Reviewerly/exHarmony huggingface dataset:
A Time-Aware Model for Legal Smart Contracts

Smart Contracts that embody real world legal contracts require not only a sound and secure implementation but also a careful analysis of the underlying contractual commitments. Temporal clauses are abundant in contracts, requiring permissions and obligations to be executed in temporal relationships with observed events. Before signing a contract a thorough analysis, whether breaches of temporal clauses are imminent, whether all temporal obligations can be fulfilled are inevitable to avoid the cost of violating temporal commitments. We present a contract model that focuses on modeling temporal commitments explicitly. And we present techniques based on these contract models to analyze the temporal properties of contracts, in particular, whether a party can guarantee to fulfill all temporal commitments under all foreseeable circumstances. We present a framework that supports the development and negotiation of contracts precluding the risk of violating temporal clauses.
Supporting Method Creation, Adaptation and Execution with a Low-code Approach

Method Engineering emerged in the 90s as a discipline to design, construct and adapt methods, techniques and tools for the development of information systems. By executing a method step by step, users follow a systematic and well-defined way to attain the results which the method was created for. To support the creation of methods in a more guided and systematic way, a method framework can be used as a template, allowing one to benefit from the expertise of method engineers who regrouped their good practices in such frameworks. However, the creation and adoption of a method may be difficult if there is no tool to support these activities. In addition, method engineers may not have the programming skills to implement such a tool. In this context, we propose an approach inspired by the low-code paradigm for Method Engineering. The approach helps method engineers in creating new methods or adapting an already existing framework that integrates some construction rules for guidance. Our approach automatically provides tool support so that method experts can actually execute the method. This paper presents the approach through a proof of concept implementation, and a first empirical evaluation through semi-structured interviews.
Co-Presence of Readiness-To-Hand &amp; Presence-At-Hand as the Shape of Things by Revisiting Technology-Aided Learning from the Heideggerian Perspective

In everyday technology-aided learning, students as Dasein are embedded in equipmentally oriented world with useful and serviceable physical and abstract technology as ready-to-hand for the sake of fulfilling their education needs, while experiencing counterparts as present-at-hand due to (1) technical problems such as system breakdowns, dilapidation or physical damage, and (2) non-technical counterparts such as contents with calculable quantities and relationships in the informationalized world to be theoretically and critically examined. This article analyses the co-presence of the ready-to-hand and present-at-hand modes in learning contexts in sequential and simultaneous ways. Sequentially, the two modes of an technological thing oscillate frequently due to students’ strong familiarity with the use and due to contents of calculable quantities and relationships that reinforce calculative thought. Simultaneously, one technology can be primordially used as ready-to-hand with intense familiarity and proficiency to support the theoretical and critical examination of the other technology as present-at-hand (149 words).
Creating Ontologies Concept in Transport and Traffic Research Environment

Ontology engineering in the field of transportation engineering deals with the construction, development, and maintenance of ontologies, which are formal descriptions of concepts and relationships in the domain of transportation technology. Algorithms that are developed in the field of artificial intelligence allow computers to think and behave like humans. Ontological engineering techniques contribute to the development of artificial intelligence in terms of semantic interpretation of textual information from a specific domain of knowledge. This scientific paper describes the conceptual foundation of building ontologies in the field of transportation based on existing data models and analyzes the possibilities of applying artificial intelligence for automated establishment and expansion of ontologies in the transportation field. Also, the paper describes the role of ontologies in semantic applications and the applicative contribution of semantic applications in transportation.
Open Quantum Systems

By definition, all realistic quantum systems are open systems, and their dynamics are governed by the master equation. The master equation for open quantum systems plays the same role as the Newtonian equation for classical mechanics, the Maxwell equation for electrodynamics, and the Schrodinger equation for quantum mechanics. It is with this as preamble that the theory of open quantum systems is the foundation for the currently developing quantum technology. However, before the twenty-first century, there exists only one exact master equation, which is the quantum Brownian model master equation. In this case, the system is modeled as a harmonic oscillator which is linearly coupled to the many harmonic oscillators. In the current chapter, leveraging the coherent states path integrals, one is able to obtain the exact master equations for fermionic, bosonic, as well as topological systems, respectively. We shall focus on the coherent states formalism of how to derive the exact master equation for bosonic and fermionic open systems. We shall also discuss the associated general non-Markovian dynamics of open quantum system and their applications to quantum transport and quantum thermodynamics.
Introduction

This chapter serves two purposes. First, it provides a history of how the concept of coherent states was germinated and who made some of the most fundamental theoretical developments in the twentieth century. Second, it outlines the most important outcome of these developments was how the underlying mathematical structure of coherent states is a branch of mathematics known as Lie groups and Lie algebras. Utilizing the coherent state concept, and leveraging the mathematical structures usability, a plethora of physical applications, such as quantum phase transitions and quantum chaos, is discussed in later chapters.
Coherent States for Fermions

As is well known, the world of elementary particles is divided into two camps: bosons and fermions. It is particular interesting that because of the nature of the physics of these two camps, the respective Lie algebras and Lie groups are obviously and distinctly different. In this chapter we shall discuss in detail the group theoretical nature of the fermions.
Spin Coherent States

Spin plays an exceedingly special role in quantum optics with the underlying Lie Algebra and Lie Group of SU(2). In this chapter, we shall discuss in detail all the physical implications of the mathematical ramifications. Remarkably, both the important concepts of geometric phases and cat states for spin systems are rooted in the spin coherent states.
Lie Group Generalizations of Coherent States

This chapter forms the core of this book. The first half of this chapter discusses in detail the general group-theoretical procedure for constructing coherent states for arbitrary Lie groups. The second half of this chapter emphasizes the geometric properties of the coherent state space, namely, the complex structure, Riemannian structure, and the symplectic structure.
Coherent States of Harmonic Oscillator

This chapter discusses in detail the mathematical structure of the coherent states of the harmonic oscillator. It then follows with a detailed discussion of the technical details of the Glauber’s coherent states. Perhaps the most spectacular outcome of the Glauber’s coherent states is that such states are the eigenstates of the non-Hermitian annihilation operator given by Eq. 2.30.
Schrödinger’s Cat States

After discussing the mathematical structure of the coherent states in the previous chapters, in this chapter we explicitly utilized it to discuss three popular and current topics of great interest: EPR paradox, cat states, and entanglement. All three in one way or another is inherently related to our current interest in quantum computing and quantum information.
Squeezed Coherent States

Squeezed states are extremely useful in modern cutting-edge technology. For instance, without using squeezed states of light, the detection of gravitational wave is extremely difficult, if not impossible. In the chapter, we shall discuss in detail how squeezed states play a role in quantum optics, as well as their various mathematical properties. We then followed with a discussion of the applications of squeezed states in the detection of gravitation wave and continuous variable quantum information. Finally, we shall discuss the fascinating topics of spin squeezing.
Supporting and Enhancing the Future of Degree Education in Graphic Design: A University of Gloucestershire Case Study

This collaborative paper authored by Claudia Brewster and Grant Pooke derives from the experience of teaching and managing the delivery of a practice-based Graphic Design (GD) BA Honours degree pathway at the University of Gloucestershire, UK. Supported by two curriculum-based case studies, it explores and discusses examples of new and emerging Design practices in Industry 5.0. The experiential timeframe extends from the start of the academic year in September 2022 through to the student assessment period in June 2023. How might some of the brand-based contexts of emerging technology and the social platforms they utilise be incorporated into a framework which supports modular learning and skills acquisition for First Year (Level 4) Graphic Design students? What additional skill sets and aptitudes might Virtual Design applications require from a student cohort and how might student learning gain be effectively assessed and developed?
Multirobot Navigation Using Partially Observable Markov Decision Processes with Belief-Based Rewards

For the safe deployment of multirobot systems, accurate position estimation is necessary. We present a multirobot navigation algorithm that accomplishes the dual objective of reaching designated goal positions and maintaining low position uncertainty. We pose the problem as a [Formula: see text]-partially observable Markov decision process ([Formula: see text]POMDP) to capture position uncertainty through a belief-based reward. The multirobot system employs a centralized extended Kalman filter (EKF) for state estimation, whose output is used as the observation as well as the belief in the [Formula: see text]POMDP. Using the EKF output as the observation and belief allows for the integration of the EKF in the action planning module of the system. To solve the [Formula: see text]POMDP, we adapt the online information particle filter tree algorithm to be compatible with the EKF closed-form output being used as both the observation and belief. We present results for the proposed navigation algorithm that show emergent behaviors for a multirobot system, where the robots move to provide inter-ranging measurements that minimize position uncertainty for all robots in the system. We also demonstrate that the proposed method outperforms a state-of-the-art solver (partially observable Monte Carlo planning with observation widening) and a direct-to-goal planner in terms of minimizing position uncertainty.
Evaluating the Extraction of Toxicological Properties with Extractive Question Answering

Preparing toxicological analysis of chemical substances is a time-consuming process that requires a safety advisor to search text documents from multiple sources for information on several properties and experiments. There has been a growing interest in using Machine Learning (ML) approaches, specifically Natural Language Processing (NLP) Techniques to improve Human-Machine integration in processes in different areas. In this paper we explore this integration in toxicological analysis. To minimise the effort of preparing toxicological analysis of chemical substances, we explore several available neural network models tuned for Extractive Question Answering (BERT, RoBERTa, BioBERT, ChemBERT) for retrieving toxicological properties from sections of the document sources. This formulation of Information Extraction as a targeted Question Answering task can be considered as a more flexible and scalable alternative to manually creating a set of (limited) extraction patterns or even training a model for chemical relation extraction. The proposed approach was tested for a set of eight properties, each containing multiple fields, in a sample of 33 reports for which golden answers were provided by a security advisor. Compared to the golden responses, the best model tested achieved a BLEU score of 0.55. When responses from different models are combined, BLEU increases to 0.59. Our results suggest that while this approach cannot yet be fully automated, it can be useful in supporting security advisor’s decisions and reducing time and manual effort.
Pruning for Power: Optimizing Energy Efficiency in IoT with Neural Network Pruning

The Internet of Things (IoT) has rapidly emerged as a crucial driver of the digital economy, generating massive amounts of data. Machine learning (ML) is an important technology to extract insights from the data generated by IoT devices. Deploying ML on low-power devices such as microcontroller units (MCUs) improves data protection, reduces bandwidth, and enables on-device data processing. However, the requirements of ML algorithms exceed the processing power, memory, and energy consumption capabilities of these devices. One solution to adapt ML networks to the limited capacities of MCUs is network pruning, the process of removing unnecessary connections or neurons from a neural network. In this work, we investigate the effect of unstructured and structured pruning methods on energy consumption. A series of experiments is conducted using a Raspberry Pi Pico to classify the FashionMNIST dataset with a LeNet-5-like convolutional neural network while applying unstructured magnitude and structured APoZ pruning approaches with various model compression rates from two to 64. We find that unstructured pruning out of the box has no effect on energy consumption, while structured pruning reduces energy consumption with increasing model compression. When structured pruning is applied to remove 75 % of the model parameters, inference consumes 59.06 % less energy, while the accuracy declines by 3.01 %. We further develop an adaption of the TensorFlow Lite framework that realizes the theoretical improvements for unstructured pruning, reducing the energy consumption by 37.59 % with a decrease of only 1.58 % in accuracy when 75 % of the parameters are removed. Our results show that both approaches are feasible to significantly reduce the energy consumption of MCUs, leading to various possible sweet spots within the trade-off between accuracy and energy consumption.
Application of Partial Differential Equation Models in English Reading Corpus

Abstract In order to understand the application of partial differential equation models, a study in English reading corpus is proposed. In this study, a quasi-experimental research method was used to select two classes of students in a school as the subjects. The two classes were taught by the same English teacher, and the two classes were comparable in terms of their basic English learning (including reading ability). The total number of English lessons per semester in the two classes was 96, including 32 reading lessons. Students were guided to apply corpus-assisted reading in the reading classes of the experimental class, while the traditional reading teaching method was used in the control class. The corpus-assisted reading method was used in the experimental class for 16 lessons. During the experiment, students’ performances in applying the corpus-assisted reading were observed, their thinking maps in class and after class assignments were collected and analyzed, and their reading scores in the midterm and final exams were used as indicators of the effectiveness of the corpus to compare and analyze the experimental and control classes. It was from the study results found that teachers were able to apply the corpus to assist reading instruction in the English reading process, and students’ application of the corpus in the reading process enhanced their participation and interest in reading lessons. The application of the corpus improved students’ higher-order reading reasoning skills, and students learned to think actively and holistically through corpus mapping.
The Application Optimization of Project based Learning Design in Ideological and Political Education in Colleges and Universities Based on ARCS Model

Abstract The author uses the ARCS model in a combination of ideological and political education and classroom teaching in colleges and universities, and the theory of the ARCS model is based on theory and based on the ARCS model, and related ideas are proposed in this article. teaching in the halls of ideological and political discipline. First of all, 460 questions were divided from the questionnaire database, 440 questions were returned, and 95.7%, the author analyzes the motivation problems of college students in the process of ideological and political learning, and then takes the ARCS motivation model as the framework, from the perspective of motivation analysis, attention, relevance, confidence and satisfaction, the paper puts forward corresponding motivation stimulation strategies, finally, it combines the ARCS model with relevant teaching, and analyzes the case. Practice has proved that ARCS model has a good fit with the teaching of ideological and political courses, which can effectively improve the level of students’ motivation to learn ideological and political courses, and thus improve the learning efficiency.
The Efficiency of Resource Allocation in Higher Education in India Based on Dea-Tobit Model

Abstract Higher education in India has grown rapidly since independence, becoming the world’s largest higher education country after the United States. In the research, the basic theory of higher education resource allocation efficiency was described. Then the regional distribution of higher education resources, the current situation of inputs and outputs in the central region were described. Then DEA analysis was used to evaluate the efficiency of higher education resources in the central region and projection analysis, and Tobit model was used to test the factors influencing the efficiency of higher education allocation in the central region. The efficiency evaluation and projection analysis of higher education resources in central region were conducted by using DEA analysis, and the Tobit model was used to test the factors influencing the efficiency of higher education allocation in central region. The empirical results show that the resource allocation efficiency of higher education in central region is not determined by education funding alone, but depends on the optimization of resource input combination.
PytonDAQ – A Python based measurement data acquisition and processing software

Abstract This paper introduces PythonDAQ, an open-source Python package for measurement data acquisition, visualization, storage, and post-processing. The code is capable of acquiring measurement data from any sensor with digital data output, performs online calculations, and stores the measured and computed data. A client for live data visualization and tools for postprocessing are also contained in the software package. First, the code is introduced explaining the software architecture and the currently implemented features. Then, the usability is demonstrated by an application at the low-speed compressor test rig FRANCC. As last step, comparisons between PythonDAQ and commercial DAQ solutions, as well as the data acquisition software of a major aero-engine manufacturer are made.
Taype: A Policy-Agnostic Language for Oblivious Computation

Secure multiparty computation (MPC) allows for joint computation over private data from multiple entities, usually backed by powerful cryptographic techniques that protect sensitive data. Several high-level programming languages have been proposed to make writing MPC applications accessible to non-experts. These languages typically require developers to enforce security policies within the logic of the secure application itself, making it difficult to update security requirements, or to experiment with different policies. This paper presents the design and implementation of Taype, a language that permits security concerns to be decoupled from the program logic. To do so, Taype provides the first implementation of oblivious algebraic data types and tape semantics, two language features recently proposed by a core calculus for oblivious computation, λ OADT + . We evaluate our implementation of Taype on a range of benchmarks, demonstrating its ability to encode a range of security polices for a rich class of data types.
One Pixel Adversarial Attacks via Sketched Programs

Neural networks are successful in various tasks but are also susceptible to adversarial examples. An adversarial example is generated by adding a small perturbation to a correctly-classified input with the goal of causing a network classifier to misclassify. In one pixel attacks, an attacker aims to fool an image classifier by modifying a single pixel. This setting is challenging for two reasons: the perturbation region is very small and the perturbation is not differentiable. To cope, one pixel attacks iteratively generate candidate adversarial examples and submit them to the network until finding a successful candidate. However, existing works require a very large number of queries, which is infeasible in many practical settings, where the attacker is limited to a few thousand queries to the network. We propose a novel approach for computing one pixel attacks. The key idea is to leverage program synthesis and identify an expressive program sketch that enables to compute adversarial examples using significantly fewer queries. We introduce OPPSLA, a synthesizer that, given a classifier and a training set, instantiates the sketch with customized conditions over the input’s pixels and the classifier’s output. OPPSLA employs a stochastic search, inspired by the Metropolis-Hastings algorithm, that synthesizes typed expressions enabling minimization of the number of queries to the classifier. We further show how to extend OPPSLA to compute few pixel attacks minimizing the number of perturbed pixels. We evaluate OPPSLA on several deep networks for CIFAR-10 and ImageNet. We show that OPPSLA obtains a state-of-the-art success rate, often with an order of magnitude fewer queries than existing attacks. We further show that OPPSLA’s programs are transferable to other classifiers, unlike existing one pixel attacks, which run from scratch on every classifier and input.