Reviewerly/exHarmony huggingface dataset:
CHANGES IN THE MICROSTRUCTURE AND MICROHARDNESS OF 65G STEEL AFTER THERMAL HARDENING AND SURFACING WITH A LOW-CARBON ELECTRODE

The article is devoted to the urgent problem of improving the methodological training of biology teachers, who are direct mentors of participants, as well as winners and prize-winners of the All-Russian Olympiad of Schoolchildren in Biology. To date, the results of the participants of this intellectual competition are not only an important indicator of the professionalism and pedagogical skills of the teacher-mentor working with them, but also an indicator of the effectiveness of the educational organization on the basis of which the training was organized. The article describes the experience of organizing and conducting advanced training courses for teachers who are mentors of participants of the All-Russian Biology Olympiad in the context of implementing additional professional programs, including using e-learning and distance learning technologies.
Practical Federated Learning for Samples with Different IDs

Federated learning is widely used in various fields, it usually consists of sample alignment phase and training phase, where sample alignment is the first phase. For example, in horizontal federated learning, if the databases of parties contain some identical samples, then parties could use sample alignment to remove these duplicate samples before training, and in vertical federated learning, parties are required to use sample alignment to put the samples of the same user on the same row of both databases before training. Meanwhile, the current sample alignment schemes in federated learning are almost ID-based, and they assume the two participants have the same ID. Consider that these schemes cannot deal with the sample alignment problem for samples with different IDs, we present a sample alignment scheme that allows two participants with different IDs to align their samples. Our sample alignment scheme is based on Oblivious Programmable PRF (OPPRF), which doesn’t have much public key operation. After aligning the samples utilizing our scheme, the two participants could accomplish a variety of secure two-party machine learning tasks. In this paper, we design the privacy-preserving logistic regression training scheme using additive homomorphic encryption, thus achieving the whole federated logistic regression process. We implement our sample alignment scheme to verify the efficiency, and the experiments show that our sample alignment scheme only requires 216 s when the set sizes of sender and receiver are $$ 2^{24} $$ and $$ 2^{20}$$ . Besides, we conduct experiments to verify the feasibility of our logistic regression training scheme.
Development of Additional Material Transport to Augmented Reality Sandbox Algorithm to Model Pollution and Contaminant Runoff

Abstract Augmented Reality (AR) sandboxes make use of play sand, a depth sensor (to sense the topography in the sand), a computer (to run calculations), and a projector (to display a responsive topographic height map onto the sand). AR sandboxes can be found in schools and museums across the world being used to teach students and patrons about topography, runoff, and other geological topics. The sandboxes provide a mixed reality experience as the users manipulate standard play sand but view simulated rain and water runoff. In this work, an additional transport equation is added to standard augmented reality sandbox algorithm. This transport equation allows modeling of contaminants as a point source or as runoff from a specified area. The transport of fluid and contaminants is driven by gravity and gradients across cells, The presence of pollution in the simulated water is displayed through a new visual texture with the color and opacity of the texture conveying the concentration of the contamination. The inclusion of this additional transport equation extends the educational application of the AR sandbox.
Control Structures

Execution of a program is generally done by computing (executing or evaluating) different parts of the program (possibly repeatedly) in some order that may be dependent on data. Control structures are features in a programming language that allows the programmer some control over this order.
CNN-Based Car Damage Detection

Identifying the damage in a vehicle is considered to be the highest priority task in redeeming insurance from insurance industries. Whenever an accident occurs the guilty must be identified and the charges must be fined on him, these activities are actually carried out by the insurance companies. In this work, we present a different approach of identifying the vehicle’s damage by capturing 2D images, hence if the body of the vehicle gets deformed, the deprived driver of the vehicle need not have to wait for the insurance company’s consent and calculation of the ensured amount, instead the driver himself can calculate the recover amount from the insurance company. This work contains the CNN-based approach to check for the intensity and severity of damage. A computer vision approach is used accurately to classify damage of a vehicle and facilitates in claiming the right amount at right time.
Image Caption Generation for Low Light Images

Image captioning is the task of understanding an image and generating semantically and grammatically correct text captions. Existing work on image caption generation is mainly done on images set in optimal conditions, i.e., bright light conditions. This paper focuses on generating image captions for twilight/low-light images. The photos are processed through a Retinex theory-based enhancement algorithm to enhance the details of low-light images. A deep learning model with encoder-decoder architecture is implemented and trained with the Flickr 8k dataset for generating captions for improved photos. The efficiency of the caption generator is verified using the BLEU score, an available metric for evaluating machine-translated text.
Priority Analysis of Local Government Content Policy using AHP

This study is started with the purpose of empirically verifying the priority of institutional design for revitalization of the content industry of local governments. This is to suggest improvement measures to pursue the effectiveness of content support policies based on the opinions of expert groups when designing systems for content policies of local governments.&#x0D; As for the method of the study, the AHP structure diagram was designed through the analysis of previous studies and the current content system of local governments. Through this structural diagram, the priority of support policies was confirmed when designing the system. In addition, policy implications according to system design were derived based on the contents described in this study.&#x0D; As a result of the research, detailed tasks for policy direction include technology development (23.280), start-up (22.488), occupancy space and rent (18.297), employment and education and training subsidies (18.00), education and training funds for manpower nurturing (16.884), advanced technology and equipment (15.768), participation and promotion of domestic and foreign exhibitions (14.056), overseas location incentives (12.347), content promotion fund installation (10.162), and tax benefits (9.585) were in the order. Experts judged that direct support and continuous support policies should be supported by companies in designing important systems for the content industry.
Next Steps

Throughout this book, we’ve explored the three areas that must be balanced for an embedded software team to deliver their software successfully:
Break and Make: Interactive Structural Understanding Using LEGO Bricks

Visual understanding of geometric structures with complex spatial relationships is a fundamental component of human intelligence. As children, we learn how to reason about structure not only from observation, but also by interacting with the world around us – by taking things apart and putting them back together again. The ability to reason about structure and compositionality allows us to not only build things, but also understand and reverse-engineer complex systems. In order to advance research in interactive reasoning for part-based geometric understanding, we propose a challenging new assembly problem using LEGO bricks that we call Break and Make. In this problem an agent is given a LEGO model and attempts to understand its structure by interactively inspecting and disassembling it. After this inspection period, the agent must then prove its understanding by rebuilding the model from scratch using low-level action primitives. In order to facilitate research on this problem we have built LTRON, a fully interactive 3D simulator that allows learning agents to assemble, disassemble and manipulate LEGO models. We pair this simulator with a new dataset of fan-made LEGO creations that have been uploaded to the internet in order to provide complex scenes containing over a thousand unique brick shapes. We take a first step towards solving this problem using sequence-to-sequence models that provide guidance for how to make progress on this challenging problem. Our simulator and data are available at github.com/aaronwalsman/ltron . Additional training code and PyTorch examples are available at github.com/aaronwalsman/ltron-torch-eccv22 .
Noise-resistant speaker recognition algorithm based on full-frequency speech features with wavelet packet

At present, most speaker recognition algorithms are performed in a clean environment, and the effect is poor in a noisy environment. In order to improve the accuracy of speaker recognition in a noisy environment, a new feature extraction method, wavelet packet &amp; Gammatone (WPGT) based model, is proposed. In this model, the wavelet packet is used to decompose high-frequency and low-frequency signals and the Gammatone filter bank simulates the human auditory system to process non-linear signals so that more complete speaker voice features are extracted, and finally, the convolutional neural network is used to train the features and complete speaker recognition. Based on the open source speech data sets and the noise fusion data sets, the proposed method is compared with the commonly used voiceprint feature extraction methods MFCC and Gammatone. The experimental results show that, in a noisy environment, WPGT has better anti-noise ability than MFCC and Gammatone. Compared with MFCC and Gammatone, the accuracy of WPGT is improved by 10.63% and 16.91%, respectively.
Neural Space-Filling Curves

We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images. Linear ordering of pixels forms the basis for many applications such as video scrambling, compression, and auto-regressive models that are used in generative modeling for images. Existing algorithms resort to a fixed scanning algorithm such as Raster scan or Hilbert scan. Instead, our work learns a spatially coherent linear ordering of pixels from the dataset of images using a graph-based neural network. The resulting Neural SFC is optimized for an objective suitable for the downstream task when the image is traversed along with the scan line order. We show the advantage of using Neural SFCs in downstream applications such as image compression. Project page: https://hywang66.github.io/publication/neuralsfc .
Sentiment forecasting based on past textual content and deep learning architectures

In social networks, detecting opinions or sentiments by giving textual content has become a classical task. Usually, the concerned methods take as input a set of words in order to return the corresponding sentiment polarity, which can be called as a posteriori prediction. In this paper, we propose an a priori prediction model. Its objective is to predict the sentiment polarity of a user about a well-defined topic based only on textual history. To this aim, we suggest a deep learning-based approach which essentially depends on two deep learning architectures namely convolutional neural network (CNN) and long short-term memory (LSTM). To test our system, we have purposely created a collection from SemEval-2017 data. The results reveal that our deep learning method outperforms our previous approach which depends on sentiment intensity features with traditional machine learning algorithms. In addition, it outperforms the approach based on word embedding features with also traditional algorithms. On the other hand, the performance of our model is relatively close to that of ideal approaches (a posteriori prediction) that deal directly with texts carrying the sentiment.
THE TELLUS PROGRAMME: AN IRISH SOLUTION TO A GLOBAL RESOURCE CHALLENGE

As the world inexorably moves towards a lower carbon future, our use of raw materials will significantly shift from fossil fuels to the metals needed to power this green transition. Previously ignored deposits and prospects have acquired an enhanced level of criticality as society begins to reassess their value within this framework. Geological Survey Ireland is working on a Mineral Prospectivity Modelling (MPM) project examining previously underexplored areas to improve our knowledge of the occurrence of economically important metals. This is a key component in support of the ‘Just Transition’ in Ireland, which seeks to secure an economically and environmentally sustainable solution to the energy transition. The initial phase of the project operated across the minerals and Tellus programmes within the survey and developed new geochemical and geophysical data layers across the northwest of the country, evaluating the potential for Irish Type base metal deposits. Subsequent phases will utilise modern Tellus data as well as historical data to produce regional, system-based mineral prospectivity assessments for key energy transition minerals, as well as producing value-added structural geology data from airborne geophysics, which will also support and de-risk deep geothermal energy explorationGeological Survey Ireland's Tellus Programme is a national scale survey gathering geochemical and geophysical data across the island to support the sustainable use of Ireland's natural resources. It works across multiple themes of mineral prospectivity, smart agriculture, environment and health, climate action and education and is funded by the Department of the Environment, Climate and Communications. Geological Survey Ireland geologists work to develop products from this data that support internal and external research along with a greater understanding of the mineralising systems and prospectivity of underexplored areas. All data is made available free of charge online.
Analysis of Requirements and Quality Modeloriented Assessment of the Explainable Ai As A Service

Existing artificial intelligence (AI) services provided by cloud providers (Artificial Intelligence as a Service (AIaaS)) and their explainability have been studied. The characteristics and provision of objective evaluation of explainable AI as a service (eXplainable AI as a Service (XAIaaS)) are defined. AIaaS solutions provided by cloud providers Amazon Web Services, Google Cloud Platform and Microsoft Azure were analyzed. Non-functional requirements for XAIaaS evaluation of such systems have been formed. A model has been developed and an example of the quality assessment of an AI system for image detection of weapons has been provided, and an example of its metric assessment has been provided. Directions for further research: parameterization of explainability and its sub-characteristics for services, development of algorithms for determining metrics for evaluating the quality of AI and XAIaaS systems, development of means for ensuring explainability.
Density Peak Clustering Based Split-and-Merge

Density peak clustering (DPC) defines cluster centers to be the objects with the highest density in their neighborhoods and far away from the objects that are with higher densities, where density of each object is determined by a pre-specified parameter. This definition may find the number of clusters and detect clusters with arbitrary shapes. However, as a drawback, it may also lead to multiple centers for one cluster and hence degrade the clustering quality. Some work in the literature try to remedy it, but this drawback can be regarded as the advantage in split-and-merge strategy-based clustering. In this paper, we make use of a modified DPC to partition a dataset into a large number of small clusters by defining a refined K nearest neighbors, and design a merge criterion to combine the small clusters. The experimental results on 6 synthetic and 6 real datasets demonstrate the proposed algorithm outperforms some traditional clustering algorithms.
Comparative Overview of Color Models for Content-Based Image Retrieval

Today CBIR (content-based image retrieval) in contrast to conventional TBIR (text-based image retrieval) has become a focusing research area in image processing due to numerous application possibilities. These applications vary from medical and security to business and SNS applications, to name a few. Color is the most widely used image characteristic since it is independent of image resolution or orientation. There is no single best color representation. The target application has a big role in determining which color model is best to use. To speed up the retrieval time and get good accuracy, one must understand which color model to employ. In this paper, we provide a critical review of existing color models, explain their attributes, analyze them from various perspectives and provide a context-aware comparative evaluation. We study when and why certain color models outperform others in certain applications under certain circumstances. Color spaces described in this paper are not only well-known e.g., RGB, CMYK, HSV, and Munsell, but also novice systems, like fuzzy color models, that can process higher color semantic levels.
A NLP-based Approach to Improve Speech Recognition Services for People with Speech Disorders

Current speech recognition services are not suitable for people with speech disorders, which present difficulties in coordinating muscles and articulating words and sentences. In this case, a speaker-dependent approach is strongly required in order to address the specific vocal disarticulation. Several Deep learning approaches have been proposed in the literature to address this problem. However, they require many voice samples of people to properly work, and this is not practical. In this paper, we present an innovative Automatic Speech Recognition (ASR) system which is able to correct failures of deep learning based solution adopting Natural Language Processing (NLP) techniques. The proposed solution can perform both single word and whole sentence corrections by analyzing the speech context. We evaluated the solution in a home automation case study and proved the good accuracy of our model.
Hierarchical Human Activity Recognition Based on Smartwatch Sensors Using Branch Convolutional Neural Networks

Human activity recognition (HAR) has become a popular research topic in artificial intelligence thanks to the development of smart wearable devices. The main goal of human activity recognition is to efficiently recognize human behavior based on available data sources such as videos and images, including sensory data from wearable devices. Recently, HAR research has achieved promising results using learning-based approaches, especially deep learning methods. However, the need for high performance is still an open problem for researchers proposing new methods. In this work, we investigated the improvement of HAR by hierarchical classification based on smartwatch sensors using deep learning (DL) methods. To achieve the research goal, we introduced branch convolutional neural networks (B-CNNs) to accurately recognize human activities hierarchically and compared them with baseline models. To evaluate the deep learning models, we used a complex HAR benchmark dataset called WISDM-HARB dataset that collects smartwatch sensor data from 18 physical activities. The experimental results showed that the B-CNNs outperformed the baseline convolutional neural network (CNN) models when the hierarchical connection between classes was not considered. Moreover, the results confirmed that branch CNNs with class hierarchy improved the recognition performance with the highest accuracy of 95.84%.
Philosophy and Science and Technology Studies: The Problem of Relationships

The response to the article by O.E. Stolyarova the author shows why the proposed justification for the place of philosophy in the structure of science and technology studies does not work well in relation to the tasks of interdisciplinary communication. It is argued that it is more effective to refer to historical examples and analyze them than to use a purely theoretical explanation of why these examples arise. It is pointed out that, despite the results of postpositivist research of science, the scientific community continues to rely on corporate “common sense”, in which science is seen as positive knowledge and on this basis is opposed to philosophy as a speculative discipline. The necessity of avoiding these ideas in the context of science policy tasks, primarily among scientific managers from among the scientists themselves, is substantiated.
A Parallel Algorithm for Solving Linear Symmetric Transformation Equations

Successive relaxation iterative algorithm (SOR) is a common iterative algorithm for solving linear symmetric transformation equations. When the coefficient matrix is positive, it has faster convergence speed. However, due to the data correlation in each iteration step, it is difficult to achieve parallel computing. The current sor parallel algorithm adopts the method of data decomposition, but because the parallel area of this method is too small, the cost of synchronous communication is high, and the parallel efficiency is low. This paper presents a new parallel algorithm for SOR, which is equivalent to the traditional SOR method and has the same convergence and iterative results. The parallel algorithm increases the area of parallel computing by matrix partition, and introduces pipeline technology, which makes use of the overlapping of communication and computing time between processors to achieve ideal parallel acceleration efficiency.
Novel context-aware methodology for risk assessment in intelligent video-surveillance systems

Nowadays, the presence of multiple cameras is steadily increasing. Therefore, video-vigilance systems need to be massively produced. These systems are controlled by human experts who spend excessive amount of time detecting problems or possible impediments. Intelligent video-surveillance systems appear to mitigate this issue. Nevertheless, these systems present their drawbacks. One of the most important drawbacks is their inability to process the obtained visual information and use it to modify their parameters according to the environmental requirements. In this paper, a novel methodology has been developed to provide information to a previously implemented intelligent video-surveillance system becoming sensible to the context. The methodology is based on the advice of experts to adapt the system for a given scenario. It includes both the information from generated alarms and knowledge about the environment. The experimental results have been promising and the proposed methodology can serve as the foundation for future enhancements.