Reviewerly/exHarmony huggingface dataset:
Behavior Model of General Secondary Schools and Factors Influencing Their Results

In this work, the function of the educational result of the school, which is also a utility function, has been developed through a survey of school headteachers. The article considers four critical tasks of maximization of schools in the Russian Federation and obtains solutions to these tasks in a general and particular forms.
Towards Human-Machine Integration for Signals Intelligence Applications

Recent advancements in radar technology and telecommunications have led to a high population of increasingly complex emitters in the electromagnetic spectrum. From the perspective of a signals intelligence (SIGINT) operator, assessing the general situation comes with several major challenges, e.g. the detection and extraction of signals of interest, the elimination of unwanted interferences, or the meaningful representation of increasingly complex modulation patterns. This paper provides a general overview of the challenges of SIGINT and suggests possible ways to support operators with automated signal processing.
Home smart medicine cabinet design

The design of the intelligent drug box system, can intelligently manage family medicine, at the same time, there is a timing alarm clock, the time of medicine will send a reminder, if there is no time to take medicine, the system will continue to alarm prompt, can effectively avoid mistake, take, omission, so as to improve people's healthy quality of life. In the first part of the article, we introduce the background of the whole system development principle and the current technology, then is our circuit design structure and master MCU principle, then is the hardware thought injection, is our code writing, after constant attempt and adjustment can get product roughly shape, after continuous optimization of the finished product, finally shows that the system internal timer interrupt technology to control the clock operation, and can be displayed on the corresponding LCD screen. If the system finds a new setting, it stops the previous setting, sets the new value before opening it, and then runs the current relevant setting value. The key adjustment after use will set the corresponding time, and in the normal clock in this process, the current time system will be compared with the previous alarm set time, if both are the same, a reminder will be displayed, the dose set Bluetooth connection mobile phone application will be displayed. If the real-time time of the system after one minute is not equal to the time recorded, the system will automatically exit the reminder to remind people when to take medicine, what medicine to take, and how much medicine to take every day.
How to Train Your SSAG in Convolutional Network

SSAG(Stochastic Stratified Average Gradient) is an optimization algorithm that achieves linear convergence with smaller storage and iteration costs. However, in the convolutional network, SSAG has two problems. One is that the training is unstable, and it has high requirements on the learning rate, which increases the training difficulty of the model. The other is that Batch Normalization does not work in the SSAG algorithm, making the SSAG algorithm unusable in many models, limiting the scope of SSAG use. For these two problems, we propose SSAGM(Stochastic Stratified Average Gradient with Momentous) and SSBN(Stochastic Stratified Batch Normalization). SSAGM draws on the SGDM (Stochastic Gradient Descent with Momentum) algorithm, and uses exponentially weighted moving average to estimate the first-order moment of the gradient of each category, which makes the model training more stable and converges faster. SSBN draws on SSAG stratified random sampling technology to change the calculation method of mean and variance in Batch Normalization, so that SSAG algorithm can also perform normalization. The experimental results show that SSAGM is better than SSAG and other algorithms, and SSBN can also play a role similar to Batch Normalization in SSAG algorithm.
Multi-Scene Robust Speaker Verification System Built on Improved ECAPA-TDNN

In order to solve the problems of cross-domain, short speech, and noise interference in industrial application scenarios of speaker recognition, this paper proposes an improved ECAPA-TDNN for a multi-scene robust speaker verification system architecture-improved DD-ECAP A-TDNN.The design of the DD-ECAPA-TDNN architecture is inspired by the model ECAPA-TDNN, which has recently become popular in ASV systems. Firstly, we use FBanks to extract acoustic features, followed by the DD-SE-Res2Net Block proposed in this paper to capture local features efficiently. Finally, the output feature mapping of all DD-SE-Res2Net Blocks aggregated at multiple scales, and finally the ASP pooling operation is performed. The experiments were based on the VoxCeleb1-dev dataset, and SC-AAMSoftmax was used to train a speaker identification model for 1211 speakers. This DD-ECAPA-TDNN model was used as speaker embedding extractor to construct an automatic speaker verification (ASV) system. We used VoxMovies and VoxCeleb1-O evaluation sets to simulate three scenarios of cross-domain, short speech and noise interference, respectively, to evaluate the performance of the DD-ECAPA-TDNN system under multiple scenarios. The system achieves an EER of 2.51% on VoxCeleb1-O. The DD-ECAPA-TDNN system significantly outperforms the ECAPA-TDNN system in terms of recognition performance in multiple scenarios. Finally, our ablation experiments show that the DD-SE-Res2N et Block has a positive impact on the performance of the ASV system, as well as that the DD-ECAPA-TDNN can extract robust and accurate speaker embedding with good scene generalization.
Named Entity Recognition in Qu Tan temple murals based on BERT-BiLSTM-CRF

In this paper, the named entity recognition (NER) technology is applied to the field of Qu Tan temple murals to identify religious entities in the text. In order to obtain a machine-readable corpus, the relevant books are scanned and optical character recognition is performed, and then the data is manually labeled to form the named entity recognition dataset needed in this study. In this paper, the bidirectional encoder representations from transformers (BERT) model is used to encode the input data, and the obtained word vector can effectively extract the features of the text by making use of the relationship between words. In order to obtain more accurate context information, the bidirectional long short term memory (BiLSTM) is used to extract features and output the prediction label corresponding to each word. After the BiLSTM model, the conditional random field (CRF) algorithm is joined, which will correct the score of the output, and the output is the optimal label sequence. In this study, the performance of the model is also improved by data enhancement.
Local Aggregative Attack on SAR Image Classification Models

Convolutional neural networks (CNN) have been widely used in the field of synthetic aperture radar (SAR) image classification for their high classification accuracy. However, since CNNs learn a fairly discontinuous input-output mapping, they are vulnerable to adversarial examples. Unlike most existing attack manners that fool CNN models with complex global perturbations, this study provides an idea for generating more dexterous adversarial perturbations. It demonstrates that minor local perturbations are also effective for attacking. We propose a new attack method called local aggregative attack (LAA), which is a black-box method based on probability label information, to reduce the range and amplitude of adversarial perturbations. Our attack introduces the differential evolution (DE) algorithm to search for the optimal perturbations and applies the maximum between-class variance method (OTSU) to accomplish pixel-level labeling of the target and background areas, enabling attackers to generate adversarial examples of SAR images (AESIs) by adding small-scale perturbations to specific areas. Meanwhile, the structural dissimilarity (DSSIM) metric optimizes the cost function to limit image distortion and improve attack stealthiness. Experiments show that our method achieves a high attack success rate against these CNN-based classifiers, and the generated AESIs are equipped with non-negligible transferability between different models.
Research on Gesture Recognition Based on F-S Algorithm

In order to further improve the accuracy of gesture authentication, based on the traditional Frechet distance and Support Vector Machine (SVM), this paper proposes a two-level authentication algorithm that combines space trajectory similarity and feature classification, which is called the F-S algorithm. The method mainly uses the linear accelerometer data of the mobile phone to obtain the three-dimensional spatial trajectory of the gesture action in the mobile phone coordinate system after preprocessing, and then extracts the time domain features of the trajectory, and then uses the F-S secondary authentication algorithm to identify and authenticate the gesture. The experimental results showed that the false rejection rate FRR of the F-S algorithm in the gesture recognition process is 4.13%, and the false acceptance rate FAR is 1.26%.
Research on Active Jamming Recognition Method Based on Fractional Fourier Transform

Compared with traditional jamming, sampling and repeater jamming with more similar echo characteristics are also widely used. Aiming at the problem that radar is difficult to correctly identify jamming and extract useful signals in a complex jamming environment, this paper studies a jamming recognition method based on the Fractional Fourier Transform (FRFT). This method first selects the fractional optimal rotation angle of the radar received signal through angle traversal, obtains the optimal fractional Fourier transform of the received signal, and estimates the signal parameters. Then, the fractional domain characteristic parameters which are easy to calculate and less sensitive to noise are extracted from the fractional domain results of the received signal. At last, we brought the parameters into the decision-making process of jamming recognition and simulated and verified the performance of jamming identification. The experimental results show that the algorithm can accurately identify the typical active jamming of radar.
Multi-Agent Deep Deterministic Policy Gradient Algorithm Based on Classification Experience Replay

In recent years, multi-agent reinforcement learning has been applied in many fields, such as urban traffic control, autonomous UAV operations, etc. Although the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm has been used in various simulation environments as a classic reinforcement algorithm, its training efficiency is low and the convergence speed is slow due to its original experience playback mechanism and network structure. The random experience replay mechanism adopted by the algorithm breaks the time series correlation between data samples. However, the experience replay mechanism does not take advantage of important samples. Therefore, the paper proposes a Multi-Agent Deep Deterministic Policy Gradient method based on classification experience replay, which modifies the traditional random experience replay into classification experience replay. Classified storage can make full use of important samples. At the same time, the Critic network and the Actor network are updated asynchronously, and the learned better Critic network is used to guide the Actor network update. Finally, to verify the effectiveness of the proposed algorithm, the improved algorithm is compared with the traditional MADDPG method in a simulation environment.
A Text Summarization Model with Enhanced Local Relevance

When generating short text summaries., it is challenging to accurately learn the global semantic information of the original text and extract the correlation features between local semantic information., and at the same time lead to too much redundant information., making the generated summaries ineffective. In addition., the existing normalization algorithm will increase the computational complexity of the text summarization model., which affects the performance of the model. Aiming at the above problems., a text summarization generation model GMELC(Generation Model for Enhancing Local Correlation) is proposed to enhance local correlation in generated summaries. First., the residual concept used in other media feature extraction networks is introduced into the text summarization model. We add the word semantic feature as a residual block to the n-gram feature., which improves the dependencies of words in phrases and strengthens the correlation between phrases and words in sentences. Secondly., we propose a scaled I <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> normalization method to normalize the data for reducing the amount of training parameters and removing the unnecessary computation caused by variance., so that the computational complexity of the model is reduced., thereby improving the computational efficiency and performance of the model. In order to verify the role of the model in enhancing the correlation between Chinese characters and words., experiments were conducted on the Chinese dataset LCSTS., the result shows that the summaries generated by GMELC have higher recall and better readability than other state-of-the-art models.
Knowledge Graph Completion Method Based on Embedding Representation and CNN

Knowledge graph embedding is to embed entities and relations into a low-dimensional vector space, thereby improving the ability of knowledge representation. Models represented by ConvE combine knowledge graph embedding with convolutional neural networks, but these models still treat each node of the triplet as an independent part, which makes the feature expression ability insufficient. Aiming at the problem of insufficient information interaction ability, a knowledge graph completion model based on embedding representation and convolutional neural network is proposed. By fusing the information of different nodes of the triplet with each other, and forming a vector matrix with the original triplet as the input of the convolutional neural network for feature extraction, the feature expression ability of the triplet is improved. The experimental results show that compared with the ConvE model on the datasets FB15K-237 and WN18RR, the MRR index is increased by 6% and 2.2%, respectively; the Hit@1 index is increased by 12.7% and 0.6%, respectively.
Analyzing and Investigating encrypted traffic for social media application Instagram

Social networking applications have significantly attributed to an increase in network traffic both from offices and homes. Different organizations deploy firewalls to improve their network utilization and efficiency of resources, however, these firewalls can only handle plain traffic, whereas social media applications have encrypted traffic. In this paper, we focus on Instagram which is a widely used social media application, and forensically analyze its network encrypted traffic to detect user activity for a modern firewall development system. We investigate the traffic and role of standard servers, content delivery networks (CDNs), and application programming interface (API) servers to find a solution to the problem. We analyze the traffic based on DNS records, its flow, and instantaneous application data from encrypted traffic packets by using simple deep packet inspection (DPI) and statistical techniques. We have successfully detected user chat activity and provided significant features to distinguish between chat and normal webpage browsing. We have also provided a new setup to generate real-time activities from both computer browsers and android mobile applications and acquire datasets automatically for research purposes.
Deep-Learning based Blood Cells Classification and Initial Edge Device Implementation

The suitability of two deep learning models for classifying microscopic medical images is investigated on four types of white blood cell images; the inference phase is imple-mented onto a single board computer. The first proposed model is custom-developed by careful selection of the hyperparameters; the second one is based on transfer learning. A dataset of 12500 images is used. For the custom model, the training and validation accuracies are 99% and 97%, respectively, with overall classification accuracy of 97.77%. For the transferred model, the training and validation accuracies are 92% and 87%, respectively, with overall classification accuracy of 92% The two models are minimized and deployed on an RPi4 (quad-core ARM Cortex-A72 processor) for classification inference; the overall accuracy and classification times are 98.54% & 48.2 ms for the custom model vs. 91.3% & 142 ms for the transferred model, confirming that the custom model outperforms the transferred one.
On Supervising Master’s Theses in Industry Context

In software engineering, students easily find internships in companies while still studying. To combine their studies and employment, many of them seek to compose their final theses in an industry context, for the benefit of the employer as well as to simplify their context switching between job and studies. This can put the student between a rock and a hard place, as on one hand the employer has certain expectations in terms of working for the company, whereas the supervising professor needs to follow the university guidelines. An additional aspect worth considering is the university as an administrative home for the thesis and owner of the thesis process. In this paper, we study how the different stakeholders – the student, the supervising professor, and the company – should act for the best possible results, so that the company problem gets solved, and the results can be reported in accordance with the best academic practices. The research builds on authors’ collective supervision experience, covering more than 1000 theses (mainly master’s level) and close to a sum of hundred years. The thesis has been mainly supervised in two universities, with the clear majority executed in this setup, but there are also several exceptions where the thesis has been eventually accepted in some other university. The results are expressed in the form of anti-patterns, which consist of a definition of symptoms of a problem, its root causes, and proposals to salvage the situation in a practical fashion.
Shadow Clones: an Unattended Visual Interface to Enhance the Task Performance in Multiple Space using Gaze-Switching

We propose Shadow Clones; a user interface with unattended visual information to accomplish multiple tasks in multiple spaces using fast-gaze-switching. Shadow Clones interface provides not only a cursor on the view area the user is gazing at but also cursors on other view areas that the user is not gazing at. To validate Shadow Clones, we designed a successive reaching task and compared our approach to single-cursor interface, in which the cursor was displayed in the view area that the user currently gaze at. The results showed that the shadow clones' condition improved performance while maintaining fast switching. Our approach is expected to be an interface design for interactions in which a single user can manipulate multiple bodies.
Localization in Immersive Analytics with World-In-Miniature, Landmarks and Constellations

Immersive analytics is a form of data visualization that allows users to observe and analyze data in multiple dimensions within the virtual environment (VE). Nonetheless, when immersing in such an environment with a large and abstract dataset, users may lose their spatial awareness and be unable to localize themselves within the VE. This issue motivates us to explore visualization techniques to improve the localization ability of the users. Based on the requirements to utilize the dataset’s intrinsic features and explore both ego-and exo-centric reference frames, we propose three techniques to help users localize themselves in VE for immersive analytics: World-in-miniature (WIM), Landmarks and Constellations.
An Instrumented Office Chair with a Steerable Projector for Personal Spatial Augmented Reality

We contribute the idea of an instrumented office chair as a platform for spatial augmented reality (SAR). Seated activities are tracked through chair position, back tilt, rotation, surface sensors, and touches along the armrest. A depth camera tracks chair position using simultaneous localization and mapping and a servo-actuated pan-tilt projector mounted on the side of the chair displays content for applications. Eleven demonstration scenarios explore usage possibilities and an online survey gathers feedback. Many respondents perceive the concept as useful and comfortable, validating it as a promising direction for personal portable SAR.
Imaging geomechanical properties of shales with infrared light

• AI-assisted hyperspectral data translation framework proposed for shales. • Infrared spectra contain hidden information mappable to geomechanical properties. • Acoustic velocity, acoustic attenuation, and X-ray attenuation are mapped spatially. • Machine learning algorithms used require as little as 1% surface coverage to train. • Implications include extrapolating micro-indents, scratch tests, and outcrop data. Hyperspectral images are data cubes that associate with each pixel an infrared (IR) reflectance spectrum. We hypothesize there is an implicit but discernable link between IR spectra of rocks and their mechanical response that can be learned by a machine learning (ML) algorithm. Our rationale is that IR spectra are sensitive to both rock composition and microstructure, two attributes that determine all hydro-thermo-mechanical properties of the rock. To test this hypothesis, we confine ourselves to shale specimens from the Green River formation, Utah, US, and aim to map acoustic velocity ( V p ), acoustic attenuation coefficient ( A p ), and X-ray attenuation coefficient ( µ ) from hyperspectral images acquired in the near-, shortwave-, and mid-IR. We propose an experimental workflow that generates large quantities of labelled data needed to train supervised ML algorithms. The trained algorithms are shown to accurately predict V p , A p , and µ directly from pixelwise IR spectra. In some cases, even a 1% random sampling of pixels is found sufficient for training. While both artificial and convolutional neural networks are probed, performances are similar, but better than linear regression. More important is ensuring the training data are representative of all lithologies to be targeted during prediction. This work implies the feasibility of extrapolating beyond a handful of point or line measurements from indentation, scratch testing, or field surveys to, respectively, cm-scale, m-scale, and potentially outcrop-scale rock faces.
Imperative Model of Use Information Technologies for Development of Students’ Personal Potential in the Process of University Education

In the model proposed in the article presents a psychological-pedagogical construct of information technologies introduction in university training. The construct is aimed at implementing the principles of using these technologies (targeting, accessibility, redundancy, diversity, dialogueness, sensitivity, synergy, updateability, systematicity, scientificity, etc.), adherence to which makes it possible to deploy a full-fledged educational communications that ensure the development of students' personal potential.