Reviewerly/exHarmony huggingface dataset:
A discrete truncated Zipf distribution

Abstract We provide a comprehensive account of fundamental properties of a truncated discrete Zipf distribution, complementing the results available in the literature. In particular, we obtain results on existence and uniqueness of maximum likelihood parameter estimators and propose new testing methodology for the shape parameter. We also include data examples illustrating applicability of this stochastic model.
Intelligent reservoir dynamic analysis management system

Taking the oil reservoir managed by an oil production plant as the main object, and mainly aiming at the problems existing in the current oil reservoir dynamic analysis, such as the heavy workload of data statistics, the incomplete function of drawing pictures, and the cumbersome filling of reports, we have fully investigated the current technical cadres of the whole plant. On the basis of understanding the current data requirements, functional requirements and operating habits, through the basic database, an intelligent management data platform for reservoir dynamic analysis is established. The system can meet the current reservoir dynamic analysis data statistical query, curve drawing, automatic report generation and other functions, provide convenience for development and analysis, and improve work efficiency and quality.
DATaR: Depth Augmented Target Redetection using Kernelized Correlation Filter

Unlike deep learning which requires large training datasets, correlation filter-based trackers like Kernelized Correlation Filter (KCF) use implicit properties of tracked images (circulant structure) for training in real time. Despite their popularity in tracking applications, there exists significant drawbacks of the tracker in cases like occlusions and out-of-view scenarios. This paper attempts to address some of these drawbacks with a novel RGB-D Kernel Correlation tracker in target re-detection. Our target re-detection framework not only re-detects the target in challenging scenarios but also intelligently adapts to avoid any boundary issues. Our results are experimentally evaluated using (a) standard dataset and (b) real time using the Microsoft Kinect V2 sensor. We believe this work will set the basis for improvement in the effectiveness of kernel-based correlation filter trackers and will further the development of a more robust tracker.
Design and Java Implementation of Intelligent Platform for English Training based on Intelligent Test Data Generation Algorithm

Firstly, the research results of genetic algorithm and particle swarm optimization algorithm, which are most frequently used in the generation of test data based on intelligent optimization algorithm, are summarized and summarized, and the research status is analyzed. Then, the test data based on intelligent optimization algorithm is briefly introduced. Generation tools. Based on the analysis of system functional requirements, the teaching resource library and the front and back functional modules of the learning platform are determined. Based on the technical foundations of Java, JSP, Hibernate framework, etc. Earn points by doing fun English quizzes and learning competitions
Interoperating Vocabularies in Multilingual Domain Data

Reducing ambiguity in data is a crucial requirement to be satisfied in domains of interest where high levels of information precision are required. For this purpose a specific Domain Data is exploited, where the information is expressed through specific Domain Vocabularies defined to reduce the ambiguity present, instead, when natural languages are used. However, different vocabularies are defined for the same domain used in different contexts, hindering the possibility to interoperate domain data expressed using such vocabularies. The main contribution of this paper is a domain vocabulary integration process, allowing for the interoperability among different vocabularies in cross-context exploitable Multilingual Domain Data. The integration process is based on a novel approach which concretely separates the semantic of concepts from their representations, thus reducing the complexity of creating mapping between different vocabularies concepts. The process is currently adopted within the InteropEHRate EU project, from which a real use case scenario has been extracted to demonstrate the execution of the process.
Discriminative Dictionary Pair Learning With Scale-Constrained Structured Representation for Image Classification

The dictionary pair learning (DPL) model aims to design a synthesis dictionary and an analysis dictionary to accomplish the goal of rapid sample encoding. In this article, we propose a novel structured representation learning algorithm based on the DPL for image classification. It is referred to as discriminative DPL with scale-constrained structured representation (DPL-SCSR). The proposed DPL-SCSR utilizes the binary label matrix of dictionary atoms to project the representation into the corresponding label space of the training samples. By imposing a non-negative constraint, the learned representation adaptively approximates a block-diagonal structure. This innovative transformation is also capable of controlling the scale of the block-diagonal representation by enforcing the sum of within-class coefficients of each sample to 1, which means that the dictionary atoms of each class compete to represent the samples from the same class. This implies that the requirement of similarity preservation is considered from the perspective of the constraint on the sum of coefficients. More importantly, the DPL-SCSR does not need to design a classifier in the representation space as the label matrix of the dictionary can also be used as an efficient linear classifier. Finally, the DPL-SCSR imposes the l2,p -norm on the analysis dictionary to make the process of feature extraction more interpretable. The DPL-SCSR seamlessly incorporates the scale-constrained structured representation learning, within-class similarity preservation of representation, and the linear classifier into one regularization term, which dramatically reduces the complexity of training and parameter tuning. The experimental results on several popular image classification datasets show that our DPL-SCSR can deliver superior performance compared with the state-of-the-art (SOTA) dictionary learning methods. The MATLAB code of this article is available at https://github.com/chenzhe207/DPL-SCSR.
On Selection of Efficient Sequential Pattern Mining Algorithm Based on Characteristics of Data

Sequential pattern mining, which is one of the core tasks in data mining, allows to gain insight into datasets with complex sequential data. As the task is computationally intensive, there are many different approaches that are suitable for various types of data. We explore the possibility of optimising the analysis of sequences based on the characteristic (quickly obtainable) properties of the analysed data. In this paper, we propose five such characteristics and explore the efficiency of three algorithms that are representatives of the three main approaches to sequential pattern mining. We discovered that it is possible to save up to 21% of the search time compared to the best-performing representative. We trained a decision tree model with 87% accuracy of choosing the best algorithm for selected data based on these characteristics.
Development of English Autonomous Listening Intelligent Learning System Based on Web database

Through the full combination of network assisted language teaching theory and the relevant knowledge of independent language learning system, based on the web database, the research and development process of English independent listening teaching system in detail, clearly understand the relevant system mainly use the technology, system and other operating environment requirements. On this basis, the development process and the functions of the system are fully introduced to ensure that the role and value of the system at the teaching level are fully presented. So that the role and value of web database can be truly used to English independent listening teaching, to provide technical support for further improving students' English listening level.
Q1Synth: A Quantum Computer Musical Instrument

This paper introduces Q1Synth, an unprecedented musical instrument that produces sounds from (a) quantum state vectors representing the properties of a qubit, and (b) its measurements. The instrument is presented on a computer screen (or mobile device, such as a tablet or smartphone) as a Bloch sphere, which is a visual representation of a qubit. The performer plays the instrument by rotating this sphere using a mouse. Alternatively, a gesture controller can be used, e.g., a VR glove. While the sphere is rotated, a continuously changing sound is produced. The instrument has a ‘measure key’. When the performer activates this key, the instrument generates a program (also known as a quantum circuit) to create the current state vector. Then, it sends the program to a quantum computer over the cloud for processing, that is, measuring, in quantum computing terminology. The computer subsequently returns the measurement, which is also rendered into sound. Currently, Q1Synth uses three different techniques to make sounds: frequency modulation (FM), subtractive synthesis, and granular synthesis. The paper explains how Q1Synth works and details its implementation. A setup developed for a musical performance, Spinnings, with three networked Q1Synth instruments is also reported. Q1Synth and Spinnings are examples of how creative practices can open the doors to new application pathways for quantum computing technology. Additionally, they illustrate how such emerging technology is leading to new approaches to musical instrument design and musical creativity.
Improved Feature Extraction and Similarity Algorithm for Video Object Detection

Video object detection is an important research direction of computer vision. The task of video object detection is to detect and classify moving objects in a sequence of images. Based on the static image object detector, most of the existing video object detection methods use the unique temporal correlation of video to solve the problem of missed detection and false detection caused by moving object occlusion and blur. Another video object detection model guided by an optical flow network is widely used. Feature aggregation of adjacent frames is performed by estimating the optical flow field. However, there are many redundant computations for feature aggregation of adjacent frames. To begin with, this paper improved Faster RCNN by Feature Pyramid and Dynamic Region Aware Convolution. Then the S-SELSA module is proposed from the perspective of semantic and feature similarity. Feature similarity is obtained by a modified SSIM algorithm. The module can aggregate the features of frames globally to avoid redundancy. Finally, the experimental results on the ImageNet VID and DET datasets show that the mAP of the method proposed in this paper is 83.55%, which is higher than the existing methods.
Aplikasi Sistem Informasi Geografis Dalam Pemetaan Zonasi Rawan Banjir Kecamatan Monano Kabupaten Gorontalo Utara

Monano Subdistrict is one of the sub-districts located in Gorontalo UtaraRegency, Gorontalo Province, which does not have a flood hazard map. Thisstudy aims to determine the geological conditions and create a flood hazardmap. This study collected field data, such as lithological data, plotting dataon flood coordinates of each village, as well as validation data of the floodhazard level. The spatial analysis used in this research scores on flood-proneparameters, such as slope, rainfall, and land use that are overlayed alongwith lithological data. As a result, four geomorphological units are obtained:volcanic hills, denudational hilss, and fluvial and marine plains. From thegeological aspect, four units of lithology constituents are produced, such asandesite units, agglomerate units, sandstone units, and alluvial deposit units.The data also exposes that: slopes range from 0-45o flat to very steep, landuse is covered by settlements, rice fields, shrubs, and forests, as well asrainfall which refers to a very high intensity of rainfall, which is 2771 mmper year. The data obtained produces a flood hazard map divided into threeclasses: low, medium, and high.
Deep Learning and Morphology Across Redshifts

This paper addresses the question, “How does galaxy morphology differ across red shifts?” Interestingly enough, astronomers can peer through time simply by searching deeper into the universe for galaxies, as the further back one looks in time, the further back they are looking back in time. We utilized this property of physics to analyze galaxies from millions of years in the past to understand how they are structured. The data collection discussed in this paper analyzes galaxies drawn from databases and the statistics collected when running a Convolutional Neural Network (CNN) that is trained on the data set. A discussion of the distribution of morphologies across redshifts is also presented, drawn from the results of the CNN model. Afterwards, an analysis of our CNN model and various distributions are mentioned with our interpretation of the results. Lastly, a reflection about our answer to the research question is put forward with possible future steps to take.
Enabling Human-like Language-Capable Robots Through Working Memory Modeling

Working Memory (WM) is a central component of cognition. It has direct impact not only on core cognitive processes, such as learning, comprehension, and reasoning, but also language-related processes, such as natural language understanding and referring expression generation. Thus, for robots to achieve human-like natural language capabilities, we argue that their cognitive models should include an accurate WM representation that plays a similarly central role. Our research investigates how different WM models from cognitive psychology affect robots' natural language capabilities. Specifically, we explore the limited capacity nature of WM and how different information forgetting strategies, namely decay and interference, impact the human-likeness of utterances formulated by robots.
Noise-aware Zero-Reference Low-light Image Enhancement for Object Detection

Computer vision advancement has proven to be able to automate many practical tasks such as object detection and recognition in challenging environments. However, most notable computer vision models are optimized to work under environment with ideal lighting conditions. Real-world scenarios are uncontrolled and it is common to encounter quality and performance deterioration due to challenges in poor lighting, especially related to the amplification of noise signals. Consequently, there is an increase of vision enhancement related works to maintain such models' performances, however there is still a gap in exploring the practical implication that such existing enhancement work has on detection models, as well as the issue and handling of noise signals. Hence, this paper investigates the incorporation of noise information into the enhancement modelling with the specific aim to improve the performance of object recognition. Building upon the zero-reference deep curve estimation (Zero-DCE) approach, a noisy data training strategy is designed in order to introduce noise priors into the curve estimation training to produce better image structure enhancement. Furthermore, the commonly implemented post-process denoising approach is also studied in this work to find out its impact and effectiveness in the context of object detection. Experiments on the ExDark dataset show that the enhanced images produced by the proposed approach is able to improve object detection in low-light images using YOLOv5 with an increase of up to 3% in precision, and comparable performance with significantly more complex state-of-the-art low-light image enhancement models.
Parents scaffold the formation of conversational pacts with their children

Adults readily form conversational pacts, or temporary agreements about referent names, over the course of conversation. Young children notoriously have difficulty forming pacts with peers, but the root cause of such difficulty has remained unclear. Here, we aim to tease apart the component processes of referential pact formation that may be implicated. In Experiment 1, we show that children (ages 4, 6, 8) are, in fact, able to coordinate on increasingly accurate and efficient pacts with parents in a director-matcher task. We analyze the interactional features that account for their success, finding that parents spontaneously initiate more clarification exchanges with younger children who, in turn, are more likely to adapt to labels introduced by the parent. We then examine whether the benefit of such interactional scaffolding primarily emerges through overcoming childrens' difficulties with comprehension (Experiment 2) or production (Experiment 3). Contrary to classical views attributing childrens' coordination failures to lexical rigidity or perspective-taking, these result support the idea that children readily form pacts when scaffolding from parents helps overcome their production difficulties.
Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

Arabic is the world’s first language, categorized by its rich and complicated grammatical formats. Furthermore, the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns. The Arabic language consists of distinct variations utilized in a community and particular situations. Social media sites are a medium for expressing opinions and social phenomena like racism, hatred, offensive language, and all kinds of verbal violence. Such conduct does not impact particular nations, communities, or groups only, extending beyond such areas into people’s everyday lives. This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection (IALODL-OHSD) on Arabic Cross-Corpora. The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media. In the IALODL-OHSD model, a three-stage process is performed, namely pre-processing, word embedding, and classification. Primarily, data pre-processing is performed to transform the Arabic social media text into a useful format. In addition, the word2vec word embedding process is utilized to produce word embeddings. The attention-based cascaded long short-term memory (ACLSTM) model is utilized for the classification process. Finally, the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results. To illustrate a brief result analysis of the IALODL-OHSD model, a detailed set of simulations were performed. The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.
Statistically Controlling for Processing Fluency Reduces the Aesthetic-Usability Effect

The aesthetic-usability effect asserts that user interfaces that appear aesthetic also appear easier to use. Most explanations for the effect see aesthetics in a causal role. In contrast, we propose that processing fluency as a third variable causes both judgements of aesthetics and usability. Processing fluency refers to the subjective ease of information processing and has been shown to influence, among others, judgements of aesthetics and usability. We tested our proposition in an experiment in which users rated screenshots of city websites. The aesthetic-usability effect was replicated by our data: aesthetics and usability correlated .79. When controlling for fluency, however, the aesthetic-usability effect was considerably diminished; the correlation decreased to .34. Future research will address the limitations of this study by investigating a wider range of website designs and adding interactivity to the interfaces.
Efficient CUDA stream management for multi-DNN real-time inference on embedded GPUs

Deep Neural Networks (DNNs) are widely used in Cyber–Physical Systems (CPS) that often involve multiple DNN tasks with varying real-time requirements. These tasks need to be deployed on a single embedded hardware platform with limited resources, such as an embedded GPU. Efficiently sharing the same embedded GPU among multiple real-time DNN tasks is a complex challenge. While existing DNN frameworks (e.g., PyTorch and TensorFlow) focus on maximizing average performance and high throughput on GPU, they lack scheduling management mechanisms considering multiple DNNs with different timing requirements. In this paper, we address this challenge by thoroughly examining and summarizing the scheduling rules for multiple kernels with different priorities in CUDA streams. Based on these rules, we design a framework that supports multi-DNN real-time inference and propose a method for allocating CUDA streams to DNN kernels to meet schedulability requirements while maximizing GPU resource utilization. Our proposed approach is implemented on an NVIDIA Jetson AGX Xavier embedded GPU system and validated using several popular DNNs. The results show that our approach achieves shorter response times compared with several state-of-the-art methods.
Wikidata Atlas: Putting Wikidata on the Map

Wikidata Atlas is an online system that allows users to explore Wikidata items on an interactive global map; for example, users can explore the global distribution of all lighthouses described by Wikidata. Designing such a system poses challenges in terms of scalability, where some classes have hundreds of thousands of instances; efficiency, where visualisations are generated live; freshness, where we want changes on Wikidata to be reflected as they happen in the system; and usability, where we aim for the system to be accessible for a broad audience. Herein we describe the design and implementation of the system in light of these challenges.