Reviewerly/exHarmony huggingface dataset:
A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations

Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, implicit similarity, similarity transitivity and global reputation concepts to expand the space of potential recommenders. This expansion will enhance the prediction accuracy and coverage of the proposed model when applied to sparse data situations. To show effectiveness of the proposed model, a set of experiments are conducted on two real-world multi-criteria datasets, Yahoo! Movies and TripAdvisor. The experimental results demonstrate the superiority of the proposed model compared to a number of existing collaborative filtering-based recommendation methods under a variety of evaluation metrics.
Ontology verification testing using lexico-syntactic patterns

Ontology verification refers to the activity where an ontology is tested against its ontology requirements to ensure that it is built correctly in compliance with its ontology requirements specification. Therefore, it is an important activity that should be performed in any ontology development process. Since manual verification can be a time-consuming and repetitive task, testing processes to automatically verify an ontology facilitate this activity. Moreover, the involvement of not only ontology engineers during the ontology verification process, but also domain experts and users, can provide valuable feedback to avoid misunderstandings and lack of information. This paper proposes a method for ontology verification that defines the testing activities to be performed. The method uses a testing language based on lexico-syntactic patterns to facilitate the definition of tests and an ontology to store and publish such tests. Moreover, this verification testing method proposes an online tool to execute tests on one or more ontologies. The method was compared in terms of time and errors by user evaluation with other tools for ontology verification; the evaluation showed that the tools that use testing languages had better results in terms of reducing errors in the verification activity compared to the tools that do not.
Hybrid bio-inspired model for fraud detection with correlation based feature selection

The credit card is one of the most used forms of online transactions accepted by almost all the service payment systems. The increasing popularity of credit card use is increasing the challenge and fraud cases. This paper proposes a hybrid model based on the feature selection method, firefly bio-inspired algorithm, and random forest ensemble classifier (namely HBRF) for fraud detection and overcomes the imbalanced dataset issue in the banking domain. The firefly bio-inspired algorithm and the correlation-based feature selection (CFS) method have been applied to optimize the predictive features. The proposed HBRF model has been implemented on the Brazilian credit card dataset, and its performance has been compared with machine learning techniques such as Naive Bayes, KNN, Logistic Regression, Support Vector Machine, and existing methods. The proposed HBRF model has achieved an accuracy of 96.23% with a low error rate of 3.77%. It has been identified that the HBRF model outperforms state-of-the-art classifiers and existing techniques.
Cloud Motion Vector System to Detect Clouds and Forecast Real-time Photovoltaic System Performance

Improving PV power production forecast capabilities is critical to cost-effectively integrate more solar into electric transmission and distribution systems. Varying weather patterns such as a cloud rolling over a PV array is the main cause of voltage fluctuation and flicker. To solve this problem, we propose an intelligent low-cost Cloud Motion Vector Sensor (CMVS) system design that utilizes an array of ambient light sensors and a real-time prediction algorithm based upon gradient matrix and peak detection methods. The algorithm will compute the cloud direction and cloud speed. Based on these cloud motion parameters, the system will be capable of detecting a moving cloud and predict its time-of-arrival at the PV site, as well as forecasting PV power output. When the forecast falls below a certain level, a signal at the output is triggered to instantaneously turn on the constant backup power supply until the optimal operation is restored. CMVS system will prevent voltage fluctuations and cloud-induced power transients in the power distribution network as well as potentially helping grid operators better understand and mitigate the effects of PV power variability on grid planning and operations. We include the video of this project with this report.
Research on Pseudo-label Technology for Multi-label News Classification

Multi-label news classification exerts a significant importance with the growing size of news containing multiple semantics. However, most of the existing multi-label classification methods rely on large-scale labeled corpus while publicly available resources for multi-label classification are limited. Although many researches have proposed the application of pseudo-label technology to expand the corpus, few studies explored it for multi-label classification since the number of labels is not prone to determine. To address these problems, we construct a multi-label news classification corpus for Indonesian language and propose a new multi-label news classification framework through using pseudo-label technology in this paper. The framework employs the BERT model as a pre-trained language model to obtain the sentence representation of the texts. Furthermore, the cosine similarity algorithm is utilized to match the text labels. On the basis of matching text labels with similarity algorithms, a pseudo-label technology is used to pick up the classes for unlabeled data. Then, we screen high-confidence pseudo-label corpus to train the model together with original training data, and we also introduce loss weights including class weight adjustment method and pseudo-label loss function balance coefficient to solve the problem of data with class imbalance, as well as reduce the impact of the quantity difference between labeled texts and pseudo-label texts on model training. Experiment results demonstrate that the framework proposed in this paper has achieved significant performance in Indonesian multi-label news classification, and each strategy can perform a certain improvement.
Bayesian Hyperparameter Optimization of Deep Neural Network Algorithms Based on Ant Colony Optimization

Within this paper we proposed a new method named BayesACO, to improve the convolutional neural network based on neural architecture search with hyperparameters optimization. At its essence BayesACO in first side uses Ant Colony Optimization (ACO) to generate the best neural architecture. In other side, it uses bayesian hyperparameters optimization to select the best hyperparameters. We applied this method on Mnist and FashionMnist datasets. Our proposed method proven competitive results with other methods of convolutional neural network optimization.
A FULLY BAYESIAN GRADIENT-FREE SUPERVISED DIMENSION REDUCTION METHOD USING GAUSSIAN PROCESSES

Modern day engineering problems are ubiquitously characterized by sophisticated computer codes that map parameters or inputs to an underlying physical process. In other situations, experimental setups are used to model the physical process in a laboratory, ensuring high precision while being costly in materials and logistics. In both scenarios, only a limited amount of data can be generated by querying the expensive information source at a finite number of inputs or designs. This problem is compounded further in the presence of a high-dimensional input space. State-of-the-art parameter space dimension reduction methods, such as active subspace, aim to identify a subspace of the original input space that is sufficient to explain the output response. These methods are restricted by their reliance on gradient evaluations or copious data, making them inadequate for expensive problems without direct access to gradients. The proposed methodology is gradient-free and fully Bayesian, as it quantifies uncertainty in both the low-dimensional subspace and the surrogate model parameters. This enables a full quantification of epistemic uncertainty and robustness to limited data availability. It is validated on multiple datasets from engineering and science and compared to two other state-of-the-art methods based on four aspects: (a) recovery of the active subspace, (b) deterministic prediction accuracy, (c) probabilistic prediction accuracy, and (d) training time. The comparison shows that the proposed method improves the active subspace recovery and predictive accuracy, in both the deterministic and probabilistic sense, when only few a model observations are available for training, at the cost of increased training time.
Applying Explainable Artificial Intelligence Techniques on Linked Open Government Data

Machine learning and artificial intelligence models have the potential to streamline public services and policy making. Frequently, however, the patterns a model uncovers can be more important than the model's performance. Explainable Artificial Intelligence (XAI) have been recently introduced as a set of techniques that enable explaining individual decisions made by a model. Although XAI has been proved important in various domains, the need of using relevant techniques in public administration has only recently emerged. The objective of this paper is to explore the value and the feasibility of creating XAI models using high quality open government data that are provided in the form of linked open statistical data. Towards this end, a process for exploiting linked open statistical data in the creation of explainable models is presented. Moreover, a case study where linked data from the Scottish open statistics portal is exploited in order to predict and interpret the probability the mean house price of a data zone to be higher than the average price in Scotland is described. The XGBoost algorithm is used to create the predictive model and the SHAP framework to explain it.
Light Field Super-Resolution Based on Spatial and Angular Attention

Light field (LF) images captured by LF cameras can store the intensity and direction information of light rays in the scene, which have advantages in many computer vision tasks, such as 3D reconstruction, target tracking and so on. But there is a trade-off between the spatial and angular resolution of LF images due to the fixed resolution of sensor in LF cameras. So LF image super-resolution (SR) is widely explored. Most of the existing methods do not consider the different degree of importance of spatial and angular information provided by other views in LF. So we propose a LF spatial-angular attention module (LFSAA) to adjust the weights of spatial and angular information in spatial and angular domain respectively. Based on this module, a LF image SR network is designed to super-resolve all views in LF simultaneously. And we further combine the LF image SR network with single image SR network to improve the ability to explore spatial information of a single image in LF. Experiments on both synthetic and real-world LF datasets have demonstrated the performance of our method.
TX-RSA: A High Performance RSA Implementation Scheme on NVIDIA Tegra X2

Driven by computer vision and autopilot industries, embedded graphics processing units (GPUs) are now rapidly achieving extraordinary computing power, such NVIDIA Tegra K1/X1/X2, which are widely used in embedded environments such as mobile phones, game console and vehicle-mounted systems. Such performance advantages give embedded GPUs the possibility of accelerating cryptography that also requires high-density computing. In this paper, we implement TX-RSA in embedded GPU platforms, i.e., NVIDIA TX2, to accelerate the most prevailing public-key cryptosystem, RSA. Various optimization methods are employed to promote the efficiency, including multi-threaded Montgomery multiplication and CRT implementation on the resource-constricted embedded GPUs. Within 20 W of power consumption, TX-RSA can deliver 6,423 ops/s of RSA encryption and 131,324 ops/s of RSA decryption, which outperforms implementations in the desktop GPUs and embedded CPUs in the perspective of performance-to-power ratio.
BreakingBED: Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks

Deploying convolutional neural networks (CNNs) for embedded applications presents many challenges in balancing resource-efficiency and task-related accuracy. These two aspects have been well-researched in the field of CNN compression. In real-world applications, a third important aspect comes into play, namely the robustness of the CNN. In this paper, we thoroughly study the robustness of uncompressed, distilled, pruned and binarized neural networks against white-box and black-box adversarial attacks (FGSM, PGD, C&W, DeepFool, LocalSearch and GenAttack). These new insights facilitate defensive training schemes or reactive filtering methods, where the attack is detected and the input is discarded and/or cleaned. Experimental results are shown for distilled CNNs, agent-based state-of-the-art pruned models, and binarized neural networks (BNNs) such as XNOR-Net and ABC-Net, trained on CIFAR-10 and ImageNet datasets. We present evaluation methods to simplify the comparison between CNNs under different attack schemes using loss/accuracy levels, stress-strain graphs, box-plots and class activation mapping (CAM). Our analysis reveals susceptible behavior of uncompressed and pruned CNNs against all kinds of attacks. The distilled models exhibit their strength against all white box attacks with an exception of C&W. Furthermore, binary neural networks exhibit resilient behavior compared to their baselines and other compressed variants.
Optimum Tilt Angles of Solar Collectors in Saudi Arabia

This paper presents the optimum solar collector tilt angles that will result in maximum solar radiation on the surface of collectors for each climatic zone in Saudi Arabia. The study has been done on the hourly diffuse and daily global solar radiation values on a horizontal surface. The percentage increase in the value of solar radiation gain for both seasonal and monthly optimum tilts are determined with respect to the annual fixed tilt for the different climatic zones of Saudi Arabia. A correlation that determines the optimum tilt angle as a function of latitude and month is developed for the different climatic zones of kingdom of Saudi Arabia.
On the limits of machine knowledge

General-purpose knowledge bases (KBs) are an important component of several data-driven applications. Pragmatically constructed from available web sources, these KBs are far from complete, which poses a set of challenges in curation as well as consumption. In this tutorial we discuss how completeness, recall and negation in DBs and KBs can be represented, extracted, and inferred. We proceed in 5 parts: (i) We introduce the logical foundations of knowledge representation and querying under partial closed-world semantics. (ii) We show how information about recall can be identified in KBs and in text, and (iii) how it can be estimated via statistical patterns. (iv) We show how interesting negative statements can be identified, and (v) how recall can be targeted in a comparative notion.
Dynamic Resource Provisioning for Iterative Workloads on Apache Spark

Apache Spark as a popular in-memory data analytic framework has been employed by various applications—such as machine learning, graph computation, and scientific computing, which benefit from the long-running process (e.g., executor) programming model to avoid system I/O overhead. However, existing resource allocation strategies mainly rely on the peak demand, which are normally specified by users. Since the resource usages of long-running applications like iterative computation vary significantly over time, we find that peak demand based resource allocation policies lead to low cloud utilization in production environments. In this article, we present a utilization aware resource provisioning approach for iterative workloads on Apache Spark (i.e., <inline-formula><tex-math notation="LaTeX">${iSpark}$</tex-math></inline-formula> ). It can identify the causes of resource underutilization due to an inflexible resource policy, and elastically adjusts the allocated executors over time according to the real-time resource usage. In general, iterative applications require more computation resources at the beginning stage and their demands for resources diminish as more iterations are completed. iSpark aims to timely scale up or scale down the number of executors in order to fully utilize the allocated resources while taking the dominant factor into consideration. It further preempts the underutilized executors and preserves the cached intermediate data to ensure the data consistency. Testbed evaluations show that iSpark averagely improves the resource utilization of individual executors by 35.2% compared to vanilla Spark. At the same time, it increases the cluster utilization from 32.1% to 51.3% and effectively reduces the overall job completion time by 20.8% for a set of representative iterative applications. Furthermore, we have extended iSpark to multi-tenancy cloud environments. Specifically, iSpark characterizes a virtual node based on two real-time measured performance statistics: I/O rate and CPU steal time. Thus, we extend the two-dimensional resource constraints (i.e., CPU and MeM) in iSpark to three-dimensional resource constraints (i.e., CPU, MeM and I/O) in the cloud environment. We consider two representative interference scenarios in the cloud: stable interference and dynamic interference. Experimental results on virtual clusters with varying interferences show that iSpark with cloud extension improves the average job completion time by 68% compared to default Spark resource allocation policies.
A Moving Target Detection and Localization Strategy Based on Optical Flow and Pin-hole Imaging Methods Using Monocular Vision

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization.
HCADecoder: A Hybrid CTC-Attention Decoder for Chinese Text Recognition

Text recognition has attracted much attention and achieved exciting results on several commonly used public English datasets in recent years. However, most of these well-established methods, such as connectionist temporal classification (CTC)-based methods and attention-based methods, pay less attention to challenges on the Chinese scene, especially for long text sequences. In this paper, we exploit the characteristic of Chinese word frequency distribution and propose a hybrid CTC-Attention decoder (HCADecoder) supervised with bigram mixture labels for Chinese text recognition. Specifically, we first add high-frequency bigram subwords into the original unigram labels to construct the mixture bigram label, which can shorten the decoding length. Then, in the decoding stage, the CTC module outputs a preliminary result, in which confused predictions are replaced with bigram subwords. The attention module utilizes the preliminary result and outputs the final result. Experimental results on four Chinese datasets demonstrate the effectiveness of the proposed method for Chinese text recognition, especially for long texts. Code will be made publicly available(https://github.com/lukecsq/hybrid-CTC-Attention).
A new algorithm for equivalence of cyclic codes and its applications

Cyclic codes are among the most important families of codes in coding theory for both theoretical and practical reasons. Despite their prominence and intensive research on cyclic codes for over a half century, there are still open problems related to cyclic codes. In this work, we use recent results on the equivalence of cyclic codes to create a more efficient algorithm to partition cyclic codes by equivalence based on cyclotomic cosets. This algorithm is then implemented to carry out computer searches for both cyclic codes and quasi-cyclic (QC) codes with good parameters. We also generalize these results to repeated-root cases. We have found several new linear codes that are cyclic or QC as an application of the new approach, as well as more desirable constructions for linear codes with best known parameters. With the additional new codes obtained through standard constructions, we have found a total of 14 new linear codes.
Rotation Invariance and Extensive Data Augmentation: A Strategy for the MItosis DOmain Generalization (MIDOG) Challenge

Automated detection of mitotic figures in histopathology images is a challenging task: here, we present the different steps that describe the strategy we applied to participate in the MIDOG 2021 competition. The purpose of the competition was to evaluate the generalization of solutions to images acquired with unseen target scanners (hidden for the participants) under the constraint of using training data from a limited set of four independent source scanners. Given this goal and constraints, we joined the challenge by proposing a straight-forward solution based on a combination of state-of-the-art deep learning methods with the aim of yielding robustness to possible scanner-related distributional shifts at inference time. Our solution combines methods that were previously shown to be efficient for mitosis detection: hard negative mining, extensive data augmentation, rotation-invariant convolutional networks. We trained five models with different splits of the provided dataset. The subsequent classifiers produced F1-score with a mean and standard deviation of $$0.747 {\pm } 0.032$$ on the test splits. The resulting ensemble constitutes our candidate algorithm: its automated evaluation on the preliminary test set of the challenge returned a F1-score of 0.6828.