Reviewerly/exHarmony huggingface dataset:
Differentially Private Kalman Filtering With Signal Aggregation

Large-scale monitoring and control systems increasingly rely on sensitive data obtained from private agents, e.g., location traces collected from the users of intelligent transportation systems. To encourage the participation of these agents, algorithms that process information in a privacy-preserving way are thus needed. This note revisits the Kalman filtering problem, subject to privacy constraints. We aim to enforce differential privacy, a formal state-of-the-art definition of privacy ensuring that the output of an algorithm is not too sensitive to the data collected from any single participating agent. A two-stage architecture is proposed that aggregates and combines individual signals before adding privacy-preserving noise and postfiltering the result to be published. We show how an optimal static aggregation stage can be computed by solving a semidefinite program and illustrate the significant performance improvement offered by this architecture over input perturbation schemes.
Optimization Method on the Tuning, Sound Quality, and Ergonomics of the Ancient Guitar Using a DSP-FEM Simulation

The key parameters musicians consider when they evaluate the quality of a musical instrument are its tuning (reproducing a musical scale), sound quality, and ergonomics.Musical instrument makers, even up to this day, primarily base their practice on empirical knowledge and costly physical experimentation.A computational model that combines all the aforementioned key parameters is presented to predict the building details of an instrument.The simulation of the instrument is introduced to a multi-objective optimizer to calculate its optimal set of geometrical and material features, considering the importance of the three key parameters.For a string musical instrument this approach is based on a hybrid model of Digital Signal Processing simulating the vibrating string and of Finite Elements Method simulating the effect of the body.The simulation technique has been validated by building an ancient guitar and comparing its recordings with its analogous digital model.The proposed method can be put into practice to investigate the building details of any instrument by introducing the relevant simulation and the objective's function parameters.This work is expected to provide a powerful tool for the musical instrument makers towards a more efficient design of a bespoke instrument.
A Coarse-to-Fine Convolutional Neural Network for Light Field Angular Super-Resolution

Densely-sampled light field (LF) images are drawing increasing attention for their wide applications, such as 3D reconstruction, virtual reality, and depth estimation. However, due to the hardware restriction, it is usually challenging and costly to capture them. In this paper, we propose a coarse-to-fine convolutional neural network (CNN) for LF angular super-resolution (SR), which aims at generating densely-sampled LF images from sparse observations. Our method contains two stages, i.e., coarse-grained novel views synthesis and fine-grained view refinement. Specifically, our method first extracts the multi-scale correspondence in the sparse views and generates coarse novel views. Then we propose a structural consistency enhancement module to regularize them for LF parallax structure preservation. Experimental results on both real-world and synthetic datasets demonstrate that our method achieves state-of-the-art performance. Furthermore, we show the promising application of the reconstructed LF images by our method on the depth estimation task.
Implementing an effective qualitative risk analysis

Operating of an IT system should be based on risk management. It means a kind of risk analysis should be applied periodically. Qualitative methods are used widespreadly. However, general experience of IT security experts that the implementation of qualitative risk analysis is achieved only formally, using minimal time and effort. This way gives false, unusable, formal result. In this article we examine how to recognise faulty solution, how to avoid faulty achievement. We determine the necessary circumstances how to implement a qualitative risk analysis which gives real, useable result which really supports optimal operation.
MPANet: A Multi-stage Pixel-Level Attention Network for Crowd Counting

Crowd counting is widely used in public security, video surveillance, smart city construction and other fields, and has important and positive significance for controlling the number of people in specific places, directing public transportation, and ensuring social stability. The traditional counting method has low accuracy, limited scene and huge volume. With the development of deep learning, the traditional method is gradually replaced by the convolutional neural network method. We built a crowd density estimation algorithm based on convolutional neural network with high intelligence and low power consumption, which takes into account the accuracy of crowd counting and the reasoning speed. Compared with the existing algorithms, our algorithm incorporates pixel-level attention mechanism at different stages, so that the neural network can pay attention to useful features, so as to improve the accuracy of crowd counting. Secondly, most existing methods based on pixel-level attention mechanism use additional networks to extract information, while the attention features and density maps of our network are generated in a unified network, which improves the reasoning speed of the model.
Towards Lightweight Pixel-Wise Hallucination for Heterogeneous Face Recognition

Cross-spectral face hallucination is an intuitive way to mitigate the modality discrepancy in Heterogeneous Face Recognition (HFR). However, due to imaging differences, the hallucination inevitably suffers from a shape misalignment between paired heterogeneous images. Rather than building complicated architectures to circumvent the problem like previous works, we propose a simple yet effective method called Shape Alignment FacE (SAFE). Specifically, given an image, we align its shape to that of the paired one under the assistance of a 3D face model. The produced aligned pair enables us to train a lightweight generator that solely concentrates on spectrum translation with a pixel-wise supervision. However, since the 3D face model is powerless to attributes like the hair and glasses, there are still pixel discrepancies between the aligned pair. Given that, in the image space, we introduce a probabilistic pixel-wise loss that incorporates the discrepancies into a probabilistic distribution. Moreover, in order to alleviate the influence of the shape misalignment on spectrum translation, a spectrum optimal transport is performed in a shape-irrelevant latent space. Note that, in the final inference phase, except the lightweight generator, all other auxiliary modules are discarded. In addition to superior performance in qualitative synthesis and quantitative recognition, extensive experiments on 6 datasets demonstrate that our method also gains other two distinct advantages over existing state-of-the-art counterparts. The first is using a more lightweight generator. Compared with the state-of-the-art method, our method can achieve higher recognition results with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">128x</b> fewer parameters and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">63x</b> fewer FLOPs with only 4.58ms latency on a single TITAN-XP. The second is training on low-shot datasets such as Oulu-CASIA NIR-VIS that just contains 1,920 images from 20 identities. To the best of our knowledge, we are the first that can perform well on such a small-scale dataset. These advantages make our method more practical in the real world and further push boundaries of heterogeneous face recognition.
Customizing Backend Logic Using a Chatbot

Developers tend to waste a lot of time building a backend, which in turn extends the entire development cycle by several hours, consuming valuable time on additional complex tasks. In this article, an approach can be developed in which one has to observe and track down a reasonable method of making a rationale that invigorates human discourse to the actual code composed by software engineers and designers. It can be utilized to tailor one’s own backend rationale for any application with an equivalent one. Consequently, the entire thought is to give an interface to developers for rearranging and computerizing code by uniting two diversified progressive thoughts, a chatbot for deciphering and handling-off the human thoughts, and a backend structure to create and make the rationale dependent on customized human requirements. It works on the synergy of NLP and text processing along with backend frameworks to stimulate a paradigm shift in backend generation.
An Application of Learned Multi-modal Product Similarity to E-Commerce

Product similarity search is an important tool for e-com-merce companies to manage their portfolios of products and to find competitive prices on large electronic market places. The specific requirements for this similarity search application are (i) the similar products should be competitive products with respect to a given query product, (ii) related and just generally similar products should be treated as not similar products. Thus, the similarity between products should be learned from data. We propose to use classification models for entity matching and image classification to learn a multi-modal model for similarity search. Further, we propose a way to construct a meaningful training data set to learn the relevant similarities between product pairs. Extensive experiments show that a transformer based language model combined with Siamese convolutional neural networks outperform competitive baseline models.
Noise Optimization in Artificial Neural Networks

Artificial neural network (ANN) has been widely used in automation. However, the vulnerability of ANN under certain attacks poses security threat for critical automation systems. Adding noises to artificial neural network has been shown to be able to improve robustness in previous work. In this work, we propose a new technique to compute the pathwise stochastic gradient estimate with respect to the standard deviation of the Gaussian noise added to each neuron of the ANN. By our proposed technique, the gradient estimation with respect to noise levels is a byproduct of the back propagation algorithm for estimating gradient with respect to synaptic weights in ANN. Thus, the noise level for each neuron can be optimized simultaneously in the processing of training the synaptic weights at nearly no extra computational cost. In numerical experiments, our proposed method can achieve significant performance improvement on robustness of several popular ANN structures under both black box and white box attacks.
A case-based study in ERP instructional model: Fostering critical thinking skills and portraying independence on solving problems

Case-based study has a lot of potential in supporting the success of learning outcomes needed in the 21st century, one of which is critical thinking skills.However, there are very few studies related to case-based study to promote critical thinking skills.Therefore, this study will analyze the effectiveness of case-based study in improving students' critical thinking skills.In this research, case-based study is carried out with engage, research, present (ERP) instructional model.The ERP model has potential and is in accordance with case-based study schemes for the university level.In addition, this study was also conducted to analyze the achievement of student independence in conducting research to solve a problem.This study used a non-equivalent control group research design with 70 samples divided into the experimental group (N = 35) and the control group (N = 35).The instrument used is a standardized test of critical thinking skills developed by Ennis, namely the Cornell Critical Thinking Test (CCTT) level X and an observation sheet for student independence in solving problems.The results showed that case-based learning using the ERP model (experimental group) was more effective than case-based learning without the ERP model (control group).However, the learning process carried out in groups is effective in improving critical thinking skills.This shows that case giving is quite effective in supporting critical thinking skills.In addition, the dominance of the achievement of student independence in conducting research (in the experimental group) to solve problems is at level 3, namely scaffolded research.The implication is that case-based learning is suitable for use in improving critical thinking skills at the university level.
Detecting Hate Speech Against Athletes in Social Media

When English clubs and the game’s governing bodies and organizations turned off their Facebook, Twitter, and Instagram accounts from April 30 to May 1, 2021, the fight against online racism regained a new momentum. However, the Tokyo Olympics revealed new aspects of online bullying that athletes may face during major sporting events. Despite the significant effort put into online hate speech detection research in general, hate speech detection against athletes requires a separate investigation. We show in this paper that abusive language directed at athletes is more varied and difficult to detect. We began with the introduction of the collected data from online comments aimed at three athletes competing in the Tokyo Olympics 2020. Followed by conducting an extensive classification experiments of the collected data to demonstrate its diversity in comparison to other hate speech datasets. This was done to demonstrate that Active Learning outperforms Supervised Learning in hate speech detection against athletes.
Distributed Multi-node Slicing Model and Application Analysis for Power Information Collection Scene

With the proposal of the goal of "carbon peaking and carbon neutralization", blockchain technology has played an important role in the new power system under the dual carbon goal. Aiming at the data security problem in the current power consumption information collection scenario, a distributed multi node slicing model based on blockchain is proposed in this paper. Firstly, the traditional power information acquisition system is introduced. Secondly, the feasibility and advantages and disadvantages of the integration of traditional power information acquisition system and blockchain system are compared and analyzed. Then, aiming at the problems of stealing, tampering and destruction in the process of data transmission and storage in the power information collection business, an example and application analysis are carried out. The results show that this model improves the security and reliability of power information data, and ensures that the data is not tampered and damaged. Finally, the full text is summarized, and the prospect of power consumption information acquisition scene under the double carbon goal in the future is prospected.
A Refactoring for Data Minimisation Using Formal Verification

Concerns about protecting private user data grow as automated data processing by software becomes more ubiquitous in our society. One important principle is data minimisation, which requires that all collected personal data must be limited to what is necessary for the respective declared purpose. For existing software systems, it can be difficult to determine which input data are indeed necessary to compute the result and which data can (in certain cases) be left out. We present a new approach that can be used to adapt an existing program to data minimisation requirements. In this framework a user transmits a set of facts about their data instead of the full data. We formally define the approach and introduce a variety of minimisation notions for it. We have implemented the approach and demonstrate its feasibility on a few selected examples.
Face Recognition Based on Deep Learning and Data Augmentation

Face recognition is one of the most popular applications in video surveillance systems and computer vision. The researches of face recognition in recent years have been shown that their applications are widely used in practice. Particularly, during the pandemic of Covid-19, there were a lot of researches relating to face recognition with and without mask. The accuracy of the face recognition algorithms is depended on technical issues, implemented solutions and models of data processing. In this paper, we propose an improved method for face recognition based on deep learning techniques and data augmentation. Our contribution of the proposed method is focused on the following steps: (1) obtaining and pre-processing data for training dataset based on image processing techniques (i.e. noise removal, mask wearing). (2) Creating a trained model of new dataset based on the Inception Resnet-v1. (3) Building an application for face recognition in timekeeping of a company. We use the two popular face datasets which are open source and publicity available: Casia-WebFace [1] for training and LFW [2] for validation. Comparing the several methods, the accuracy of our method is higher in case with mask and the processing time is very fast in the real time.
TWO-YEAR ARTIFICIAL INTELLIGENCE TEACHING IN THE SECONDARY SCHOOL

For two years, due to the rapid development of digital technologies in the Bulgarian school, the teaching of artificial intelligence began. The article summarizes the two-year experience of the authors in supporting the teaching of artificial intelligence in high school. In Plovdiv district, two schools conduct training as an optional module in the specialized preparation of the profile „Software and Hardware Sciences“. These two schools conduct training on developed textbooks in 11th and 12th grade, respectively: „Solving problems by searching“ and „Presenting knowledge through logic“. Logical programming. Based on the observations on AI training, the article analyzes the learning outcomes, shares the experience in applying this training and draws conclusions. The results of the conducted survey and test studies are summarized.
Research on Speckle Reduction of SAR Image by Convolutional Neural Network

The synthetic aperture radar (SAR) images are contaminated by noise called speckle, making the contaminated images difficult to understand and use.Since SAR images have the advantages of long range and all-weather, which can make up for the poor imaging effect of optical cameras in bad weather conditions, it is meaningful to study the de-speckling algorithm of SAR images.In order to solve the above problem, this paper proposes a method to improve the ability of removing speckles from SAR images using convolutional neural networks.And we use the attention mechanism to further improve the denoising ability based on the proposed denoising network CDNet.After experimental comparison, the speckle removal capability of CDNet proposed in this paper is better than the existing mainstream algorithms.
The Copyright Dilemma and Solution for “Data-Driven Creation”

Data-driven creation" means that data plays an important role in the creation of works and distribution of content.Big data has brought new challenges to the copyright legal regime while driving innovation in the copyright industry.First, the data-driven creation of works has shifted from "supply-oriented" to "demand-oriented", and the main body of creation has shifted from "author-centrism" to "reader-centrism".Second, the free use of data is the key to creative quality, and the boundaries of fair use should be appropriately expanded.Finally, data-driven creation brings negative problems such as algorithmic bias, information cocoon, and data monopoly, and a collaborative governance approach should be sought to ensure the healthy development of the industry.
Fraud Detection in Blockchains using Machine Learning

Crypto crimes peaked in 2021, and recent years have shown that blockchain is not going to die but will stay with us. In addition, blockchain transaction throughputs are expected to rise, which will make the manual investigation of crimes next to impossible in the future. We need effective and automated solutions to monitor and detect fraudulent activities happening on blockchains. In this paper, we propose a system to detect the blacklisted addresses in the Ethereum blockchain. First, we collected Ethereum blockchain transaction data and blacklisted addresses. Then, we constructed the transaction graph of Ethereum and extracted features of addresses, including some global features like pagerank. Finally, we trained the models of standard machine learning algorithms and predicted the class of the addresses. Our results show that with more than 97% accuracy we can predict blacklisted addresses.
Early Identification of Invalid Bug Reports in Industrial Settings – A Case Study

Software development companies spend considerable time resolving bug reports. However, bug reports might be invalid, i.e., not point to a valid flaw. Expensive resources and time might be expended on invalid bug reports before discovering that they are invalid. In this case study, we explore the impact of invalid bug reports and develop and assess the use of machine learning (ML) to indicate whether a bug report is likely invalid. We found that about 15% of bug reports at the case company are invalid, and that their resolution time is similar to valid bug reports. Among the ML-based techniques we used, logistic regression and SVM show promising results. In the feedback, practitioners indicated an interest in using the tool to identify invalid bug reports at early stages. However, they emphasized the need to improve the explainability of ML-based recommendations and to reduce the maintenance cost of the tool.
Research on Efficient Software Defect Prediction Using Deep Learning Approaches

Software Defect prediction results provide a list of source code artifacts that are prone to defects. Quality assurance teams can effectively devote more energy and allocate limited resources to defect-prone source code verification software products. A module that identifies defect prediction methods for frequent defects before the start of the testing phase. Measurement-based defect-prone modules improve software quality and reduce costs, leading to effective resource allocation. The previous method doesn't analyze the defect pattern, and it has less performance during software development. This work introduces a deep learning-based Pattern-based Modified Hidden Markova Fault Tree (PMHMFT) framework to extract the hidden fault analysis during cross-project validation. The proposed Modified Hidden Markova Fault Tree algorithm constructs the defect fault tree to analyze the cross-project code defect. To prevent defect based on software metrics software prediction model are used. Hidden Markova Fault tree-based classification categorize component as defective and non-defective. Using a Levy flight, optimize the method to search the fault classes efficiently compared to another method. The Markova Fault Tree model construct fault tree based given data; it is easy to identify the fault in software platform. The proposed PMHMFT to implement evaluate the performance using k-fold validation. Thus, the proposed work on software defect prediction achieves higher accuracy in true classification and prediction with less error rate. The software defects are predicted, and these predicted defects are optimized by using Levy flight optimization. Our proposed PMHMFT technique is very useful technique for predicting software defect and gives the better prediction rates in effective manner.