our proposal can deal with both OOP aspects (e.g., behavioural subtyping and casting) and under-approximation aspects (e.g., dropping paths). To demonstrate how the proposed proof system can soundly verify the specifications, we prove its soundness, prototype the proof system, and report on experimental results. The results show that our system can precisely reason about the incorrectness of programs with OOP aspects, such as proving the presence of casting errors and null-pointer-exceptions.
Revisiting State Estimation for Linear Systems with State and/or Filter Equality Constraints*

This article revisits optimal state estimation, in the mean squared error matrix sense, for linear systems with state and/or filter subject to linear equality constraints (LECs). First, it is shown that the conventional Wiener filter (WF) form incorporates any LECs on the state, thus yielding a filter subject to the same LECs. Conversely, an optimal linear filter subject to LECs (or linear equality gain constraints) in general does not exist. Therefore, adding LECs on the WF or WF gain matrix either leaves unchanged or degrades the constrained WF performance w.r.t. the unconstrained WF. Since the Kalman filter (KF) and Kalman predictor (KP) are recursive WF forms for linear discrete state-space (LDSS) systems, the same results hold for both estimators, which is in contradiction with several existing results in the literature. Actually, even if these existing results are mathematically correct, however, they have been derived for unsuitable assumed LDSS models where the state is surprisingly not compliant with the assumed LECs. Indeed, it is shown that for suitable assumed state models, both standard KF and KP forms satisfy the assumed LECs, making any additional projection step superfluous.
Spatio-Temporal Analysis of Overactuated Motion Systems: A Mechanical Modeling Approach

Flexible dynamics in motion systems lead to inherent spatio-temporal system behavior. The aim of this paper is to develop an unified approach for the Identification of modal models of spatio-temporal overactuated systems. The approach exploits the modal modeling framework and the overactuated setting to enhance the estimation of the spatial system behavior. The proposed approach is applied in an experimental case study. The case study considers an experimental overactuated stage and illustrates the effectiveness of the proposed approach.
The effect of feature space separation at different training states of CNN

Convolutional neural networks (CNNs) are successful in many different applications, however, such model decisions can be easily changed by slight modification on the inputs. The robustness needs to be guaranteed for the safety critical fields like medicine, therefore, it is necessary to understand the decision making procedure of CNN models. As the CNN model automatically extracts the image features and makes the corresponding predictions, observing the learned features space can approximately represent the decision boundary. In this paper, the use of linear interpolation to monitor the learned feature space is applied to analyze the separability property of a CNN model at different classes. By forcing the CNN to learn to separate the extracted features at different layer depths by adding the conformity loss, the classification distribution was more separable and stable to enhance the robustness of the model. The performance of linear interpolation showed the model had better classification abilities, where there are fewer perturbed classes appearing.
Attack Identification for Cyber-Physical Security in Dynamic Games under Cognitive Hierarchy

This paper considers the problem of identifying the profiles and capabilities of attackers injecting adversarial inputs to a cyber-physical system. The system in question interacts with attackers of different levels of intelligence, each employing different feedback controllers against the system. Principles of behavioral game theory – specifically the concept of level-k thinking – is employed to construct a database of potential attack vectors. By observing the state trajectories under sequential interactions with different adversaries, the defender adaptively estimates both the number and profiles of the different attack signals using an online deterministic annealing approach. This information is used to dynamically estimate the level of intelligence of the attackers. Simulation results showcase the efficacy of the proposed method.
Learning Heterogeneous Agent Cooperation via Multiagent League Training

Many multiagent systems in the real world include multiple types of agents with different abilities and functionality. Such heterogeneous multiagent systems have significant practical advantages. However, they also come with challenges compared with homogeneous systems for multiagent reinforcement learning, such as the non-stationary problem and the policy version iteration issue. This work proposes a general-purpose reinforcement learning algorithm named Heterogeneous League Training (HLT) to address heterogeneous multiagent problems. HLT keeps track of a pool of policies that agents have explored during training, gathering a league of heterogeneous policies to facilitate future policy optimization. Moreover, a hyper-network is introduced to increase the diversity of agent behaviors when collaborating with teammates having different levels of cooperation skills. We use heterogeneous benchmark tasks to demonstrate that (1) HLT promotes the success rate in cooperative heterogeneous tasks; (2) HLT is an effective approach to solving the policy version iteration problem; (3) HLT provides a practical way to assess the difficulty of learning each role in a heterogeneous team.
Delay Embedded Echo-State Network: A Predictor for Partially Observed Systems

This paper considers the problem of data-driven prediction of partially observed systems using a recurrent neural network. While neural network based dynamic predictors perform well with full-state training data, prediction with partial observation during training phase poses a significant challenge. Here a predictor for partial observations is developed using an echo-state network (ESN) and time delay embedding of the partially observed state. The proposed method is theoretically justified with Taken's embedding theorem and strong observability of a nonlinear system. The efficacy of the proposed method is demonstrated on three systems: two synthetic datasets from chaotic dynamical systems and a set of real-time traffic data.
A Tool-Assisted Approach for User-Friendly Definition of FB Constraints

Model-based software engineering is a promising approach for controlling complex automation systems. Automation engineers, unfortunately, do not routinely model the expected behavior, especially if they are not trained in software engineering. They rather focus only on the implementation. In this paper, we analyze potential causes for the low adoption of service sequences within IEC 61499, which define behavioral constraints for building blocks of control applications. We propose a methodology for mining constraints of software components, which are then classified by an engineer. Furthermore, we suggest extending the language to allow for forbidden and mandatory scenarios. Our approach facilitates the constraint definition for legacy software, where the effort of manual specification is extremely high. Tool support for all described extensions and modeling techniques is provided as part of the open source IDE Eclipse 4diac to facilitate applying the approach in practice.
Influence of artificial intelligence (AI) perception on career resilience and informal learning

Purpose Artificial intelligence (AI) and big data analysis may further enhance the automated and smart features of tourism and hospitality services. However, it also poses new challenges to human resource management. This study aims to explore the direct and indirect effects of employees’ AI perception on career resilience and informal learning as well as the mediating effect of career resilience. Design/methodology/approach This paper proposed a theoretical model of AI perception, career resilience and informal learning with perceived AI as the antecedent variable, career resilience as the mediate variable and informal learning as the endogenous variable. Targeting the employees working with AI, a total of 472 valid data were collected. Data were analyzed using structural equation modeling with AMOS software. Findings Findings indicated that employees’ perception of AI positively contributes to career resilience and informal learning. Apart from the direct effect on informal learning, career resilience also mediates the relationship between AI perception and informal learning. Originality/value Research findings provide both theoretical and practical implications by revealing the impact of AI perception on employees’ career development, leaning activities, explaining how AI transforms the nature of work and career development and shedding lights on human resource management in the tourism and hospitality field.
Language augmentation approach for code-mixed text classification

The usage of more than one language in the same text is referred to as Code Mixed. It is evident that there is a growing degree of adaption of the use of code-mixed data, especially English with a regional language, on social media platforms. Existing deep-learning models do not take advantage of the implicit language information in the code-mixed text. Our study aims to improve BERT-based models’ performance on low-resource Code-Mixed Hindi-English Datasets by experimenting with language augmentation approaches. For language augmentation in BERT models, we explore word-level interleaving and post-sentence placement of language information. We have examined the performance of vanilla BERT-based models and their code-mixed HingBERT counterparts on respective benchmark datasets, comparing their results with and without using word-level language information. The models were evaluated using metrics such as accuracy, precision, recall, and F1 score. Our findings show that the proposed language augmentation approaches work well across different BERT models. We demonstrate the importance of augmenting code-mixed text with language information on five different code-mixed Hindi-English classification datasets based on sentiment analysis, hate speech detection, and emotion detection.
Zef: Low-latency, Scalable, Private Payments

Zef is the first Byzantine-Fault Tolerant (BFT) protocol to support payments in anonymous digital coins at arbitrary scale. Zef achieves its performance by forgoing the expense of BFT consensus and using instead Byzantine Consistent Broadcast as its core primitive. Zef is asynchronous, low-latency, linearly-scalable, and powered by partially-trusted sharded authorities. Zef introduces opaque coins represented as off-chain certificates that are bound to user accounts. In order to hide the values of coins when a payment operation consumes or creates them, Zef uses cryptographically hiding commitments and NIZK proofs. Coins creation and spending are unlinkable through the Coconut blind and randomizable threshold anonymous credentials scheme. To control storage costs associated with coin replay prevention, Zef allows safe accounts deletion once the account is deactivated. Our extensive benchmarks on AWS confirm textbook linear scalability and demonstrate a confirmation time under one second at nominal capacity. Compared to existing anonymous payment systems based on a blockchain, this represents a latency speedup of three orders of magnitude, with no theoretical limit on throughput.
PENGEMBANGAN MEDIA POP UP BOOK MATERI MINYAK BUMI BERBASIS SOSIO-SCIENTIFIC ISSUE (SSI)

This research were investigated by the lack of socio-scientific issue (SSI)-based pop up book media. This research aimed at designing SSI-based pop up book media on crude oil lesson. Research and development (R&amp;D) method was used in this research with borg and gall model that was limited to the fifth stage. The stages were collecting the data, planning, developing the product, testing the product, and revising. SSI-based pop up book media on crude oil lesson was tested valid with the percentage 94.4% (very valid), it was tested practical with the percentage 96.1% (very practical), and it got good response to overall content of pop up book with the percentage 96.7% (very practical). Based on these findings, it could be concluded that SSI-based pop up book media on crude oil lesson was proper to be used as a learning media for students at school.Keywords : Pop Up Book, Socio-Scientific Issue (SSI), Crude Oil
Oriented object detection in satellite images using convolutional neural network based on ResNeXt

Abstract Most object detection methods use a horizontal bounding box that causes problems between adjacent objects with arbitrary directions, resulting in misaligned detection. Hence, the horizontal anchor should be replaced by a rotating anchor to determine oriented bounding boxes. A two‐stage process of delineating a horizontal bounding box and then converting it into an oriented bounding box is inefficient. To improve detection, a box‐boundary‐aware vector can be estimated based on a convolutional neural network. Specifically, we propose a ResNeXt101 encoder to overcome the weaknesses of the conventional ResNet, which is less effective as the network depth and complexity increase. Owing to the cardinality of using a homogeneous design and multibranch architecture with few hyperparameters, ResNeXt captures better information than ResNet. Experimental results demonstrate more accurate and faster oriented object detection of our proposal compared with a baseline, achieving a mean average precision of 89.41% and inference rate of 23.67 fps.