Reviewerly/exHarmony huggingface dataset:
REAL-TIME DENSE 3D RECONSTRUCTION FROM MONOCULAR VIDEO DATA CAPTURED BY LOW-COST UAVS

Abstract. Real-time 3D reconstruction enables fast dense mapping of the environment which benefits numerous applications, such as navigation or live evaluation of an emergency. In contrast to most real-time capable approaches, our method does not need an explicit depth sensor. Instead, we only rely on a video stream from a camera and its intrinsic calibration. By exploiting the self-motion of the unmanned aerial vehicle (UAV) flying with oblique view around buildings, we estimate both camera trajectory and depth for selected images with enough novel content. To create a 3D model of the scene, we rely on a three-stage processing chain. First, we estimate the rough camera trajectory using a simultaneous localization and mapping (SLAM) algorithm. Once a suitable constellation is found, we estimate depth for local bundles of images using a Multi-View Stereo (MVS) approach and then fuse this depth into a global surfel-based model. For our evaluation, we use 55 video sequences with diverse settings, consisting of both synthetic and real scenes. We evaluate not only the generated reconstruction but also the intermediate products and achieve competitive results both qualitatively and quantitatively. At the same time, our method can keep up with a 30 fps video for a resolution of 768 × 448 pixels.
Performance Investigation of Digital Lowpass IIR Filter Based on Different Platforms

The work presented in this paper illuminates the design and simulation of a recursive or Infinite Impulse Response (IIR) filter. The proposed design algorithm employs the Genetic Algorithm to determine the filter coefficients to satisfy the required performance. The effectiveness of different platforms on filter design and performance has been studied in this paper. Three different platforms are considered to implement and verify the designed filter’s work through simulation. The first platform is the MATLAB/SIMULINK software package used to implement the Biquad form filter. This technique is the basis for the software implementation of the designed IIR filter. The HDL – Cosimulation technique is considered the second one; it inspired to take advantage of the existing tools in SIMULINK to convert the designed filter algorithm to the Very high-speed integrated circuit Hardware Description Language (VHDL) format. The System Generator is employed as the third technique, in which the designed filter is implemented as a hardware structure based on basic unit blocks provided by Xilinx System Generator. This technique facilitates the implementation of the designed filter in the FPGA target device. Simulation results show that the performance of the designed filter is remarkably reliable even with severe noise levels.
Safety Assurance for Systems with Machine Learning Components

The use of machine learning components in safety-critical systems creates reliability concerns. My thesis focuses on developing algorithms to address these concerns. Because the assurance of a safety-critical system generally requires multiple types of validation, my research takes three directions: safe deep learning algorithms, formal verification of neural networks, and adaptive testing methods.
Detection and Identification of Obfuscated Obscene Language with Character Level Transformers

We present O-Norm (Offense Normalizer), a system for normalizing offensive words and phrases by reversing user-created obfuscations.O-Norm is intended to be a preprocessing tool for pipelines that run on user-created text, particularly those tasks where obscenities may be of significant value such as sentiment, toxicity, and abuse detection.O-Norm is constructed with a purely generated, context-free dataset derived from a curse dictionary.The generative dataset allows O-Norm to be flexible to the addition of new words as language adapts, and also allows it to be readily retrained in other Latin alphabet-based languages as it does not require manual annotations to train.O-Norm is based on a character-level, transformer network which attempts de-obfuscation only on out-of-vocabulary (OOV ) tokens.In an 80/20 train-test split, O-Norm achieves an F1 score of 89.6% over 141 curses in a generated dataset with 2.16 million unique training points.An inspection of O-Norm's output on a sample of social media posts from Kaggle's Jigsaw corpus reveals accuracy of 95.7% on de-obfuscating transformations in toxic user-created text.
Scaling-Up Robust Gradient Descent Techniques

We study a scalable alternative to robust gradient descent (RGD) techniques that can be used when losses and/or gradients can be heavy-tailed, though this will be unknown to the learner. The core technique is simple: instead of trying to robustly aggregate gradients at each step, which is costly and leads to sub-optimal dimension dependence in risk bounds, we choose a candidate which does not diverge too far from the majority of cheap stochastic sub-processes run over partitioned data. This lets us retain the formal strength of RGD methods at a fraction of the cost.
Design of True Random Number Circuit with Controllable Frequency

True random number generators (TRNGs) have been a research hotspot due to secure encryption algorithm requirements. Therefore, such circuits are necessary building blocks in state-of-the-art security controllers. In this paper, a TRNG based on random telegraph noise (RTN) with a controllable rate is proposed. A novel method of noise array circuits is presented, which consists of digital decoder circuits and RTN noise circuits. The frequency of generating random numbers is controlled by the speed of selecting different gating signals. The results of simulation show that the array circuits consist of 64 noise source circuits that can generate random numbers by a frequency from 1 kHz to 16 kHz.
Penerapan Logika Fuzzy sebagai Alat Deteksi Hipotermia dan Hipertermia Pada Manusia Berbasis Internet Of Thing (Iot)

Health is an important thing for human life. One way to determine a person’s health can be done by examining vital signs (TTV) to find out clinical signs by measuring the most basic body functions. TTV examination is also useful in determining the appropriate medical planning and can be used to strengthen the diagnosis of a disease. Most of the illness or even death related to environmental exposure temperature can be prevented, but often the victim’s decision is wrong, or the patient’s reporting is delayed. Although a person in good health may experience varying degrees of exposure to heat or cold at different times, a person will be relatively at risk for illness and death in extreme environmental temperatures. One solution to overcome this problem is the use of Internet of Thing (IoT)-based hypothermia and hyperthermia detection tools that can detect disease early so that prevention can be done before it becomes more dangerous. The features of this tool are the measurement of body temperature, heart rate, and the detection of hypothermia or hyperthermia. This study, testing the application of fuzzy logic as a hypothermia and hyperthermia detection tool based on IoT-based body temperature and heart rate. The results of system testing with 10 subjects from instrumentation tools showed the same results as the medical officer’s statement with a 100% success rate.
New recommender system evaluation approaches based on user selections factor

Currently, due to the increasing importance of recommender systems (RSs), especially in the fields of social networking and e-commerce, these systems represent one of the most interesting subjects in computer programming. Although many research reports have previously been published in this subject area, because of lack of clarity regarding their algorithms or limited comparisons with the literature, most of them are difficult to extend for similar applications in the future. Therefore, in the present study, we have attempted to improve two novel RS evaluation measures (variety and newness) developed from previous evaluator rules (namely, diversity and novelty) based on human behavior so as to be more reliable and compatible with various developments in RSs. The new rules provide higher weighting for suggestions and respect for users' behavior and can be used in place of diversity and novelty rules with better precision and centralization, by 22.54% for variety and by 14.84% for newness. In addition, we aim to use the developed measures to improve new RSs and support better comparative analyses in this field in the future. This contribution is expected to facilitate better RS research and competition, especially in the social networking domain.
Analysis of dielectric losses in chromespinelide samples from the ophiolite complexes of the Urals

Relevance is determined by the need to search for scientifically based criteria for the identification of chromite mineralization. The purpose of this work is to study the features of dielectric losses and their temperature dependence of ore-forming chromespinelides in relation to the chemical composition and phase transformations in their structure. Research methodology. To study the dielectric properties of chromespinelides, samples were prepared in the form of a cube with an edge of 0.02 m (two cube samples were prepared from each sample). Measurements were performed in an open system at atmospheric pressure. Dielectric losses were measured with a two-electrode setup every 10 degrees in the temperature range 20–900 °C. Heating rate is 4 deg/min. The temperature in the system was determined with a platinum-piatinum-rhodium thermoeiectric coupie at 0.01 m from the sampie. As a measuring device for determining the tangent of the angie of dielectric losses (tg δ) at alternating voitage, the “Digitai L, C, R Meter” E7-8 was used. The operating frequency of the device is 1 kHz. In the initial sample and the duplicate sampie after its roasting to 900 °C, the content of oxides of ferrous and ferric iron was determined. The reiative change in the ratios of oxide and ferrous iron in the initial sample and in the dupiicate sample after roasting was compared with the position of the maximum dielectric losses on the temperature scale. Results. Samples of chromite ore from various deposits and ore occurrences of the Urals were studied by physical, physicochemical, mineralogical and petrographic methods. The temperature dependences of dielectric losses of 21 samples of chromite ore, consisting of 80–90% of chromespinelides, were obtained. All curves clearly show the maximum dieiectric iosses. This indicates that the losses are of a relaxation nature. The position of the maximum on the temperature scaie is different for the studied chromespinelides and is mainly associated with a change in the ratio H. H value is the relative change in the ratio of oxide and ferrous iron FeO/Fe2 O3 in a chromespinelide sample during its heating to 900 °C. The position of the maximum dielectric losses for the studied samples varies from 450 °C (sampie 1 – massive chromitite from the Podenny mine III deposit – Alapaevsi&lt;y massif) to 842 °C (sample 21 – massive chromite ore from the Tsentrainy deposit – Ray-lz massif). In this case, H vaiue varies from 1.61 to 11.14, respectively. A relationship was revealed between the position of the maximum dieiectric losses on the temperature scale of the studied samples of chromite ore and the parameter H. Conclusions. The above results in combination with other physicochemical parameters can be used as an indicator of a rapid assessment of the type of mineralization.
IMPLEMENTATION OF THE PRINCIPLES OF ACADEMIC INTEGRITY IN HIGHER EDUCATION INSTITUTIONS OF UKRAINE

Taking into account the current state of development of higher education and science, the peculiarities of the introduction of the principles of academic integrity in the structure of higher education institutions are indicated in the study. The essence of the concept of academic integrity was clarified basing on the study of normative documents and regulations (Law of Ukraine on Education and Law of Ukraine on Higher Education), recommendations for higher education institutions on the development and implementation of a university system of academic integrity, approved by the National Agency for Quality Assurance in Higher Education. Main vocation of academic integrity, its features of compliance and application in educational and scientific activities of all participants in the educational process are revealed in the study. On the example of H.S. Skovoroda Kharkiv National Pedagogical University it was found that higher education institutions, guided by the above laws and recommendations, operates on their own principles of academic integrity in view of the peculiarities of the free economic zone, its specifics, areas of training future professionals and scientific activities. An example of a university system for ensuring the principles of academic integrity and ethics of academic relations is revealed, which consists of five components: regulatory framework, information base, structural units and authorized commissions, tools for implementation and control. It is noted about the creation of such a system on the basis of the recommendations for higher education institutions on the development and implementation of the university system of academic integrity, approved by the decision of the National Agency for Quality Assurance in Higher Education. The activity of separate structural links of the educational institution on realization of the principles of academic integrity is analyzed. A number of promising ways to implement the principles of academic integrity in higher education institutions is proposed. Among them are the development of normative documents of the Free Economic Zone, information materials, banners, methodical instructions, preparation of videos, holding popular science events, etc.
Deep Semantic Cross Modal Hashing Based on Graph Similarity of Modal-Specific

With the advantages of low storage cost and fast query speed, cross modal hashing has attracted increasing attention recently. However, most existing cross-modal hashing methods adopt the same measurement metric when processing data of different modalities or cannot explore heterogeneous correlation between different modalities well, which will result in information loss and heterogeneous correlation cannot be solved. In this paper, we propose a Deep semantic Cross Modal Hashing based on Graph similarity of Modal-Specific (DCMHGMS) method, which not only considers the inter-modal similarity but also designs two graphs to characterize the intra-modal similarity of modal-specific models. First, we use a weighted measurement metric of Euclidean distance and cosine distance to measure the inter-modal similarity between image and text, which can solve the heterogeneous correlation problem. Next, for image graph, we build the intra-modal similarity with Euclidean distance function. Then, for text graph, we build the intra-modal similarity with cosine distance function. Paying attention to the specifics of each modality can improve the retrieval accuracy, thus solving the problem of information loss. Moreover, the semantic information embedding, quantization loss, and bit balanced constraint are considered in this model. Experimental results on two datasets show the effectiveness of our proposed DCMHGMS method.
Learning the Parameters of Bayesian Networks from Uncertain Data

The creation of Bayesian networks often requires the specification of a large number of parameters, making it highly desirable to be able to learn these parameters from historical data. In many cases, such data has uncertainty associated with it, including cases in which this data comes from unstructured analysis or from sensors. When creating diagnosis networks, for example, unstructured analysis algorithms can be run on the historical text descriptions or images of previous cases so as to extract data for learning Bayesian network parameters, but such derived data has inherent uncertainty associated with it due to the nature of such algorithms. Because of the inability of current Bayesian network parameter learning algorithms to incorporate such uncertainty, common approaches either ignore this uncertainty, thus reducing the resulting accuracy, or completely disregard such data. We present an approach for learning Bayesian network parameters that explicitly incorporates such uncertainty, and which is a natural extension of the Bayesian network formalism. We present a generalization of the Expectation Maximization parameter learning algorithm that enables it to handle any historical data with likelihood-evidence-based uncertainty, as well as an empirical validation demonstrating the improved accuracy and convergence enabled by our approach. We also prove that our extended algorithm maintains the convergence and correctness properties of the original EM algorithm, while explicitly incorporating data uncertainty in the learning process.
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet

CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge.In this paper, we propose an information extraction toolkit, called CogIE, which is a bridge connecting raw texts and CogNet.CogIE has three features: versatile, knowledge-grounded and extensible.First, CogIE is a versatile toolkit with a rich set of functional modules, including named entity recognition, entity typing, entity linking, relation extraction, event extraction and framesemantic parsing.Second, as a knowledgegrounded toolkit, CogIE can ground the extracted facts to CogNet and leverage different types of knowledge to enrich extracted results.Third, for extensibility, owing to the design of three-tier architecture, CogIE is not only a plug-and-play toolkit for developers but also an extensible programming framework for researchers.We release an open-access online system 1 to visually extract information from texts.Source code, datasets and pre-trained models are publicly available at GitHub 2 , with a short instruction video 3 .
AdvantageNAS: Efficient Neural Architecture Search with Credit Assignment

Neural architecture search (NAS) is an approach for automatically designing a neural network architecture without human effort or expert knowledge. However, the high computational cost of NAS limits its use in commercial applications. Two recent NAS paradigms, namely one-shot and sparse propagation, which reduce the time and space complexities, respectively, provide clues for solving this problem. In this paper, we propose a novel search strategy for one-shot and sparse propagation NAS, namely AdvantageNAS, which further reduces the time complexity of NAS by reducing the number of search iterations. AdvantageNAS is a gradient-based approach that improves the search efficiency by introducing credit assignment in gradient estimation for architecture updates. Experiments on the NAS-Bench-201 and PTB dataset show that AdvantageNAS discovers an architecture with higher performance under a limited time budget compared to existing sparse propagation NAS. To further reveal the reliabilities of AdvantageNAS, we investigate it theoretically and find that it monotonically improves the expected loss and thus converges.
Preservation of the intangible cultural heritage through the implementation of additional general education programs in the field of fine arts

The problem of preserving the traditions of academic art and folk culture as part of the intangible cultural heritage of the country is becoming more urgent every year. One of the solutions to this problem is the transfer of knowledge and technology using additional general education programs.&#x0D; Implementation of additional general education programs reveals the practical possibilities of children’s creativity and shows the effectiveness of forms and methods for preserving the intangible cultural heritage through the practical development of techniques and technologies of artistic creation by children, which are based on academic or folk traditions. This, in the authors’ opinion, introduces the scientific novelty of this study. The theoretical basis of pedagogical mastery is studied and the need for a competency-based approach in the implementation of additional education programs on the example of the experience of children’s art schools is shown. In addition, the study substantiates the need for the effective construction of educational and methodological support for educational programs, the actualization and development of teacher’s competencies, as well as the preservation and development of methods containing the basic principles and technologies of the academic foundations of fine arts and folk crafts. Proposals to organize support for the implementation of additional general education programs in the field of fine and decorative arts and folk crafts are made.
Finnish Paraphrase Corpus

In this paper, we introduce the first fully manually annotated paraphrase corpus for Finnish containing 53,572 paraphrase pairs harvested from alternative subtitles and news headings. Out of all paraphrase pairs in our corpus 98% are manually classified to be paraphrases at least in their given context, if not in all contexts. Additionally, we establish a manual candidate selection method and demonstrate its feasibility in high quality paraphrase selection in terms of both cost and quality.
Predictive Analytics for Product Configurations in Software Product Lines

A Software Product Line (SPL) is a collection of software for configuring software products in which sets of features are configured by different teams of product developers.This process often leads to inconsistencies (or dissatisfaction of constraints) in the resulting product configurations, whose resolution consumes considerable business resources.In this paper, we aim to solve this problem by learning, or mathematically modeling, all previous patterns of feature selection by SPL developers, and then use these patterns to predict inconsistent configuration patterns at runtime.We propose and implement an informative Predictive Analytics tool called predictive Software Product LIne Tool (p-SPLIT) which provides runtime decision support to SPL developers in three ways: 1) by identifying configurations of feature selections (patterns) that lead to inconsistent product configurations, 2) by identifying feature selection patterns that lead to consistent product configurations, and 3) by predicting feature inconsistencies in the product that is currently being configured (at runtime).p-SPLIT provides the first application of Predictive Analytics for the SPL feature modeling domain at the application engineering level.With different experiments in representative SPL settings, we obtained 85% predictive accuracy for p-SPLIT and a 98% Area Under the Curve (AUC) score.We also obtained subjective feedback from the practitioners who validate the usability of p-SPLIT in providing runtime decision support to SPL developers.Our results prove that p-SPLIT technology is a potential addition for the global SPL product configuration community, and we further validate this by comparing p-SPLIT's characteristics with state-of-the-art SPL development solutions.