Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Simple Reflex Agents Using Stigmergy with Model-Based Agents in Self-Organizing Transportation

Multi-agent systems utilizing simple reflex agents are assumed to have a significant competitive disadvantage when compared to more sophisticated agent-based approaches. However, in terms of resilience and adaptivity, this simple design turns out be an advantage when used together with stigmergy. In this paper we show that simple reflex agents that use stigmergy, are fit and flexible enough to outperform rivaling model-based agents in a disturbed transportation setting that simulates a dynamic, real-world industrial shop floor, and have a performance closer to a centralized, monolithic approach which we compare to as gold standard. This leads to opportunities for simpler, but nevertheless more robust agent design for self-organizing, decentralized multiagent approaches just by sharing knowledge of the world and exploiting their environment.
A simple method for sampling random Clifford operators

We describe a simple algorithm for sampling n-qubit Clifford operators uniformly at random. The algorithm outputs the Clifford operators in the form of quantum circuits with at most 5n + 2n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> elementary gates and a maximum depth of ${\mathcal{O}}\left({n\,{\text{log}}\,n}\right)$ on fully connected topologies. The circuit can be output in a streaming fashion as the algorithm proceeds, and different parts of the circuit can be generated in parallel. The algorithm has an ${\mathcal{O}}\left({{n^2}}\right)$ time complexity, which matches the current state of the art. The main advantage of the proposed algorithm, however, lies in its simplicity and elementary derivation.
Research on ship dynamic map based on AIS

With the development of cloud computing, big data and other technologies, WebGIS has been widely used as an effective tool for processing and analyzing spatial data. AIS data covers the core elements of ship navigation, and its visualization can track the ship trajectory and count the traffic density. For effectively analyzing AIS data, this paper designs and implements a web ship dynamic map system based on AIS data. This system consists of data layer, data service layer, business logic layer and presentation layer. Data layer realizes data storage through big data technology and relational database. The data service layer provides data through GeoServer and Spring Dao technology. The business logic layer realizes business logic through Spring Boot, which converts the data into JSON format and provides it to the presentation layer. The presentation layer realizes map display through Openlayers. Practice shows that the system can meet the requirements of real-time display and has high scalability.
Fuzzy clustering-based neural networks modelling reinforced with the aid of support vectors-based clustering and regularization technique

In recent years, classical fuzzy clustering-based neural networks (FCNNs) have been successfully applied to regression tasks. The determination of the parameters such as cluster centers of the existing hard c-means (HCM) or fuzzy c-means (FCM), leads to the performance deterioration of the model because of the sensitivity of HCM or FCM to noise and outliers. Moreover, there are also several factors for over-fitting and degradation of the robustness of the ensuing model. To solve such problems, two improved clustering techniques and L2 norm-regularization are considered in the proposed robust fuzzy clustering-based neural networks (RFCNNs) modeling. SVs-based hard c-means (SVs-based HCM) and SVs-based fuzzy c-means (SVs-based FCM) designed with support vectors (SVs) can reduce the interference of uncorrelated data, including noise and outliers, thereby enhancing the main data characteristics effectively, as well as leading to the construction on the improved network model. L2 norm-regularization can be used to alleviate the degradation of robustness caused by overfitting. In terms of improving the performance of the model through SVs-based HCM or SVs-based FCM, as well as robustness completed through L2 norm-regularization, the superiority of RFCNNs was verified by experimenting with synthetic data and publicly available data from machine learning datasets.
Identify High-Priority Barriers to Effective Digital Transformation in Higher Education: A Case Study at Private University in Indonesia

Some barriers negatively affect the implementation of digital transformation in higher education institutions. This research aims to investigate these barriers in a particular context: a private university in Indonesia. The barriers diagnostic framework (BDF) has been applied to identify and prioritize barriers. It is determined that ‘Actionable plans based on strategy translation’, ‘The ability to embed ICT into the education system’, and ‘Limitations of institutional policies' have high priority barriers and therefore meet critical concerns in the implementation of digital transformation at the case study. The main contribution of this study is providing empirical evidence on barriers to digital transformation in the higher education sector. More understanding of the high-priority barriers will help the management of higher education to find effective and efficient strategies to manage the resources.
Region of Interest Based Medical Image Compression Using DCT and Capsule Autoencoder for Telemedicine Applications

Medical imaging has a significant role in today’s healthcare system. Advancement in imaging modalities leads to a remarkable proliferation of resolution and volume of digital data. Telemedicine based on a centralized database requires effective storage, processing, and digital data transmission over the internet. Even after the tremendous advancement in electronic communication techniques, it is still challenging to transmit this huge volume of medical data over the internet efficiently. This leads to an inevitable requirement of image compression that reduces the data size and hence the transmission requirements. In this paper, we offer an ROI-based image compression technique for MR images, which tackles the frequency components present in the processed medical image. The Fuzzy C-Means clustering approach is used to separate the region of interest from the non-region of interest. Capsule autoencoder method is used for compressing the non-ROI and Discrete Cosine Transform with HuffmanRun-length encoding is used for the compression of the region of interest. A detailed analysis of the compression process in terms of various evaluation functions like peak signal to ratio and compression ratio has been carried out and the result shows superior performance over the previous methods.
Efficient Quering Blockchain Applications

Industrial blockchain applications have recently risen to the top of the scientific and industrial communities' priority lists. This is due to their practical capabilities in resolving many issues in various industrial domains. Visibility and traceability to a large volume of trusted data benefit members of a consortium of industrial companies and associated organizations. Data scientists who query data and use statistics and Machine Learning to solve a wide range of interesting problems benefit as well. This paper introduces a blockchain application Query Engine based on upgradable Smart Contracts with a lightweight, flexible, and interoperable query framework. This query engine specifically is designed to facilitate the storage and querying of application data recorded on blockchain networks.
Solving Sensor Placement Problems In Real Water Distribution Networks Using Adiabatic Quantum Computation

Quantum annealing has emerged in the last few years as a promising quantum computing approach to solving large-scale combinatorial optimization problems. We formulate the problem of correctly placing pressure sensors on a Water Distribution Network (WDN) as a combinatorial optimization problem in the form of a Quadratic Unconstrained Binary Optimization (QUBO) or Ising model. Optimal sensor placement is indeed key to detect and isolate fault events. We outline the QUBO and Ising formulations for the sensor placement problem starting from the network topology and few other features. We present a detailed procedure to solve the problem by minimizing its Hamiltonian using PyQUBO, an open-source Python Library. We then apply our methods to the case of a real Water Distribution Network. Both simulated annealing and a hybrid quantum-classical approach on a D-Wave machine are employed.
Revealing the structure of prediction models through feature interaction detection

In recent years, machine learning models have been employed for prediction in various domains. While the prediction performance has obviously improved, some models have become too complex to understand, and these models are called black-box models. Detecting the feature interactions is a useful technique to gain insight into the structure of black-box models. In this paper, we propose a method based on high dimensional model representation (HDMR) to reveal the structure of prediction models by detecting the feature interactions that are embedded in the models. The HDMR-based method can detect the k-way interactions without any constraints on k and can detect the interactions from both classification and regression models. Moreover, this method is model-agnostic and can detect both global and local interactions. Experiments on some synthetic and real datasets demonstrate that the HDMR-based method can detect feature interactions effectively and improve the model interpretability.
A Deep Learning Model for Forecasting Photovoltaic Energy with Uncertainties

Accurate forecasting of photovoltaic (PV) power generation is crucial for integrating this renewable energy into existing energy systems. Predicting PV output is a challenging task because of the uncertain and stochastic behavior of solar energy systems (e.g., unstable climatic conditions, errors in irradiance measurements). Conventional PV production forecasting approaches, such as non-deep neural networks, solely rely on high-precision and ideal data, and they may fail in predicting uncertain PV profiles. In this study, an uncertainty-aware deep neural network is proposed for predicting stochastic PV generation profiles. We combined convolutional neural networks (CNN) with long short-term memory (LSTM) to improve the robustness of our prediction model against uncertainties in the data. In the first step, CNN is utilized to capture local features in PV datasets. In the second stage, CNN outputs are injected into the LSTM neural network. LSTM is responsible for learning the temporal relationships in the data. To evaluate the performance of our proposed approach, it is applied to a real-world dataset. The effectiveness of our proposed hybrid model is compared to that of a baseline approach (a single LSTM neural network). In contrast to the existing PV forecasting approaches presented in the literature, our proposed framework provides highly accurate predictions of PV generation with flexible prediction horizons, and it is robust against noises in measurement devices and missing profiles.
Performance Analysis of SSD and Faster RCNN Multi-class Object Detection Model for Autonomous Driving Vehicle Research Using CARLA Simulator

Autonomous vehicle research has grown exponentially over the years with researchers working on different object detection algorithms to realize safe and competent self-driving systems while legal authorities are simultaneously looking into the ways of mitigating the risks posed by fully autonomous vehicles. These advancements can result in a much safer commuting environment, reduced accidents and also eliminate the necessity for human driving. Recent developments in the field show that object detection models combined with an on-vehicle camera module provides more robustness and accuracy than other methods such as LiDAR or RADAR. This paper proposes two object detection algorithms, SSD and Faster RCNN for autonomous driving applications through various performance parameters. CARLA Simulator was used to generate synthetic data to train and test the models. Results shows that that Faster-RCNN was found to have a mean Average Precision (mAP) value of 94.32% while SSD has a mAP of 88.998%. However, SSD had a speed of 30 ms/image while Faster-RCNN had a speed of 106 ms/image. Taking into consideration the real-time and speed constraints in autonomous driving, it was inferred that the SSD algorithm is much better suited for this problem as the difference in accuracy between the models was relatively lesser compared to the difference in computation speeds.
Data Augmented 3D Semantic Scene Completion with 2D Segmentation Priors

Semantic scene completion (SSC) is a challenging Computer Vision task with many practical applications, from robotics to assistive computing. Its goal is to infer the 3D geometry in a field of view of a scene and the semantic labels of voxels, including occluded regions. In this work, we present SPAwN, a novel lightweight multimodal 3D deep CNN that seamlessly fuses structural data from the depth component of RGB-D images with semantic priors from a bimodal 2D segmentation network. A crucial difficulty in this field is the lack of fully labeled real-world 3D datasets which are large enough to train the current data-hungry deep 3D CNNs. In 2D computer vision tasks, many data augmentation strategies have been proposed to improve the generalization ability of CNNs. However those approaches cannot be directly applied to the RGB-D input and output volume of SSC solutions. In this paper, we introduce the use of a 3D data augmentation strategy that can be applied to multimodal SSC networks. We validate our contributions with a comprehensive and reproducible ablation study. Our solution consistently surpasses previous works with a similar level of complexity.
Application of Software Engineering in Intermediate and Higher Education through Web Apps Development

The development of software engineering has helped a lot of human work and made technological growth around the world. Software engineering can be applied to support various fields of work including education. The purpose of this research is to develop a web-based educational application that aims to improve online learning for students. There are several methods used to make this research, namely observation, research survey, industry survey, literature review, data analysis, and others. This research opens up various learning opportunities, especially during this pandemic, where every student must study at home. There are several steps in the development of this webapp, namely creating a conceptual design, frontend website, backend website, database, and security. Developing a WebApp to accommodate the studies of students will have an important role in the future with technological advancements. Online studies will help students study more efficiently without the need to bother with location and time. The result of this research will be a fully functional WebApp to assist students in online study for high schoolers and university.
3D Printing an Accessory Dock for XR Controllers and its Exemplary Use as XR Stylus

This article introduces the accessory dock, a 3D printed multi-purpose extension for consumer-grade XR controllers that enables flexible mounting of self-made and commercial accessories. The uniform design of our concept opens new opportunities for XR systems being used for more diverse purposes, e.g., researchers and practitioners could use and compare arbitrary XR controllers within their experiments while ensuring access to buttons and battery housing. As a first example, we present a stylus tip accessory to build an XR Stylus, which can be directly used with frameworks for handwriting, sketching, and UI interaction on physically aligned virtual surfaces. For new XR controllers, we provide instructions on how to adjust the accessory dock to the controller's form factor. A video tutorial for the construction and the source files for 3D printing are publicly available for reuse, replication, and extension (https://go.uniwue.de/hci-otss-accessory-dock).
<i>Flash</i>: Joint Flow Scheduling and Congestion Control in Data Center Networks

Flow scheduling and congestion control are two important techniques to reduce flow completion time in data center networks. While existing works largely treat them independently, the interactions between flow scheduling and congestion control are in general overlooked which leads to sub-optimal solutions, especially given that the link capacity is increasing faster than the switch port buffer size. In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flash</i> , a simple yet effective scheme that integrates scheduling and congestion control. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flash</i> puts forward a congestion-aware scheduling scheme to determine the priority of flows based on the latest network congestion extent and the flow’s bytes sent. Besides, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flash</i> proposes a priority-based packet dropping scheme in switch port buffers and implements a priority-aware congestion control scheme. Experiment results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flash</i> has superior performance: (1) it has 35.8% lower tail latency than PIAS and performs similar with pFabric in a 10G network without knowing the flow size, (2) in 100G networks with shallow buffers, the information agnostic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flash</i> has 6.8% lower average FCT than the information-aware pFabric, (3) it outperforms pFabric by 13.5% in FCT if flow size is also known to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flash</i> .
Investigating how we read translations

Abstract Since its inception, Translation Studies has hinged on theoretical concepts of effects and reception, with various reader-oriented notions such as equivalent effect, skopos , acceptability and adequacy, and user-centredness, to name but a few, having pervaded the discipline for decades. Despite this preoccupation with the phenomenology of translations, we still know very little about how translations are actually experienced – written translations especially. This article calls for an expansion of research into the reception and experience of source texts and their translations, reviewing the opportunities afforded by recent technological developments in eye-tracking, galvanic skin response sensors, echocardiogram monitors, and other multi-sensory devices. Using a short case study, a number of research questions and an outline of an experimental method are proposed to contrast the reading experience of two translations of the same source text, serving as a prompt for future research of this kind. By drawing inspiration from the few existing examples of research in this incipient paradigm and the considerations offered in the example, this article aims to stimulate future research to explore the vast untapped potential in this area and to arrive at a better understanding of the effects that different translation approaches yield and the potential variation in effects between source and target text.
An English Teaching Resource Recommendation System Based on Network Behavior Analysis

The sharing of English teaching resources has always been a concern. In order to further improve the value of different English teaching resources, this paper proposes a resource management system based on an improved collaborative recommendation algorithm. The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN). The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information. Therefore, the model is constructed by combining GNN and RNN to obtain the advantages of both. In order to prove the effectiveness of the model, we used CNGrid’s real user behavior dataset in the experiment and finally compared the results with other methods. The different deep learning-based models achieved a precision of up to 88% and outperformed other traditional models. The experimental results show that this new deep learning model has good sharing value.
Anomaly Detection in Electromechanical Systems by means of Deep-Autoencoder

Anomaly detection in manufacturing processes is one of the main concerns in the new era of the Industry 4.0 framework. The detection of uncharacterized events represents a major challenge within the operation monitoring of electrical rotatory machinery. In this regard, although several machine learning techniques have been classically considered, the recent appearance of deep-learning approaches represents an opportunity in the field to increase the anomaly detection capabilities in front of complex electromechanical systems. However, each anomaly detection technique considers its own data interpretability and modelling strategy, which should be analyzed in front of the specificities of the data generated in an industrial environment and, specifically, by an electromechanical actuator. Thus, in this study, a comparison framework is considered including multiple fault scenarios in order to analyze the performance of four representative anomaly detection techniques, that is, one-class support vector machine, k-nearest neighbor, Gaussian mixture model and, finally, deep-autoencoder. The experimental results suggest that the use of the deep-autoencoder in the task of detecting anomalies of operation in electromechanical systems has a higher performance compared to the state of the art methods.
CLTracer: A Cross-Ledger Tracing framework based on address relationships

With the proliferation of cryptocurrency, many automated cross-ledger trading platforms were set up. These platforms introduce new challenges in tracing the money flows and getting evidence of illicit behaviors. Yousaf, Kappos, and Meiklejohn (USENIX Security'19) are the first to link the cross-ledger money flows. However, their scheme is only applicable to one platform and requires real-time monitoring to obtain transaction lists. To extend the cross-ledger tracing techniques, we design CLTracer, a general and non-real-time framework based on address relationships. In our implementation, we discover more than 1.7 million cross-ledger transactions on ShapeShift. We further design a combined heuristic of cross-ledger clustering and obtain 24,925 cross-ledger clusters. Two methods are then proposed to analyze the false positives, and the biggest clusters are inspected to understand their behaviors. Finally, we study the deposit and withdrawal mechanisms of 19 other trading platforms and adapt our techniques to nine of them. Our work could provide insights to the supervising authority in collecting evidence of illicit cross-ledger trading behaviors.