Reviewerly/exHarmony huggingface dataset:
Ontology based sentiment analysis for fake review detection

Majority of customers and manufacturers who tend to purchase and trade via e-commerce websites primarily rely on reviews before making purchasing decisions and product improvements. Deceptive reviewers consider this opportunity to write fake reviews to mislead customers and manufacturers. This calls for the necessity of identifying fake reviews before making them available for decision making. Accordingly, this research focuses on a fake review detection method that incorporates review-related features including linguistic features, Part-of-Speech (POS) features, and sentiment analysis features. A domain feature ontology is used in the feature-level sentiment analysis and all the review-related features are extracted and integrated into the ontology. The fake review detection is enhanced through a rule-based classifier by inferencing the ontology. Due to the lack of a labeled dataset for model training, the Mahalanobis distance method was used to detect outliers from an unlabeled dataset where the outliers were selected as fake reviews for model training. The performance measures of the rule-based classifier were improved by integrating linguistic features, POS features, and sentiment analysis features, in spite of considering them separately.
Detection and analysis types of DDoS attack

The problem of detecting types of DDOS attacks in large-scale networks is considered. The complexity of detection is explained by the presence of a large number of connected and diverse devices, the high volume of incoming traffic, the need to introduce special restrictions when searching for anomalies. The technology of developing information security models using data mining (DM) methods is proposed. The features of machine learning of DM algorithms are related to the choice of methods for preprocessing big data (Big Data). A technique for analyzing the structure of relations between types of DDOS attacks has been developed. Within the framework of this technique, a procedure for pairwise comparison of data by types of attacks with normal traffic is implemented. The result of the comparison is the stability of features, the values of which are invariant to the measurement scales. The analysis of the structure of relations by grouping algorithms was carried out according to the stability values on the determined sets of features. When forming the sets, the stability ranking was used. For classification, various existing methods of machine learning are analyzed.
LMRAOA: An improved arithmetic optimization algorithm with multi-leader and high-speed jumping based on opposition-based learning solving engineering and numerical problems

This paper proposes an improved variant of the arithmetic optimization algorithm (AOA), called LMRAOA, which is used to solve numerical and engineering problems. Various strategies are proposed to improve AOA. First, Multi-Leader Wandering Around Search Strategy (MLWAS) is proposed to improve the exploration ability of the algorithm on global scale. Then, Random High-Speed Jumping Strategy (RHSJ) is proposed, and the search agent performs high-speed search in the current neighborhood to improve the exploitation ability. Finally, in order to avoid local optima, adaptive lens opposition-based learning strategy is proposed, and linear changes are proposed in its parameters to further satisfy the dynamic changes. 27 classic benchmark functions, 6 engineering optimization problems, and CEC2014, CEC2019 and CEC2020 competition functions are tested by LMRAOA algorithm and comparison algorithm. The experimental results show that, in most cases, the LMRAOA outperforms other algorithms in solving engineering and numerical problems, and can provide effective solutions.
Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS)

Credit card fraud is a growing problem nowadays and it has escalated during COVID-19 due to the authorities in many countries requiring people to use cashless transactions. Every year, billions of Euros are lost due to credit card fraud transactions, therefore, fraud detection systems are essential for financial institutions. As the classes' distribution is not equally represented in the credit card dataset, the machine learning trains the model according to the majority class which leads to inaccurate fraud predictions. For that, in this research, we mainly focus on processing unbalanced data by using an under-sampling technique to get more accurate and better results with different machine learning algorithms. We propose a framework that is based on clustering the dataset using fuzzy C-means and selecting similar fraud and normal instances that have the same features, which guarantees the integrity between the data features.
StepConf: SLO-Aware Dynamic Resource Configuration for Serverless Function Workflows

Function-as-a-Service (FaaS) offers a fine-grained resource provision model, enabling developers to build highly elastic cloud applications. User requests are handled by a series of serverless functions step by step, which forms a function-based workflow. The developers are required to set proper resource configuration for functions, so as to meet service level objectives (SLOs) and save cost. However, developing the resource configuration strategy is challenging. It is mainly because execution of cloud functions often suffers from cold start and performance fluctuation, which requires a dynamic configuration strategy to guarantee the SLOs. In this paper, we present StepConf, a framework that automates the resource configuration for functions as the workflow runs. StepConf optimizes memory size for each function step in the workflow and takes inter and intra-function parallelism into consideration. We evaluate StepConf on AWS Lambda. Compared with baselines, the experimental results show that StepConf can save cost up to 40.9% while ensuring the SLOs.
DARC: Deep adaptive regularized clustering for histopathological image classification

In recent years, deep learning as a state-of-the-art machine learning technique has made great success in histopathological image classification. However, most of deep learning approaches rely heavily on the substantial task-specific annotations, which require experienced pathologists' manual labelling. As a result, they are laborious and time-consuming, and many unlabeled pathological images are difficult to use without experts' annotations. To mitigate the requirement for data annotation, we propose a self-supervised Deep Adaptive Regularized Clustering (DARC) framework to pre-train a neural network. DARC iteratively clusters the learned representations and utilizes the cluster assignments as pseudo-labels to learn the parameters of the network. To learn feasible representations and encourage the representations to become more discriminative, we design an objective function combining a network loss with a clustering loss using an adaptive regularization function, which is updated adaptively throughout the training process to learn feasible representations. The proposed DARC is evaluated on three public datasets, including NCT-CRC-HE-100K, PCam and LC25000. Compared to the strategy of training from scratch, fine-tuning using the pre-trained weights of DARC can obviously boost the accuracy of neural networks on histopathological classification. The accuracy of using the network trained using DARC pre-trained weights with only 10% labeled data is already comparable to the network trained from scratch with 100% training data. The network using DARC pre-trained weights achieves the fastest convergence speed on the downstream classification task. Moreover, visualization through t-distributed stochastic neighbor embedding (t-SNE) shows that the learned representations are generalizable and discriminative.
Cardinality-constrained portfolio selection via two-timescale duplex neurodynamic optimization

This paper addresses portfolio selection based on neurodynamic optimization. The portfolio selection problem is formulated as a biconvex optimization problem with a variable weight in the Markowitz risk-return framework. In addition, the cardinality-constrained portfolio selection problem is formulated as a mixed-integer optimization problem and reformulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is customized and applied for solving the reformulated portfolio optimization problem. In the two-timescale duplex neurodynamic approach, two recurrent neural networks operating at two timescales are employed for local searches, and their neuronal states are reinitialized upon local convergence using a particle swarm optimization rule to escape from local optima toward global ones. Experimental results on four datasets of world stock markets are elaborated to demonstrate the superior performance of the neurodynamic optimization approach to three baselines in terms of two major risk-adjusted performance criteria and portfolio returns.
Meta-Reinforcement Learning in Non-Stationary and Dynamic Environments

In recent years, the subject of deep reinforcement learning (DRL) has developed very rapidly, and is now applied in various fields, such as decision making and control tasks. However, artificial agents trained with RL algorithms require great amounts of training data, unlike humans that are able to learn new skills from very few examples. The concept of meta-reinforcement learning (meta-RL) has been recently proposed to enable agents to learn similar but new skills from a small amount of experience by leveraging a set of tasks with a shared structure. Due to the task representation learning strategy with few-shot adaptation, most recent work is limited to narrow task distributions and stationary environments, where tasks do not change within episodes. In this work, we address those limitations and introduce a training strategy that is applicable to non-stationary environments, as well as a task representation based on Gaussian mixture models to model clustered task distributions. We evaluate our method on several continuous robotic control benchmarks. Compared with state-of-the-art literature that is only applicable to stationary environments with few-shot adaption, our algorithm first achieves competitive asymptotic performance and superior sample efficiency in stationary environments with zero-shot adaption. Second, our algorithm learns to perform successfully in non-stationary settings as well as a continual learning setting, while learning well-structured task representations. Last, our algorithm learns basic distinct behaviors and well-structured task representations in task distributions with multiple qualitatively distinct tasks.
Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19

The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for identifying people who do not wear a face mask. However, not all face masks or coverings are equally effective in preventing virus transmission or illness caused by viruses and therefore, it appears important for those systems to incorporate the ability to distinguish between the different types of face masks. This paper implements four pre-trained deep transfer learning models (NasNetMobile, MobileNetv2, ResNet101v2, and ResNet152v2) to classify images based on the type of face mask (KN95, N95, surgical and cloth) worn by people. Experimental results indicate that the deep residual networks (ResNet101v2 and ResNet152v2) provide the best performance with the highest accuracy and the lowest loss.
Memento 2.0: An Improved Lifelog Search Engine for LSC'22

In this paper, we present Memento 2.0, an improved version of our system which first participated in the Lifelog Search Challenge 2021. Memento 2.0 employs image-text embeddings derived from two CLIP models (ViT-L/14 and ResNet-50x64) and adopts a weighted ensemble approach to derive a combined final ranking. Our approach significantly improves the performance over the baseline LSC'21 system. We additionally make important updates to the system's user interface after analysing the shortcomings to make it more efficient and better suited to the needs of the Lifelog Search Challenge.
Bendlet Transform Based Adaptive Denoising Method for Microsection Images

Magnetic resonance imaging (MRI) plays an important role in disease diagnosis. The noise that appears in MRI images is commonly governed by a Rician distribution. The bendlets system is a second-order shearlet transform with bent elements. Thus, the bendlets system is a powerful tool with which to represent images with curve contours, such as the brain MRI images, sparsely. By means of the characteristic of bendlets, an adaptive denoising method for microsection images with Rician noise is proposed. In this method, the curve contour and texture can be identified as low-frequency components, which is not the case with other methods, such as the wavelet, shearlet, and so on. It is well known that the Rician noise belongs to a high-frequency channel, so it can be easily removed without blurring the clarity of the contour. Compared with other algorithms, such as the shearlet transform, block matching 3D, bilateral filtering, and Wiener filtering, the values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) obtained by the proposed method are better than those of other methods.
Entangling a Hole Spin with a Time-Bin Photon: A Waveguide Approach for Quantum Dot Sources of Multiphoton Entanglement

Deterministic sources of multiphoton entanglement are highly attractive for quantum information processing but are challenging to realize experimentally. In this Letter, we demonstrate a route toward a scaleable source of time-bin encoded Greenberger-Horne-Zeilinger and linear cluster states from a solid-state quantum dot embedded in a nanophotonic crystal waveguide. By utilizing a self-stabilizing double-pass interferometer, we measure a spin-photon Bell state with (67.8±0.4)% fidelity and devise steps for significant further improvements. By employing strict resonant excitation, we demonstrate a photon indistinguishability of (95.7±0.8)%, which is conducive to fusion of multiple cluster states for scaling up the technology and producing more general graph states.
Attributes impacting cybersecurity policy development: An evidence from seven nations

Cyber threats have risen as a result of the growing usage of the Internet. Organizations must have effective cybersecurity policies in place to respond to escalating cyber threats. Individual users and corporations are not the only ones who are affected by cyber-attacks; national security is also a serious concern. Different nations' cybersecurity rules make it simpler for cybercriminals to carry out damaging actions while making it tougher for governments to track them down. Hence, a comprehensive cybersecurity policy is needed to enable governments to take a proactive approach to all types of cyber threats. This study investigates cybersecurity regulations and attributes used in seven nations in an attempt to fill this research gap. This paper identified fourteen common cybersecurity attributes such as telecommunication, network, Cloud computing, online banking, E-commerce, identity theft, privacy, and smart grid. Some nations seemed to focus, based on the study of key available policies, on certain cybersecurity attributes more than others. For example, the USA has scored the highest in terms of online banking policy, but Canada has scored the highest in terms of E-commerce and spam policies. Identifying the common policies across several nations may assist academics and policymakers in developing cybersecurity policies. A survey of other nations' cybersecurity policies might be included in the future research.
Correlogram, predictability error growth, and bounds of mean square error of solar irradiance forecasts

Solar forecast verification must incorporate a notion of “relativity.” Particularly for deterministic forecasts, one wishes to know the upper and lower bounds of the mean square error (MSE) for the forecasting situation of concern, such that the performance of some forecasts of interest can be quantified relative to these bounds. This work proposes a method for estimating these bounds. For the upper bound of MSE, the theory builds upon the premise that no forecast worse than that provided by the standard of reference should be used. Under some mild assumptions, the MSE of the standard of reference, which is herein defined to be the optimal convex combination of climatology and persistence, varies solely with the lag-h autocorrelation of the clear-sky index. Then, by fitting a theoretical isotropic correlogram with a nugget effect to the empirical autocorrelations, the upper bound of MSE, as a function of forecast horizon, can be derived. For the lower bound of MSE, it can be approximated by the predictability error growth, which is the difference between the controlled and perturbed forecasts from a perfect dynamical weather model. The empirical part of this work considers the irradiance forecasts from the European Centre for Medium-Range Weather Forecasts, at seven locations in United States, over a period of two years (2019–2020). Various MSEs reported in the literature are put in perspective of the estimated bounds of MSE. The proposed bounds have a profound impact on predictability quantification and skill score computation, which are essential for comparative forecast verification.
Blockchain adoption in a supply chain system to combat counterfeiting

The ongoing boom in platform retailing has been accompanied by widespread counterfeiting. Blockchain technology has attracted much attention as a way to combat this practice. We consider a supply chain system consisting of one brand seller, one counterfeiter and one platform retailer and examine how blockchain adoption affects the decisions of firms in this system and the performance of the whole system. We show that the platform’s use of blockchain reduces both products’ selling price and increases the brand seller’s demand while also increasing the counterfeiter’s demand under certain conditions when a wholesale contract is adopted. Moreover, we find that blockchain technology could be an effective anticounterfeit tool but that the platform does not always use it, even if its implementation is costless. The platform is more willing to use blockchain when imitation good’s quality is relatively low, and an agency contract can better incentivize the platform to deploy blockchain than a wholesale contract if the commission fee is large. Finally, consumer surplus and social welfare may decrease after the platform adopts blockchain, but the whole supply chain system can perform better through the platform’s blockchain implementation if the imitation good’s quality and consumers’ trust in the platform are low. • Considering blockchain to combat counterfeiting under different contracts. • Exploring the conditions of blockchain adoption of the platform. • Discussing the impacts of the platform’s blockchain implementation. • Investigating the effects of the blockchain adoption on the performance.
DeepDiagnosis

Deep Neural Networks (DNNs) are used in a wide variety of applications. However, as in any software application, DNN-based apps are afflicted with bugs. Previous work observed that DNN bug fix patterns are different from traditional bug fix patterns. Furthermore, those buggy models are non-trivial to diagnose and fix due to inexplicit errors with several options to fix them. To support developers in locating and fixing bugs, we propose DeepDiagnosis, a novel debugging approach that localizes the faults, reports error symptoms and suggests fixes for DNN programs. In the first phase, our technique monitors a training model, periodically checking for eight types of error conditions. Then, in case of problems, it reports messages containing sufficient information to perform actionable repairs to the model. In the evaluation, we thoroughly examine 444 models - 53 real-world from GitHub and Stack Overflow, and 391 curated by AUTOTRAINER. DeepDiagnosis provides superior accuracy when compared to UMLUAT and DeepLocalize. Our technique is faster than AUTOTRAINER for fault localization. The results show that our approach can support additional types of models, while state-of-the-art was only able to handle classification ones. Our technique was able to report bugs that do not manifest as numerical errors during training. Also, it can provide actionable insights for fix whereas DeepLocalize can only report faults that lead to numerical errors during training. DeepDiagnosis manifests the best capabilities of fault detection, bug localization, and symptoms identification when compared to other approaches.
Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation

Learning which Point-of-Interest (POI) a user will visit next is a challenging task for personalized recommender systems due to the large search space of possible POIs in the region. A recurring problem among existing works that makes it difficult to learn and perform well is the sparsity of the User-POI matrix. In this paper, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the data sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we also propose a novel selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our model significantly outperforms baseline and the state-of-the-art methods.
Classroom Behavior Detection Based on Improved YOLOv5 Algorithm Combining Multi-Scale Feature Fusion and Attention Mechanism

The detection of students’ behaviors in classroom can provide a guideline for assessing the effectiveness of classroom teaching. This study proposes a classroom behavior detection algorithm using an improved object detection model (i.e., YOLOv5). First, the feature pyramid structure (FPN+PAN) in the neck network of the original YOLOv5 model is combined with a weighted bidirectional feature pyramid network (BiFPN). They are subsequently processed with feature fusion of different scales of the object to mine the fine-grained features of different behaviors. Second, a spatial and channel convolutional attention mechanism (CBAM) is added between the neck network and the prediction network to make the model focus on the object information to improve the detection accuracy. Finally, the original non-maximum suppression is improved using the distance-based intersection ratio (DIoU) to improve the discrimination of occluded objects. A series of experiments were conducted on our new established dataset which includes four types of behaviors: listening, looking down, lying down, and standing. The results demonstrated that the algorithm proposed in this study can accurately detect various student behaviors, and the accuracy was higher than that of the YOLOv5 model. By comparing the effects of student behavior detection in different scenarios, the improved algorithm had an average accuracy of 89.8% and a recall of 90.4%, both of which were better than the compared detection algorithms.