Reviewerly/exHarmony huggingface dataset:
Automatic Algorithm Programming Model Based on the Improved Morgan's Refinement Calculus

The automatic algorithm programming model can increase the dependability and efficiency of algorithm program development, including specification generation, program refinement, and formal verification. However, the existing model has two flaws: incompleteness of program refinement and inadequate automation of formal verification. This paper proposes an automatic algorithm programming model based on the improved Morgan's refinement calculus. It extends the Morgan's refinement calculus rules and designs the C++ generation system for realizing the complete process of refinement. Meanwhile, the automation tools VCG (Verification Condition Generator) and Isabelle are used to improve the automation of formal verification. An example of a stock's maximum income demonstrates the effectiveness of the proposed model. Furthermore, the proposed model has some relevance for automatic software generation.
Improving Problem Decomposition and Regulation in Distributed Multi-Agent Path Finder (DMAPF)

Distributed Multi-Agent Path Finder (DMAPF) is a novel distributed algorithm to solve the Multi-Agent Path Finding (MAPF) problem, where the objective is to find a sequence of movements for agents to reach their assigned locations without colliding with obstacles, which include other agents. The idea of DMAPF is to decompose a given MAPF problem into smaller sub-problems, then solve them in parallel. It has been shown that DMAPF can achieve higher scalability compared to centralized methods. This paper addresses two problems in the previous works. First, the previous works only divide problem maps in a simple, rectangular manner. This can create sub-problems with unbalanced numbers of locations in their maps when the shape of the original map is not rectangular or when the obstacles are not uniformly distributed. Having sub-problems that vary in sizes diminishes the effectiveness of parallelism. Second, the idea of DMAPF is to have agents move across sub-problems until they reach the sub-problems that contain their goals, but the previous works do not have a mechanism to regulate the number of agents residing in the sub-problems, thus it may fail to find the solution when a sub-problem is overcrowded. To mitigate the problems, we introduce (i) a method to decompose MAPF problems with balanced numbers of vertices; and (ii) a mechanism to regulate the number of agents in sub-problems. We also improve the performance of the Answer Set Programming (ASP) encoding, that was used in previous DMAPF implementations to solve MAPF sub-problem instances, by eliminating unnecessary parameters. The results show that the new solver scales better and is more efficient than the previous versions.
Train track maps on graphs of groups

In this paper we develop the theory of train track maps on graphs of groups. Expanding a definition of Bass, we define a notion of a map of a graph of groups, and of a homotopy equivalence. We prove that under one of two technical hypotheses, any homotopy equivalence of a graph of groups may be represented by a relative train track map. The first applies in particular to graphs of groups with finite edge groups, while the second applies in particular to certain generalized Baumslag-Solitar groups.
Radar Intra–Pulse Signal Modulation Classification with Contrastive Learning

The existing research on deep learning for radar signal intra–pulse modulation classification is mainly based on supervised leaning techniques, which performance mainly relies on a large number of labeled samples. To overcome this limitation, a self–supervised leaning framework, contrastive learning (CL), combined with the convolutional neural network (CNN) and focal loss function is proposed, called CL––CNN. A two–stage training strategy is adopted by CL–CNN. In the first stage, the model is pretrained using abundant unlabeled time–frequency images, and data augmentation is used to introduce positive–pair and negative–pair samples for self–supervised learning. In the second stage, the pretrained model is fine–tuned for classification, which only uses a small number of labeled time–frequency images. The simulation results demonstrate that CL–CNN outperforms the other deep models and traditional methods in scenarios with Gaussian noise and impulsive noise–affected signals, respectively. In addition, the proposed CL–CNN also shows good generalization ability, i.e., the model pretrained with Gaussian noise–affected samples also performs well on impulsive noise–affected samples.
Rethinking the design of (food) conferences: The case of Creative Tastebuds 2021

Climate change, food systems and sustainability are nowadays connected themes at many conferences. In response to these societal challenges, we arranged the symposium Creative Tastebuds 2021 to qualify questions and studies across disciplines on how we can all eat sustainably for both pleasure and planet. But how can conferences be designed in ways that build up the new knowledge that are needed today? Based on a critique of the traditional concept of conferences we discuss how alternative designs can contribute to new understandings and new answers. With inspiration from design anthropology, we discuss how knowledge production can be stimulated based on three basic elements: conceptual curation of the event, experimental and interdisciplinary collaboration and a sensuous scenography that fits the content of the conference. The symposium was designed to create an ambitious, interdisciplinary and creative atmosphere, where dialogues were facilitated in innovative ways and across virtual and physical participants. The reasoning is that audiences need to be engaged in new ways in order to foster creativity and be able to invent new solutions. Food conferences are especially suitable for playing with formats, as food is a topic everyone can relate to, and in addition, it points to the sensuous aspects of knowledge exchange. In this article, we explore conference design around the subjects of food and taste through examples of the dialogue-creating instruments used at the symposium Creative Tastebuds 2021.
Imperatives, Trends and Dynamics of Digital Transformation as Banks Adopt Technology and Intelligent Systems

The substitution of physical interactions with digital channels in services industries has resulted in a rapid evolution of processes—to simplify and enrich the customer journey with new functional, experiential, and intelligent capabilities. In Banking, this transition to digital processes faces a variety of deployment considerations due to issues of trust, transparency, nature of interactions, compliance, and technological adaptation of customers. This chapter highlights the key imperatives for digital transformation in banking, enabling technologies, and presents a view of the transition to intelligent operating models of the future. Included are perspectives based on industry observations and experience across transformations of global banks. The chapter establishes the adoption of intelligent digital systems through an assessment of strategic fitment and digital technology maturity. While there can be no single way for this transformation, this chapter establishes the underlying technology infrastructure and their related manifestations in customer engagements and internal operations through several use-cases and potential future scenarios.
Zero-Shot Remote Sensing Image Dehazing Based on a Re-Degradation Haze Imaging Model

Image dehazing is crucial for improving the advanced applications on remote sensing (RS) images. However, collecting paired RS images to train the deep neural networks (DNNs) is scarcely available, and the synthetic datasets may suffer from domain-shift issues. In this paper, we propose a zero-shot RS image dehazing method based on a re-degradation haze imaging model, which directly restores the haze-free image from a single hazy image. Based on layer disentanglement, we design a dehazing framework consisting of three joint sub-modules to disentangle the hazy input image into three components: the atmospheric light, the transmission map, and the recovered haze-free image. We then generate a re-degraded hazy image by mixing up the hazy input image and the recovered haze-free image. By the proposed re-degradation haze imaging model, we theoretically demonstrate that the hazy input and the re-degraded hazy image follow a similar haze imaging model. This finding helps us to train the dehazing network in a zero-shot manner. The dehazing network is optimized to generate outputs that satisfy the relationship between the hazy input image and the re-degraded hazy image in the re-degradation haze imaging model. Therefore, given a hazy RS image, the dehazing network directly infers the haze-free image by minimizing a specific loss function. Using uniform hazy datasets, non-uniform hazy datasets, and real-world hazy images, we conducted comprehensive experiments to show that our method outperforms many state-of-the-art (SOTA) methods in processing uniform or slight/moderate non-uniform RS hazy images. In addition, evaluation on a high-level vision task (RS image road extraction) further demonstrates the effectiveness and promising performance of the proposed zero-shot dehazing method.
Conventional photon blockade in a four-wave mixing system with Kerr nonlinearity

Abstract We investigate conventional photon blockade in a non-degenerate four wave mixing system with Kerr non-linearity. By analyzing the Hamiltonian of the system, we have acquired the corresponding condition to implement photon anti-bunching effect. And we solve the master equation with the purpose of obtaining solutions numerically. We find that the analytic conditions could be consistent with numerical results, and photon blockade could be realized in this system. Furthermore, we discuss the effect of several parameters on the realization of conventional photon blockade in detail. The results show that four wave mixing interaction and Kerr non-linearity could significantly enhance the conventional photon blockade effect, whereas the dissipation rate has a negative effect on implementing photon blockade.
A dynamic failure mode and effects analysis for train systems failures risk assessment using FCM and prospect theory

Abstract Improving the reliability of railway train systems and preventing potential failures in the train operation process is one of the most significant tasks. The failure mode and effects analysis (FMEA) is the most effective and widely applied technique for identification, evaluation, and prevention risk of potential failures in diverse fields. Nevertheless, current risk prioritization approaches for FMEA overlook the transfer of decision makers’ risk preferences under different risk states of potential failures. In addition, little attrition has been paid to addressing the risk prioritization problems in FMEA under a dynamic environment. In order to bridge these research gaps, this paper proposes a dynamic prioritization approach for FMEA by integrating the Fuzzy Cognitive Map (FCM) and the prospect theory. First, improved weighted arithmetic averaging (WAA) operator based on the similarity measure is constructed to aggregate each decision maker’s evaluation information. Then, the FCM is applied to obtain the risk matrix and interaction relationships among failures under different risk states. Next, the dynamic prospect theory is built to determine the risk priority of each failure by considering the risk preference of decision makers, in which the dynamic weight functions are derived based on the risk matrix under different risk states. Finally, the proposed dynamic risk prioritization approach for FMEA is tested by the failures risk analysis of the railway train bogie system in the railway train systems. The comparison study is conducted to demonstrate the reliability and rationality of the proposed risk prioritization approach.
The Impact of National Culture on Innovation: A Comparative Analysis between Developed and Developing Nations during the Pre- and Post-Crisis Period 2007–2021

This empirical study investigates the impact of the Hofstede cultural dimensions (HCD) on the Global Innovation Index (GII) scores in four different years (2007, 2009, 2019 and 2021) to compare the impacts during the pre- and post-crisis (financial and COVID-19) period by employing ordinary least square (OLS) and robust least square (Robust) analyses. The purpose of this study is to identify the impact of cultural factors on the innovation development for different income groups during the pre- and post-crisis period. We found that, in general, the same cultural properties were required for countries to enhance innovation inputs and outputs regardless of pre- and post-crisis periods and time variances. The significant cultural factors (driving forces) of the innovation performance do not change over time. However, our empirical results revealed that not the crisis itself but the income group (either developed or developing) is the factor that influences the relationship between cultural properties and innovation. It is also worth noting that cultural properties have lost much of their impact on innovation, particularly in developing countries, during recent periods. It is highly likely that in terms of innovation, no cultural development or change can significantly impact the innovation output of developing countries without the construction of the appropriate systems.
Locating robust patterns based on invariant of LTP-based features

Efficiently representing Dynamic Textures (DTs) based on salient features is one of the considerable challenges in computer vision. Locating these features can be obstructed due to the impact of encoding factors. In this article, a novel concept of Robust Local Ternary Patterns (RLTP) is introduced in consideration of the invariance of Local Ternary Patterns (LTP) subject to the deviation of thresholds. Our locating process is able to simultaneously encapsulate the discrimination of local features, and deal with the noise sensibility caused by a small gray-scale change of local neighbors. RLTP is then adapted to the completed LTP model to form an efficient operator for capturing completely properties of RLTP. Finally, RLTP is taken into account for the DT description, where the robust patterns of spatial-temporal features and optical-flow-based motions are exploited to improve the performance. Experiments have clearly corroborated the efficacy of our proposed approach.
Real-life evaluation of deep learning models trained on two datasets for Plasmodium falciparum detection with thin blood smear images at 500x magnification

Malaria is a fatal disease transmitted by bites from mosquito-type vectors. Biologists examined blood smears under a microscope at high magnification (1000×) to identify the presence of parasites in red blood cells (RBCs). Such an examination is laborious and time-consuming. Moreover, microscopists sometimes have difficulty identifying parasitized RBCs due to a lack of skill or practice. Deep learning, especially convolutional neural networks (CNNs) applied for malaria diagnosis, are able to identify complex features of a large number of medical images. The proposed work focuses on the construction of a dataset of blood components images representative of the diagnostic reality captured from 202 patients at 500x magnification. We evaluated through a cross-validation study different deep learning networks for the classification of Plasmodium falciparum-infected RBCs and uninfected blood components. These models include a custom-built CNN, VGG-19, ResNet-50 and EfficientNet-B7. In addition, we conducted the same experiments on a public dataset and compared the performance of the resultant models through a patient-level inference including 200 extra patients. The models trained on our dataset show better performance in terms of generalization and achieved better accuracy, sensitivity and specificity scores of 99.7%, 77.9% and 99.8%, respectively.
Geohash-Based Rapid Query Method of Regional Transactions in Blockchain for Internet of Vehicles

Many researchers have introduced blockchain into the Internet of Vehicles (IoV) to support trading or other authentication applications between vehicles. However, the traditional blockchain cannot well support the query of transactions that occur in a specified area which is important for vehicle users since they are bound to the geolocations. Therefore, the querying efficiency of the geolocation attribute of transactions is vital for blockchain-based applications. Existing work does not well handle the geolocation of vehicles in the blockchain, and thus the querying efficiency is questionable. In this paper, we design a rapid query method of regional transactions in blockchain for IoV, including data structures and query algorithms. The main idea is to utilize the Geohash code to represent the area and serve as the key for transaction indexing and querying, and the geolocation is marked as one of the attributes of transactions in the blockchain. To further verify and evaluate the proposed design, on the basis of the implementation of Ethereum, which is a well-known blockchain, the results show that the proposed design achieves significantly better-querying speed than Ethereum.
Multi-Strategy Improved Sparrow Search Algorithm and Application

The sparrow search algorithm (SSA) is a metaheuristic algorithm developed based on the foraging and anti-predatory behavior of sparrow populations. Compared with other metaheuristic algorithms, SSA also suffers from poor population diversity, has weak global comprehensive search ability, and easily falls into local optimality. To address the problems whereby the sparrow search algorithm tends to fall into local optimum and the population diversity decreases in the later stage of the search, an improved sparrow search algorithm (PGL-SSA) based on piecewise chaotic mapping, Gaussian difference variation, and linear differential decreasing inertia weight fusion is proposed. Firstly, we analyze the improvement of six chaotic mappings on the overall performance of the sparrow search algorithm, and we finally determine the initialization of the population by piecewise chaotic mapping to increase the initial population richness and improve the initial solution quality. Secondly, we introduce Gaussian difference variation in the process of individual iterative update and use Gaussian difference variation to perturb the individuals to generate a diversity of individuals so that the algorithm can converge quickly and avoid falling into localization. Finally, linear differential decreasing inertia weights are introduced globally to adjust the weights so that the algorithm can fully traverse the solution space with larger weights in the first iteration to avoid falling into local optimum, and we enhance the local search ability with smaller weights in the later iteration to improve the search accuracy of the optimal solution. The results show that the proposed algorithm has a faster convergence speed and higher search accuracy than the comparison algorithm, the global search capability is significantly enhanced, and it is easier to jump out of the local optimum. The improved algorithm is also applied to the Heating, Ventilation and Air Conditioning (HVAC) system control optimization direction, and the improved algorithm is used to optimize the parameters of the HVAC system Proportion Integral Differential (PID) controller. The results show that the PID controller optimized by the improved algorithm has higher control accuracy and system stability, which verifies the feasibility of the improved algorithm in practical engineering applications.
Quality improvement path and countermeasures for future-oriented film and animation teaching: based on fuzzy comprehensive evaluation method

As the latest and hottest concept in the international arena, the metaverse concept has attracted the attention of various industries including information, economy, art, management, education and teaching for its application and technology integration research, but whether to define metaverse as a technology or a scenario has not yet reached a unified understanding in the academic and scientific communities. We believe that metaverse should be used as a key concept and emerging theory in building the future intelligent field. Therefore, we introduce the concept of metaverse in future film and animation teaching as a novel, strategic and disruptive teaching field with great potential, and the constructed metaverse self-directed learning field will become an important part of school education resource optimization. In this study, we focus on the quality improvement path of film and animation teaching in the context of metaverse, and conduct a study on the assessment method of teaching quality after the introduction of metaverse concept. Specifically, we discuss the quality improvement measures in the future teaching of film and animation, construct a teaching field of film and animation based on the metaverse, and propose a related teaching quality assessment model and establish an index system for the quality assessment of film and animation teaching in the context of the metaverse. The index system is composed of 3 primary indicators, 9 secondary indicators and 27 tertiary indicators, and the quantitative calculation is carried out by the Analytic Hierarchy Process (AHP) in fuzzy mathematics, and the weighting results of the indicators are calculated. The research goal of combining quantitative analysis and qualitative research was achieved. What can be seen through our research is that the metaverse online classroom built with virtual reality and other technologies will have more advantages than the traditional teaching classroom. In the future, similar learning devices can be introduced in film and animation teaching, and diversified learning modules can be established. Not only can the learning efficiency of offline classroom be improved, but also more learning space can be opened for online classroom. This study bridges the gap in the theory of quality assessment of film and animation teaching after the introduction of the future metaverse concept, innovates the analysis of the new concept and the improvement of the old method, builds a new scenario of organic combination of new technology and traditional education teaching, and provides a new idea for international and domestic future education research.
Multi-robot Implicit Control of Massive Herds

This paper solves the problem of herding countless evaders by means of a few robots. The objective is to steer all the evaders towards a desired tracking reference while avoiding escapes. The problem is very challenging due to the highly complex repulsive evaders’ dynamics and the underdetermined states to control. We propose a solution that is based on Implicit Control and a novel dynamic assignment strategy to select the evaders to be directly controlled. The former is a general technique that explicitly computes control inputs even in highly complex input-nonaffine dynamics. The latter is built upon a convex-hull dynamic clustering inspired by the Voronoi tessellation problem. The combination of both allows to choose the best evaders to directly control, while the others are indirectly controlled by exploiting the repulsive interactions among them. Simulations show that massive herds can be herd throughout complex patterns by means of a few herders.