Reviewerly/exHarmony huggingface dataset:
High-speed phase-shifting profilometry under fluorescent light

Abstract Phase-shifting profilometry (PSP) has been widely used in three-dimensional (3-D) shape measurement because of its attributes of high-accuracy and high-resolution. However, PSP typically works under fluorescent light, flickering with a double frequency of the alternating current frequency, which may generate a non-ignorable phase error, and result in a PSP's measurement error, especially for PSP using a high sampling speed. We first mathematically describe the fluorescent light problem, model the PSP's phase error, and state the problem in high-speed PSP. Next, a fluorescent light error suppression (FLES) algorithm is proposed to suppress the phase error. Both the phase error model and the proposed algorithm are theoretically analyzed, and then numerically and experimentally verified. The provided numerical and experimental results coincide with the theoretical analysis. The proposed algorithm can reduce the phase error from 0.12 rad to a comparable level with system noise of 0.02 rad, which is significant for the accurate measurement of 3-D shapes for high-speed PSP in a fluorescent light environment.
Harmonic convolutional networks based on discrete cosine transform

Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. We propose to learn these filters as combinations of preset spectral filters defined by the Discrete Cosine Transform (DCT). Our proposed DCT-based harmonic blocks replace conventional convolutional layers to produce partially or fully harmonic versions of new or existing CNN architectures. Using DCT energy compaction properties, we demonstrate how the harmonic networks can be efficiently compressed by truncating high-frequency information in harmonic blocks thanks to the redundancies in the spectral domain. We report extensive experimental validation demonstrating benefits of the introduction of harmonic blocks into state-of-the-art CNN models in image classification, object detection and semantic segmentation applications.
Does AI Qualify for the Job?

In this paper we present a setting for examining the relation be-tween the distribution of research intensity in AI research and the relevance for a range of work tasks (and occupations) in current and simulated scenarios. We perform a mapping between labourand AI using a set of cognitive abilities as an intermediate layer. This setting favours a two-way interpretation to analyse (1) what impact current or simulated AI research activity has or would have on labour-related tasks and occupations, and (2) what areas of AI research activity would be responsible for a desired or undesired effect on specific labour tasks and occupations. Concretely, in our analysis we map 59 generic labour-related tasks from several worker surveys and databases to 14 cognitive abilities from the cognitive science literature, and these to a comprehensive list of 328 AI benchmarks used to evaluate progress in AI techniques. We provide this model and its implementation as a tool for simulations. We also show the effectiveness of our setting with some illustrative examples.
Modified fuzzy sentiment analysis approach based on user ranking suitable for online social networks

The rapidly increasing of sentiment analysis in social networks has lead business owners and decision makers to value opinion leaders who can influence people's impressions concerning certain business or commodity. Nevertheless, decision makers are being misled by inaccurate results due to the ignorance of perspectivism. Considering perspectivism, while computing text polarity, can help machines to reflect the human perceived sentiment within the content. This emphasises the need for integrating social behaviour (user's influence factor) with sentiment analysis (text polarity scores), providing a more pragmatic portrayal of how the writer's audience comprehend the message. In this study, a new model is proposed to intensify sentiment analysis process on Twitter. In the achievement of such, social network analysis is done using UCINET tool followed by artificial neural networks for ranking users. For sentiment classification, a hybrid approach is presented, where lexicon-based technique is combined with a fuzzy classification technique to handle language vagueness as well as for an inclusive analysis of tweets into seven classes; for the purpose of enhancing final results. The proposed model is practiced on data collected from Twitter. Results show a significant enhancement in tweets polarity scores represent more realistic sentiments.
Convex Relaxations for Consensus and Non-Minimal Problems in 3D Vision

In this paper, we formulate a generic non-minimal solver using the existing tools of Polynomials Optimization Problems (POP) from computational algebraic geometry. The proposed method exploits the well known Shor's or Lasserre's relaxations, whose theoretical aspects are also discussed. Notably, we further exploit the POP formulation of non-minimal solver also for the generic consensus maximization problems in 3D vision. Our framework is simple and straightforward to implement, which is also supported by three diverse applications in 3D vision, namely rigid body transformation estimation, Non-Rigid Structure-from-Motion (NRSfM), and camera autocalibration. In all three cases, both non-minimal and consensus maximization are tested, which are also compared against the state-of-the-art methods. Our results are competitive to the compared methods, and are also coherent with our theoretical analysis. The main contribution of this paper is the claim that a good approximate solution for many polynomial problems involved in 3D vision can be obtained using the existing theory of numerical computational algebra. This claim leads us to reason about why many relaxed methods in 3D vision behave so well? And also allows us to offer a generic relaxed solver in a rather straightforward way. We further show that the convex relaxation of these polynomials can easily be used for maximizing consensus in a deterministic manner. We support our claim using several experiments for aforementioned three diverse problems in 3D vision.
Practical Key-Recovery Attacks On Round-Reduced Ketje Jr, Xoodoo-AE And Xoodyak

Abstract A new conditional cube attack was proposed by Li et al. at ToSC 2019 for cryptanalysis of Keccak keyed modes. In this paper, we find a new property of Li et al.’s method. The conditional cube attack is modified and applied to cryptanalysis of 5-round Ketje Jr, 6-round Xoodoo-AE and Xoodyak, where Ketje Jr is among the third round CAESAR competition candidates and Xoodyak is a Round 2 submission of the ongoing NIST lightweight cryptography project. For the updated conditional cube attack, all our results are shown to be of practical time complexity with negligible memory cost, and test codes are provided. Notably, our results on Xoodyak represent the first third-party cryptanalysis for Xoodyak.
Multi-dimensional dynamic trust evaluation scheme for cloud environment

In cloud computing environment, Cloud Customers (CCs) and Cloud Service Providers (CSPs) require to evaluate the trust levels of potential partner prior to appealing in communications. The accurateness of trust evaluation significantly influences the success velocity of the communication. Determining trustworthiness dynamically is a demanding problem in an open and dynamic environment (such as cloud computing) because of huge number of CSPs offering similar types of services. Also it is a challenging job for both CCs and CSPs to mutually recognize and distinguish between the trustworthy and untrustworthy CSPs and CCs. Presently, there are very less number of dynamic trust evaluation scheme that permits CCs to evaluate the trustworthiness of CSPs from multi-dimensional perspectives (i.e., perspectives from Cloud Auditors (CAs), Cloud Brokers (CBs), Service Level Agreement Agents (SLAAs) and Peers). Similarly, there is no scheme that permits CSPs to evaluate trustworthiness of CCs. This paper proposes a Multi-dimensional Dynamic Trust Evaluation Scheme (MDTES) that facilitates CCs to evaluate the trustworthiness of CSPs from various viewpoints. Similar approach can be employed by CSPs to evaluate the trustworthiness of CCs. The proposed MDTES helps CCs to choose trustworthy CSP which provides desired QoS and CSPs to choose desired and legal CCs. The experimental results illustrate that the MDTES is dynamic, efficient and steady in distinguishing trustworthy and untrustworthy CSPs and CCs compared to other trust models.
Decision Support System To Determine The Student Achievement Scholarship Recipients Using Fuzzy Multiple Attribute Decision Making (FMADM) With SAW

Higher education is an education unit that organizes higher education. College students are called students, while college educators are called lecturers. Higher education as a place of learning and self-development Students provide facilities to improve student achievement in the form of student achievement scholarships. This scholarship program is expected to stimulate students' interest in learning to be better. In determining scholarship recipients, the student division selects prospective scholarship recipients based on existing criteria. In the selection process, the student division has difficulty choosing the data of prospective recipients. Because each type of scholarship criteria and has different criteria weights. Calculation of the value of weighting criteria requires precision. Based on the problems that occur, the author designed a decision support system to determine the recipients of achievement scholarships using the SAW method. This system can help student affairs to carry out criteria for recipients of achievement scholarships based on administrative data of prospective scholarship recipients in determining criteria for weighting value calculations. With this system, it can minimize errors to determine prospective scholarship recipients who excel. After testing the use of support systems for deciding scholarship decisions using the SAW method, it can be concluded that a decision support system can assist the student's part in carrying out their duties.
Efficient Ising Model Mapping for Induced Subgraph Isomorphism Problems Using Ising Machines

Ising machines have attracted attention as they are expected to solve combinatorial optimization problems at high speed with Ising models corresponding to those problems. An induced subgraph isomorphism problem is one of the decision problems, which determines whether a specific graph structure is included in a whole graph or not. The problem can be represented by equality constraints. By using the penalty functions correspond to the equality constraints, we can utilize an Ising machine to the induced subgraph isomorphism problem. The induced subgraph isomorphism problem can be seen in many practical problems, for example, finding out a particular malicious circuit in a device or particular network structure of chemical bonds in a compound. However, due to the limitation of the number of spin variables in the current Ising machines, reducing the number of spin variables is a major concern. Here, we propose an efficient Ising model mapping method to solve the induced subgraph isomorphism problem by Ising machines. Our proposed method theoretically solves the induced subgraph isomorphism problem. Furthermore, the number of spin variables in the Ising model generated by our proposed method is theoretically smaller than that of the conventional method. Experimental results demonstrate that our proposed method can successfully solve the induced subgraph isomorphism problem using the Ising-model based simulated annealing.
Bayesian attack graphs for platform virtualized infrastructures in clouds

Virtualization security is an important aspect to be carefully addressed while provisioning cloud services. In this paper, we propose a novel model using Bayesian Attack Graphs (BAG) to perform security risk assessment for platform virtualized infrastructures that are used for building cloud services. BAGs are powerful mechanisms that can be used to model the uncertainties inherent in security attacks. We build upon the reference conditional probability tables for the BAG nodes using the reported attacks on virtualized systems from the Common Vulnerabilities and Exposures (CVE) database. We employ Bayesian probabilistic inference techniques on the model presented and showcase the results obtained that can be used by system architects for the risk assessment of such infrastructures. In addition to the probabilistic model, we also present a deterministic approach with security metrics for attack graphs and derive the values for the modeled BAG, which can be used for assessing and comparing with other architectures. The approach described here to draw inferences from the BAG can be employed by system architects to find explanations to critical queries in security design and also to carefully select the countermeasures to be installed. The model can also be used to learn from future a-posteriori evidence data from actual security breaches to provide an efficient risk assessment.
Deep Reinforcement Learning for Real-world Anomaly Detection in Surveillance Videos

Surveillance videos are considered as a very important mechanism in a smart city project. In recent years, a method called deep learning was combined with reinforcement learning techniques to learn useful representations for the problems with high dimensional raw data input. In this paper, we develop a Deep Q Learning Network (DQN) to localize anomalies in videos by enabling the agent to learn how to detect and recognize the abnormalities in videos. Our idea is inspired by multiple instance learning (MIL) techniques based on common share features with reinforcement learning. We consider normal and abnormal videos as bags and the selection of videos clips as actions. In our DQN architecture we will design a fully connected layer, which compute probability for each video segment in both positive (anomalous) and negative(normal) bags indicating how likely a clip is containing an anomaly. Our method is applied to a new large-scale dataset of 128 hours of videos called UCF-Anomaly-Detection-Dataset, it is about of 1900 long and untrimmed real-world surveillance videos, with 13 cases of realistic anomalies.
Predicting healthcare professionals’ intention to use poison information system in a Malaysian public hospital

The main objective of this paper is to determine the predicting factors that influence the intention to use Poison Information System (PIS) among healthcare professionals.A quantitative approach was applied, using a five-point Likert scale questionnaire, adapted from previous studies. Data were collected from 167 healthcare professionals working for Malaysian Public Hospitals in Penang. Smart Partial Least Square (PLS) version 3.2.7 were used to analyse the proposed relationships.The results indicated that attitude and computer anxiety had a significant positive relation to the intention to use PIS among healthcare professionals wherein computer knowledge was found to have had a significant relationship with attitude and computer habit. Apart from that, technical support and training had a positive relationship with perceived ease of use. Surprisingly, computer habit, perceived usefulness, perceived ease of use, compatibility and facilitating condition did not significantly influence intention to use PIS.The results of this study provided useful insights for healthcare agencies to understand the underlying elements that could improve the poison information management. The results proved that attitude and computer anxiety were critical factors among healthcare professionals managing poisoning cases in a highly stressful and unpredictable work environment. These factors must, therefore, be considered before implementing PIS in managing poisoning cases. The study also provided an understanding of how to improve system development by utilising the end user's expectation on the implementation of the system.
NF-RCNN: Heart localization and right ventricle wall motion abnormality detection in cardiac MRI

<h2>Abstract</h2> Convolutional neural networks (CNNs) are extensively used in cardiac image analysis. However, heart localization has become a prerequisite to these networks since it decreases the size of input images. Accordingly, recent CNNs benefit from deeper architectures in gaining abstract semantic information. In the present study, a deep learning-based method was developed for heart localization in cardiac MR images. Further, Network in Network (NIN) was used as the region proposal network (RPN) of the faster R-CNN, and then NIN Faster-RCNN (NF-RCNN) was proposed. NIN architecture is formed based on "MLPCONV" layer, a combination of convolutional network and multilayer perceptron (MLP). Therefore, it could deal with the complicated structures of MR images. Furthermore, two sets of cardiac MRI dataset were used to evaluate the network, and all the evaluation metrics indicated an absolute superiority of the proposed network over all related networks. In addition, FROC curve, precision-recall (PR) analysis, and mean localization error were employed to evaluate the proposed network. In brief, the results included an AUC value of 0.98 for FROC curve, a mean average precision of 0.96 for precision-recall curve, and a mean localization error of 6.17 mm. Moreover, a deep learning-based approach for the right ventricle wall motion analysis (WMA) was performed on the first dataset and the effect of the heart localization on this algorithm was studied. The results revealed that NF-RCNN increased the speed and decreased the required memory significantly.
Improve BIRCH algorithm for big data clustering

Abstract Big Data is a collection of data with super large data volumes, has a very high diversity of data sources, so needs to be managed with methods and devices that help perform accordingly. Clustering is one of the effective techniques for dealing with Big Data. The hierarchical method with the BIRCH algorithm is able to produce a short time in data execution. The BIRCH algorithm is a matching grouping algorithm for very large data sets. In an algorithm, a CF-tree is built in which all entries in each leaf node must meet same T threshold, and the CF-tree is rebuilt at each stage with a different threshold. But using a static (fixed) threshold produces poor cluster quality, in this paper proposes a solution to this deficiency by modifying the Threshold value to dynamic so that it can produce good cluster quality and be validated using silhouette coefficient (SC). There is a very clear difference between the standard BIRCH algorithm and the BIRCH algorithm on the modified T parameter (BIRCH (CF-Leaf (modif)). The CF-Node result, the total CF-Entries and Total CF-Leaf Entries produced 60% less than CF-Node, the total CF-Entries and Total CF-Leaf Entries in the standard BIRCH algorithm.
An Indoor Crowd Detection Network Framework Based on Feature Aggregation Module and Hybrid Attention Selection Module

In this paper, we present an indoor crowd detection network framework based on feature aggregation module and hybrid attention selection module (HSFA2Net). In order to better provide the details needed for small scale pupulation detection, we propose a novel feature aggregation module (FAM), which uses the idea of fusion and decomposition to aggregate contextual feature information. Since the indoor population feature and background feature overlap and the classification boundaries are not obvious, the proposed improved hybrid attention selection module (HASM) combines the selection mechanism with the previously proposed mixed attention module. Ultimately, we implement an indoor crowd detection network framework and achieve a recall rate of 0.92 and an F1 score of 0.92 on a public dataset SCUT-HEAD.
Intuitionistic fuzzy cognitive map approach for the evaluation of supply chain configuration criteria

Because of the growing global competence and effectiveness concepts, supply chain becomes more important for organizations. Therefore, managers object to find best supply chain configuration for their firms. This study proposes a comprehensive configuration for supply chain management process, and it enables to understand relationships among supply chain integration, supply chain strategies, supply chain risk factors, and performance criteria. By reviewing the literature and using experts' knowledge, supply chain configuration criteria are determined. Intuitionistic fuzzy cognitive map methodology is employed to consider the interrelations between criteria. Intuitionistic fuzzy cognitive map methodology is a suitable tool due to the presence of causalities and relationships among criteria and the difficulty of expressing the interrelations with crisp numbers. It also deals with uncertain and vague data and allows representing hesitation. The application is conducted in an automobile factory, which is one of the largest manufacturers in Turkey. The results show that selection of proper supplier is the most significant supply chain configuration criteria. Thus, the importance of supplier selection criteria is also analyzed as the second phase of the study.
Bitcoin Price Prediction Using Machine Learning Methods

Bitcoin is a new currency that is recognized as a creative payment network. The operating system functions independently of a central structure or bank. Bitcoin is managed by the developed network. Bitcoin's open source code structure allows it to be uncontrolled and uncontrollable by an individual. The use of bitcoin requires virtual wallet. Verification of all payments is secured using public key encryption. Fluctuate in Bitcoin prices at a high rate, making it difficult to predict. For this reason, new cryptology methods related to bitcoin price estimation and security are tried in the literature. In our study, bitcoin price estimation was made by using machine learning methods using KAGGLE Bitcoin Dataset 2010-2019 data set. The methods used are long-short term memory networks, support vector machines, artificial neural networks, Naive Bayes, decision trees and the nearest neighbor algorithm. Obtained accuracy rates are 97.2%, 91.8%, 86.6%, 85%, 81.2% respectively.
Channels of corruption in Africa: analytical review of trends in financial crimes

Purpose The purpose of this paper is to identify and analytically review the trends of financial crimes as channels of corruption that impact the development process and economic progress in Africa. Design/methodology/approach This paper outlines the trends in financial crimes by compiling statistics derived from a scan of the publicly available survey and other published data related. Findings The financial crimes of embezzlement and theft, bribes and kickbacks, money laundering and illicit financial flows and state capture are all channels of corruption that are prevalent in Africa with significant negative effects on the continent’s socio-economic development progress. The magnitude of these crimes has been trending upward with the resultant effect that corruption continues to have significant negative impacts on Africa’s current and future development prospects. To develop policies to minimise these negative effects, it is necessary to quantify and continuously monitor the magnitude of these various corruption channels. Originality/value The main value of the study is the insights it provides on the nature and extent of the trends in financial crimes as channels of corruption in Africa, and the resultant negative impact on socio-economic development in the region.