Reviewerly/exHarmony huggingface dataset:
Discrete Coordinate Descent (DCD)

As many real-world optimization problems are large-scale and expensive, the large search space and expensive gradient computation may lead to failure of metaheuristic and classical algorithms. The problem even gets more crucial as we move from continuous domain to the discrete or mixed type one, because most of the discrete optimization problems are NP-hard and cannot be treated as convex or linear optimization, therefore there exists no cost-effective algorithm to cope with large-scale discrete global optimization (LSDGO) problems. However, due to the low memory demand and computational cost of coordinate descent (CD) search methods they are appropriate algorithms for optimizing large-scale expensive problems. In this paper, we propose a discrete version of CD algorithm called Discrete Coordinate Descent (DCD) as an effective method for solving LSDGO problems. Our proposed algorithm makes the most of two essential phases referred to as finding the region of interest and folding the search space, which shrinks it into two halves per variable and results in ${\left( {\frac{1}{2}} \right)^D}$ shrinking of the whole search space at each iteration (D indicates the problem's dimension). Since the proposed algorithm shrinks the search space rapidly, it requires a low computational budget to find the optimal value for each coordinate. In order to investigate the efficiency of our algorithm precisely, we tested it on 20 well-known large-scale problems with dimensions of 30, 50, 100, and 1000. The results demonstrate the potency of DCD not only in low-scale discrete problems, but in large-scale discrete optimization problems as well.
Privacy preserving classification over differentially private data

Abstract Privacy preserving data classification is an important research area in data mining field. The goal of a privacy preserving classification algorithm is to protect the sensitive information as much as possible, while providing satisfactory classification accuracy. Differential privacy is a strong privacy guarantee that enables privacy of sensitive data stored in a database by determining the ratio of sensitive information leakage with respect to an ɛ parameter. In this study, our aim is to investigate the classification performance of the state‐of‐the‐art classification algorithms such as C4.5, Naïve Bayes, One Rule, Bayesian Networks, PART, Ripper, K*, IBk, and Random tree for performing privacy preserving classification. To preserve privacy of the data to be classified, we applied input perturbation technique coming from differential privacy, and observed the relationship between the ɛ parameter values and accuracy of the classifiers. To our best knowledge, this article is the first study that analyzes the performances of the well‐known classification algorithms over differentially private data, and discovers which datasets are more suitable for privacy preserving classification when input perturbation is applied to provide data privacy. The classification algorithms are compared by using the differentially private versions of the well‐known datasets from the UCI repository. According to the experimental results, we observed that, as ɛ parameter value increases, better classification accuracies are achieved with lower privacy levels. When the classifiers are compared, Naïve Bayes classifier is the most successful method. The ɛ parameter should be greater than or equal to 2 (i.e., ɛ ≥2) to achieve cloud server is malicious and untrusted, sensitive data will satisfactory classification accuracies. This article is categorized under: Commercial, Legal, and Ethical Issues &gt; Security and Privacy Technologies &gt; Classification
Learning Multipart Attention Neural Network for Zero-Shot Classification

Zero-shot learning (ZSL) models typically learn a cross-modal mapping between the visual feature space and the semantic embedding space. Despite promising performance achieved by existing methods, they usually take visual features from the whole image as the main proposed inputs, while pay little attention to image regions which are relevant to human’s visual response to the whole image. In this article, we propose a neural network-based ZSL model which incorporates an attention mechanism to discover the discriminative parts for each image. The proposed model allows us to automatically generate attention maps for visual parts, which provides a flexible way of encoding the salient visual aspects to distinguish the categories. Moreover, we introduce a simple yet effective objective function to exploit the pairwise label information between images and classes, resulting in substantial performance improvement. When multiple semantic spaces are available, a multiple-attention scheme is provided to fuse different semantic spaces, which helps to achieve further improvement in performance. On the widely used CUB-2010-2011 data set for fine-grained image classification, we demonstrate the advantages of using attention mechanism and semantic parts in our model for ZSL. Comprehensive experimental results show that our proposed approach achieves superior performance than the state-of-the-art methods.
A Method for Cleaning Power Grid Operation Data Based on Spatiotemporal Correlation Constraints

Bad data in a power system fail to accurately reflect its state of operation.Cleaning bad data is thus necessary for the situational analysis of a data-based power system.Methods for the identification and modification of bad data based on state estimation may fail to converge in iterative calculations, and yield poor results when the errors are significant.State estimation also involves non-linear computation, which incurs a significant computational overhead and makes it difficult to efficiently handle large volumes of data.To solve these problems, this paper proposes a method for the detection and identification of bad data based on constraints on spatial-temporal correlation.In the spatial domain, we fix the equivalent power balance condition through the topology of the network.In sequential distribution, we establish data constraints based on the similarity of data among temporal sections.We then check the data in combination with the spatial-temporal correlation constraints.The proposed method was applied to an IEEE 14-bus system and a provincial grid, and the results show that it can reliably detect and modify bad data to significantly improve their quality.The proposed approach avoids non-linear calculations, is computationally efficient, and can quickly detect and modify bad data.
Obtaining Value From The Constraints in Finding User Habitual Words

The traffics of social media posts are very high per second. The posts are a resource, if managed properly, will get value. Social media posts represent the user's own personality. So far, the researches on social media are related to sentiment. If the habits of users can be seen from their' posts, they can be used for industry, health and other fields. To get them all, experiments must be done, which can result success or failure. Ontology teaches us about the value of a study i.e. the success has value and so has failure, thus we don't notice the research from the results only. Unwanted results are obtained from the experiments performed, where the habit of words that users often write is not yet visible. But the results of these experiments can be used as a reference for future experiments.
Design and Analysis of a Novel Microspeaker with Enhanced Low-Frequency SPL and Size Reduction

In the era of multimedia devices, smartphones have become the primary device for consuming multimedia content. As technological developments have facilitated a more immersive multimedia experience, enlarged displays and the use of several sensors have limited the allowable size of microspeakers. Although sound plays an important role when consuming multimedia content, the limited space for microspeakers in modern devices leads to poor acoustic performance, especially at low frequencies. To address this issue, this paper proposes a novel microspeaker structure that enhances the low-frequency sound pressure level (SPL), while also featuring reduced exterior dimensions. The structure was designed and analyzed using 3D finite element analysis. Through coupling analysis, the simulation results were verified on the basis of the experimental results. The novel microspeaker has one outer magnet surrounding the entire coil, unlike in prototype microspeakers, which have two outer magnets. The gap between the top plates and coil is reduced, and a new type of coil is introduced for the purpose of increasing electromagnetic force. The samples were manufactured, and their SPLs were tested in an anechoic chamber. The experimental results prove that the proposed microspeaker offers an improved SPL at low frequencies compared with prototype microspeakers.
A New Decryption Failure Attack Against HQC

HQC is an IND-CCA2 KEM running for standardization in NIST's post-quantum cryptography project and has advanced to the second round. It is a code-based scheme in the class of public key encryptions, with given sets of parameters spanning NIST security strength 1, 3 and 5, corresponding to 128, 192 and 256 bits of classic security. In this paper we present an attack recovering the secret key of an HQC instance named hqc-256-1. The attack requires a single precomputation performed once and then never again. The online attack on an HQC instance then submits about $$2^{64}$$ special ciphertexts for decryption (obtained from the precomputation) and a phase of analysis studies the subset of ciphertexts that are not correctly decrypted. In this phase, the secret key of the HQC instance is determined. The overall complexity is estimated to be $$2^{246}$$ if the attacker balances the costs of precomputation and post-processing, thereby claiming a successful attack on hqc-256-1 in the NIST setting. If we allow the precomputation cost to be $$2^{254}$$ , which is below exhaustive key search on a 256 bit secret key, the computational complexity of the later parts can be no more than $$2^{64}$$ . This is a setting relevant to practical security since the large precomputation needs to be done only once. Also, we note that the complexity of the precomputation can be lower if the online attack is allowed to submit more than $$2^{64}$$ ciphertexts for decryption.
Manta Ray Foraging Optimization for Hyper-Parameter Selection in Convolutional Neural Network

Abstract Convolutional neural networks (CNNs) have been attracting attention as one of the most common deep learning techniques used for different applications like images classifications, objects recognition, face recognition, etc. The performance and efficiency of the CNN model depend directly on their hyper-parameter, which must be selected by an expert or using one of the models that proposed and improved. This makes it very important to determine the optimal hyper-parameters. In this work, the Manta Ray Foraging Optimization (MRFO) algorithm is used to select CNN’s Hyper-Parameter. We demonstrate that MRFO efficiently explores the solution space. This allowing CNN with simple architecture to achieve a good classification accuracy over Cifar_10 dataset. In the presented experiment Cifar_10 dataset used as the benchmark data sets. By optimizing CNN hyper-parameters with MRFO algorithm and comparing the obtained results with other CNN, it was approved that the accuracy was improved.
Low-Latency ASIC Algorithms of Modular Squaring of Large Integers for VDF Evaluation

This article is an attempt in quest of the fastest hardware algorithms for the computation of the evaluation component of verifiable delay functions (VDFs), <inline-formula><tex-math notation="LaTeX">$a^{2^T} \bmod N$</tex-math></inline-formula> , proposed for use in various distributed protocols, in which no party is assumed to compute it significantly faster than other participants. To this end, we propose a class of modular squaring algorithms suitable for low-latency ASIC implementations. The proposed algorithms aim to achieve highest levels of parallelization that have not been explored in previous works in the literature, which usually pursue more balanced optimization of speed and area. For this, we utilize redundant representations of integers and introduce three modular squaring algorithms that work with integers in redundant forms: i) Montgomery algorithm, ii) memory-based algorithm and iii) direct reduction algorithm for fixed moduli. All algorithms enable <inline-formula><tex-math notation="LaTeX">$O(\log k)$</tex-math></inline-formula> depth circuit implementations, where <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is the bit-size of the modulus <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> in the VDF function. We analyze and compare gate level-circuits of the proposed algorithms and provide estimates for their critical path delay and gate count.
Recommender System Based on User's Tweets Sentiment Analysis

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. Nowadays, people from all around the world use social media sites to share information. Twitter, for example, is a social network in which users send, read posts known as 'tweets' and interact with different communities. Users share their daily lives, post their opinions on everything such as brands and places. Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this paper, we present a new paradigm of e-commerce recommender systems, which can utilize information in social networks. In this study, we have combined sentiment analysis of twitter data with the collaborative filtering in order to increase system accuracy. The proposed system uses lexical approach to analyze sentiment. In order to design the recommender system, we have replaced the missing values of the ratings matrix with the averages of the ratings assigned to the items, to solve the sparsity and cold-start problems inherent in collaborative filtering. The results show that our proposed method improves CF performance. In this experiment we demonstrate how relevant social media can be for recommender systems.
Pixel co-ordinate-based secret image sharing scheme with constant size shadow images

In this paper, we propose a secret image sharing scheme with constant size shadow images. In the proposed scheme, the size of the shadow images is 23 × 23 irrespective of the secret image size. The scheme also offers a lossless reconstruction of the secret image without requiring pre-sharing of a key between the share generator and combiner. The scheme is based on a multi-secret sharing scheme and uses pixel value co-ordinate sharing. We prove the security of the proposed secret image sharing scheme by reducing it to the underlying multi-secret sharing scheme. Finally, we evaluate the correctness of the scheme by conducting experiments on standard images.
NoSQL Breakdown: A Large-scale Analysis of Misconfigured NoSQL Services

In the last years, NoSQL databases have grown in popularity due to their easy-to-deploy, reliable, and scalable storage mechanism. While most NoSQL services offer access control mechanisms, their default configurations grant access without any form of authentication, resulting in misconfigurations that may expose data to the Internet, as demonstrated by the recent high-profile data leaks.
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation

Partial domain adaptation which assumes that the unknown target label space is a subset of the source label space has attracted much attention in computer vision. Despite recent progress, existing methods often suffer from three key problems: negative transfer, lack of discriminability, and domain invariance in the latent space. To alleviate the above issues, we develop a novel ‘Select, Label, and Mix’ (SLM) framework that aims to learn discriminative invariant feature representations for partial domain adaptation. First, we present an efficient "select" module that automatically filters out the outlier source samples to avoid negative transfer while aligning distributions across both domains. Second, the "label" module iteratively trains the classifier using both the labeled source domain data and the generated pseudo-labels for the target domain to enhance the discriminability of the latent space. Finally, the "mix" module utilizes domain mixup regularization jointly with the other two modules to explore more intrinsic structures across domains leading to a domain-invariant latent space for partial domain adaptation. Extensive experiments on several benchmark datasets for partial domain adaptation demonstrate the superiority of our proposed framework over state-of-the-art methods. Project page: https://cvir.github.io/projects/slm.
A Brief Analysis of Collaborative and Content Based Filtering Algorithms used in Recommender Systems

Abstract In the modern age and many prestigious applications use the recommendation method to play an important role. The system of recommendations collected apps, built a global village and provided enough information for development. This paper presents an overview of the approaches and techniques produced in the recommendation framework for collaborative filtering. Collaborative filtering, material and hybrid methods were the method of recommendation. In producing personalised recommendation the technique of collaborative filtering is particularly effective. There have been several algorithms over ten years of study, but no distinctions have been made between the various strategies. Indeed, there is not yet a widely agreed way to test a collaborative filtering algorithm. In this work we compare various literature techniques and review each one’s characteristics to emphasise their key strengths and weaknesses.
Robust multiclass least squares support vector classifier with optimal error distribution

Robust least squares support vector regression (RLSSVR), minimizing the variance and mean of the global modeling errors, has achieved the excellent performance in dealing with outliers. However, generalizing the RLSSVR for solving the binary classification problems is easily misguided by the outliers because the differences in the modeling errors of the different classes are not considered. To address this issue, a robust least squares support vector classifier (RLSSVC) with optimal error distribution is proposed. RLSSVC minimizes the mean and variance of the modeling errors class-wisely, and considers the difference in the modeling errors of the different classes. Specifically, the binary classification problems are considered at first, the variance analysis indicates that the variance of the modeling errors of RLSSVC is smaller than that of RLSSVR. According to the validity in solving the binary classification problems, RLSSVC is naturally generalized for solving the multiclass classification problems by introducing multiple error adjusting factors. The robustness analysis provides a theoretical guarantee for the robustness of RLSSVC, which delivers that RLSSVC assigns the smaller weights for the training instances with the larger errors, while the larger weights for the training instances with the smaller errors. Furthermore, our optimization objective function is strictly convex and thus can obtain their corresponding closed-form solutions, resulting in higher computational performance. Finally, the performance of RLSSVC is further improved by introducing the metric learning and kernel trick. Theoretical and experimental results indicate that the proposed RLSSVC achieves the better classification effect with the lower computational costs.
Semantic Code Clone Detection Via Event Embedding Tree and GAT Network

Semantic code clone detection is an important yet challenging task in software engineering. Traditional methods rely on expert experience and cannot automatically determine which features are better for semantic code clone detection. Moreover, the program dynamics (e.g., the execution characteristics and execution order of statements) are not considered in these methods. As a result, this limits their ability to detect semantic clones. To address this issue, we propose a code clone detection method based on event embedding tree and Graph Attention Network. Our method uses a program control flow graph to capture the execution characteristics of each statement and extract the context relationship of different statements in the control flow. Based on such information, our method can calculate the functional similarity of two pieces of code, thereby identifying semantically similar code fragments. Experimental results show that our method is superior to state-of-the-art open source methods for Type-3 (syntactic) / Type-4 (semantic) clone detection.
A semi-automatic methodology for analysing distributed and private biobanks

Privacy issues limit the analysis and cross-exploration of most distributed and private biobanks, often raised by the multiple dimensionality and sensitivity of the data associated with access restrictions and policies. These characteristics prevent collaboration between entities, constituting a barrier to emergent personalized and public health challenges, namely the discovery of new druggable targets, identification of disease-causing genetic variants, or the study of rare diseases. In this paper, we propose a semi-automatic methodology for the analysis of distributed and private biobanks. The strategies involved in the proposed methodology efficiently enable the creation and execution of unified genomic studies using distributed repositories, without compromising the information present in the datasets. We apply the methodology to a case study in the current Covid-19, ensuring the combination of the diagnostics from multiple entities while maintaining privacy through a completely identical procedure. Moreover, we show that the methodology follows a simple, intuitive, and practical scheme.
Artifact Detection in Endoscopic Video with Deep Convolutional Neural Networks

The following topics are dealt with: learning (artificial intelligence); graph theory; convolutional neural nets; mobile computing; object detection; Internet; neural nets; health care; medical image processing; optimisation.
eXplainable AI (XAI)

• Do Machine Learning algorithms have a Soul?
Development of migrant workers in construction based on machine learning and artificial intelligence technology

Artificial intelligence technology has been widely used in all aspects of our life. Similarly, the application of artificial intelligence in the field of construction engineering is a necessary trend in the development of engineering industry, especially in the traditional construction engineering department. Under the background of the times, from the perspective of knowledge, artificial intelligence technology has appeared a huge development, which may have an impact on the employment of Chinese labor force, may create new jobs, or replace traditional jobs. This effect on employment is essential. From the perspective of machine learning and artificial intelligence, this paper reviews the transformation prospects of engineering industry and the development of agricultural industry in construction industry, and examines the intellectual transformation of individual human capital in Chinese labor force.