Reviewerly/exHarmony huggingface dataset:
Enhancing the Efficiency and Sensitivity in Discrete Cosine Transform based Image Compression and Comparing with Discrete Wavelet Transform

This work aims to compress the color images using Novel DC transform (Discrete Cosine Transform) and compare the efficiency and sensitivity with Discrete Wavelet Transform (DWT). Novel DCT and DWT algorithms were implemented to compress the image without losing quality. Group 1 with 20 samples using DWT and group 2 with 20 pieces using Novel DCT. These two groups can be performed with the help of collecting 40 samples. These algorithms were implemented to evaluate the efficiency and sensitivity of the compressed images. From the simulation result, the proposed algorithm achieves the image compression ratio of 23:1 with 96% efficiency and 98% sensitivity. The DWT algorithm achieves an image compression ratio of 21:1 with 94 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> of efficiency and 93% of sensitivity. The significance value obtained was 0.02, which is less than 0.05. The novel DCT algorithm has significantly better efficiency and sensitivity than the DWT algorithm.
Comparing the Efficacy of Machine Learning Models on Potentially Hazardous Objects

An asteroid or a comet is termed as a potentially hazardous object (PHO) if its orbit is such that it can closely approach the Earth and is huge enough to cause serious regional damage in the event of an impact. There are currently nearly 1,500 asteroids documented that have the potential to cause serious damage, but these are solely the ones we know about. In an event like this, it becomes a necessity to keep a check on all celestial bodies near earth so that we can take appropriate measures to minimize the damage. This project aims at analyzing data gathered by NASA about various asteroids or PHOs and predicting whether an Earth approaching body is hazardous or not. On finding an appropriate dataset, extensive data pre-processing was done on Machine Learning workspace on Azure. The Azure platform for machine learning algorithm provide a quick and easy way to apply machine learning algorithms as well as for modelling of data. Experimental results obtained by using diverse Machine Learning models were compared. The Two-Class Decision Forest provided the highest accuracy, a ROC-AUC score of 0.99.
Federated Learning Approach towards Sentiment Analysis

Smartphones have access to a tremendous quantity of data relevant to models, henceforth enhancing the consumer experience. General ML algorithms require centralization of data and models but pose difficulty in GDPR compliance, lack of trust from users' end, and limited transparency. FL is a collective and decentralized approach to Machine Learning that enhances privacy and security of data, complies with GDPR. It also advocates low power consumption and latency. This paper delivers the application of Federated Learning in sentiment analysis, in comparison to the Deep Learning algorithms - RNN, CNN used. The sentiments of the text are segmented in accordance with the spectrum of emotions they articulate - positive, negative, and neutral using Deep Learning algorithms. We have also added a web component (a dynamic web-app) to the sentiment analysis model to automate the prediction process.
Relaxed Locally Decodable and Correctable Codes: Beyond Tensoring

In their highly influential paper, Ben-Sasson, Goldreich, Harsha, Sudan, and Vadhan (STOC 2004) introduced the notion of a relaxed locally decodable code (RLDC). Similarly to a locally decodable code (Katz-Trevisan; STOC 2000), the former admits access to any desired message symbol with only a few queries to a possibly corrupted codeword. An RLDC, however, is allowed to abort when identifying corruption. The natural analog to locally correctable codes, dubbed relaxed locally correctable codes (RLCC), was introduced by Gur, Ramnarayan and Rothblum (ITCS 2018) who constructed asymptotically-good length-nRLCC and RLDC with $(\log n)^{O(\log\log n)}$ queries.In this work we construct asymptotically-good RLDC and RLCC with an improved query complexity of $(\log n)^{O(\log\log\log n)}$. To achieve this, we devise a mechanism-an alternative to the tensor product-that squares the length of a given code. Compared to the tensor product that was used by Gur et al. and by many other constructions, our mechanism is significantly more efficient in terms of rate deterioration, allowing us to obtain our improved construction.
Testing Positive Semidefiniteness Using Linear Measurements

We study the problem of testing whether a symmetric $d\times d$ input matrix A is symmetric positive semidefinite (PSD), or is $\epsilon$-far from the PSD cone, meaning that $\lambda_{min}(A)\leq-\epsilon\Vert A\Vert_{p}$, where $\Vert A\Vert_{p}$ is the Schatten-p norm of A. In applications one often needs to quickly tell if an input matrix is PSD, and a small distance from the PSD cone may be tolerable. We consider two well-studied query models for measuring efficiency, namely, the matrix-vector and vector-matrix-vector query models. We first consider one-sided testers, which are testers that correctly classify any PSD input, but may fail on a non-PSD input with a tiny failure probability. Up to logarithmic factors, in the matrix-vector query model we show a tight $\tilde{\Theta}(1/\epsilon^{p/(2p+1)})$ bound, while in the vector-matrix-vector query model we show a tight $\tilde{\Theta}(d^{1-1/p}/\epsilon)$ bound, for every $p\geq 1$. We also show a strong separation between one-sided and two-sided testers in the vector-matrix-vector model, where a two-sided tester can fail on both PSD and non-PSD inputs with a tiny failure probability. In particular, for the important case of the Frobenius norm, we show that any one-sided tester requires $\tilde{\Omega}(\sqrt{d}/\epsilon)$ queries. However we introduce a bilinear sketch for two-sided testing from which we construct a Frobenius norm tester achieving the optimal $\tilde{O}(1/\epsilon^{2})$ queries. We also give a number of additional separations between adaptive and non-adaptive testers. Our techniques have implications beyond testing, providing new methods to approximate the spectrum of a matrix with Frobenius norm error using dimensionality reduction in a way that preserves the signs of eigenvalues.
Geometry of Secure Two-party Computation

What is the round and communication complexity of secure computation? The seminal results of Chor-Kushilevitz-Beaver (STOC-1989, FOCS-1989, DIMACS-1989) answer this question for computations with deterministic output. However, this question has remained unanswered for computations with randomized output. Our work answers this question for two-party secure function evaluation functionalities. We introduce a geometric encoding of all candidate secure protocols for a given computation as points in a high-dimensional space. The following results follow by analyzing the properties of these sets of points.1)It is decidable to determine if a given computation has a secure protocol within round or communication constraints.2)We construct one such protocol if it exists.3)Otherwise, we present an obstruction to achieving security.Our technical contributions imply new information complexity bounds for secure computation.
FDR: An Automated System for Finding Missing People

According to the National Missing and Unidentified Persons (NamUS) database, on an average 600,000 individuals of all ages go missing every year. With the assistance of surveillance technology like CCTV cameras, our proposed model can help to make the search process easier and faster-using Face Detection and Recognition (FDR) techniques. It can be used for locating missing children, mentally challenged and old-aged individuals with Alzheimer's, and criminals by providing correct datasets. The face recognition model matches the face encodings of the uploaded image with the face encodings that are already available in the database to find a suitable match. Here we tend to use HAAR for face detection, LBP for face recognition, and a cloud system for storing the information. The disadvantage posed here is that generally they are susceptible to false detection and need parameter calibration. Further research will be conducted to overcome these drawbacks because achieving high accuracy is important.
Memory Bounds for Continual Learning

Continual learning, or lifelong learning, is a formidable current challenge to machine learning. It requires the learner to solve a sequence of k different learning tasks, one after the other, while retaining its aptitude for earlier tasks; the continual learner should scale better than the obvious solution of developing and maintaining a separate learner for each of the k tasks. We embark on a complexity-theoretic study of continual learning in the PAC framework. We make novel uses of communication complexity to establish that any continual learner, even an improper one, needs memory that grows linearly with k, strongly suggesting that the problem is intractable. When logarithmically many passes over the learning tasks are allowed, we provide an algorithm based on multiplicative weights update whose memory requirement scales well; we also establish that improper learning is necessary for such performance. We conjecture that these results may lead to new promising approaches to continual learning.
Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance

Computing the edit distance of two strings is one of the most basic problems in computer science and combinatorial optimization. Tree edit distance is a natural generalization of edit distance in which the task is to compute a measure of dissimilarity between two (unweighted) rooted trees with node labels. Perhaps the most notable recent application of tree edit distance is in NoSQL big databases, such as MongoDB, where each row of the database is a JSON document represented as a labeled rooted tree and finding dissimilarity between two rows is a basic operation. Until recently, the fastest algorithm for tree edit distance ran in cubic time (Demaine, Mozes, Rossman, Weimann; TALG’10); however, Mao (FOCS’21) broke the cubic barrier for the tree edit distance problem using fast matrix multiplication.Given a parameter k as an upper bound on the distance, an $\mathcal{O}(n+k^{2})$-time algorithm for edit distance has been known since the 1980s due to works of Myers (Algorithmica’86) and Landau and Vishkin (JCSS’88). The existence of an $\tilde{\mathcal{O}}(n+poly(k))$-time algorithm for tree edit distance has been posed as open question, e.g., by Akmal and Jin (ICALP’21), who give a stateof-the-art $O(nk^{2})$-time algorithm. In this paper, we answer this question positively.
Application of Cluster Analysis Using Agglomerative Method

Improving the quality of human resources is the main supporting factor in increasing national productivity in various fields and development sectors. The government's productive investment activities that spur the nation's competitiveness in the global era prioritize Indonesia's education development. This study aims to cluster provinces in Indonesia based on educational indicators using the Agglomerative method consisting of the Average Linkage and Ward methods. Data collection is based on documentation techniques obtained from Statistics Indonesia in 2018. Data analysis used hierarchical cluster analysis consisting of data standardization, determining the size of the similarity or dissimilarity between data, the clustering process with a distance matrix, and seeing the characteristics of the cluster results formed. The second clustering method is by doing the initial grouping and determining the excellent cluster based on the average standard deviation ratio to the standard deviation between groups. Clustering results show the Ward method with the number of collections as many as 4 clusters and produces a ratio with a value of 0.01 smaller than the Average Linkage method. It shows that the cluster analysis method using the Ward method has better group accuracy quality than the Average Linkage method.
Privacy‐preserving and fine‐grained data sharing for resource‐constrained healthcare <scp>CPS</scp> devices

Abstract Medical cyber‐physical systems (CPS) provide the possibility for real‐time health monitoring of patients and flexible diagnostic services based on expert systems by collaboratively integrating and connecting various physical devices including sensors, terminals, and cloud infrastructure. However, the ubiquitous security threats in cyberspace have raised concerns about data security and user privacy. Although related works propose to protect data security and user privacy with cryptographic protocols, their heavy computational and storage overheads incur performance and battery life challenges for resource‐constrained devices in the healthcare CPS. This article proposes an energy‐saving and privacy‐preserving data sharing (ESPPDS) scheme to address the challenge. ESPPDS inherits the anonymous fine‐grained access control from attribute‐based encryption (ABE) while protecting data integrity and supporting efficient user revocation. We also eliminate the repetitive computations of ciphertext components by utilizing the online/ offline encryption technology, and design a subtle and secure trick to delegate the decryption operations to the edge device, thereby reducing the computational overheads of the resource‐constrained devices. We then show the security proof, and discuss the construction in the untrusted/ compromised server setting. The comparison and experiment indicate that ESPPDS is practical and more efficient than related schemes.
Extremal ternary self-dual codes of length 36 and symmetric 2-(36, 15, 6) designs with an automorphism of order 2

In this note, we report the classification of all symmetric 2-(36, 15, 6) designs that admit an automorphism of order 2 and their incidence matrices generate an extremal ternary self-dual code. It is shown that up to isomorphism, there exists only one such design, having a full automorphism group of order 24, and the ternary code spanned by its incidence matrix is equivalent to the Pless symmetry code.
Fast Bayesian inference for large occupancy datasets

Abstract In recent years, the study of species' occurrence has benefited from the increased availability of large-scale citizen-science data. While abundance data from standardized monitoring schemes are biased toward well-studied taxa and locations, opportunistic data are available for many taxonomic groups, from a large number of locations and across long timescales. Hence, these data provide opportunities to measure species' changes in occurrence, particularly through the use of occupancy models, which account for imperfect detection. These opportunistic datasets can be substantially large, numbering hundreds of thousands of sites, and hence present a challenge from a computational perspective, especially within a Bayesian framework. In this paper, we develop a unifying framework for Bayesian inference in occupancy models that account for both spatial and temporal autocorrelation. We make use of the Pólya-Gamma scheme, which allows for fast inference, and incorporate spatio-temporal random effects using Gaussian processes (GPs), for which we consider two efficient approximations: subset of regressors and nearest neighbor GPs. We apply our model to data on two UK butterfly species, one common and widespread and one rare, using records from the Butterflies for the New Millennium database, producing occupancy indices spanning 45 years. Our framework can be applied to a wide range of taxa, providing measures of variation in species' occurrence, which are used to assess biodiversity change.
Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability.In this background, the current research article designs a Deep Learning enabled Intelligent Modulation Recognition of Communication Signal (DLIMR-CS) technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition, the fractal feature extraction process is applied with the help of the Sevcik Fractal Dimension (SFD) approach.Then, the extracted features are fed into the Deep Variational Autoencoder (DVAE) model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model, the Tunicate Swarm Algorithm (TSA) is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.
An optimization for adaptive multi-filter estimation in medical images and EEG based signal denoising

Classical denoising techniques are efficient to extinguish the Gaussian noise but are unable to handle the impulse and additive noise. The blurring of edges in denoised data is critical in both images and signals. It has been found that edge blurring occurs due to limited spatial information. Adaptive multi-filtering methods can accommodate large spatial information. But the estimation of filter window sizes and their subsequent information fusion is a challenging task. To overcome this issue, an adaptive multi-filter estimation technique that fuses large information has been explored. The fusion function is formulated for multiple sizes of the filter as an objective function. The objective function takes the spatial information from the neighborhood of noisy data using multiple filters and fuses them while minimizing the objective function. The objective function is optimized for various sizes of filter windows with a stochastic flower pollination Algorithm (FPA). Impulse noise is efficiently removed using optimized filters. An empirical study is conducted with recent state-of-the-art using evaluation indices such as MSE, PSNR, MAXERR, L2RAT, SSIM, computational time, and complexity. For the proposed setup, the MSE, PSNR, MAXERR, L2RAT, and SSIM are evaluated as 1.33, 26.90, 201, 0.9964, 0.8922, and 0.9312 respectively that are outperforming the results obtained in the previous state-of-the-art denoising algorithms such as wavelet, compression, box averaging, tabu search and simulated annealing. Further, it is interesting to observe the improvement in complexity. The experimental study demonstrated the performance of the framework with previous qualitative indices in both Alzheimer’s images and EEG-based neural signal data.
Assessing the writing quality of English research articles based on absolute and relative measures of syntactic complexity

Prediction of writing quality based on various linguistic features has greatly supported writing assessment in academic contexts. Research article (RA) is an important academic genre widely taught and assessed in EAP (English for Academic Purposes) writing courses, but there is little research exploring the assessment of RA writing quality on the basis of linguistic features, which may have the potential to assist teachers’ evaluation of students’ RA drafts. The present study aims to explore whether features of syntactic complexity can effectively predict RA writing quality. To represent syntactic complexity as a multidimensional construct, we employed traditional absolute measures and the newly proposed relative measures related to the use of verb argument constructions (VACs). Robust predictors of writing quality were identified at both the whole RA level and the part-genre level. It is found that VAC-based measures are more useful for indexing the writing quality of RAs at whole text level, while absolute measures have a stronger predictive power at part-genre level. Our research also shows that the relationships between writing quality and syntactic complexity are subject to part-genre variation. Implications for language teaching and assessment in the EAP context are discussed.
HistoSSL: Self-Supervised Representation Learning for Classifying Histopathology Images

The success of image classification depends on copious annotated images for training. Annotating histopathology images is costly and laborious. Although several successful self-supervised representation learning approaches have been introduced, they are still insufficient to consider the unique characteristics of histopathology images. In this work, we propose the novel histopathology-oriented self-supervised representation learning framework (HistoSSL) to efficiently extract representations from unlabeled histopathology images at three levels: global, cell, and stain. The model transfers remarkably to downstream tasks: colorectal tissue phenotyping on the NCTCRC dataset and breast cancer metastasis recognition on the CAMELYON16 dataset. HistoSSL achieved higher accuracies than state-of-the-art self-supervised learning approaches, which proved the robustness of the learned representations.
OPS Effect of the Use of Audio Visual Media on the Learning Outcomes of Class IV Students at Negeri 091299 Sipoldas

This study aims to determine the effect of using audio-visual media on the learning outcomes of fourth grade students at SD Negeri 091299 Sipoldas. In this study, the researcher used quantitative research with a pre-experimental design in the form of one group pre-test post-test design. The population in this study were all fourth grade students of SD Negeri 091299 Sipoldas. The sample in this study amounted to 27 students. The sampling technique in this study is a saturated sampling technique. Data collection techniques in this study are documentation and tests (pre-test post-test). The data analysis technique used is descriptive statistical test, normality and homogeneity. This data was analyzed with the help of Windows SPSS 21. The hypothesis testing of this study used the calculation of the independent sample T-Test test. The results showed that the tcount value was 8.930 ttable 2.052, so Ha was accepted and Ho was rejected. It can be concluded that the hypothesis of this research is accepted, namely that there is an effect of using audio-visual media on student learning outcomes in sub-theme 1 types of work in class IV SD Negeri 091299 Sipoldas.