Reviewerly/exHarmony huggingface dataset:
Exploiting Smart Meter Water Consumption Measurements for Human Activity Event Recognition

Human activity event recognition (HAER) within a residence is a topic of significant interest in the field of ambient assisted living (AAL). Commonly, various sensors are installed within a residence to enable the monitoring of people. This work presents a new approach for HAER within a residence by (re-)using measurements from commercial smart water meters. Our approach is based on the assumption that changes in water flow within a residence, specifically the transition from no flow to flow above a certain threshold, indicate human activity. Using a separate, labeled evaluation data set from three households that was collected under controlled/laboratory-like conditions, we assess the performance of our HAER method. Our results showed that the approach has a high precision (0.86) and recall (1.00). Within this work, we further recorded a new open data set of water consumption data in 17 German households with a median sample rate of 0.083¯ Hz to demonstrate that water flow data are sufficient to detect activity events within a regular daily routine. Overall, this article demonstrates that smart water meter data can be effectively used for HAER within a residence.
Toward Real-Time UAV Multi-Target Tracking Using Joint Detection and Tracking

Multiple object tracking (MOT) of unmanned aerial vehicle (UAV) systems is essential for both defense and civilian applications. As drone technology moves towards real-time, conventional tracking algorithms cannot be directly applied to UAV videos due to limited computational resources and the unstable movements of UAVs in dynamic environments. These challenges lead to blurry video frames, object occlusion, scale changes, and biased data distribution of object classes and samples, resulting in poor tracking accuracy for non-representative classes. Therefore, in this study, we present a deep learning multiple object tracking model for UAV aerial videos to achieve real-time performance. Our approach combines detection and tracking methods using adjacent frame pairs as inputs with shared features to reduce computational time. We also employed a multi-loss function to address the imbalance between the challenging classes and samples. To associate objects between frames, a dual regression bounding box method that considers the center distance of objects rather than just their areas was adopted. This enables the proposed model to perform object ID verification and movement forecasting via single regression. In addition, our model can perform online tracking by predicting the position of an object within the next video frame. By exploiting both low- and high-quality detection techniques to locate the same object across frames, more accurate tracking of objects within the video is attained. The proposed method achieved real-time tracking with a running time of 77 frames per second. The testing results have demonstrated that our approach outperformed the state-of-the-art on the VisDrone2019 test-dev dataset for all ten object categories. In particular, the multiple object tracking accuracy (MOTA) score and the F1 score both increased in comparison to earlier work by 8.7 and 5.3 percent, respectively.
Implementasi Natural Language Processing (NLP) dan Algoritma Cosine Similarity dalam Penilaian Ujian Esai Otomatis

Evaluation of learning is an activity that is routinely carried out in the lecture process. The essay exam is a test in the form of questions that aim so that the answers given are in the form of descriptions based on student’s understanding in accordance with what they know. The results of the various answers are a separate consideration in correcting whether the answer is in accordance with the answer key or not. This resulted in each question on the essay exam having its own weight which would later be added up cumulatively to get a total score. This study implements Natural Language Processing (NLP) and Cosine Similarity algorithms to automatically assess essay exams. Document Similarity is one of the tasks in Natural Language Processing (NLP) to check the degree of document similarity. The algorithm used to check the level of similarity is Cosine Similarity which uses two vectors to measure the degree of similarity of documents with the results ranging from 0 to 1. Processing student answer data for three essay questions gets the expected results. The results of the Cosine Similarity calculation in question no 1 show that M3 students have answers with a similarity level of 90.58%. Whereas for question no 2 M1 students had answers with a similarity level of 87.71% and finally for question no 3 M1 students had answers with a similarity level of 76.70%.
A method of face texture fusion based on visibility weight

In the reconstruction process from 2D images to 3D face models, texture completion still suffers from pixel blurring and color inconsistency when face images are under different perspectives. In this paper, we propose a method based on visibility weights for face texture fusion. Meanwhile, for the complex geometric structure of the ear region where the traditional texture mapping algorithm is inapplicable, a skin color probability method with Gaussian model is used for pixel completion, and jointly optimized with the texture fusion band. Finally, we generate a complete and high-fidelity face texture model. The simulation experiment shows that the novel face texture fusion and completion method generates the perfect texture under multiple viewpoints. Our face texture model outperforms state-of-the-art techniques under the same rendering conditions.
Technical Perspective: (Pre-) Semirings Come to the Recursion Party

(This article is an imagined conversation with my U. at Buffalo UG algorithms class students.)
Sistem Pendukung Keputusan Pemilihan Aplikasi Chat Terbaik Dalam Mendukung Pembelajaran Daring di Masa Pandemi Covid Menggunakan Metode Multi Attribute Utility Theory

The Chating application is a virtual communication service either through messages, voice calls, or video calls that can assist in conducting online lectures during a pandemic. There are many types of applications that can be used to communicate during online lectures. Such as the Telegram application, Whatsapp application, Discord, KaKaoTalk, Line or WeChat and other social media applications that can be used to communicate personally or in groups. However, these applications have their own advantages and disadvantages that can affect the comfort and optimization of learning. Therefore, to determine the best Chating application, each alternative must have its own criteria as a requirement for the optimization of the application. As in this study, the authors added some data as criteria, namely Group Support, Member Capacity, Security, Account Privacy and Services provided by the application. Which, each criterion has a predetermined weight and is carried out with a settlement process using the Multi Attribute Utility Theory (MAUT) method for the ranking process. From calculations using the Multi Attribute Utility Theory (MAUT) method, it will produce sequential alternatives from the highest to the lowest. The alternative value which is Qi or the highest ranking will be used as an alternative recommendation for chat applications that will be used as a tool to support online learning during the Covid pandemic.
Predictive Analysis and Correction Control of CCT for a Power System Based on a Broad Learning System

Transient stability is an important factor for the stability of a power system. With improvements in voltage levels, and the expansion of power network scales, the problem of transient stability is particularly prominent. When a power system circuit fails, if the operation time of the relay protection device is higher than the critical clearing time (CCT), the relay protection device cannot cut the fault line in a timely manner. It is essential to forecast and adjust the CCT to improve the stability of the system; therefore, a method is proposed in this paper to predict and evaluate the critical clearing time using the broad learning system (BLS). The sensitivity of the critical clearing time can be easily calculated based on the prediction results of the critical clearing time using BLS. Moreover, the critical clearing time can be modified using the BLS correction control model. The proposed method was verified using a 4-machine 11-node system and a 10-machine 39-node system. According to the experimental results, the proposed model can predict, evaluate, and correct the CCT very well.
Using copies can improve precision in continuous-time quantum computing

In the quantum optimisation setting, we build on a scheme introduced by Young et al [PRA 88, 062314, 2013], where physical qubits in multiple copies of a problem encoded into an Ising spin Hamiltonian are linked together to increase the logical system's robustness to error. We introduce several innovations that improve this scheme significantly. First, we note that only one copy needs to be correct by the end of the computation, since solution quality can be checked efficiently. Second, we find that ferromagnetic links do not generally help in this "one correct copy" setting, but anti-ferromagnetic links do help on average, by suppressing the chance of the same error being present on all of the copies. Third, we find that minimum-strength anti-ferromagnetic links perform best, by counteracting the spin-flips induced by the errors. We have numerically tested our innovations on small instances of spin glasses from Callison et al [NJP 21, 123022, 2019], and we find improved error tolerance for three or more copies in configurations that include frustration. Interpreted as an effective precision increase, we obtain several extra bits of precision for three copies connected in a triangle. This provides proof-of-concept of a method for scaling quantum annealing beyond the precision limits of hardware, a step towards fault tolerance in this setting.
Optimal Design of Antennas based on CNN and BLS

Abstract Surrogate can greatly reduce the computing cost of full-wave electromagnetic simulation software in antenna design, and neural networks as a kind of surrogate have become very popular in recent years, among which the Convolutional Neural Network (CNN) is a very hot topic due to its better feature extraction and generalization capability. In this paper, Broad Learning System (BLS) is introduced to address the problems that CNN may fall into with local optimization and overfitting when dealing with antenna data. It provides a learning method to increase nodes in width for regression prediction, which has good generalization ability. Specifically, in antenna design, feature extraction is first applied using the convolution and pooling of CNN. The original dataset is combined with the extracted feature data to realize data augmentation, and then the BLS is used for regression training. Compared with the traditional deep CNN, this model uses lightweight convolutional layers, which is promising in solving the problem of the network falling into local optimum or overfitting. The simulation results show that the accuracy reaches 93.91% and 92.34% respectively on the Abalone benchmark and antenna data, proving the effectiveness of the proposed model in this paper.
Objective model selection with parallel genetic algorithms using an eradication strategy

Abstract In supervised learning, feature selection methods identify the most relevant predictors to include in a model. For linear models, the inclusion or exclusion of each variable may be represented as a vector of bits playing the role of the genetic material that defines the model. Genetic algorithms reproduce the strategies of natural selection on a population of models to identify the best. We derive the distribution of the importance scores for parallel genetic algorithms under the null hypothesis that none of the features has predictive power. They, hence, provide an objective threshold for feature selection that does not require the visual inspection of a bubble plot. We also introduce the eradication strategy, akin to forward stepwise selection, where the genes of useful variables are sequentially forced into the models. The method is illustrated on real data, and simulation studies are run to describe its performance.
A Deep Learning-Based Visual Map Generation for Mobile Robot Navigation

Visual map-based robot navigation is a strategy that only uses the robot vision system, involving four fundamental stages: learning or mapping, localization, planning, and navigation. Therefore, it is paramount to model the environment optimally to perform the aforementioned stages. In this paper, we propose a novel framework to generate a visual map for environments both indoors and outdoors. The visual map comprises key images sharing visual information between consecutive key images. This learning stage employs a pre-trained local feature transformer (LoFTR) constrained with a 3D projective transformation (a fundamental matrix) between two consecutive key images. Outliers are efficiently detected using marginalizing sample consensus (MAGSAC) while estimating the fundamental matrix. We conducted extensive experiments to validate our approach in six different datasets and compare its performance against hand-crafted methods.
Enhanced entanglement and quantum teleportation of two-mode squeezed vacuum state via multistage non-Gaussian operations

The improvement of quantum features and processes, prominent as entanglement and quantum teleportation, of the two-mode squeezed vacuum state (TMSVS) in the small squeezed parameter domain is of great importance. Therefore, it has attracted the attention of scientists in the field of quantum information in recent years. In this paper, we introduce a new optical scheme to enhance the degree of entanglement and quantum teleportation fidelity of such a state using ordinary quantum devices including beam splitters and photon-number-resolving detectors. The scheme allows us to sequentially perform non-Gaussian operations, namely, photon addition, photon subtraction, quantum optical catalysis, and combinations of these techniques, into both modes of the TMSVS. The investigated results show that the degree of entanglement and fidelity in the novel two-mode non-Gaussian state is enhanced, especially in the small regions of the squeezed parameter compared with the original TMSVS and non-Gaussian states constructed from it in previous works. They also indicate that the enhanced zones recede to the small squeezed parameter areas with increasing the number of stages of the non-Gaussian effect.
Self-education Competence of Future Teachers: Diagnosing by Computer Analyses of Student Essays

Our research is aimed at finding adequate ways to diagnose self-education competence of future teachers. Writing a prognostic essay, revealing students’ positions, was chosen as the final diagnosis in their training. This research consists of the development and testing of diagnostic tools and the diagnosis of self-education competence of future teachers by means of a computer analysis of student essays. The fundamental basis of this study is predictive and didactic in nature. The leading research methods were the following: a theoretical analysis of the essence, structure and main features of future teacher's self-education competence, technical and technological methods of designing computer analytical tools for the content analysis of essays (with the elements of formalization and intellectual analysis of text documents), methods of expert systems (used to solve the problem of choosing and systematizing marker words), experimental pedagogical methods (including a pilot experiment and formative assessment). The research involved students of the Institute of Psychology and Education of Kazan Federal University: 263 students - at the stage of the pilot study, 134 students – at the stage of the formative assessment (the participants are enrolled on the program 44.04.01 – Teacher Education (Master's). At the stage of the formative assessment, using the final essay content analysis, tables with the results of diagnosis of motivational-value, reflective-evaluative, and cognitive-activity components of self-education competence were created. A reflective comparison of the results of student essays computer analysis and own assessment of self-education competence provided data for the further improvement of diagnostic tools. Statistical data processing provides results of the self-education competence both of an individual student and groups, cohorts, and all students of a specific training program. The development of self-education competence allows a person to obtain the required authority in the field of education, to preserve and increase his/her competence in solving professional problems. The materials of the experiment made it possible to systematize the key words prevailing in student essays and combine them into the basic blocks of markers. Presence and occurrence frequency of these markers in a certain part of the analyzed text indicates a certain development level of self-education competence. The materials presented in the article describe the proposed tools for content analysis, which is built on the basis of specially grouped marker words, can be used in pedagogical practices. A free-form essay is digitized which allows us to quantitatively assess the given components of subject's self-education competence.
Design and Implementation of Text Understanding System Based on Semantic Tagging Instances

There are many challenges in text semantic understanding research, such as sparse semantic features, massive representation forms, complex semantic redundancy, diverse echo phenomena, etc. Based on explicit representation of human thinking responses, the importance of semantic tagging and the principles of tagging methods were emphasized in this paper; The routes of large-scale semantic tagging and text semantic understanding system were designed; The concept of semantic scene was defined; By the theory of semantic scene segmentation and recognition, the default information of the semantic scene context was supplemented; Taking elementary math word problems as examples, the prototype system of semantic tagging and understanding was implemented by integrating common sense knowledge.
An improved ant colony optimization for UAV minimum time search path planning

In this paper, an improved ant colony optimization algorithm for UAV minimum time search path planning for moving targets is proposed. Based on Bayesian theory, the path planning of UAVs is transformed into an optimization problem for the expected time of target detection. According to the principle of the simulated annealing algorithm, the probability of ants choosing a non-optimal path is improved after the algorithm converges, to improve the ability of the algorithm to jump out of the local optimum. Simulation results show that the proposed algorithm has a shorter searching time and is more robust than the existing algorithms.
Super-Resolution Algorithm Based on Image Separation

Aiming at the shortcoming of traditional learning-based super-resolution (SR) reconstruction algorithms, a super-resolution algorithm based on image separation is proposed. The proposed algorithm in this paper is used to process the structural features of the image, which separate the smoothing and detail of the image. And we generate the final high-resolution image by reconstructing the image separately. Experimental results show that the proposed algorithm not only improves the reconstruction quality and visual effect, but also improves the training and reconstruction time.
Improved A* Algorithm based on Multi-Layers and Multi-Constraints

In this study, an improved A* algorithm based on multi-layers and multi-constraints is proposed for solving the problem that A* algorithm must detour when encounters obstacles due to the deficiency of vertical dimension. Detailed obstacle classification and shift method among layers are introduced to make the mathematical environment closer to the real environment and support the calculation of proposed cost function. In the procedure of planning, more interested points are taken into consideration to increase the opportunity of find more reasonable path and achieve lower route cost during approaching the target. Shared forbidden area and shift cost mechanism are set to make sure the trend is right without shift too often. The improved algorithm can provide a better and flexible solution to cross obstacles other than make a detour, so the optimal route find by it has a shorter length than that by the traditional one.
Differentiating between Trustless and Custodial Digital Assets: A Solution to the Diminishing Trust in Cryptocurrency

The paper attempts to distinguish between Trustless and Custodial Digital Assets in an attempt to assuage some of the reservations that exist towards cryptocurrency.
NODE-Imgnet: A PDE-Informed Effective and Robust Model for Image Denoising

Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neural network (CNN) blocks. NODE-ImgNet is intrinsically a PDE model, where the dynamic system is learned implicitly without the explicit specification of the PDE. This naturally circumvents the typical issues associated with introducing artifacts during the learning process. By invoking such a NODE structure, which can also be viewed as a continuous variant of a residual network (ResNet) and inherits its advantage in image denoising, our model achieves enhanced accuracy and parameter efficiency. In particular, our model exhibits consistent effectiveness in different scenarios, including denoising gray and color images perturbed by Gaussian noise, as well as real-noisy images, and demonstrates superiority in learning from small image datasets.
호소에서 상수원수의 수심선택 취수를 통해 이상수질 배제를 위한 룰 기반 시스템 개발

The water quality of raw water taken from lakes can vary depending on the water depth, and if water can be intaked from the water depth with the best water quality, it will not only reduce the cost of water purification but also increase the safety of tap water. In lakes, abnormal water quality can occur due to algal occur, rainfall, and water layer turn-over, and as a countermeasure for these problems, water depth-selective intake is being implemented. In this study, it was found that this selective intake can be applied when algae, turbidity and manganese increase in the raw water. In addition, in this study, 14 items such as turbidity, EC, Chl-a, etc. related to the selection of water intake depth were set, the criteria for each item were established, and a rule-based system was developed by interconnecting all items. Therefore, the system can be a useful decision-making tool for choosing the optimal water depth.