Reviewerly/exHarmony huggingface dataset:
On Automorphisms of Chain Rings

Suppose R is a finite chain ring with invaraints p,n,r,k,k′,m. Suppose G is also the subset of all φ∈ Aut(R), the automorphism group of R, such that φ(πk′)=πk′, where π is a generator of the maximal ideal of R. It was found that G is a group that is, in some sense, the set of all symmetries of {πk′}. The main purpose of this article is to describe the structure of G. The subgroup G helps us understand the structure of Aut(R) in the general case which in turn provides immediate results in classifying chain rings.
Harris Hawks Optimization-Based Multilevel Thresholding Segmentation of Magnetic Resonance Brain Images

The segmentation role is inevitable in computer vision and image processing for delineating the specific region of interest. In the healthcare sector, the segmentation role is vital in the diagnosis and prediction of diseases. This research work proposes Harris Hawks optimization-based multilevel thresholding segmentation approach for the MR brain images. Multilevel thresholding is an improved version classical thresholding algorithm, and the optimization algorithm chooses the optimum threshold values for the segmentation. The segmentation model was tested on Alzheimer’s MR brain images and validated in terms of performance computer-aided threshold level of 5 which was found to generate satisfactory results in terms of performance metrics.
Differentially Private Range Query on Shortest Paths

We consider range queries on a graph under the constraints of differential privacy and query ranges are defined as the set of edges on the shortest path of the graph. Edges in the graph carry sensitive attributes and the goal is to report the sum of these attributes on the shortest path for counting query or the minimum of the attributes in a bottleneck query. We use differential privacy to ensure that answering these queries does not violate the privacy of the sensitive edge attributes. Our goal is to design mechanisms that minimize the additive error of the output with the given privacy budget. For this, we develop the first set of non-trivial results for private range queries on shortest paths. For counting range queries we can achieve an additive error of $$\widetilde{O}(n^{1/3})$$ for $$\varepsilon $$ -DP and $$\widetilde{O}(n^{1/4})$$ for $$(\varepsilon , \delta )$$ -DP. We present two algorithms where we control the final error by carefully balancing perturbation added to the edge attributes directly versus perturbation added to (a subset of) range query answers. Bottleneck range queries are easier and can be answered with polylogarithmic additive errors.
Coherent and incoherent trapped-ion quantum control in structured lightfields

Spatially structured lightfields enable enhanced laser cooling and quantum coherent control of trapped ions as compared to simple plane waves. Integrated photonics offers a route to stable delivery, facilitating application in quantum computing and metrology.
On the efficient implementation of classification rule learning

Abstract Rule learning methods have a long history of active research in the machine learning community. They are not only a common choice in applications that demand human-interpretable classification models but have also been shown to achieve state-of-the-art performance when used in ensemble methods. Unfortunately, only little information can be found in the literature about the various implementation details that are crucial for the efficient induction of rule-based models. This work provides a detailed discussion of algorithmic concepts and approximations that enable applying rule learning techniques to large amounts of data. To demonstrate the advantages and limitations of these individual concepts in a series of experiments, we rely on BOOMER—a flexible and publicly available implementation for the efficient induction of gradient boosted single- or multi-label classification rules.
Dynamically controlled time-bin entanglement source for quantum key distribution

We propose and demonstrate a simple protocol to upgrade a time-bin entanglement resource with access-control through binary phase randomization at the optical pump. The enhanced source can effectively control all users at a negligible cost.
Using deep learning in pathology image analysis: A novel active learning strategy based on latent representation

&lt;abstract&gt;&lt;p&gt;Most countries worldwide continue to encounter a pathologist shortage, significantly impeding the timely diagnosis and effective treatment of cancer patients. Deep learning techniques have performed remarkably well in pathology image analysis; however, they require expert pathologists to annotate substantial pathology image data. This study aims to minimize the need for data annotation to analyze pathology images. Active learning (AL) is an iterative approach to search for a few high-quality samples to train a model. We propose our active learning framework, which first learns latent representations of all pathology images by an auto-encoder to train a binary classification model, and then selects samples through a novel ALHS (Active Learning Hybrid Sampling) strategy. This strategy can effectively alleviate the sample redundancy problem and allows for more informative and diverse examples to be selected. We validate the effectiveness of our method by undertaking classification tasks on two cancer pathology image datasets. We achieve the target performance of 90% accuracy using 25% labeled samples in Kather's dataset and reach 88% accuracy using 65% labeled data in BreakHis dataset, which means our method can save 75% and 35% of the annotation budget in the two datasets, respectively.&lt;/p&gt;&lt;/abstract&gt;
Experimental demonstration of multiparty reference-frame-independent QKD

We report that RFI-QKD protocol can be extended to multiparty system using Greenberger-Horne-Zeilinger (GHZ) state. We derive the asymptotic key rate and perform proof-of-principle experiment to verify the feasibility of secure quantum network.
Designing large scale quantum networks

In this presentation we explore the core design concepts required for building large scale quantum networks and a future quantum internet. Our focus will include both the quantum and classical resources required.
Quantitative Comparison of Translation by Transformers-Based Neural Network Models

One of the major tasks in producer-customer relations is a processing of customers’ feedback. Since for the international companies provide their products for a lot of countries the feedback could be provided with various languages. That can produce a number of difficulties during automatic text processing. One of the solutions is to use one common language to process feedback and automatically translate feedbacks from the various languages to the common one. Then the translated feedback could be processed with the configured pipeline. This paper compares existing open models for automatic text translation. Only language models with Transformer architecture were considered due to the best results of translation over other existing approaches. The models are: M2M100, mBART, OPUS-MT (Helsinki NLP). Own test data was built due to requirement of translation for texts specific to the subject area. To create dataset Microsoft Azure Translation was chosen as the reference translation with manual translation verification for grammar. Translations produced by each model were compared with the reference translation using two metrics: BLEU and METEOR. The possibility of fast fine-tuning of models was also investigated to improve the quality of address the translation on specific lexicon of the problem area. Among the reviewed models, M2M100 turned out to be the best in terms of translation quality, but it is also the most difficult to fine-tune it.
Modelling Software Tasks for Supporting Resource-Driven Adaptation

Software systems execute tasks that depend on different types of resources. The variability of resources hinders the ability of software systems to execute important tasks. For example, in automated warehouses, malfunctioning robots could delay product deliveries and cause financial losses due to customer dissatisfaction. Resource-driven adaptation addresses the negative implications of resource variability. Hence, this paper presents a task modelling notation called SERIES, which is used for representing task models that support resource-driven adaptation in software systems. SERIES is complemented by a tool that enables software practitioners to create and modify task models. SERIES was evaluated through a study with software practitioners. The participants of this study were asked to explain and create task models and then provide their feedback on the usability of SERIES and the clarity of its semantic constructs. The results showed a very good user performance in explaining and creating task models using SERIES. These results were reflected in the feedback of the participants and the activities that they performed using SERIES.
Sentiment-Based Simplification of Legal Text

Legal documents have always played an important role for liabilities and safety, but the length of the document and the use of complex terms have become a complication for apprehension. Hence, if one possesses a tool to facilitate improvement in understanding, it could be a possible way of securing impartiality. The legal text is challenging since it is entirely different from the ordinary natural language in vocabulary, morphology, syntax, and semantics. Thus, a text simplification method that is to the legal domain can help make the legal text more accessible and save people from taking time to read legal documents. Thus, this study aims to find a text simplification method that would best suit legal text conversion. The proposed model is identifying complex words with the help of statistics and then replacing them with simpler and readable phrases or words which would be done with the help of a word2Vec model and sentiment analysis. The model is trained using a dataset that contains combined data from Indian Legal Documents Corpus (ILDCsingle and ILDCmulti) which has a corpus count of 1,906,365 which groups vectors of similar words together in the vector space. We propose an approach that successfully detects complex words in a paragraph and replaces it with a less complex word with the same sentiment as the complex word.
Transfer Learning for Driver Pose Estimation from Synthetic Data

The training of computer vision models for human pose estimation requires large amounts of data. Since labelling image data with pose keypoints is very time consuming and costly, we aim to alleviate this requirement by using synthetic data during pre-training and thus relax the need for large amounts of real data samples during fine-tuning. To this end, we investigate the impact of synthetic data on the performance of a 2D keypoint detection model in the context of driver body pose estimation. We present our approach for synthetic data generation to automatically provide large amounts of in-cabin views as training data. The utilization of the generated synthetic data is evaluated in different learning schemes. We achieve a notable performance gain of +30.5% by pre-training with our in-cabin synthetic data when only 1% of real training data from the DriPE dataset is available. The proposed approach also outperforms pre-training with PeopleSansPeople by +8.3% when the reduced DriPE dataset is used for fine-tuning.
A multi-grained unsupervised domain adaptation approach for semantic segmentation

When transferring knowledge between different datasets, domain mismatch greatly hinders model’s performance. So domain adaption has been brought up to tackle the problem. Traditional methods focusing either on global or local alignment play a limited role in improving model’s performance. In this paper, we propose a multi-grained unsupervised domain adaptation approach (Muda) for semantic segmentation. Muda aims to enforce multi-grained semantic consistency between domains by aligning domains at both global and category level. Specifically, coarse-grained adaptation uses global adversarial learning on an image translation model and a main segmentation model, which respectively attempts to eliminate appearance differences and to get similar segmentation maps from two domains. While fine-grained adaptation employs an auxiliary model to adapt category information to refine pseudo labels of target data. Experiments and ablation studies are conducted on two synthetic-to-real benchmarks: GTA5 → Cityscapes and SYNTHIA → Cityscapes, which show that our model outperforms the state-of-the-art methods.
Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM

Social media is currently being used by many individuals online as a major source of information. However, not all information shared online is true, even photos and videos can be doctored. Deepfakes have recently risen with the rise of technological advancement and have allowed nefarious online users to replace one's face with a computer-generated face of anyone they would like, including important political and cultural figures. Deepfakes are now a tool to be able to spread mass misinformation. There is now an immense need to create models that are able to detect deepfakes and keep them from being spread as seemingly real images or videos. In this paper, we propose a new deepfake detection schema using two popular machine learning algorithms; support vector machine and convolutional neural network, along with a publicly available dataset named the 140k Real and Fake Faces to accurately detect deepfakes in images with accuracy rates reaching as high as 88.33%.
A Hybrid Transdimensional Evolutionary Algorithm for Dynamical System Control Multicriteria Optimization

The article develops a new hybrid evolutionary algorithm for the optimal control law multicriteria synthesis of a dynamical system based on transdimensional search models. The transdimensional search model implies the combined usage of finite-dimensional and infinite-dimensional multicriteria optimization evolutionary algorithms, implementing the stages of the global approximate and local clarifying search for optimal solutions. A comparative analysis of the effectiveness of various hybrid transdimensional models of the evolutionary search is carried out for the problem of the bioreactor program control optimal law multicriteria synthesis. It is shown that the transdimensional hybridization of evolutionary algorithms using infinite-dimensional search models provides a higher solving accuracy of the problem.
Theory of localization-hindered thermalization in nonlinear multimode photonics

Abstract Our society’s appetite for ultra-high bandwidth communication networks and high-power optical sources, together with recent breakthroughs in mode multiplexing/demultiplexing schemes, forced the photonics community to reconsider the deployment of nonlinear multimode systems. These developments pose fundamental challenges stemming from the complexity of nonlinear mode-mode mixing by which they exchange energy in the process towards an equilibrium Rayleigh-Jeans (RJ) distribution. Here we develop a universal one-parameter scaling theory for the relaxation rates of out-of-equilibrium excitations towards their RJ thermal state. The theory predicts an exponential suppression of the rates with increasing disorder due to the formation of stable localization clusters resisting the nonlinear mode-mode interactions that tend to separate them. For low optical temperatures, the rates experience a crossover from linear to nonlinear temperature dependence which reflects a disorder-induced reorganization of the low frequency eigenmodes. Our theory will guide the design of nonlinear multimode photonic networks with tailored relaxation-scales.
Evidential deep learning-based multi-modal environment perception for intelligent vehicles

Intelligent vehicles (IVs) are pursued in both research laboratories and industries to revolutionize transportation systems. Since the driving surroundings can be cluttered and the weather conditions may vary, environment perception in IVs represents a challenging task. Therefore, multi-modal sensors are engaged. In perception, outstanding performance is obtained by employing deep learning algorithms. However, deep learning often relies on probabilities while there is a better formalism to handle prediction uncertainty. To circumvent this, in this work, evidence theory is combined with a camera-lidar-based deep learning fusion architecture. The coupling is based on generating basic belief functions using distance to prototypes. It also uses a distance-based decision rule. Because IVs have constrained computational power, a reduced deep-learning architecture is leveraged in this formulation. In the task of road detection, the evidential approach outperforms the probabilistic one. Besides, ambiguous features can be prudently settled as ignorance rather than making a possibly wrong decision using probability. The coupling is also extended to the task of semantic segmentation. This shows how evidential formulation can be easily adapted to the multi-class case. Therefore, the evidential formulation is generic and produces a more accurate and versatile prediction while maintaining the trade-off between performances and computational costs in IVs. This work uses the KITTI dataset.
Space-Efficient Functional Offline-Partially-Persistent Trees with Applications to Planar Point Location

In 1989 Driscoll, Sarnak, Sleator, and Tarjan presented general space-efficient transformations for making ephemeral data structures persistent. The main contribution of this paper is to adapt this transformation to the functional model. We present a general transformation of an ephemeral, linked data structure into an offline, partially persistent, purely functional data structure with additive $$\mathcal {O}\!\left( n\log n \right) $$ construction time and $$\mathcal {O}\!\left( n \right) $$ space overhead; with n denoting the number of ephemeral updates. An application of our transformation allows the elegant slab-based algorithm for planar point location by Sarnak and Tarjan 1986 to be implemented space efficiently in the functional model using linear space.
Study on the Development and Implementation of Different Big Data Clustering Methods

Clustering is an unsupervised learning method used to organize raw data in such a way that those with the same (similar) characteristics are found in the same class and those that are dissimilar are found in different classes. In this day and age, the very rapid increase in the amount of data being produced brings new challenges in the analysis and storage of this data. Recently, there is a growing interest in key areas such as real-time data mining, which reveal an urgent need to process very large data under strict performance constraints. The objective of this paper is to survey four algorithms including K-Means algorithm, FCM algorithm, EM algorithm and BIRCH, used for data clustering and then show their strengths and weaknesses. Another task is to compare the results obtained by applying each of these algorithms to the same data and to give a conclusion based on these results.
Learning Consensus and Complementary Information for Multi-aspect Data Clustering

One of the most challenging facets of learning multi-aspect data is to effectively capture and maintain the consensus and complementary information present among multiple views in the projected low-dimensional representation. Consensus information refers to the shared common information among views, while complementary information refers to the distinct characteristics provided by each view. This chapter focuses on various approaches that aim to preserve this information while learning a low-dimensional representation of multi-view data. It explores different types of constraints and regularizations that are used to achieve this objective.
Augmented 3D arrows for visualizing off-screen Points of Interest without clutter

When navigating through 3D environments from a first-person perspective, the limited field-of-view poses challenges in locating off-screen Points Of Interest (POIs), particularly when using the compact screen of a mobile phone. Existing techniques for visualizing off-screen POIs tend to clutter the screen, negatively affecting the visualization’s overall effectiveness. To resolve this problem, we propose a method for displaying off-screen POIs in high-density settings (with over 15 POIs). The proposed approach uses 3D arrows placed along the edges of the smartphone screen to point to the POIs. To minimize clutter, a cluster of POIs is represented by a single 3D arrow pointing to the cluster’s centroid. A model known as the cluttering threshold has also been developed to determine the number of POIs to include in a cluster while maintaining direction information. Additionally, a distance filter is implemented to reduce clutter in dense environments. The effectiveness of the proposed system was evaluated using a within-subject study involving 16 participants. The results indicate that our approach is considerably more efficient and accurate than the current state-of-the-art systems, namely, 3DWedge+ and Halo3D, particularly when the number of POIs in the environment is high.
Neural network based image object detection and tracking for security and surveillance

The saliency map obtained from the source image determines the efficacy of the traditional seam carving process. The importance map proposed in this paper is used to highlight the shadows and important objects in the images. It combines the saliency map, shadow map, and gradient map acquired from the image to discover the image’s prominent regions. The proposed map, when compared to others, highlights more distinct details with the state-of-the-art. The improved seam carving technique is paired with cropping and warping image retargeting operators in the suggested hybrid sequence. By labelling a picture with a class label and object localisation, the coordinates of the objects are generated using R-CNN object detection techniques. This will help in identifying the non-salient objects from the image for security and surveillance purposes with pin-point accuracy.
Analysis of cost estimation using the web metrics and cost driver in the high performance of web developers

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Pengembangan Media Pembelajaran Spinner Word Berbasis Kontekstual Untuk Siswa Kelas II di Sekolah Dasar

This development research aims to produce Spinner Word Learning Media products. The aims of this study were to: (1) explain the design of Spinner Word Learning Media for Indonesian Language Content for Class II Students; and (2) determine its validity. This development study uses the ADDIE development model, which consists of five stages: analysis, design, development, implementation, and evaluation. Due to the many students are unable to engage in research, which is limited to individual trials. In this study, data was collected through a non-test method using a questionnaire. Quantitative and qualitative descriptive data analysis was used. The results of the development research include (1) a description of the development process of Word Spinner Learning Media; (2) Spinner Word Learning Media was declared valid based on the assessment of learning content experts at 90.9% with very good qualifications; 85.9% learning design experts with very good qualifications; and learning media experts at 90.6% with very good qualifications.