Reviewerly/exHarmony huggingface dataset:
Two-phase flow pattern online monitoring system based on convolutional neural network and transfer learning

Two-phase flow may almost exist in every branch of the energy industry. For the corresponding engineering design, it is very essential and crucial to monitor flow patterns and their transitions accurately. With the high-speed development and success of deep learning based on convolutional neural network (CNN), the study of flow pattern identification recently almost focused on this methodology. Additionally, the photographing technique has attractive implementation features as well, since it is normally considerably less expensive than other techniques. The development of such a two-phase flow pattern online monitoring system is the objective of this work, which seldom studied before. The ongoing preliminary engineering design (including hardware and software) of the system are introduced. The flow pattern identification method based on CNNs and transfer learning was discussed in detail. Several potential CNN candidates such as ALexNet, VggNet16 and ResNets were introduced and compared with each other based on a flow pattern dataset. According to the results, ResNet50 is the most promising CNN network for the system owing to its high precision, fast classification and strong robustness. This work can be a reference for the online monitoring system design in the energy system.
Variational Determination of Multiqubit Geometrical Entanglement in Noisy Intermediate-Scale Quantum Computers

Current noise levels in physical realizations of qubits and quantum operations limit the applicability of conventional methods to characterize entanglement. In this adverse scenario, we follow a quantum variational approach to estimate the geometric measure of entanglement of multiqubit pure states. The algorithm requires only single-qubit gates and measurements, so it is well suited for noisy intermediate-scale quantum devices. This is demonstrated by successfully implementing the method on IBM Quantum devices for Greenberger-Horne-Zeilinger states of three, four, and five qubits. Numerical simulations with random states show the robustness and accuracy of the method. The scalability of the protocol is numerically demonstrated via matrix-product-state techniques up to $25$ qubits.
Federated edge learning for the wireless physical layer: Opportunities and challenges

Deep learning (DL) has been applied to the physical layer of wireless communication systems, which directly extracts environment knowledge from data and outperforms conventional methods either in accuracy or computation complexity. However, most related research works employ centralized training that inevitably involves collecting training data from edge devices. The data uploading process usually results in excessive communication overhead and privacy disclosure. Alternatively, a distributed learning approach named federated edge learning (FEEL) is introduced to physical layer designs. In FEEL, all devices collaborate to train a global model only by exchanging parameters with a nearby access point. Because all datasets are kept local, data privacy is better protected and data transmission overhead can be reduced. This paper reviews the studies on applying FEEL to the wireless physical layer including channel state information acquisition, transmitter, and receiver design, which represent a paradigm shift of the DL-based physical layer design. In the meantime they also reveal several limitations inherent in FEEL, particularly when applied to the wireless physical layer, thus motivating further research efforts in the field.
Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval

Pre-training and fine-tuning have achieved significant advances in the information retrieval (IR). A typical approach is to fine-tune all the parameters of large-scale pre-trained models (PTMs) on downstream tasks. As the model size and the number of tasks increase greatly, such approach becomes less feasible and prohibitively expensive. Recently, a variety of parameter-efficient tuning methods have been proposed in natural language processing (NLP) that only fine-tune a small number of parameters while still attaining strong performance. Yet there has been little effort to explore parameter-efficient tuning for IR.
Distance-Aware Occlusion Detection With Focused Attention

For humans, understanding the relationships between objects using visual signals is intuitive. For artificial intelligence, however, this task remains challenging. Researchers have made significant progress studying semantic relationship detection, such as human-object interaction detection and visual relationship detection. We take the study of visual relationships a step further from semantic to geometric. In specific, we predict relative occlusion and relative distance relationships. However, detecting these relationships from a single image is challenging. Enforcing focused attention to task-specific regions plays a critical role in successfully detecting these relationships. In this work, (1) we propose a novel three-decoder architecture as the infrastructure for focused attention; 2) we use the generalized intersection box prediction task to effectively guide our model to focus on occlusion-specific regions; 3) our model achieves a new state-of-the-art performance on distance-aware relationship detection. Specifically, our model increases the distance F1-score from 33.8% to 38.6% and boosts the occlusion F1-score from 34.4% to 41.2%. Our code and data will be publicly available.
Heisenberg treatment of multiphoton pulses in waveguide QED with time-delayed feedback

The dynamics of waveguide-QED systems involving coherent time-delayed feedback give rise to a hierarchy of multi-time correlations within the Heisenberg picture due to the induced non-Markovianity. We propose to perform a projection onto a complete set of states in the Hilbert space to decompose the multi-time correlations into single-time matrix elements. To illustrate the procedure, we consider the paradigmatic example of a two-level system that couples to a semi-infinite waveguide and interacts with quantum light pulses. Our approach complements the range of available methods as it allows calculating the dynamics under the inclusion of additional dissipation channels in a numerically exact and efficient manner for multiphoton pulses of arbitrary shape where memory requirements are known in advance.
Influence of seasonal effect on dust accumulation on Photovoltaic panels that operate light posts

Dust accumulation on Photovoltaic (PV) panels is a severe threat that decreases the energy production of PV panels and therefore, lowers their efficiency especially in the Middle East and North Africa (MENA) region. Cleaning of the PV panels has always been a challenge for most of the countries in this region due to scarcity of water resources and high dust accumulation rates throughout the year. A new dust mitigation technique has been developed in this research, which consists of a wind shield, antistatic hydrophilic coating and a mechanical vibrator. The objective of this research is to study the seasonal effects on the performance of this new dust mitigation technique for PV panels that operate street light posts. Two experiments were conducted for six weeks in 2021; the first one was in winter while the other one was in summer. In both experiments, two PV panels were examined. The first PV panel is used as a reference panel for comparison and the second panel is provided with a wind shield and a mechanical vibrator, in addition to being coated with an antistatic hydrophilic coating. The function of the wind shield is to obstruct the wind coming from the South direction. The antistatic coating prevents the dust particles from adhering to the panel’s surface. The role of the mechanical vibrator is to shake off the deposited dust particles on the panel’s surface. The experimental results show that applying the antistatic coating together with the wind shield with vibrating the panel in case of the winter experiment preserved the efficiency of the panel, such that the drop in efficiency did not exceed the maintenance limit of 10% during the experiment. While in the summer experiment, the maintenance limit has been exceeded in the fifth week. This is mainly due to the effect of wind direction and rainfalls, which were present in the winter, unlike the summer experiment where winds and rainfalls are rarely found. This clearly shows that the combination of the wind shield and the mechanical vibrator together with the coating is a promising dust mitigation technique for PV panels operating light posts especially in winter.
Auto-Encoder-Enabled Anomaly Detection in Acceleration Data: Use Case Study in Container Handling Operations

The sudden increase in containerization volumes around the globe has increased the overall number of cargo losses, infrastructure damage, and human errors. Most critical losses occur during handling procedures performed by port cranes while sliding the containers to the inner bays of the ship along the vertical cell guides, damaging the main metal frames and causing the structure to deform and lose its integrity and stability. Strong physical impacts may occur at any given moment, thus in-time information is critical to ensure the clarity of the processes without halting operations. This problem has not been addressed fully in the recent literature, either by researchers of the engineering community or by the logistics companies’ representatives. In this paper, we have analyzed the conventional means used to detect these critical impacts and found that they are outdated, having no real-time assessment capability, only post-factum visual evaluation results. More reliable and in-time information could benefit many actors in the transportation chain, making transportation processes more efficient, safer, and reliable. The proposed solution incorporates the monitoring hardware unit and the analytics mechanism, namely the auto-encoder technology, that uses the acceleration parameter to identify sensor data anomalies and informs the end-user if these critical impacts occurred during handling procedures. The proposed auto-encoder analytical method is compared with the impacts detection methodology (IDM), and the result indicates that the proposed solution is well capable of detecting critical events by analyzing the curves of reshaped signals, detecting the same impacts as the IDM, while improving the speed of the short-term detection periods. We managed to detect–predict between 9 and 18 impacts, depending on the axis of container sway. An experimental study suggests that if programmed correctly, the auto-encoder (AE) can be used to detect deviations in time-series events in different container handling scenarios.
5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model

Resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as machine learning (ML) and deep learning, must be applied to make the model customisable. Moreover, improvisation allows the prediction system to generate the most accurate outcomes and valuable insights from data whilst enabling effective decisions. In this study, we first provide a literature study on the applications of ML and a summary of the hyperparameters influencing the prediction capabilities of the ML models for the communication system. We demonstrate the behaviour of four ML models: k nearest neighbour, classification and regression trees, random forest and support vector machine. Then, we observe and elaborate on the suitable hyperparameter values for each model based on the accuracy in prediction performance. Based on our observation, the optimal hyperparameter setting for ML models is essential because it directly impacts the model’s performance. Therefore, understanding how the ML models are expected to respond to the system utilised is critical.
Planning and visual-servoing for robotic manipulators in ROS

Abstract This article presents a probabilistic road map (PRM) and visual servo control (visual-servoing) based path planning strategy that allows a Motoman HP20D industrial robot to move from an initial positional to a random final position in the presence of fixed obstacles. The process begins with an application of the PRM algorithm to take the robot from an initial position to a point in space where it has a free line of sight to the target, to then apply visual servoing and end up, finally, at the desired position, where an image captured by a camera located at the robot’s end effector matches a reference image, located on the upper surface of a rectangular prismatic object. Algorithms and experiments were developed in simulation, specifically, the visual servo control that includes the dynamic model of the robot and the image sensor subject to realistic lighting were developed in robot operating system (ROS) environment.
Arigatō: Effects of Adaptive Guidance on Engagement and Performance in Augmented Reality Learning Environments

Experiential learning (ExL) is the process of learning through experience or more specifically "learning through reflection on doing". In this paper, we propose a simulation of these experiences, in Augmented Reality (AR), addressing the problem of language learning. Such systems provide an excellent setting to support "adaptive guidance", in a digital form, within a real environment. Adaptive guidance allows the instructions and learning content to be customised for the individual learner, thus creating a unique learning experience. We developed an adaptive guidance AR system for language learning, we call Arigato (Augmented Reality Instructional ¯ Guidance & Tailored Omniverse), which offers immediate assistance, resources specific to the learner's needs, manipulation of these resources, and relevant feedback. Considering guidance, we employ this prototype to investigate the effect of the amount of guidance (fixed vs. adaptive-amount) and the type of guidance (fixed vs. adaptive-associations) on the engagement and consequently the learning outcomes of language learning in an AR environment. The results for the amount of guidance show that compared to the adaptive-amount, the fixed-amount of guidance group scored better in the immediate and delayed (after 7 days) recall tests. However, this group also invested a significantly higher mental effort to complete the task. The results for the type of guidance show that the adaptive-associations group outperforms the fixed-associations group in the immediate, delayed (after 7 days) recall tests, and learning efficiency. The adaptive-associations group also showed significantly lower mental effort and spent less time to complete the task.
Perceptions on the use of mixed reality in mobile environments in secondary education

Purpose The beliefs about this subject of N = 223 secondary education teachers were collected through an ex post facto research method using a descriptive and correlational design and a 27-item questionnaire. Design/methodology/approach The arrival of online learning, in this case m-learning, to secondary education, has made educators in this stage incorporate digital resources, such as Smartphones or mixed reality (MR), to their classroom methodologies. The present study describes the results obtained in the project design, implementation and evaluation of MR materials in learning environments (PID2019-108933GB-I00). The starting general objective is to determine the perception of Compulsory Education teachers-in-training from the areas of Experimental Sciences, Engineering and Architecture, and Health Sciences, on the use of MR as a teaching tool under the m-learning modality. Findings The main conclusion obtained was that neither sex nor age had an influence on the use of MR in the classroom, and its use was determined by elements associated with the classroom methodology, such as collaborative work and attention to diversity, which took place in the education center. Research limitations/implications The main obstacle found when conducting the research was being able to access the general population of teachers-in-training in the aforementioned Master's program. Originality/value The value of the article lies in publicizing the knowledge that secondary school teachers have of MR and giving clues to create training actions that encourage its incorporation into the classroom methodology.
Data encryption based on 7D complex chaotic system with cubic memristor for smart grid

The information security has an irreplaceable position in the smart grid (SG). In order to avoid the malicious attack and ensure the information security, the cryptographic techniques are essential. This paper focuses on the encryption techniques to ensure the information security of SG. Firstly, an unusual 7-dimensional complex chaotic system (7D-CCS) combined with the cubic memristor is introduced. Besides its phase portraits, Lyapunov exponent, 0–1 test, complexity, and bifurcation diagram are investigated. Then, with the proposed 7D-CCS, we design a data encryption algorithm to ensure the encryption security. Finally, the data and monitoring images in SG are encrypted by the designed encryption scheme. Besides, the encryption performance is given in detailed. The experimental results show that the proposed encryption scheme has quite good encryption performance. Therefore, it can ensure the information security of SG.
A Novel Nonparametric Feature Selection Approach Based on Mutual Information Transfer Network

The filter feature selection algorithm is habitually used as an effective way to reduce the computational cost of data analysis by selecting and implementing only a subset of original features into the study. Mutual information (MI) is a popular measurement adopted to quantify the dependence among features. MI-based greedy forward methods (MIGFMs) have been widely applied to escape from computational complexity and exhaustion of high-dimensional data. However, most MIGFMs are parametric methods that necessitate proper preset parameters and stopping criteria. Improper parameters may lead to ignorance of better results. This paper proposes a novel nonparametric feature selection method based on mutual information and mixed-integer linear programming (MILP). By forming a mutual information network, we transform the feature selection problem into a maximum flow problem, which can be solved with the Gurobi solver in a reasonable time. The proposed method attempts to prevent negligence on obtaining a superior feature subset while keeping the computational cost in an affordable range. Analytical comparison of the proposed method with six feature selection methods reveals significantly better results compared to MIGFMs, considering classification accuracy.
MaxMatch: Semi-Supervised Learning With Worst-Case Consistency

In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-art models are based on the idea of pursuing consistent model predictions over unlabeled data toward the input noise, which is called consistency regularization. Nonetheless, there is a lack of theoretical insights into the reason behind its success. To bridge the gap between theoretical and practical results, we propose a worst-case consistency regularization technique for SSL in this article. Specifically, we first present a generalization bound for SSL consisting of the empirical loss terms observed on labeled and unlabeled training data separately. Motivated by this bound, we derive an SSL objective that minimizes the largest inconsistency between an original unlabeled sample and its multiple augmented variants. We then provide a simple but effective algorithm to solve the proposed minimax problem, and theoretically prove that it converges to a stationary point. Experiments on five popular benchmark datasets validate the effectiveness of our proposed method.
Entanglement distribution using a biphoton frequency comb compatible with DWDM technology

We demonstrate a distribution of frequency-multiplexed polarization-entangled photon pairs over 16 frequency channels using demultiplexers for the signal and idler photons with a frequency spacing of 25 GHz, which is compatible with dense wavelength division multiplexing (DWDM) technology. Unlike conventional frequency-multiplexed photon-pair distribution by a broadband spontaneous parametric down-conversion (SPDC) process, we use photon pairs produced as a biphoton frequency comb by SPDC inside a cavity where one of the paired photons is confined. Owing to the free spectral range of 12.5 GHz and the finesse of over 10 of the cavity, the generated photons having a narrow linewidth in one channel are separated well from those in the other channels, which minimizes channel cross-talk in advance. The observed fidelities of the photon pairs range from 81 % to 96 % in the 16 channels. The results show the usefulness of the polarization-entangled biphoton frequency comb for frequency-multiplexed entanglement distribution via a DWDM system.
Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python

This paper introduces the multi-freq-ldpy Python package for multiple frequency estimation under Local Differential Privacy (LDP) guarantees. LDP is a gold standard for achieving local privacy with several real-world implementations by big tech companies such as Google, Apple, and Microsoft. The primary application of LDP is frequency (or histogram) estimation, in which the aggregator estimates the number of times each value has been reported. The presented package provides an easy-to-use and fast implementation of state-of-the-art solutions and LDP protocols for frequency estimation of: single attribute (i.e., the building blocks), multiple attributes (i.e., multidimensional data), multiple collections (i.e., longitudinal data), and both multiple attributes/collections. Multi-freq-ldpy is built on the well-established Numpy package – a de facto standard for scientific computing in Python – and the Numba package for fast execution. These features are described and illustrated in this paper with two worked examples. This package is open-source and publicly available under an MIT license via GitHub ( https://github.com/hharcolezi/multi-freq-ldpy ) and can be installed via PyPi ( https://pypi.org/project/multi-freq-ldpy/ ).
Research on visual question answering based on dynamic memory network model of multiple attention mechanisms

Since the existing visual question answering model lacks long-term memory modules for answering complex questions, it is easy to cause the loss of effective information. In order to further improve the accuracy of the visual question answering model, this paper applies the multiple attention mechanism combining channel attention and spatial attention to memory networks for the first time and proposes a dynamic memory network model (DMN-MA) based on the multiple attention mechanism. The model uses the multiple attention mechanism in the situational memory module to obtain the most relevant visual vectors for answering questions based on continuous memory updating, storage and iterative inference of the questions, and effectively uses contextual information for answer inference. The experimental results show that the accuracy of the model in this paper reaches 64.57% and 67.18% on the large-scale public datasets COCO-QA and VQA2.0, respectively.