Reviewerly/exHarmony huggingface dataset:
A Generalized Nesterov-Accelerated Hessian-Vector-Based Latent Factor Analysis Model for QoS Prediction

User-side Quality-of-Service (QoS) data are vital for efficient cloud service selection, while available QoS data in real applications are commonly described by a high-dimensional and Sparse (HiDS) matrix due to a) increasing users and services, and b) the impossibility of observing the full invoking mapping among users and services. A latent factor analysis (LFA) model has proven to be efficient in performing representation learning on such an HiDS QoS matrix. However, existing LFA models commonly adopt a first-order optimizer that cannot makes it approach the second-order stationary point of the learning objective, thereby resulting in accuracy loss. Aiming at addressing this issue, this study proposes to incorporate a Generalized Nesterov's Acceleration (GNA) method in to a Hessian-vector algorithm for LFA, thereby establishing a GNA-incorporated Hessian-vector-based LFA (GNHL) model with two-fold ideas: a) adopting the principle of a Hessian-vector method to acquire a proper Newton step efficiently, and b) incorporating a GNA method into its linear search for accelerating its convergence rate. Experimental results on six real QoS datasets demonstrate that a GNHL model outperforms state-of-the-art LFA models in generating highly accurate predictions for missing QoS data with low computational burden.
Mitigating Data Poisoning in Text Classification with Differential Privacy

NLP models are vulnerable to data poisoning attacks.One type of attack can plant a backdoor in a model by injecting poisoned examples in training, causing the victim model to misclassify test instances which include a specific pattern.Although defences exist to counter these attacks, they are specific to an attack type or pattern.In this paper, we propose a generic defence mechanism by making the training process robust to poisoning attacks through gradient shaping methods, based on differentially private training.We show that our method is highly effective in mitigating, or even eliminating, poisoning attacks on text classification, with only a small cost in predictive accuracy.
A kernel-free fuzzy support vector machine with Universum

&lt;p style='text-indent:20px;'&gt;Support vector machines with Universum are attractive for dealing with classification problems by incorporating prior information. In this paper, a quadratic function based kernel-free support vector machine with Universum is proposed for binary classification. To deal with noise and outliers, two fuzzy membership functions considering both information entropy and distance information are constructed for labeled and Universum data, respectively. The fuzzy membership function for Universum is also adopted for further selecting Universum data to improve the robustness. The proposed model corresponds to an efficiently solved convex quadratic programming. In the meanwhile, by avoiding the issue of choosing kernel functions, the proposed model saves more computational time when compared with other Universum-based support vector machines. Finally, some numerical tests are implemented on several data sets to validate the classification effectiveness of the proposed method. The numerical results illustrate the competitive performance when compared with some state-of-the-art support vector machines. Applications on two credit rating data sets are also conducted to distinguish the classification performance of the proposed method.&lt;/p&gt;
Self-calibration method of gyroscope and camera in video stabilization

With the rapid development of image acquisition technology, video stabilization becomes increasingly important. The gyroscope-based video stabilization methods obtain camera motion path through gyroscope, which can avoid the poor image quality affecting the video stabilization effect. However, the camera motion path obtained by the gyroscope is in the gyroscope coordinate system, and there is a rotation relationship between the gyroscope coordinate system and the camera coordinate system. Therefore, a self-calibration method of gyroscope and camera in video stabilization is proposed. By collecting the video sequence with rich features, the camera motion is estimated according to the image information. At the same time, the camera motion in the gyroscope coordinate system is obtained by using the gyroscope data, and the rotation relationship between two coordinate systems is obtained by optimizing the minimum distance between the paths in two coordinate systems. The experimental results show that the proposed method can improve the video stabilization accuracy, and is more flexible.
Implementation of Temperature Data Denoising Operator Using Steepest Descent Method

In this paper, a distributed implementation of temperature data denoising operator using the steepest descent method is presented. First, the smoothness-based denoising method using normalized Laplacian matrix is described and the conventional Neumann series implementation is reviewed briefly. Then, the steepest descent method is applied to develop a distributed implementation of denoising operator and its convergence condition is studied. It can be also shown that the Neumann series method is a special case of steepest descent method. Next, the momentum term is added to the update term of the steepest descent method for speeding up the convergence of implementation method. Finally, the real temperature data collected from the sensor network at Taiwan is used to demonstrate the effectiveness of the proposed denoising method and some discussions are made.
Impact on JPEG Image Steganalysis using Transfer Learning

Image steganography is a convenient candidate for information hiding with the upsurge of using images on social media platforms and the internet. It is equally important that detecting these images through steganalysis so that the illegal activities could be monitored. In this work, approaches that aren’t widely explored in the image steganalysis domain were analyzed. The concept of Transfer learning using recent ImageNet models such as EfficientNet, MobileNetV3, DenseNet and MixNet and attempts on developing ensemble models using the above-pretrained architectures were experimented. Results and findings of these experiments are documented through this work. These findings on the unexplored areas could potentially help in building a universal image steganalysis tool for the domain.
Research on Technical Innovation of Emergency Supply Chain Management Based on Blockchain Technology

This paper analyzes the emergency in China's fujian province in response to the outbreak of the main problems in the supply chain operation and difficulties, through the investigation of both for many times, discusses how to on the basis of the existing cloud computing platform, using block chain technology, raise the emergency of the fujian province and even the whole of China supply chain management efficiency of the technical solution. This paper focuses on research on technology innovation of emergency supply chain management based on blockchain Technology. After thorough investigation and analysis, the research group put forward several feasible schemes.
Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction

The software cost prediction is a crucial element for a project’s success because it helps the project managers to efficiently estimate the needed effort for any project. There exist in literature many machine learning methods like decision trees, artificial neural networks (ANN), and support vector regressors (SVR), etc. However, many studies confirm that accurate estimations greatly depend on hyperparameters optimization, and on the proper input feature selection that impacts highly the accuracy of software cost prediction models (SCPM). In this paper, we propose an enhanced model using SVR and the Optainet algorithm. The Optainet is used at the same time for 1-selecting the best set of features and 2-for tuning the parameters of the SVR model. The experimental evaluation was conducted using a 30% holdout over seven datasets. The performance of the suggested model is then compared to the tuned SVR model using Optainet without feature selection. The results were also compared to the Boruta and random forest features selection methods. The experiments show that for overall datasets, the Optainet-based method improves significantly the accuracy of the SVR model and it outperforms the random forest and Boruta feature selection methods.
Participatory Mapping of Tsunami Evacuation Routes (Case Study of Karangbenda Village Cilacap Regency)

Abstract Indonesia is one of the countries with a high tsunami risk, in this case the community plays an important role in disaster management, one of which can be done through the preparation of a tsunami evacuation route map which can be done through participatory mapping. This research was conducted in Karangbenda Village, Adipala District, Cilacap Regency, the purpose of this study was to determine how the skills of the community in determining tsunami evacuation routes were compared with the criteria for evacuation locations and effective evacuation routes and to test the effectiveness of tsunami evacuation routes prepared by the community. Data collection in this study was carried out through focus group discussions (FGD) and field surveys. The results show that the community can arrange a tsunami evacuation route properly, the evacuation route consists of the evacuation location and evacuation route, the designated evacuation location is in accordance with the evacuation location criteria and the evacuation route is in accordance with the route obtained through network analysis. The effectiveness level of the tsunami evacuation route was 98.82% and there were some people who were unable to reach the evacuation location within the specified time, totaling 36 people who were in the area of Dusun Congot to be precise in RT 02 / RW 02 because of the long distance from the evacuation location.
Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care

We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time of admission and can be provided by the patients themselves, namely gender, ethnicity, type of insurance, language, and marital status, and a full model that additionally includes clinical information like diagnoses, medication, and procedures. The method was tested and validated on MIMIC-IV, a publicly available clinical dataset. The minimal prediction model achieved an area under the receiver operating characteristic curve value of 0.69, while the full prediction model achieved a value of 0.98. The models serve different purposes. The minimal model can be used as a first risk assessment based on patient-reported information. The full model expands on this and provides an updated risk assessment each time a new variable occurs in the clinical case. In addition, the rules in the models allow us to analyze the dataset based on data-backed rules. We provide several examples of interesting rules, including rules that hint at errors in the underlying data, rules that correspond to existing epidemiological research, and rules that were previously unknown and can serve as starting points for future studies.
Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.
Learning Parametric Time-Vertex Graph Processes from Incomplete Realizations

We consider the problem of estimating time-varying graph signals with missing observations, which is of interest in many applications involving data acquisition on irregular topologies. We model time-varying graph signals as jointly stationary time-vertex ARMA graph processes. We formulate the learning of ARMA process parameters as an optimization problem where the joint power spectral density of the model is fit to a rough empirical estimate of the process covariance matrix. We propose a convex relaxation of this problem, which results in an algorithm more flexible than existing methods regarding the pattern of available and missing observations of the process. Experimental results on meteorological signals show that the proposed method compares favorably to reference state-of-the-art algorithms.
Information-Theoretically Secure MPC Against Mixed Dynamic Adversaries

In this work we consider information-theoretically secure MPC against an mixed adversary who can corrupt \(t_p\) parties passively, \(t_a\) parties actively, and can make \(t_f\) parties fail-stop. With perfect security, it is known that every function can be computed securely if and only if \(3t_a + 2t_p + t_f < n\), and for statistical security the bound is \(2t_a + 2t_p + t_f < n\).
Fault Tolerance in Cloud Computing: A Survey

Using cloud computing services has many advantages, such as improving efficiency, reducing costs, compatibility with multiple formats, unlimited storage capacity, and easy access to services anytime and anywhere. It should be mentioned that the fault tolerance is the main restriction of all varieties of cloud computing services. Cloud service providers need to effectively handle performance-related reliability, availability, and throughput issues to maximize the potential of using cloud computing services.This paper provides a comprehensive overview of the issues related to fault tolerance in cloud computing. It focuses on important advanced technologies, and methods. Its purpose is to provide insight into traditional fault-tolerant approaches and the challenges that still need to be overcome. This investigation enumerates several promising methods that can be used for efficient solutions.
Analysis of Different Consensus Algorithms with Development of Blockchain based Cryptocurrency and Initial Coin Offering

Blockchain, a system of connected blocks, originally used as a distributed, immutable, decentralized and peer to peer database but has advanced over the years and many use cases are now seen in the market. To put more simply, blockchain offers a peer-to-peer transactions kept out in open so that everyone can see each of the transactions. These transactions are secured by the cryptographic hashes which are unique for every block formed. Each transaction is timestamped and linked to the prior event, making it exceedingly resilient to malicious activities by making alter the extremely difficult but not impossible. In addition to financial services, blockchain has also developed many use cases in fields like voting, real estate, supply chain management and most essentially in healthcare. The current work compares two most common algorithms i.e. proof of work (PoW) and Proof of Stake for Infant Care Applications. In addition to this the work also includes development of cryptocurrency with its applications and numerous functions. This work also highlights on how startups are benefited from this in terms of easy crowd funding and how this implementation will bring a boom in the Indian Startups. Further this work also the concept of Initial Coin Offering to ERC-20 token standard contract using Solidity Programming Language.
Cluster-centroid-based mutation strategies for Differential Evolution

Mutation is the main operator of differential evolution (DE), as it is responsible for combining the information of distinct solutions to generate a donor vector. Aiming at improving the search effectivity of DE, previous research incorporated the calculation of centroids into the DE mutations. In some of the existing methods, the centroids are simply calculated as the center of some selected solutions (or, the entire population); in other cases, one-step clustering is used to perform a local search, or the centroids themselves are used as actual solutions in the population. As opposed to these methods, in this paper we extend some traditional DE mutation strategies to incorporate centroids calculated by deterministic hierarchical clustering. Experimental results on two sets of well-known benchmark problems show that the proposed cluster-centroid-based mutation strategies outperform, in general, the traditional rand/1 strategy, as well as several metaheuristics from the literature. Therefore, the use of clustering is an effective way to improve the search performance that could be exploited also in other population-based metaheuristics.
Learning Intuitive Physics and One-Shot Imitation Using State-Action-Prediction Self-Organizing Maps

Human learning and intelligence work differently from the supervised pattern recognition approach adopted in most deep learning architectures. Humans seem to learn rich representations by exploration and imitation, build causal models of the world, and use both to flexibly solve new tasks. We suggest a simple but effective unsupervised model which develops such characteristics. The agent learns to represent the dynamical physical properties of its environment by intrinsically motivated exploration and performs inference on this representation to reach goals. For this, a set of self-organizing maps which represent state-action pairs is combined with a causal model for sequence prediction. The proposed system is evaluated in the cartpole environment. After an initial phase of playful exploration, the agent can execute kinematic simulations of the environment’s future and use those for action planning. We demonstrate its performance on a set of several related, but different one-shot imitation tasks, which the agent flexibly solves in an active inference style.
Towards Strategic Reengineering the Global Computer Environment for Control of Sustainable Development of Social Systems

This article presents a study of the general trends of expansion of the global computer environment (GCE) as the primary tool of global digitalization and its impact on social systems. The authors identify and analyze the causes of intra-system imbalances in GCE development and their destructive impact on social systems development sustainability. They show ways to eliminate the combinatorial barriers of the complexity of network resources integration by eliminating the fundamental causes of GCE’s heterogeneity and forming in the GCE a universal algorithmic space of distributed computing based on computers with the non-microprocessor architecture.
The discovery of structural elements and zones of hydrothermal alterations by using ASTER satellite data in the margins of Gadabay and Murovdag ore districts (Lesser Caucasus, Azerbaijan)

&#x0D; &#x0D; &#x0D; The article considers the discovery of structural elements, zones of hydrothermal alterations and mineralization in the margins of Gadabay and Murovdag regions and the study of prospective areas for mineralization characterized by them by using ASTER satellite data. The main purpose of the research is the definition of the lithological composition of rocks, ring and linear structures and zones of hydrothermal alterations with the help of remote-sensing methods and by better technological development of special radiometric data. In order to detect the lithological composition of associated rocks of alteration zone of endogenous ores, 14 channel data of ASTER has been used. By using stereoscopic images obtained from ASTER data, it was determined that hydrothermal alterations are mainly represented by alunitization, kaolinization, sericitization, silication, pyropilitization and silification in the studied ore regions. These changes were shown to be characteristic for copper-porphyry, gold- copper pyrite, copper-polymetal and gold-copper-porphyry deposits. Strip ratios, principal component analysis (PCA), minimum of noise fraction (MNF) and selection method for least squares (LS) have been used to map hydrothermal alteration zones. The ring structures detected in the area, different fault fractures and lineaments are assessed as favourable geological structural factors for alteration and mineralization. The results obtained by ASTER image analysis confirm the presence of derivative minerals which are considered to be the product of hydrothermal alterations which are densely concentrated (kalium spathization, kaolinization, serictization, pyrophyllitization, alunitization) has been confirmed. The points each with private coordinates have been determined (defined) within separate areas with the help of remote data and relevant anomalous maps prepared. These points are considered favourable geological condition in terms of the type and intensity of hydrothermal alterations in the area. Such areas are considered a sign of indirect search significant for the detection of copper-polymetallic, copper-porphyry, copper-pyrite and gold-pyrite type of mineralization with epithermal origin in the region.&#x0D; &#x0D; &#x0D;
Geometrically Adaptive Dictionary Attack on Face Recognition

CNN-based face recognition models have brought remarkable performance improvement, but they are vulnerable to adversarial perturbations. Recent studies have shown that adversaries can fool the models even if they can only access the models’ hard-label output. However, since many queries are needed to find imperceptible adversarial noise, reducing the number of queries is crucial for these attacks. In this paper, we point out two limitations of existing decision-based black-box attacks. We observe that they waste queries for background noise optimization, and they do not take advantage of adversarial perturbations generated for other images. We exploit 3D face alignment to overcome these limitations and propose a general strategy for query-efficient black-box attacks on face recognition named Geometrically Adaptive Dictionary Attack (GADA). Our core idea is to create an adversarial perturbation in the UV texture map and project it onto the face in the image. It greatly improves query efficiency by limiting the perturbation search space to the facial area and effectively recycling previous perturbations. We apply the GADA strategy to two existing attack methods and show overwhelming performance improvement in the experiments on the LFW and CPLFW datasets. Furthermore, we also present a novel attack strategy that can circumvent query similarity-based stateful detection that identifies the process of query-based black-box attacks.