Reviewerly/exHarmony huggingface dataset:
K-medoids Crazy Firefly Algorithm For Unsupervised Data Clustering

Data clustering is an unsupervised process in which identical data are collected in groups. A clustering algorithm divides a set of objects into different subsets, these subsets are non-overlapping in nature, and each subset is considered as a cluster. In this paper, a modified clustering approach is developed on integration of k-medoids clustering algorithm with crazy firefly algorithm. The proposed algorithm is experimented on four different datasets. The main aim of the suggested algorithm is to give fast and accurate user specified clustering results from given datasets. Finally, the outcome obtained from the proposed algorithm is compared with both k-medoids clustering algorithm and k-medoids firefly clustering algorithm. The results of the suggested algorithm are better than the k-medoids algorithm and the k-medoids firefly clustering algorithm.
Automated evaluation of the quality of ideas in compositions based on concept maps

Abstract Nowadays, automated essay evaluation (AEE) systems play an important role in evaluating essays and have been successfully used in large-scale writing assessments. However, existing AEE systems mostly focus on grammar or shallow content measurements rather than higher-order traits such as ideas. This paper proposes a new formulation of graph-based features for concept maps using word embeddings to evaluate the quality of ideas for Chinese compositions. The concept map derived from the student’s composition is composed of the concepts appearing in the essay and the co-occurrence relationship between the concepts. By utilizing real compositions written by eighth-grade students from a large-scale assessment, the scoring accuracy of the computer evaluation system (named AECC-I: Automated Evaluation for Chinese Compositions—Ideas) is higher than the baselines. The results indicate that the proposed method deepens the construct-relevant coverage of automatic ideas evaluation in compositions and that it can provide constructive feedback for students.
Determination of The Shortest Route Based on BFS Algorithm for Purpose to Disaster Evacuation Shelter

Purus village in Padang City which has an area of 0.86 km2 consisting of 8 RW and 28 RT has a population of 8,075 people with a density of 11,875. As a result, people are always haunted by fear and always feel threatened if an earthquake occurs. If an earthquake and tsunami occur, what the community needs at that time is information about a safe zone that can be reached to save themselves and their families. For this reason, there needs to be an educational process for the community so that they have a culture of disaster awareness in the form of a system that is able to inform the community where the closest safe zone they can reach is the route they must take when a disaster strikes so that it can provide a sense of security because of safety guarantees. This study aims to determine the shortest route that can be taken by the user (community) during a disaster to reduce the risk of a greater number of victims by using the Breadth first search algorithm which is integrated into a web-based GIS application. By determining the starting point which is the user's position when the disaster occurs, and then determining the end point which is the location of the closest shelter, it will be possible to calculate the shortest distance that can be reached by the user at that time. The method chosen in this study is a waterfall because each step of the research carried out must be sequential and structured to avoid the risk of errors in each sequence of processes carried out. The results of this study can be proven by doing manual calculations to determine the shortest distance which will later be compared with the results of applications that have been designed using the BFS algorithm.
LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products

The remarkable success of convolutional neural networks (CNNs) in computer vision tasks is shown in large-scale datasets and high-performance computing platforms. However, it is infeasible to deploy large CNNs on resource constrained platforms, such as embedded devices, on account of the huge overhead. To recognize the label numbers of industrial black material product and deploy deep CNNs in real-world applications, this research uses an efficient method to simultaneously (a) reduce the network model size and (b) lower the amount of calculation without compromising accuracy. More specifically, the method is implemented by pruning channels and corresponding filters that are identified as having a trivial effect on the output accuracy. In this paper, we prune VGG-16 to obtain a compact network called LeanNet, which gives a 25× reduction in model size and a 4.5× reduction in float point operations (FLOPs), while the accuracy on our dataset is close to the original accuracy by retraining the network. Besides, we also find that LeanNet could achieve better performance on reductions in model size and computation compared to some lightweight networks like MobileNet and SqueezeNet, which are widely used in engineering applications. This research has good application value in the field of industrial production.
Multi-Criteria Recommendation Systems to Foster Online Grocery

With the exponential increase in information, it has become imperative to design mechanisms that allow users to access what matters to them as quickly as possible. The recommendation system ($RS$) with information technology development is the solution, it is an intelligent system. Various types of data can be collected on items of interest to users and presented as recommendations. $RS$ also play a very important role in e-commerce. The purpose of recommending a product is to designate the most appropriate designation for a specific product. The major challenges when recommending products are insufficient information about the products and the categories to which they belong. In this paper, we transform the product data using two methods of document representation: bag-of-words (BOW) and the neural network-based document combination known as vector-based (Doc2Vec). We propose three-criteria recommendation systems (product, package, and health) for each document representation method to foster online grocery, which depends on product characteristics such as (composition, packaging, nutrition table, allergen, etc.). For our evaluation, we conducted a user and expert survey. Finally, we have compared the performance of these three criteria for each document representation method, discovering that the neural network-based (Doc2Vec) performs better and completely alters the results.
Graph Attention Networks with Positional Embeddings

Graph Neural Networks (GNNs) are deep learning methods which provide the current state of the art performance in node classification tasks. GNNs often assume homophily – neighboring nodes having similar features and labels–, and therefore may not be at their full potential when dealing with non-homophilic graphs. In this work, we focus on addressing this limitation and enable Graph Attention Networks (GAT), a commonly used variant of GNNs, to explore the structural information within each graph locality. Inspired by the positional encoding in the Transformers, we propose a framework, termed Graph Attentional Networks with Positional Embeddings (GAT-POS), to enhance GATs with positional embeddings which capture structural and positional information of the nodes in the graph. In this framework, the positional embeddings are learned by a model predictive of the graph context, plugged into an enhanced GAT architecture, which is able to leverage both the positional and content information of each node. The model is trained jointly to optimize for the task of node classification as well as the task of predicting graph context. Experimental results show that GAT-POS reaches remarkable improvement compared to strong GNN baselines and recent structural embedding enhanced GNNs on non-homophilic graphs.
Migrazione stagionale, bilinguismo e politiche linguistico-educative nelle valli dei gelatieri

The following contribution analyses a particular form of bilingualism present in the mountains and foothills of Veneto (Belluno and Treviso), which originated from a century-old tradition of ice-cream makers’ seasonal migration to German-speaking countries. The children of ice-cream makers are also ‘naturally’ bilingual, although this is not given adequate recognition within educational policies.
Efficient Classical Simulation and Benchmarking of Quantum Processes in the Weyl Basis

One of the crucial steps in building a scalable quantum computer is to identify the noise sources which lead to errors in the process of quantum evolution. Different implementations come with multiple hardware-dependent sources of noise and decoherence making the problem of their detection manyfoldly more complex. We develop a randomized benchmarking algorithm which uses Weyl unitaries to efficiently identify and learn a mixture of error models which occur during the computation. We provide an efficiently computable estimate of the overhead required to compute expectation values on outputs of the noisy circuit relying only on the locality of the interactions and no further assumptions on the circuit structure. The overhead decreases with the noise rate and this enables us to compute analytic noise bounds that imply efficient classical simulability. We apply our methods to ansatz circuits that appear in the variational quantum eigensolver and establish an upper bound on classical simulation complexity as a function of noise, identifying regimes when they become classically efficiently simulatable.
An efficient bilevel differential evolution algorithm with adaptation of lower level population size and search radius

Bilevel optimization has been recognized as one of the most difficult and challenging tasks to deal with because a solution to the upper level problem may be feasible only if it is also an optimal solution to the lower level problem. In recent years, evolutionary bilevel optimization has attracted increasing interest. In this paper, an efficient self-adaptive bilevel differential evolution (SABiLDE) with k-nearest neighbors (kNN) based interpolation is proposed to solve bilevel optimization problems. The kNN approximation is applied to estimate the optimal lower level variables for any newly generated upper candidates to improve the computational efficiency. A similarity based self-adaptive strategy for the dynamic control of lower level population size and search radius is introduced to further enhance the efficiency of the lower level function evaluations. A test set with 10 standard test problems and the SMD suite with controllable complexities are used to evaluate the performance of the proposed approach. Compared with four recent state-of-the-art methods, the numerical results produced by the proposed method are promising and show great potential for solving generic bilevel optimization problems.
The development status of ship wall-climbing robot

The ship wall-climbing robot belongs to a branch of intelligent robots, and its application objects are large military and civilian ships. It is mainly engaged in special operations such as testing, welding, cleaning and descaling. With the rapid development of science and technology, ship wall-climbing robots have also developed from the original negative pressure adsorption wall-climbing robots to the current bionic foot-type wall-climbing robots. In practical applications, it is gradually replacing manual operations, greatly improving work efficiency. This article introduces various ship-climbing robots with adsorption, movement, and driving methods as the main categories. This article focuses on the magnetic adsorption method, combined with other adsorption methods, and analyzes the performance characteristics and existing problems of various wall-climbing robots. Finally, we summarize the current technical problems of wall-climbing robots, and then make a prospect for the future development of wall-climbing robots.
UiLab, a Workbench for Conducting and Reproducing Experiments in GUI Visual Design

With the continuously increasing number and variety of devices, the study of visual design of their Graphical User Interfaces grows in importance and scope, particularly for new devices, including smartphones, tablets, and large screens. Conducting a visual design experiment typically requires defining and building a GUI dataset with different resolutions for different devices, computing visual design measures for the various configurations, and analyzing their results. This workflow is very time- and resource-consuming, therefore limiting its reproducibility. To address this problem, we present UiLab, a cloud-based workbench that parameterizes the settings for conducting an experiment on visual design of Graphical User Interfaces, for facilitating the design of such experiments by automating some workflow stages, and for fostering their reproduction by automating their deployment. Based on requirements elicited for UiLab, we define its conceptual model to delineate the borders of services of the software architecture to support the new workflow. We exemplify it by demonstrating a system walkthrough and we assess its impact on experiment reproducibility in terms of design and development time saved with respect to a classical workflow. Finally, we discuss potential benefits brought by this workbench with respect to reproducing experiments in GUI visual design and existing shortcomings to initiate future avenues. We publicly release UiLab source code on a GitHub repository.
Energy-Aware Real-Time Tasks Processing for FPGA-Based Heterogeneous Cloud

Cloud computing is becoming a popular model of computing. Due to the increasing complexity of the cloud service request, it often exploits heterogeneous architecture. Moreover, some service requests (SRs)/tasks exhibit real-time features, which are required to be handled within a specified duration. Along with the stipulated temporal management, the strategy should also be energy efficient, as energy consumption in cloud computing is challenging. In this paper, we have proposed a strategy, called “Efficient Resource Allocation of Service Request” (ERASER) for energy efficient allocation and scheduling of periodic real-time SRs on cloud platform. The cloud platform is consists of Field Programmable Gate Arrays (FPGAs) as Processing Elements (PEs) along with the General Purpose Processors (GPP). We have further proposed, an SR migration technique to reduce the tasks rejection by serving maximum SRs. Simulation based experimental results demonstrate that the proposed methodology is capable to achieve upto 90 percent resource utilization with only 26 percent SR rejection rate over different experimental scenarios. Comparison results with other state-of-the-art techniques reveal that the proposed strategy outperforms the existing technique with 17 percent reduction in SR rejection rate and 21 percent reduction in energy consumption. Further, the simulation outcomes have been validated on real FPGA test-bed based on Xilinx Zynq SoC with standard benchmark tasks.
MASS: Multi-task anthropomorphic speech synthesis framework

Text-to-Speech (TTS) synthesis plays an important role in human-computer interaction. Currently, most TTS technologies focus on the naturalness of speech, namely, making the speeches sound like humans. However, the key tasks of the expression of emotion and the speaker identity are ignored, which limits the application scenarios of TTS synthesis technology. To make the synthesized speech more realistic and expand the application scenarios, we propose a multi-task anthropomorphic speech synthesis framework (MASS), which can synthesize speeches from text with specified emotion and speaker identity. The MASS framework consists of a base TTS module and two novel voice conversion modules: the emotional voice conversion module and the speaker voice conversion module. We propose deep emotion voice conversion model (DEVC) and deep speaker voice conversion model (DSVC) based on convolution residual networks. It solves the problem of feature loss during voice conversion. The model trainings are independent of parallel datasets, and are capable of many-to-many voice conversion. In the emotional voice conversion, speaker voice conversion experiments, as well as the multi-task speech synthesis experiments, experimental results show DEVC and DSVC convert speech effectively. The quantitative and qualitative evaluation results of multi-task speech synthesis experiments show MASS can effectively synthesis speech with specified text, emotion and speaker identity.
Explainable and Ethical AI: A Perspective on Argumentation and Logic Programming

In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques.In particular, we show how argumentation could be combined with different extensions of logic programming – namely, abduction, inductive logic programming, and probabilistic logic programming – to address the issues of explainable AI as well as some ethical concerns about AI.
Hidden multi-distance loss-based full-convolution hashing

To improve retrieval efficiency and quality, learning to hash has been widely used in approximate nearest neighbor queries. Deep learning is characterized by high precision in extracting data features; therefore, deep-learning-based hashing has attracted more attention. Existing methods have some weaknesses, such as complex training and losing spatial information. We design a new deep hashing algorithm named HLFH, which is very simple technique but achieves amazingly good performance. HLFH is optimized and improved in two aspects: network structure and hashing loss. Concerning network structure, a new full convolutional hashing network is proposed to preserve spatial information of features. A smooth activation function is used in the hashing layer to reduce the quantization error. Concerning hashing loss, the semantic information of data is then used to generate binary codes by hidden multi-distance loss, i.e., combination of triplet loss and quadruplet loss. With these two new techniques, our method is more accurate than many other state-of-the-art methods.
Evolving Schools in a Post-pandemic Context

The Covid-19 pandemic has required schools and students to radically and immediately adapt teaching and learning processes to more distributed, digital, and home-based learning strategies for an indefinite period of time. This situation provides an unprecedented opportunity for students and teachers around the world to reflect on the purpose and design of schools post COVID-19, particularly on the kinds of school design elements that may best meet the needs of all students and teachers in the future. Before the pandemic emerged, the Community Science Lab at the Max Planck Institute for Evolutionary Anthropology had been developing a student-led effort, the Evolving Schools project, for exploring the everyday cooperation dynamics of teaching and learning within their own school communities. This chapter offers an illustrative case study of this project as a novel approach to student participation in school improvement efforts. By involving a small group of students and teachers in reflecting on their experience of changing schooling practices during the COVID-19 pandemic, as well as in exploring scientific perspectives on human learning and cooperation, a new theoretical foundation for the practical empowerment of students and teachers is framed within the aims of Education for Sustainable Development.
Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers

Generating knowledge grounded responses in both goal and non-goal oriented dialogue systems is an important research challenge. Knowledge Graphs (KG) can be viewed as an abstraction of the real world, which can potentially facilitate a dialogue system to produce knowledge grounded responses. However, integrating KGs into the dialogue generation process in an end-to-end manner is a non-trivial task. This paper proposes a novel architecture for integrating KGs into the response generation process by training a BERT model that learns to answer using the elements of the KG (entities and relations) in a multi-task, end-to-end setting. The k-hop subgraph of the KG is incorporated into the model during training and inference using Graph Laplacian. Empirical evaluation suggests that the model achieves better knowledge groundedness (measured via Entity F1 score) compared to other state-of-the-art models for both goal and non-goal oriented dialogues.
Structured Semantic Modeling of Scientific Citation Intents

The search for relevant information within large scholarly databases is becoming an unaffordable task where deeper semantic representations of citations could give impactful contributions. While some researchers have already proposed models and categories of citations, this often remains at a theoretical level only or it simply reduces the problem to a short-text classification of the context sentence. In this work, we propose CiTelling: a radically new model of fine-grained semantic structures lying behind citational sentences able to represent their intent and features. After an extensive and multiple annotation of 1380 citations (https://github.com/rogerferrod/CiTelling), we tested the validity and the reliability of the proposal through both qualitative and quantitative analyses. In particular, we were able to 1) extend the current depth of existing semantic representations when used in computational scenarios, 2) achieve high inter-annotator agreement and 3) obtain state-of-the-art classification results with straightforward neural network models.
Can3d: Fast 3d Knee Mri Segmentation Via Compact Context Aggregation

Automated segmentation using deep learning approaches have shown significant promise for medical images. However, existing methods generally suffer from high computational complexity when utilised in 3D due to their large memory requirements, thus restricting training to high-performing computing hardware only. We present an extremely compact convolutional neural network with a shallow memory footprint to address this problem and train the model with a novel loss function to segment imbalanced classes with extra shape constrain in 3D MR images. The proposed approaches can directly process large full-size 3D input volumes (no patches) and allow inference times within just seconds using the CPU. The proposed network efficiently retains model parameters required to outperform other methods for 3D segmentation (U-Net3D, improved U-Net3D and V-Net) under severe memory limitations, while achieving several times faster inference times. It can also achieve favourable performance compared to a complex pipeline for knee cartilage segmentation with hundred times faster inference.