Reviewerly/exHarmony huggingface dataset:
ASPPMVSNet: A high‐receptive‐field multiview stereo network for dense three‐dimensional reconstruction

The learning-based multiview stereo (MVS) methods for three-dimensional (3D) reconstruction generally use 3D volumes for depth inference. The quality of the reconstructed depth maps and the corresponding point clouds is directly influenced by the spatial resolution of the 3D volume. Consequently, these methods produce point clouds with sparse local regions because of the lack of the memory required to encode a high volume of information. Here, we apply the atrous spatial pyramid pooling (ASPP) module in MVS methods to obtain dense feature maps with multiscale, long-range, contextual information using high receptive fields. For a given 3D volume with the same spatial resolution as that in the MVS methods, the dense feature maps from the ASPP module encoded with superior information can produce dense point clouds without a high memory footprint. Furthermore, we propose a 3D loss for training the MVS networks, which improves the predicted depth values by 24.44%. The ASPP module provides state-of-the-art qualitative results by constructing relatively dense point clouds, which improves the DTU MVS dataset benchmarks by 2.25% compared with those achieved in the previous MVS methods.
Transformer-based Extraction of Deep Image Models

Model extraction attacks pose a threat to the security of ML models and to the privacy of the data used for training. Previous research has shown that such attacks can be either monetarily motivated to gain an edge over competitors or maliciously in order to mount subsequent attacks on the extracted model. In this paper, recent advances in the field of transformers are exploited to propose an attack tailored to the task of image classification that allows stealing complex convolutional neural network models without any knowledge of their architecture. The attack was performed on a range of datasets and target architectures to evaluate the robustness of the proposed attack. With only 100k queries, we were able to recover up to 99.2% of the black-box target network's accuracy on the test set. We conclude that it is possible to effectively steal complex neural networks with relatively little expertise and conventional means – even without knowledge of the target's architecture. Recently proposed defences have also been examined for their effectiveness in preventing the attack proposed in this paper.
Combination of Post-Processing Methods to Improve High-Resolution NWP Solar Irradiance Forecasts in French Guiana

Efforts have been made to improve Numerical Weather Prediction (NWP) forecasts using post-processing techniques, relying on statistical models to refine the weather forecasts. Most approaches used in the literature suffer from two main deficiencies when applied to high-resolution data: (1) they high capacity models to retain nonlinear data fluctuations; (2) some are known to reduce the mean random error; however, they may still generate subsequent biased forecasts. In this study, methods from three different approaches are compared to improve 10-min resolution NWP solar irradiance forecasts, namely a neural network and a linear statistical model as Model Output Statistics, Kalman Filter and Kernel Conditional Density Estimation. The results show that none of the methods, if used individually, improve the mean absolute error (MAE) and mean bias (MBE) jointly. However, a combination of a neural network followed by Kalman filter post-processing results in significant improvements both in the mean random error and the systematic mean bias of original forecasts, reducing the MAE by 45% and the MBE by 91%, respectively.
Finite-state super transducers for compact language resource representation in edge voice-AI

Finite-state transducers have been proven to yield compact representations of pronunciation dictionaries used for grapheme-to-phoneme conversion in speech engines running on low-resource embedded platforms. However, for highly inflected languages even more efficient language resource reduction methods are needed. In the paper, we demonstrate that the size of finite-state transducers tends to decrease when the number of word forms in the modelled pronunciation dictionary reaches a certain threshold. Motivated by this finding, we propose and evaluate a new type of finite-state transducers, called ‘finite-state super transducers’, which allow for the representation of pronunciation dictionaries by a smaller number of states and transitions, thereby significantly reducing the size of the language resource representation in comparison to minimal deterministic final-state transducers by up to 25%. Further, we demonstrate that finite-state super transducers exhibit a generalization capability as they may accept and thereby phonetically transform even inflected word forms that had not been initially represented in the original pronunciation dictionary used for building the finite-state super transducer. This method is suitable for speech engines operating on platforms at the edge of an AI system with restricted memory capabilities and processing power, where efficient speech processing methods based on compact language resources must be implemented.
A model of learning the online course “Creative Thinking through Learning Elementary Maths”

Abstract The article considers the development of the online educational model “Creative Thinking through Learning Elementary Maths” and the results of the effciency to implement this course for students of the specialization “Mathematics” while learning Elementary Mathematics. The research represents the development of learning materials for the online course posted in free access on the educational platform “Higher School Mathematics Teacher”. The course components (theoretical, practical, and feedback) are focused on the organization of motivated and consistent students’ activity; open and integrative Rich tasks of three levels of complexity. The course participants’ activity, focused on the recognition, classification, solution, creation of Rich tasks, encourages the formation of the main components of creative thinking that include a problem statement, fluency, flexibility, originality, and elaboration. The first version of the course materials was improved following course users’ wishes, and then the effciency of online course education was checked using an experiment. The analysis of results of the experiment allows concluding about the effciency of the developed model of learning an online course.
Gravitational wave signal recognition and ring-down time estimation via Artificial Neural Networks

Laser Interferometer Gravitational-Wave Observatory (LIGO) was the first laboratory to measure the gravitational waves successfully. An exceptional experimental design was needed to measure distance changes less than an atomic nucleus. In the same way, the data analyses to confirm and extract information is a tremendously challenging task. This article shows a computational procedure based on Artificial Neural Networks (ANN) to recognize a black hole-black hole gravitation wave event signal from the LIGO data. With the ANN introduced methodology, it is possible to define a numerical score, like a thermometer. High score values are associated with gravitational wave observation and small values with noise. Building a time series from these scores values, physical information about the astronomical system’s damping time, the ring-down time, can be estimated at a first approximation, based on a damped harmonic oscillator modeling. Here, the ring-down time is estimated, at a first approximation, with a direct data measure on the ANN score time series, without using numerical relativity techniques and high computational power.
Activity recognition using unsupervised learning

In developing a high-performance generic algorithm capable of managing multiple environments, the main challenges are the large and very different data sets and the problem of different types of residential spaces. The human activity recognition domain in residential environments has registered an exponential growth lately. In developing a high-performance generic algorithm capable of managing multiple environments, the main challenges are the large and very different data sets and the problem of different types of residential spaces. The training process of supervised algorithms is also limited since most of the available datasets do not have the activities data labeled. This article describes a classification method for dataset data. The used dataset is Kyoto generated by CASAS. The raw data is cleaned in the pre-processing phase, and the new features are extracted. These extracted features form a new dataset of input data fed to a cluster classification neural network. The processed dataset is labeled in order to simplify the validation of the obtained results. The dataset will contain 120 activities comprised of five main actions: Make a phone call in the dining room, Wash hands, Eat, and Clean. Since we are using an unsupervised classification algorithm, we can also use unlabeled data-sets as well.
Risk Prediction Model of Enterprise Financial Data Based upon Sensor Signal Fusion

The prediction of financial distress has always been a topic of concern because it is very important to listed companies, stakeholders, and even a country’s economy. This research mainly discusses the risk prediction model of corporate financial data based on sensor signal fusion. This article analyzes the factors that affect financial risks through the study of related theories. Based on the design principles and qualitative analysis of the predictive index system, this paper constructs the financial risk prediction index system of listed companies from five aspects: profitability, solvency, asset management capability, operating capability, and growth capability. And it verifies the applicability of financial indicators through independent sample t-test. In order to effectively prevent the occurrence of corporate financial risks, this article takes listed companies as the research object. Starting from multiple angles that affect the occurrence of corporate financial risks, this article constructs a comprehensive and effective financial risk prediction index system for listed companies. And it uses sensor signal fusion algorithm to build an effective financial risk prediction model. Through this model, the enterprise's risk management ability can be effectively improved, the enterprise's risk prevention mechanism can be improved, and it can be successfully applied to the actual management of the enterprise, and the enterprise risk management mechanism can be enhanced. In general, the probability of making a type I error is slightly greater than the probability of making a type II error, but the misjudgment rate is below 25%, which is acceptable. This research provides scientific and effective guidance for corporate finance.
DSMCA: Deep Supervised Model with the Channel Attention Module for Cross-modal Retrieval

Cross-modal retrieval has become a highlighted research topic, to provide flexible retrieval experience across multimedia data. It is challenging to retrieve information on massive multimodal data due to the heterogeneity and semantic gap of multimodal data. In this paper, we propose a novel cross-modal retrieval method, called Deep Supervised Model with the Channel Attention module (DSMCA). It aims to efficiently learn a common representation of heterogeneous data while maintaining semantic discriminability and modality invariance. Specifically, to improve the representation capability of the network, the squeeze and excitation block is implemented to explicitly establish inter-channel correlations and adaptively adjust the weights of the feature channels. A weight-sharing strategy is used between two branches of the network to reduce cross-modal heterogeneity. Furthermore, our model combines the complementary losses in different levels for improving cross-modal retrieval performance. The extensive experiments demonstrate the effectiveness of our method in the application of cross-modal retrieval.
Transparent DIFC: Harnessing Innate Application Event Logging for Fine-Grained Decentralized Information Flow Control

Information flow control is a canonical approach to access control in systems, allowing administrators to assure confidentiality and integrity through restricting the flow of data. Decentralized Information Flow Control (DIFC) harnesses application-layer semantics to allow more precise and accurate mediation of data. Unfortunately, past approaches to DIFC have depended on dedicated instrumentation efforts or developer buy-in. Thus, while DIFC has existed for decades, it has seen little-to-no adoption in commodity systems; the requirement for complete redesign or retrofitting of programs has proven too high a barrier. In this work, we make the surprising observation that developers have already unwittingly performed the instrumentation efforts required for DIFC — application event logging, a software development best practice used for telemetry and debugging, often contains the information needed to identify application-layer event processes that DIFC mediates. We present T-difc, a kernel-layer reference monitor framework that leverages the insights of application event logs to perform precise decentralized flow control. T-difc identifies and extracts these application events as they are created by monitoring application I/O to log files, then references an administrator-specified security policy to assign data labels and mediate the flow of data through the system. To our knowledge, T-difc is the first approach to DIFC that does not require developer support or custom instrumentation. In a survey of 15 popular open source applications, we demonstrate that T-difc works seamlessly on a variety of popular open source programs while imposing negligible runtime overhead on realistic policies and workloads. Thus, T-difc demonstrates a transparent and non-invasive path forward for the dissemination of decentralized information flow controls.
Video2Subtitle: Matching Weakly-Synchronized Sequences via Dynamic Temporal Alignment

This paper investigates a new research task in multimedia analysis, dubbed as Video2Subtitle. The goal of this task is to finding the most plausible subtitle from a large pool for a querying video clip. We assume that the temporal duration of each sentence in a subtitle is unknown. Compared with existing cross-modal matching tasks, the proposed Video2Subtitle confronts several new challenges. In particular, video frames / subtitle sentences are temporally ordered, respectively, yet no precise synchronization is available. This casts Video2Subtitle into a problem of matching weakly-synchronized sequences. In this work, our technical contributions are two-fold. First, we construct a large-scale benchmark for the Video2Subtitle task. It consists of about 100K video clip / subtitle pairs with a full duration of 759 hours. All data are automatically trimmed from conversational sub-parts of movies and youtube videos. Secondly, an ideal algorithm for tackling Video2Subtitle requires both temporal synchronization of the visual / textual sequences, but also strong semantic consistency between two modalities. To this end, we propose a novel algorithm with the key traits of heterogeneous multi-cue fusion and dynamic temporal alignment. The proposed method demonstrates excellent performances in comparison with several state-of-the-art cross-modal matching methods. Additionally, we also depict a few interesting applications of Video2Subtitle, such as re-generating subtitle for given videos.
A Working Theory of a Learned Model in a Partially Observable Environment for Cognitive Decision-Making

To survive in our unpredictable, evolving world, cognitive beings learn to make decisions with the limited knowledge of the world they process. Reflective of an individual's view of the world, a cognitive decision-making model is explored in a partially observable, stochastic environment. The cognitive model uses the Partially Observable Markov Decision Process problem formulation, which is a framework for neurological models and considered implementable in neural circuitry [26] [16]. To structure a planning model comparable to that of DeepMind's MuZero in a partially observable environment, a belief function will translate the observations to a vector of belief states that will be discretized so as to be used as the observations of a MuZero-based machine learning algorithm [29]. The belief states are computed recursively from the previous belief state using Bayesian inference. Bayes rule is thought to capture the neurological and cognitive levels of reasoning [26]. Components of the planning, training, and action methods of the cognitive model will follow those of MuZero. The model could then be trained and act, in way parallel to that of MuZero, in a partially observable environment. Cognitive insights from a model structured in this form and additional considerations are discussed.
Person Search by Uncertain Attributes

This paper presents a person search system by uncertain attributes. Attribute-based person search aims at finding person images that are the best matched with a set of attributes specified by a user as a query. The specified query attributes are inherently uncertain due to many factors such as the difficulty of retrieving characteristics of a target person from brain-memory and environmental variations like light and viewpoint. Also, existing attribute recognition techniques typically extract confidence scores along with attributes. Most of state-of-art approaches for attribute-based person search ignore the confidence scores or simply use a threshold to filter out attributes with low confidence scores. Moreover, they do not consider the uncertainty of query attributes. In this work, we resolve this uncertainty by enabling users to specify a level of confidence with each query attribute and consider uncertainty in both query attributes and attributes extracted from person images. We define a novel matching score to measure the degree of a person matching with query attribute conditions by leveraging the knowledge of probabilistic databases. Furthermore, we propose a novel definition of Critical Point of Confidence and compute it for each query attribute to show the impact of confidence levels on rankings of results. We develop a web-based demonstration system and show its effectiveness using real-world surveillance videos.
DRIIS: MapReduce Parameter Optimization of Hadoop Using Genetic Algorithm

In this era of massive knowledge, Hadoop, jointly of the foremost unremarkably used massive processing platforms, features a variety of parameters that are closely associated with resource utilization, particularly mainframe and memory. Calibration of these parameters through improvement may increase Hadoop’s resource utilization. Manual calibration of those parameters is just about not possible, thanks to the time price. Within the massive knowledge business, there’s a requirement to mechanically set up parameters and thereby maximize resource usage. The previous automatic calibration strategies take an extended time to realize the optimum configuration, reducing the cluster’s overall performance. With the help of a novel perceptive procedure, each genetic programming and a genetic rule are supported with a view to enhancing the performance of a Hadoop MapReduce work, we propose an assistant in nursing the best configuration finder. We have a tendency to use the algorithms on top to search out the simplest values for parameter settings. Experiments were performed on four common applications, WordCount, TeraSort, Index and Grep, and eight virtual machines (VMs) in a very typical Hadoop cluster. Our projected methodology will increase MapReduce job potency by 53.63% for a one GB dataset and by 67.4% for a five GB dataset, in keeping with the findings, and by 73.68% on a 10 GB dataset; additionally, for a TeraSort programmed application, MapReduce job potency will increase by 52.62% for an one GB dataset, 61.2% for a five GB dataset, and 55.17% for a 10[Formula: see text]GB dataset. MapReduce jobs boost the performance of Grep applications by 44.4% for a one GB dataset, 56.25% for a five GB dataset, and 49.44% for a 10 GB dataset.
Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

Purpose: Relevance ranking algorithms rank retrieved documents based on the degrees of topical similarity (relevance) between search queries and documents. This paper aims to introduce a new relevance ranking method combining a probabilistic topic modeling algorithm with the “pennant retrieval” method using citation data. Data and Method: We applied this method to the iSearch corpus consisting of c. 435,000 physics papers. We first ran the topic modeling algorithm on titles and summaries of all papers for 65 search queries and obtained the relevance ranking lists. We then used the pennant retrieval to fuse the citation data with the existing relevance rankings, thereby incrementally refining the results. The outcome produced better relevance rankings with papers covering various aspects of the topic searched as well as the more marginal ones. The Maximal Marginal Relevance (MMR) algorithm was used to evaluate the retrieval performance of the proposed method by finding out its effect on relevance ranking algorithms that we used. Findings: Findings suggest that the terms used in different contexts in the papers might sometimes be overlooked by the topic modeling algorithm. Yet, the fusion of citation data to relevance ranking lists provides additional contextual information, thereby further enriching the results with diverse (interdisciplinary) papers of higher relevance. Moreover, results can easily be re-ranked and personalized. Implications: We argue that once it is tested on dynamic corpora for computational load, robustness, replicability, and scalability, the proposed method can in time be used in both local and international information systems such as TR-Dizin, Web of Science, and Scopus. Originality: The proposed method is, as far as we know, the first one that shows that relevance rankings produced with a topic modeling algorithm can be incrementally refined using pennant retrieval techniques based on citation data.
Bringing Serverless Computing to the HPC Community

The next wave of cloud computing - the serverless computing model - is enjoying adoption at scale by different cloud computing vendors. The serverless computing model is already rapidly accelerating the development and deployment of enterprise applications. Unfortunately, the HPC community appears to be left behind in the revolution. The widespread assumption or opinion is that the serverless computing model is not beneficial for the HPC community because the serverless computing model was not primarily designed for scientific computing and users.
Implementation of Educational Programs in the Field of Training IT Specialists on the Orders of Companies

The paper gives the description of the program of additional education for students in the field of information technology, implemented by the university in collaboration with IT companies. The following prerequisites for the development of such educational programs are noted: trends in the information technology market and the growing demand for qualified specialists. The needs of organizations in competencies of their employee lead to a personalized additional educational program for students developed by the university for the company. The conclusion of agreements between the student, the company and the university allows guaranteeing students - additional scholarships and employment, companies - reducing the adaptation time for new employees due to the availability of relevant competencies and getting to know the company in the learning process, the university - additional funding and developing the competencies of university teachers. An example of such a program implemented by the Lipetsk State Technical University in collaboration with RetailDriver LLC for students educated on Bachelor's programs in IT areas is given.