Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Database Fingerprinting

When sharing relational databases with other parties, in addition to providing high quality (utility) database to the recipients, a database owner also aims to have (i) privacy guarantees for the data entries and (ii) liability guarantees (via fingerprinting) in case of unauthorized redistribution.However, (i) and (ii) are orthogonal objectives, because when sharing a database with multiple recipients, privacy via data sanitization requires adding noise once (and sharing the same noisy version with all recipients), whereas liability via unique fingerprint insertion requires adding different noises to each shared copy to distinguish all recipients.Although achieving (i) and (ii) together is possible in a naïve way (e.g., either differentially-private database perturbation or synthesis followed by fingerprinting), this approach results in significant degradation in the utility of shared databases.In this paper, we achieve privacy and liability guarantees simultaneously by proposing a novel entrylevel differentially-private (DP) fingerprinting mechanism for relational databases without causing large utility degradation.The proposed mechanism fulfills the privacy and liability requirements by leveraging the randomization nature of fingerprinting and transforming it into provable privacy guarantees.Specifically, we devise a bit-level random response scheme to achieve differential privacy guarantee for arbitrary data entries when sharing the entire database, and then, based on this, we develop an ϵ-entry-level DP fingerprinting mechanism.We theoretically analyze the connections between privacy, fingerprint robustness, and database utility by deriving closed form expressions.We also propose a sparse vector technique-based solution to control the cumulative privacy loss when fingerprinted copies of a database are shared with multiple recipients.We experimentally show that our mechanism achieves strong fingerprint robustness (e.g., the fingerprint cannot be compromised even if the malicious database recipient modifies/distorts more than half of the entries in its received fingerprinted copy), and higher database utility compared to various baseline methods (e.g., application-dependent database utility of the shared database achieved by the proposed mechanism is higher than that of the considered baselines).
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models

Transfer learning has remarkably improved computer vision. These advances also promise improvements in neuroimaging, where training set sizes are often small. However, various difficulties arise in directly applying models pretrained on natural images to radiologic images, such as MRIs. In particular, a mismatch in the input space (2D images vs. 3D MRIs) restricts the direct transfer of models, often forcing us to consider only a few MRI slices as input. To this end, we leverage the 2D-Slice-CNN architecture of Gupta et al. (2021), which embeds all the MRI slices with 2D encoders (neural networks that take 2D image input) and combines them via permutation-invariant layers. With the insight that the pretrained model can serve as the 2D encoder, we initialize the 2D encoder with ImageNet pretrained weights that outperform those initialized and trained from scratch on two neuroimaging tasks — brain age prediction on the UK Biobank dataset and Alzheimer’s disease detection on the ADNI dataset. Further, we improve the modeling capabilities of 2D-Slice models by incorporating spatial information through position embeddings, which can improve the performance in some cases.
An Explicitly Weighted GCN Aggregator based on Temporal and Popularity Features for Recommendation

Graph convolutional network (GCN) has been extensively applied to recommender systems (RS) and achieved significant performance improvements through iteratively aggregating high-order neighbors to model the relevance between users and items as well as their characteristics. In the aggregation process, GCN models usually give neighbors the same or trainable weights based on implicit features, ignoring explicit ones. In this work, we take the features with explicit meanings or extracted with specific purpose as explicit ones (e.g., temporal features) and the others contained in user-item network as implicit ones (e.g., user preferences). However, some explicit features and knowledge play an essential role in improving the model representation ability and explainability in recommendation systems. To deal with the limitation, we propose a GCN based framework to embed the explicit features or those extracted with explicit intentions in this work. We also provide specific implementations based on two commonly researched features, temporal evolution and popularity bias. Specifically, we first experimentally analyze the popularity bias of the representation learning in RS based on two commonly used GCN models. Secondly, we propose a general framework to weigh neighbors based on explicit features or intentions. Thirdly, we implement a Temporal and Popularity weighted Aggregator (TPA) for GCN. The Interest-Forgetting Curve is utilized to capture temporal evolution as temporal weights and the data-driven Beta distribution is employed to tune the weights based on the node popularity flexibly. At last, we conduct extensive experiments on three real-world datasets to demonstrate the effectiveness of TPA in improving recommendation accuracy and alleviating the popularity bias.
Image Restoration Quality Measurement using Noise Filters

The aim of this project is to implement different features of image restoration.We consider a vectorised picture and take pictures of it with our mobile for two configurations, the first lit by natural light, the second without the light.Those photos apply different transformations on the original image as rotation, change of the scale, integration of unwanted environment in the picture, etc.We will see a method to restore those photos and compare them to the original image.The comparison will be made by using two indicators: the Peak Signal Noise Ratio (PSNR), measuring the quality of reconstruction of the image, and the Structural Similarity Index (SSIM), evaluating the similarity between pixels.Finally, the complementary analysis will be performed to increase the restoration quality between the pictures, like using a noise reduction filter or function to increase the correspondence between histograms.
Robust Dual Digital Watermark Applied to Antique Digitized Cinema Images: Resistant to Print-Scan Attack

Nowadays, advances in information and communication technologies along with easy access to electronic devices such as smartphones have achieved an agile and efficient storing, edition, and distribution of digital multimedia files. However, lack of regulation has led to several problems associated with intellectual property authentication and copyright protection. Furthermore, the problem becomes complex in a scenario of illegal printed exploitation, which involves printing and scanning processes. To solve these problems, several digital watermarking in combination with cryptographic algorithms has been proposed. In this paper, a strategy of robust watermarking is defined consisting of the administration and detection of unauthorized use of digitized cinematographic images from Mexican cultural heritage. The proposed strategy is based on the combination of two types of digital watermarking, one of visible-camouflaged type based on spatial domain and another of invisible type based on frequency domain, together with a particle swarm optimization. The experimental results show the high performance of the proposed algorithm faced to printing-scanning processes or digital-analogue attack, and common image geometric and image processing attacks such as JPEG compression. Additionally, the imperceptibility of the watermark is evaluated by PSNR and compared with other previously proposed algorithms.
Colour Image Encryption Based on HACS and Knuth-Durstenfeld Algorithm

This research proposes an algorithm for an image encryption strategy based on a HACS and a scrambling algorithm to solve the issues of limited key space, insecure encryption frameworks, and easy-to-break present image encryption techniques utilising chaotic systems and bit wise XoR encryption. To begin, the image is encrypted using the sequence generated using HACS. The image is disordered using the shuffling method, then the bit wise XoR sequence technique is utilised to dilute the image’s pixel value. Experiments demonstrate that the scheme’s key space exceeds 2327 and that it is very sensitive to small changes in keys. The encrypted images’ histogram is equally distributed. Almost every neighbouring pixel has a correlation coefficient of zero. The UACI and NPCR values are both close to ideal, and the entropy values of the encrypted images are all close to eight. Experiments show that the encryption method described in this paper can survive extensive cryptanalysis, such as statistical and differential types. The research’s results show that the system and algorithm have made encryption more effective and that the suggested method is useful and doable for image encryption.
Secure Image Data Encryption Scheme for 5G Internet of Things Applications

The foundation of telecommunications for linking things in the Internet of Things (IoT) ecosystem is fifth-generation (5G) communications. The 5G network is being developed to deliver massive capacity, large bandwidth, and reduced latency. The creation and invention of new 5G-IoT approaches will undoubtedly result in massive new security and privacy issues. 5G-IoT technologies will need secure data transfer mechanisms as the foundation to solve these issues. Chaos theory-based image encryption and DNA sequence computations are combined in the suggested encryption approach. The encryption process is performed independently on three colour image channels (R, G, and B) with an appropriate chaotic map. Here the selection algorithm chooses an appropriate chaotic map according to the entropy values of image attributes and few other factors. Following chaotic encryption, a DNA encoding approach is used. According to simulation and security analysis, the suggested image encryption technique performs very well in terms of security.
An Analysis of Codebook Optimization for Image Compression: Modified Genetic Algorithm and Particle Swarm Optimization Algorithm

Billions of images are uploaded daily, and it requires a large storage space. Utilization of better storage capacity and to improve uploading/downloading time, researchers have designed an image compression model. Many researchers have implemented various approaches to improve the image compression ratio of an image. This paper presents an analysis of various optimization algorithms based on vector quantization (VQ). The first algorithm is a modified genetic algorithm. It is based on Darwin’s principle which is natural characteristics. Those who are fit can survive and use it to optimize the codebook. A second algorithm for optimization of the codebook is particle swarm optimization (PSO). PSO algorithm is superior to finding the codeword vectors of codebook from the training image samples for image compression. In the PSO algorithm, the selection approach plays an important role to select the particle based on the fitness of the population. Training images from the standard image database are used for the design of the codebook. The input image set is 4 × 4 or 8 × 8 blocks and is represented as vectors. They are referred to as codewords in the codebook, and it is a component of a code. The codebook size is measured by codewords. The block size is decided by the length of the codeword. These codewords generate the codebook by entering the vector value. Compression is done with the help of sending indices to the decoder. Likewise, analysis of quality measures is presented to the modified GA and PSO algorithms based on mean square error, peak signal-to-noise ratio, structural similarity index, and average difference. In this work, we have calculated bits per pixel (BPP), the compression ratio (CR), and the % compression ratio. The experimental results are validated.
A Dropout Optimization Algorithm to Prevent Overfitting in Machine Learning

In the computer field, data encryption has always been a hot research topic, and TreScriptic algorithm has become the object of attention and research because of its simple parameters, clear classification and other advantages.This paper mainly introduces the web server which is built based on different models and has good performance, does not affect the system performance, has strong portability, and can be widely used in most occasions.This paper proposes a genetic NSBP neural network based on evolutionary algorithm, which is over fitting multi-dimensional and has global optimization characteristics.In this algorithm, parameters are regarded as input variables rather than directly interacting with output samples.Then this paper designs the Dropout optimization algorithm process and tests the performance.The test results show that the performance of the Dropout optimization algorithm based on machine learning to prevent over fitting takes about 5 seconds to process data, and the time to prevent over fitting is at least 3 seconds.This method can improve the biological signal processing effect of the over fitting.
SOCIOCULTURAL FUNCTION OF THE LIBRARY IN RURAL AREAS (THE CASE OF THE SLAVSK MUNICIPALITY OF THE KALININGRAD REGION)

Being in close proximity to the local community and its needs, a modern rural library in the Russian space is designed to be not only a center for users to access information, but also a place of education, cultural and intellectual activities, adaptation of residents to rapidly changing political, socio-economic, and psychological conditions. The rural library should act as an institution of socialization through developing a system of traditional values and pre­serving local culture, positioning itself as an open platform and involving residents in an active social and business life. In the article, the author considers the socio-economic develop­ment of the Slavsk municipal district based on the array of statistical and analytical data of the Slavsk library network, which is a valuable information resource. It is concluded that the library in rural areas is actively developing the principles of communication, education, me­morialization, and information that are familiar to us, as well as newer ones — consolidation, education, adaptation, and the acquisition of integrity by the community.
Offline Signature Recognition and It’s Forgery Detection using Machine Learning Technique

Signature verification is an important aspect in today’s World. Signature has been verified in Banks, Government Agencies, Universities (Degree Verification) etc. Signature can involve in its shape, size, pressure, speed and angle. From a population of Signatures an original signature can be found out and distinguished. In this Paper for Forgery signature Detection we use two algorithm viz.Harris Algorithm and Surf Algorithm. We have also discussed about CNN Algorithm. Moreover in this paper we take the x-y co-ordinate of the real signature and also the x-y co-ordinate of the forged signature and compare among the two. We have used Python Programming for plotting the graphs whereas the graph can be plot using Matlab, R, Microsoft Excel and Python.
Implementing a Text-to-Speech synthesis model on a Raspberry Pi for Industrial Applications

Text-to-Speech (TTS) technology produces human-like speech from input text.It has recently acquired prominence by applying deep neural networks.Nowadays, endto-end TTS models produce highly natural synthesized speech but require extremely high computational resources.Deploying such high-quality TTS models in a real-time environment has been a challenging problem due to the limited resources of embedding systems and cell phones.This paper demonstrated the implementation of an end-to-end TTS model (FastSpeech 2) in an embedded device (Raspberry Pi4 B+).The objective experimental results showed that the TTS model is compatible with the Raspberry Pi (RPi) with high-quality synthesized speech and acceptable performance in terms of processing speed.Our proposed model could be used in many real-life applications if used together with a mechanism for caching, such as railway announcements and industrial purposes.
Application of intelligent time series prediction method to dew point forecast

&lt;abstract&gt; &lt;p&gt;With the rapid development of meteorology, there requires a great need to better forecast dew point temperatures contributing to mild building surface and rational chemical control, while researches on time series forecasting barely catch the attention of meteorology. This paper would employ the seasonal-trend decomposition-based simplified dendritic neuron model (STLDNM*) to predict the dew point temperature. We utilize the seasonal-trend decomposition based on LOESS (STL) to extract three subseries from the original sequence, among which the residual part is considered as an input of an improved dendritic neuron model (DNM*). Then the back-propagation algorithm (BP) is used for training DNM* and the output is added to another two series disposed. Four datasets, which record dew points of four cities, along with eight algorithms are put into the experiments for comparison. Consequently, the combination of STL and simplified DNM achieves the best speed and accuracy.&lt;/p&gt; &lt;/abstract&gt;
Factors associated with acceptance of a digital contact tracing application for COVID-19 in the Japanese working-age population.

The working-age population is at the epicenter of coronavirus disease 2019 (COVID-19) infections. Therefore, it is important to increase the acceptance of digital contact tracing apps in this population. Contact-Confirming Application (COCOA) is the only digital contact tracing app in Japan. This study aimed to determine factors associated with acceptance of the COCOA for COVID-19 in the Japanese working-age population. A cross-sectional study was performed for 27,036 full-time workers registered with an internet survey company during December 2020 in Japan. Factors associated with COCOA adoption were evaluated by multivariate logistic regression analysis. The rate of downloading the COCOA was 25.1%. The COCOA was more likely to be accepted by people with married status, university graduation or above, higher income, and occupations involving desk work. Fear of COVID-19 transmission, wearing a mask, using hand disinfection, willingness to be vaccinated against COVID-19, and presence of an acquaintance infected with COVID-19 were also associated with a greater likelihood of adopting the app. The rate of downloading the COCOA in Japan was not very high. The present findings have important implications for widespread adoption of digital contact tracing apps in working-age populations in Japan and elsewhere.
[Spatial Distribution Characteristics and Source Analysis of Heavy Metals in Soil of a Lead Plant in Sanmenxia].

At present, a large-scale relocation of industrial enterprises is taking place in major cities in China, and a large number of contaminated relocation sites are being generated, among which the heavy metal pollution is particularly serious. In order to analyze the pollution status, spatial distribution, and sources of heavy metals in the soil of a lead factory in Sanmenxia, the spatial variation and distribution characteristics of heavy metals in the soil were analyzed using geostatistics, and the main sources of heavy metals in the soil were analyzed using a PMF model. The results showed that the average values of As, Cd, Cu, Pb, Hg, and Ni in the soil far exceeded the background values of the soil environment in Henan province; the contents of As, Cd, Pb, and Hg exceeded the screening values of soil pollution risk; and the contents of As, Pb, and Hg exceeded the control values of soil pollution risk. The high-value area was located on the northern part of the slag yard; the Cr, Ni, and Cd high-value area was located in the north and south of the slag yard; the high-value As area was located in the slag yard between the southern area and the living quarters; the Cu and Pb high-value area was relatively scattered, mainly concentrated in the central part of the raw material storage area and furnace area; and Ni and Cd and Cu and Pb had the same spatial distribution characteristics. Based on the PMF model, it can be seen that there were three main sources of the seven heavy metals, and Cd was mainly from waste residue accumulation, with a contributing rate of 87.60%. Cu, Pb, and Hg were mainly soil parent material, with contribution rates of 92.50%, 75.20%, and 95.40%, respectively. Cr, Ni, and As were mainly raw material dust exhaust gas sources, with contribution rates of 80.80%, 83.30%, and 62.00%, respectively.
Fusing Syntax and Semantics-Based Graph Convolutional Network for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence or document, which is a fine-grained task of natural language processing. Recent ABSA methods mainly focus on exploiting the syntactic information, the semantic information and both. Research on cognition theory reveals that the syntax an*/874d the semantics have effects on each other. In this work, a graph convolutional network-based model that fuses the syntactic information and semantic information in line with the cognitive practice is proposed. To start with, the GCN is taken to extract syntactic information on the syntax dependency tree. Then, the semantic graph is constructed via a multi-head self-attention mechanism and encoded by GCN. Furthermore, a parameter-sharing GCN is developed to capture the common information between the semantics and the syntax. Experiments conducted on three benchmark datasets (Laptop14, Restaurant14 and Twitter) validate that the proposed model achieves compelling performance comparing with the state-of-the-art models.
Analysis of Wildfire Visualization Systems for Research and Training: Are They up for the Challenge of the Current State of Wildfires?

Wildfires affect many regions across the world. The accelerated progression of global warming has amplified their frequency and scale, deepening their impact on human life, the economy, and the environment. The temperature rise has been driving wildfires to behave unpredictably compared to those previously observed, challenging researchers and fire management agencies to understand the factors behind this behavioral change. Furthermore, this change has rendered fire personnel training outdated and lost its ability to adequately prepare personnel to respond to these new fires. Immersive visualization can play a key role in tackling the growing issue of wildfires. Therefore, this survey reviews various studies that use immersive and non-immersive data visualization techniques to depict wildfire behavior and train first responders and planners. This paper identifies the most useful characteristics of these systems. While these studies support knowledge creation for certain situations, there is still scope to comprehensively improve immersive systems to address the unforeseen dynamics of wildfires.
Equivalence for generalized Boolean functions

Equivalence plays a key-role for the classification of functions between elementary abelian groups V (p) n