Reviewerly/exHarmony huggingface dataset:
Bounding first-order quantum phase transitions in adiabatic quantum computing

In the context of adiabatic quantum computation (AQC), it has been argued that first-order quantum phase transitions (QPTs) due to localization phenomena cause AQC to fail by exponentially decreasing the minimal spectral gap of the Hamiltonian along the annealing path as a function of the qubit number. The vanishing of the spectral gap is often linked to the localization of the ground state in a local minimum, requiring the system to tunnel into the global minimum at a later stage of the annealing. Recent methods have been proposed to avoid this phenomenon by carefully designing the involved Hamiltonians. However, it remains a challenge to formulate a comprehensive theory of the effect of the various parameters and the conditions under which QPTs make the AQC algorithm fail. Equipped with concepts from graph theory, in this work we link graph quantities associated with the Hamiltonians along the annealing path with the occurrence of QPTs. These links allow us to derive bounds on the location of the minimal spectral gap along the annealing path, augmenting the toolbox for the analysis of strategies to improve the runtime of AQC algorithms.
Quantum phase processing and its applications in estimating phase and entropies

Quantum computing can provide speedups in solving many problems as the evolution of a quantum system is described by a unitary operator in an exponentially large Hilbert space. Such unitary operators change the phase of their eigenstates and make quantum algorithms fundamentally different from their classical counterparts. Based on this unique principle of quantum computing, we develop an algorithmic toolbox, quantum phase processing, that can directly apply arbitrary trigonometric transformations to eigenphases of a unitary operator. The quantum phase processing circuit is constructed simply, consisting of single-qubit rotations and controlled unitaries, typically using only one ancilla qubit. Besides the capability of phase transformation, quantum phase processing in particular can extract the eigeninformation of quantum systems by simply measuring the ancilla qubit, making it naturally compatible with indirect measurement. Quantum phase processing complements another powerful framework known as quantum singular value transformation and leads to more intuitive and efficient quantum algorithms for solving problems that are particularly phase related. As a notable application, we propose a quantum phase estimation algorithm without quantum Fourier transform, which requires the fewest ancilla qubits and matches the best performance so far. We further exploit the power of our method by investigating a plethora of applications in Hamiltonian simulation, entanglement spectroscopy, and quantum entropy estimation, demonstrating improvements or optimality for almost all cases.
ChatGPT in the Loop: A Natural Language Extension for Domain-Specific Modeling Languages

This paper presents an approach to no-code development based on the interplay of formally defined (graphical) Domain-Specific Languages and informal, intuitive Natural Language which is enriched with contextual information to enable referencing of formally defined entities. The paper focuses on the use and automated integration of these enriched intuitive languages via ChatGPT-based code generation to exploit the best of both language paradigms for domain-specific application development. To compensate for the lack of control over the intuitive languages we apply automated system-level validation via automata learning and subsequent model checking. All this is illustrated using the development of point-and-click adventures as a minimal viable example.
Benchmark: Formal Verification of Semantic Segmentation Neural Networks

Formal verification utilizes a rigorous approach to ensure the absence of critical errors and validate models against predefined properties. While significant progress has been made in verification methods for various deep neural networks (DNNs), such as feed-forward neural networks (FFNNs) and convolutional neural networks (CNNs), the application of these techniques to semantic segmentation remains largely unexplored. Semantic segmentation networks are vital in computer vision applications, where they assign semantic labels to individual pixels within an image. Given their deployment in safety-critical domains, ensuring the correctness of these networks becomes paramount. This paper presents a comprehensive benchmark study on applying formal verification techniques to semantic segmentation networks. We explore a diverse set of state-of-the-art semantic segmentation datasets and generate neural network models, including fully-convolutional networks and encoder-decoder architectures. Our investigation encompasses a wide range of verification properties, focusing on the robustness of these models against bounded adversarial vulnerabilities. To evaluate the networks’ performance, we employ set-based reachability algorithms to calculate the output reachable set(s) and some state-of-the-art performance measures for a comparative study among the networks. This benchmark paper aims to provide the formal verification community with several semantic segmentation networks and their robustness specifications for future use cases in different neural network verification competitions.
Web computer model of optimal control of the fish production process in the correctional facilities of the Russian Federation

The paper deals with the problems of ensuring the food security of correctional facilities through the development of fish farms in the correctional system of the Russian Federation. The rational use of fish stocks in inland waters and increasing their productivity is one of the most important and urgent fisheries problems, the complexity and versatility of which require a systematic approach and the use of program methods to solve it. The paper proposes a statement of the problem of fish catching control in the form of an optimal control problem, for the solution of which a numerical optimization method implemented in the software is used. The results of the software tool operation for various values of input parameters are given. The developed software tool can be used to provide decision support in the production of fish products.
The pedagogical principles and effectiveness of utilizing ChatGPT for language learning

The rapid advancement of artificial intelligence (AI) has engendered innovative approaches to language learning, revolutionizing conventional instructional methodologies. This study delves into the fusion of ChatGPT, an AI-powered language model, with pedagogical principles to ascertain its efficacy in augmenting language learning experiences. The research explores the symbiotic relationship between ChatGPT and established language acquisition theories, dissecting how it facilitates interactive, personalized, and engaging language instruction. Through a comprehensive mixed-methods approach involving both learners and educators, the study unveils a substantial positive correlation between ChatGPT integration and perceived effectiveness, substantiating its potential to revolutionize language pedagogy. The findings illuminate the significance of synergizing pedagogical underpinnings with technological innovation, advocating for the seamless integration of ChatGPT to invigorate language learning and elevate learner outcomes.
Trace contracts

Abstract Behavioral software contracts allow programmers to strengthen the obligations and promises that they express with conventional types. They lack expressive power, though, when it comes to invariants that hold across several function calls. Trace contracts narrow this expressiveness gap. A trace contract is a predicate over the sequence of values that flow through function calls and returns. This paper presents a principled design, an implementation, and an evaluation of trace contracts.
Virtual reality as a technology of memory: Immersive presence in Polish politics of memory

Abstract Experiences that take place in virtual reality (VR) become part of users’ autobiographical memory. As memories can impact users’ self-perception, personal beliefs, and social interactions, storyliving in VR narratives can be used to manipulate memory and mould users’ self according to the preferences of the VR narrative creators. Poland's Ministry of Culture and National Heritage has, in recent years, generously invested in the production of VR films that depict events from the Polish historical canon. VR is a spatial technology that positions users ‘inside’ a virtual storyworld and leverages users’ sense of body position and movement to enhance memory and a sense of presence. Three dimensions of presence – self, social, and spatial – are relevant to understanding the role of VR in the politics of memory. The article interrogates a recent Polish VR production, Wiktoria 1920 (2020, dir. Tomasz Dobosz), and argues that VR representations exploit a sense of presence to shape users’ memory, perspective, and emotions in relation to the past.
Absorption of <scp>PV</scp> Power Prediction Errors with Headroom Control by Statistical, Machine Learning and Combined Models

Owing to the mass penetration of renewable energy (RE) in Japan, the replacement reserve for feed‐in tariff (RR‐FIT) was introduced in 2021. To promote the further introduction of RE, we attempted to provide reserve power value to the balancing market by controlling the headroom of photovoltaic (PV) power with a PV power plant as a research subject. At the planning stage, the information available is the predicted value, and PV prediction error always occur on the day. Therefore, in this research, we proposed an algorithm to determine the absorption of prediction errors headroom in PV output, thereby maximally reducing negative imbalance. In addition, we developed a statistical model, SVR model, and combined models to calculate and compare the number of imbalance spots and amount of imbalance. Accordingly, the proposed methods can significantly reduce imbalance compared to planning with predicted values, without setting the headroom of prediction errors. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Integrating Distributed Component-Based Systems Through Deep Reinforcement Learning

Modern system design and development often consists of combining different components developed by separate vendors under some known constraints that allow them to operate together. Such a system may further benefit from further refinement when the components are integrated together. We suggest a learning-open architecture that employs deep reinforcement learning performed under weak assumptions. The components are “black boxes”, where their internal structure is not known, and the learning is performed in a distributed way, where each process is aware only on its local execution information and the global utility value of the system, calculated after complete executions. We employ the proximal policy optimization (PPO) as our learning architecture adapted to our case of training control for black box components. We start by applying the PPO architecture to a simplified case, where we need to train a single component that is connected to a black box environment; we show a stark improvement when compared to a previous attempt. Then we move to study the case of multiple components.
The Role of Deep Learning Models in the Detection of Anti-Social Behaviours towards Women in Public Transport from Surveillance Videos: A Scoping Review

Increasing women’s active participation in economic, educational, and social spheres requires ensuring safe public transport environments. This study investigates the potential of machine learning-based models in addressing behaviours impacting the safety perception of women commuters. Specifically, we conduct a comprehensive review of the existing literature concerning the utilisation of deep learning models for identifying anti-social behaviours in public spaces. Employing a scoping review methodology, our study synthesises the current landscape, highlighting both the advantages and challenges associated with the automated detection of such behaviours. Additionally, we assess available video and audio datasets suitable for training detection algorithms in this context. The findings not only shed light on the feasibility of leveraging deep learning for recognising anti-social behaviours but also provide critical insights for researchers, developers, and transport operators. Our work aims to facilitate future studies focused on the development and implementation of deep learning models, enhancing safety for all passengers in public transportation systems.
SiamMAN: Siamese Multi-Phase Aware Network for Real-Time Unmanned Aerial Vehicle Tracking

In this paper, we address aerial tracking tasks by designing multi-phase aware networks to obtain rich long-range dependencies. For aerial tracking tasks, the existing methods are prone to tracking drift in scenarios with high demand for multi-layer long-range feature dependencies such as viewpoint change caused by the characteristics of the UAV shooting perspective, low resolution, etc. In contrast to the previous works that only used multi-scale feature fusion to obtain contextual information, we designed a new architecture to adapt the characteristics of different levels of features in challenging scenarios to adaptively integrate regional features and the corresponding global dependencies information. Specifically, for the proposed tracker (SiamMAN), we first propose a two-stage aware neck (TAN), where first a cascaded splitting encoder (CSE) is used to obtain the distributed long-range relevance among the sub-branches by the splitting of feature channels, and then a multi-level contextual decoder (MCD) is used to achieve further global dependency fusion. Finally, we design the response map context encoder (RCE) utilizing long-range contextual information in backpropagation to accomplish pixel-level updating for the deeper features and better balance the semantic and spatial information. Several experiments on well-known tracking benchmarks illustrate that the proposed method outperforms SOTA trackers, which results from the effective utilization of the proposed multi-phase aware network for different levels of features.
Knowledge graph‐guided object detection with semantic distance network

Abstract In this research study, the inadequacies of current object detection techniques are analyzed. These techniques solely recognize individual objects without considering their interrelationships. To address this issue, a novel solution called the knowledge graph‐guided semantic distance network (KGSDN) approach is proposed. By utilizing a knowledge graph, KGSDN provides semantic contextual cues, leading to enhanced object detection accuracy. The KGSDN framework seamlessly integrates the knowledge graph and object detection network and employs an attention‐based network to evaluate the semantic distance between objects. As a result, the conditional object probability of every bounding box is updated, and the joint probability of all objects in the image is determined. The empirical findings indicate that this approach significantly improves the performance of deep learning‐based object detection methods.
Development of mathematical and dynamic logical models in complex special-purpose organizational systems of monitoring environmental health

This article examines the problem of monitoring environmental health using complex special-purpose organizational systems. To make effective management decisions about the functioning of complex special-purpose organizational systems, it is necessary to develop dynamic logical and mathematical models for them to recognize and predict the functioning of these systems. The article is referred to the specialists of natural resources management.
Let’s just work together! Paper, laptop, and tablet as equally effective tools for groupwork in college

Abstract Students often recruit digital devices to complete their classroom activities. To determine best practices for the classroom, the present study explored the use of tablets relative to laptops and paper in the understudied context of brief, in-person, small group academic tasks. One-hundred twenty small groups of two to three undergraduate students were randomly assigned to one device (paper, laptop, or tablet) and one set-up (a single shared device or multiple individual devices) in a 3 × 2 between-subjects design. Data were collected in 2019 –2021. Students completed one group task that culminated in a knowledge quiz and a second group task that culminated in writing their own quiz together. They then individually rated group members as well as reported their perceptions of satisfaction, effort, and difficulty. Open-ended questions further assessed their perceptions of technology and the three devices in question. Results showed equivalency in group outcomes and individual perceptions across devices and set-ups, with very few exceptions. Quantitative and qualitative data supported that students found advantages and drawbacks to each device and believed that all were effective tools in such a context. Educators must consider the affordances of different technologies when encouraging their use in the classroom but should be open to students using their device of choice for groupwork.
Now You See Me: Robust Approach to Partial Occlusions

Object occlusion presents a significant and complex challenge in computer vision, requiring effective solutions. While convolutional neural networks (CNNs) have shown success in regular image classification tasks, they often struggle when confronted with images containing partial occlusions. Partial occlusion refers to instances where an object is partially obscured by another object or space. Solving this problem holds immense potential for various applications, with a particular focus on the domain of autonomous driving. Autonomous vehicle research has emerged as a prominent topic in recent years, and it involves numerous situations where partial occlusions occur. These occlusions can manifest in the form of obstructed driving signs, pedestrians, or other objects, occurring at varying angles. Beyond the realm of autonomous driving, addressing the challenges of partial occlusion can also contribute to video analytics in traffic data, enabling crime prevention, income-level prediction for different groups, and more. In this study, we present our approach, which involves creating a synthetic dataset by augmenting the Stanford Car Dataset with occlusions of various sizes and types. Subsequently, we conduct a comprehensive analysis employing state-of-the-art models, investigating the impact of different occlusion proportions and types on model performance. Our research aims to shed light on the robustness of various models in the face of partial occlusions and their performance when trained on both occluded and unoccluded images. These insights can play a vital role in the development of more reliable autonomous driving systems and other computer vision applications.
An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements.
UAV path planning based on third-party risk modeling

Drones play an important role in modern cities, they bring convenience but also bring corresponding risks. Falling drones can cause casualties or damage to urban facilities and serious property damage, which increases the third-party risk problem. An effective way to reduce these third-party risks is to avoid high-risk areas in path planning, but most current path planning methods are optimized to minimize flight distance, and third-party risk costs are rarely considered. In this paper, a comprehensive risk-cost UAV path planning method is proposed, which evaluates urban risk by establishing a third-party risk model that incorporates obstacle risk, death risk, and property loss risk. This paper proposes a Min-cost A* algorithm based on city risk assessment, and smooths the generated low-risk path through the improved Floyd algorithm. The results show that the path planning method can effectively reduce the risk in the flight path, improve the reliability of the UAV flight path in the urban environment, and solve the problem of planning the third-party risk path of the UAV.
Exploring the NFT market on ethereum: a comprehensive analysis and daily volume forecasting

With the popularity of Non-Fungible Tokens (NFTs), which has now become a financial market that has attracted extensive attention worldwide. A large number of investors and creators are flocking to this emerging market in search of investment opportunities. Nowadays, many studies have analysed this phenomenon from an economic perspective. However, we know little about the players and ecosystem characteristics of this market. To fill this knowledge gap, we first provide a processed large-scale dataset of the Ethereum blockchain-based NFT market, containing more than 80 million NFT transaction records from January 2018 to April 2022. Second, we constructed the NFT creator graph (NCG) and NFT holder graph (THG) to delve into the characteristics of the NFT market. Further, we analyse the market preferences and trends using statistical methods to reveal the development trends of the NFT market. Finally, we focus on predicting the transaction volume of the NFT market and analyse the influence factors. This study provides data support for participants and researchers to explore the NFT market, while our analysis promotes a deeper understanding of the NFT market among the public.