Reviewerly/exHarmony huggingface dataset:
Machine Translation of Electrical Terminology Constraints

In practical applications, the accuracy of domain terminology translation is an important criterion for the performance evaluation of domain machine translation models. Aiming at the problem of phrase mismatch and improper translation caused by word-by-word translation of English terminology phrases, this paper constructs a dictionary of terminology phrases in the field of electrical engineering and proposes three schemes to integrate the dictionary knowledge into the translation model. Scheme 1 replaces the terminology phrases of the source language. Scheme 2 uses the residual connection at the encoder end after the terminology phrase is replaced. Scheme 3 uses a segmentation method of combining character segmentation and terminology segmentation for the target language and uses an additional loss module in the training process. The results show that all three schemes are superior to the baseline model in two aspects: BLEU value and correct translation rate of terminology words. In the test set, the highest accuracy of terminology words was 48.3% higher than that of the baseline model. The BLEU value is up to 3.6 higher than the baseline model. The phenomenon is also analyzed and discussed in this paper.
Prototyping of Utilization Model for KOMPSAT-3/3A Analysis Ready Data Based on the Open Data Cube Platform in Multi-Cloud Computing Environment: A Case Study

This study introduces a multi-cloud model that combines private and public cloud services for processing and managing satellite images. The multi-cloud service is established by incorporating private clouds within organizations and integrating them with external public cloud services to utilize the data. Private clouds can maintain data security within an organization or between organizations, while public clouds offer easy processing options for general users with access accounts. The model for the private cloud service utilizes open-source OpenStack software to create virtual machines, allowing users to manage analysis ready data (ARD) of the Korea Multi-Purpose Satellite (KOMPSAT)-3/3A images simultaneously. The public cloud service through Amazon Web Services (AWS) offers four services and uses the Open Data Cube (ODC) to manage data and provide web-based time-series visualization and processing. The model utilizes OpenStack to create virtual machines, and the public cloud service through AWS offers various services using ODC to manage data. A system that handles large amounts of satellite imagery in a multi-cloud environment has benefits such as improved availability, cost savings through open-source, and enhanced scalability. We present a prototyped utilization model that can be used with the ODC user interface (UI) that applies the proposed multi-cloud model. The multi-cloud model of this study can be applied to constructing a country-scale data cube system, that deals with large-scale satellite image data. It can also be applied to systems that need to be built with data that is tailored to a specific user’s needs at any institution.
Patch-Level Consistency Regularization in Self-Supervised Transfer Learning for Fine-Grained Image Recognition

Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass differences under large intraclass variances. Most previous approaches are based on supervised learning, which requires a large-scale labeled dataset. However, such large-scale annotated datasets for fine-grained image recognition are difficult to collect because they generally require domain expertise during the labeling process. In this study, we propose a self-supervised transfer learning method based on Vision Transformer (ViT) to learn finer representations without human annotations. Interestingly, it is observed that existing self-supervised learning methods using ViT (e.g., DINO) show poor patch-level semantic consistency, which may be detrimental to learning finer representations. Motivated by this observation, we propose a consistency loss function that encourages patch embeddings of the overlapping area between two augmented views to be similar to each other during self-supervised learning on fine-grained datasets. In addition, we explore effective transfer learning strategies to fully leverage existing self-supervised models trained on large-scale labeled datasets. Contrary to the previous literature, our findings indicate that training only the last block of ViT is effective for self-supervised transfer learning. We demonstrate the effectiveness of our proposed approach through extensive experiments using six fine-grained image classification benchmark datasets, including FGVC Aircraft, CUB-200-2011, Food-101, Oxford 102 Flowers, Stanford Cars, and Stanford Dogs. Under the linear evaluation protocol, our method achieves an average accuracy of 78.5%, outperforming the existing transfer learning method, which yields 77.2%.
Security, Privacy, and Reliability in Ubiquitous Intelligence Empowered Internet of Vehicles

Ubiquitous intelligence empowered internet of vehicles (UIIoV) is an emerging paradigm where network entities such as mobile vehicles, edge/cloud servers, and intermediate nodes interact to achieve effective data sensing and make intelligent decisions based on artificial intelligence techniques. Unlike the conventional internet of vehicles, artificial intelligence is introduced to improve the ability of data processing and analysis, and further enhance the accuracy of decision-making. In this article, we study the architecture of UIIoV, and the security, privacy, and reliability challenges it confronts. To be specific, we start by describing the overall infrastructure, exploring the security, privacy, and reliability demands of UIIoV, and further give state-of-the-art solutions to enable security guarantees, privacy protection, and prediction reliability. Finally, this article presents several promising future research directions to hopefully attract more attention to this emergent field.
Six Sigma Application on Software Improvement Process

This study proposes a quality development method based on six sigma to solve software quality problems of the software. The six sigma DMADV framework is used to analyze the root causes that have a major influence on user complaints against the software. Based on the results of joint elaboration with the company stakeholders, proposed improvements will be designed that can reduce user complaints. The suggestions for improvement that will be designed are the design of a junior programmer training program and the design of technical documents. The program will be designed by adapting the Windows training framework and developing the SKKNI 615-2012 programmer curriculum. Meanwhile, technical documents will be designed based on the IEEE 1063-2001 standard for software user documentation. The results of the proposed design are expected to increase the effectiveness of the script writing of the software and minimize technical repair activities.
Analyzing Trends and Topics of Sinhala Hate Speech on Twitter: A Time Series Approach

With the exponential increase in popularity, social media has become an influential platform for expressing opinions on various topics. However, these conversations may contain both hate-promoting and non-hate-promoting content, and excessive promotion of hate content can significantly impact the masses. Hence, it is critical to identify trending periods of hate-promoting topics to avoid harmful incidents among individuals or society. Considering the previous experiences in Sri Lanka, it would be helpful to detect such discussions early. This paper proposes a deep learning-based approach to identify trending periods of hate topics on Twitter, specifically focusing on hate-promoting Sinhala topics. The proposed model can identify trending periods of hate-promoting content in real-time, providing valuable insights for policymakers and stakeholders to counter the spread of hate speech. This research has significant implications for mitigating the impact of hate speech in the Sinhala-speaking community, contributing towards a more inclusive and tolerant society.
Online learning of smooth functions

In this paper, we study the online learning of real-valued functions where the hidden function is known to have certain smoothness properties. Specifically, for q≥1, let Fq be the class of absolutely continuous functions f:[0,1]→R such that ‖f′‖q≤1. For q≥1 and d∈Z+, let Fq,d be the class of functions f:[0,1]d→R such that any function g:[0,1]→R formed by fixing all but one parameter of f is in Fq. For any class of real-valued functions F and p>0, let optp(F) be the best upper bound on the sum of pth powers of absolute prediction errors that a learner can guarantee in the worst case. In the single-variable setup, we find new bounds for optp(Fq) that are sharp up to a constant factor. We show for all ε∈(0,1) that opt1+ε(F∞)=Θ(ε−12) and opt1+ε(Fq)=Θ(ε−12) for all q≥2. We also show for ε∈(0,1) that opt2(F1+ε)=Θ(ε−1). In addition, we obtain new exact results by proving that optp(Fq)=1 for q∈(1,2) and p≥2+1q−1. In the multi-variable setup, we establish inequalities relating optp(Fq,d) to optp(Fq) and show that optp(F∞,d) is infinite when p<d and finite when p>d. We also obtain sharp bounds on learning F∞,d for p<d when the number of trials is bounded.
Deterministic realization of trace-preserving channels in linear-optical systems

A quantum channel describes the general evolution of quantum systems, which is therefore a critical issue for quantum simulation and a fundamental element of quantum information. To realize quantum channels, an efficient method is to randomly implement Kraus operators, which does not require any ancillary quantum system. For open systems, it is natural to extend the method from implementation of unitary Kraus operators to nonunitary ones. However, when some Kraus operators are not proportional to unitary ones, this method becomes probabilistic even if the channel is trace preserving. In this paper, to overcome this drawback, we propose an algorithm to deterministically realize arbitrary trace-preserving channels with only one ancillary qubit and finite iterations of evolutions of the combined states of the system and the ancilla. Moreover, to show the validity of the method, we experimentally realize conventional and modified Landau-Streater channels based on the algorithm. Our results shed light on quantum simulation of quantum channels for open systems.
Efficient quantum process tomography for Clifford circuits

A Clifford circuit is a pivotal tool in quantum computing and has extensive applications in quantum error correction codes and topological quantum computing. Hence, it is essential to benchmark and verify the effect of Clifford circuits against noise and errors. Standard quantum process tomography is a fundamental technique for fully characterizing quantum dynamics, but at the cost of exponential time, space, and computation with an increasing number of qubits. Here, we propose an efficient quantum process tomography method for Clifford circuits. Combining with the stabilizer formalism, we prove theoretically that, for an $n$-qubit Clifford circuit, our method merely needs $m$ ancillary qubits and $\ensuremath{\lceil}n/m\ensuremath{\rceil}$ input stabilizer states to obtain the quantum process. Numerical simulation results show that our method could perfectly rebuild the unknown Clifford quantum circuit with fidelity over $99.99%$ to six qubit cases. Our work provides an efficient and practical approach to benchmark and verify Clifford circuits.
Molecular Identification of Malaria Vector in Gusau Township, Nigeria

There about 460 Anopheles species recognized, over 100 can transmit human malaria but only 30–40 commonly transmit parasites of the genus Plasmodium, which cause malaria. Anopheles gambiae is the principal vector of the most dangerous malaria parasite species in Africa, which is Plasmodium falciparum. Anopheles gambiae are a complex consisting of eight morphologically indistinguishable species and each of the members of the complex having unique biology, ecology and behaviour and should be studied and differentiated. The research objective was to identify the species of malaria vector in Gusau township Nigeria using molecular biological technique to know the exact malaria vector. Knowledge of the exact malaria vector in a given environment enables formulating a carefully designed tailor-made vector control measure. Indoor and outdoor mosquito samples were collected from selected areas in five wards of Gusau township using standard collection methods, for a period of twelve months. The samples were identified using polymerase chain reaction (PCR) technique and the result showed Anopheles gambiae in all sites of sample collection. It is therefore recommended that integrated malaria vector control should be adopted in the mosquito control programme because Anopheles gambiae is rugged and can be difficult to control and/or eradicate because of the emerging insecticide resistance and its close association with human host.
Model-Free Ground Velocity and Position Estimation for Manned and Unmanned Aircraft

Navigation is a critical task for the operations of both manned and unmanned aircraft systems. Current positioning systems rely primarily on satellite systems such as the Global Positioning System (GPS) or alternative sensor fusion algorithms, which typically require vision sensing and processing. Due to the possibility of temporary GPS outages and/or GPS jamming, it is critical for aircraft sensing systems to predict the position as well as the ground velocity of the aircraft in the absence of GPS signals. This work proposes two state estimation algorithms for predicting the position and ground velocity of aircraft. These methods do not require vision sensors or aircraft dynamic model information, thus providing a portable approach applicable to any aircraft. The proposed methods consider infrequent GPS position updates. Although not completely GPS free, these algorithms do not require GPS velocity measurements and can predict the aircraft position in between the position updates. The proposed methods use the information filter and unscented information filter; they are first validated using unmanned aircraft flight data and later applied to flight data from a high-speed manned military trainer jet. The results indicate the effectiveness of this approach for model-free position and ground velocity estimation.
Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction

Multi-modal regression is important in forecasting nonstationary processes or with a complex mixture of distributions. It can be tackled with multiple hypotheses frameworks but with the difficulty of combining them efficiently in a learning model. A Structured Radial Basis Function Network is presented as an ensemble of multiple hypotheses predictors for regression problems. The predictors are regression models of any type that can form centroidal Voronoi tessellations which are a function of their losses during training. It is proved that this structured model can efficiently interpolate this tessellation and approximate the multiple hypotheses target distribution and is equivalent to interpolating the meta-loss of the predictors, the loss being a zero set of the interpolation error. This model has a fixed-point iteration algorithm between the predictors and the centers of the basis functions. Diversity in learning can be controlled parametrically by truncating the tessellation formation with the losses of individual predictors. A closed-form solution with least-squares is presented, which to the authors’ knowledge, is the fastest solution in the literature for multiple hypotheses and structured predictions. Superior generalization performance and computational efficiency is achieved using only two-layer neural networks as predictors controlling diversity as a key component of success. A gradient-descent approach is introduced which is loss-agnostic regarding the predictors. The expected value for the loss of the structured model with Gaussian basis functions is computed, finding that correlation between predictors is not an appropriate tool for diversification. The experiments show outperformance with respect to the top competitors in the literature.
Transferring four-dimensional atomic states at one step between separated cavities

Abstract This paper is concentrated on one-step transferring four-dimensional atomic states between atoms trapped in distant cavities connected by an optical fiber. Compared with the conventional schemes, one-step transferring four-dimensional atomic states means higher efficiency in preparing quantum network. In addition, we also calculate the effects of photon leakage out of cavity and fiber in this quantum process. The result shows the high fidelity can be reached based on the current experimental conditions.
Adopting multiple vision transformer layers for fine-grained image representation

Abstract Accurate discriminative regions proposal has an important effect for fine-grained image recognition. The vision transformer (ViT) brings about a striking effect in computer vision duo to its innate muti-head self-attention mechanism. However, the attention maps are gradually similar after certain layers and since ViT adds classification token for perform classification, it is unable to effectively select discriminative image patches for fine-grained image classification. To accurately detect discriminative regions, we propose a novel network AMTrans, which efficiently increases layers to learn diverse features and utilizes integrated raw attention maps to capture more salient feature. Specifically, we employ DeepViT as backbone to solve the attention collapse issue. Then, we fuse each head attention weight within each layer to produce attention weight map. After that, we alternatively use recurrent residual refinement blocks to promote salient feature detection and then utilize semantic grouping method to propose the discriminative feature region. A lot of experiments prove that AMTrans acquires the SOTA performance on three widely used fine-grained datasets under the same settings, involving Stanford-Cars, Stanford-Dogs and CUB-200-2011.
Optimization of ICANet lightweight human pose estimation based on HRNet

Abstract The existing high-resolution human pose estimation models have low applicability in practical applications due to their large parameter quantity and high computational complexity. To address these issues, this paper proposes a human pose estimation network with a lightweight inverted residual coordinate attention network (ICANet) based on the high-resolution network (HRNet). With the introduction of CoordAttention mechanism and the inverted residual module, this paper proposes two lightweight network modules, namely ICAneck and ICAblock, which not only reduce model parameter quantity and computational complexity, but also achieve feature enhancement of long-range dependence and precise position information in spatial directions of the feature map. Experimental results show that compared to HRNet, the ICANet model proposed in this paper reduces its parameter quantity by 53.7% and computational complexity by 32.4% on the COCO validation set, and lowers its parameter amount by 53.7% and computational complexity by 32.6% on the MPII validation set. Practical applications prove that ICANet still achieves high-precision detection of human key points with fewer parameters and lower computational complexity, and has higher applicability and practicality compared with common human pose estimation networks such as the Stacked Hourglass Network (Hourglass), Cascaded Pyramid Network (CPN), and SimpleBaseline, and therefore has better applicability and practicality.
Distributed Energy Grid-Connected Dense Data Forecasting Technology Based on Federated Learning

Abstract Photovoltaic power generation system is one of the main clean energy power generation systems at present, which plays an important role in daily production and life. However, the photovoltaic power generation system is easily affected by various factors, and the output power will be unstable in the practical application process, which will affect the power generation efficiency. In this paper, a prediction method of distributed energy grid-connected dense data based on federated learning is constructed. This method can not only realize the short-term prediction of distributed photovoltaic power generation data, but also ensure that the data can be encrypted and modeled, thus solving the “digital island” problem. The model evaluation shows that the method in this paper performs well in short-term photovoltaic power generation prediction, and it can predict the short-term power generation of different photovoltaic power stations with high prediction accuracy. This method is of great significance to improve the management and scheduling ability and energy utilization rate of distributed photovoltaic power generation systems.
Wokół badań nad „Commentariolum Petitionis”

Doubts concerning the authorship of the work contemporarily entitled Commentariolum Petitionis were raised even in the late nineteenth century. Both those researchers who regarded Quintus (Cicero’s brother) as the author and those who questioned his authorship found their supporters. In this context, Michael Alexander stands out, arguing in his publications that the intention behind the pamphlet was to provide a distanced or even warped image of late-Republican electioneering. The debate continues, and the importance of the present-day interpretation must be emphasised. Even if perhaps satisfactory for those who attribute the authorship to Marcus Cicero, recent computerassisted stylometric studies in fact fail to provide a conclusive answer as to who the author of the Commentariolum Petitionis actually was. Stylometric evidence may even be quoted to prove that it might have been written by someone skilful enough to imitate the eminent orator’s style.
Detecting covert channel attacks on cyber‐physical systems

Abstract Cyberattacks on cyber‐physical systems (CPS) have the potential to cause widespread disruption and affect the safety of millions of people. Machine learning can be an effective tool for detecting attacks on CPS, including the most stealthy types of attacks, known as covert channel attacks. In this study, the authors describe a novel hierarchical ensemble architecture for detecting covert channel attacks in CPS. Our proposed approach uses a combination of TCP payload entropy and network flows for feature engineering. Our approach achieves high detection performance, shortens the model training duration, and shows promise for effective detection of covert channel communications. This novel architecture closely mirrors the CPS attack stages in real‐life, providing flexibility and adaptability in detecting new types of attacks.