Reviewerly/exHarmony huggingface dataset:
Enhancing extended belief rule-based systems for classification problems using decomposition strategy and overlap function

Multi-class and multi-attribute are two important features of classification problems and have different effects on the requirements and performance of the classifier. Decomposition strategy and overlap function are two effective ways to enhance the performance of classifiers, because the former decomposes a complex multi-class problem into multiple simple sub-problems; the latter uses various functions to specify the conjunctive relationship of input variables in a multi-attribute problem. Extended belief rule-based system (EBRBS) is an advanced rule-based system that has been widely used in classification problems. In order to apply decomposition strategies and overlap functions to enhance the performance of EBRBSs, the present work focuses on the investigative research and comparative evaluation of the commonly used one-versus-one (OVO) decomposition strategy and five common overlap functions to improve the performance of EBRBSs on multi-class and multi-attribute problems. More specifically, three typical kinds of EBRBSs, namely original EBRBS (O-EBRBS), EBRBS with dynamic rule activation (DRA-EBRBS), and a latest EBRBS for big data (Micro-EBRBS), are selected to conduct extensive experimental studies on twenty classification problems. To best of our knowledge, this present work is the first time to provide a meaningful and useful study in revealing the potential capability of the EBRBSs with decomposition strategy and overlap function for multi-class and multi-attribute problems. Experimental results demonstrate that the square product overlap function and the OVO strategy can enhance the performance of EBRBSs over others for twenty classification problems.
Biometric E-Voting System for Cybersecurity

The manual voting system currently witnessed across Africa elections can now be carried out electronically. History has shown that an E-based voting system provides efficiency, accuracy, compliance, ease, and security. The developed Biometric E-Voting system (BIO-EVS) has the ability to prevent opportunities for fraud and protects voters' privacy. The voting protocol designed for this system coalesced all the benefits of the existing protocols and techniques while at the same time, removing most of the known deficiencies and harms of the present system. The artificial intelligence (AI) pattern recognition and the cyber security algorithms for encryption and decryption of data where adapted in this research. Specifically, there are two-way approach at design stage of the new system namely the preliminary and detailed design stages. During the preliminary design stage, some important features of the system and the inherent cost of implementation were estimated adequately. This will guide Independent National Electoral Commission (INEC) in appropriating the allocation of resources. There is also provision for capital and content insurance cover against the fear of manipulation by electoral officials. Overall, the impact of BIO-EVS was discovered to be substantial, as the Independent National Electoral Commission, State electoral bodies in charge of local government elections, and Educational Institutions can adopt and promote this system of voting.
Towards corpus and model: Hierarchical structured-attention-based features for Indonesian named entity recognition

Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.
Cyclic Autoencoder for Multimodal Data Alignment Using Custom Datasets

The subtitle recognition under multimodal data fusion in this paper aims to recognize text lines from image and audio data.Most existing multimodal fusion methods tend to be associated with pre-fusion as well as post-fusion, which is not reasonable and difficult to interpret.We believe that fusing images and audio before the decision layer, i.e., intermediate fusion, to take advantage of the complementary multimodal data, will benefit text line recognition.To this end, we propose: (i) a novel cyclic autoencoder based on convolutional neural network.The feature dimensions of the two modal data are aligned under the premise of stabilizing the compressed image features, thus the high-dimensional features of different modal data are fused at the shallow level of the model.(ii) A residual attention mechanism that helps us improve the performance of the recognition.Regions of interest in the image are enhanced and regions of disinterest are weakened, thus we can extract the features of the text regions without further increasing the depth of the model (iii) a fully convolutional network for video subtitle recognition.We choose DenseNet-121 as the backbone network for feature extraction, which effectively enabling the recognition of video subtitles in complex backgrounds.The experiments are performed on our custom datasets, and the automatic and manual evaluation results show that our method reaches the state-of-the-art.
Smart Accounts for Decentralized Governance on Smart Cities

This paper introduces state-of-the-art possibilities for using smart contracts capabilities for governance. Assisted by blockchain, the use of these tools can provide a transition that society currently needs due the huge amount of information that reaches citizens. The core mechanism of this study lies within the scope of smart accounts and digital identities. These topics enclose smart cities trends that seek to increase citizens’ participation in the social decision making process, in a transparent way that is usually managed throughout decentralized systems. We define a set of available features that can automatically guide the flow of resources, after the conclusions of voting processes also conducted on trusted environments of distributed ledgers. By presenting innovative ideas and didactically describing the possibilities, we aim to promote awareness of blockchain capabilities among readers, students, decisions makers and, mainly, the younger generation.
Parallelizing DNN Training on GPUs: Challenges and Opportunities

In recent years, Deep Neural Networks (DNNs) have emerged as a widely adopted approach in many application domains. Training DNN models is also becoming a significant fraction of the datacenter workload. Recent evidence has demonstrated that modern DNNs are becoming more complex and the size of DNN parameters (i.e., weights) is also increasing. In addition, a large amount of input data is required to train the DNN models to reach target accuracy. As a result, the training performance becomes one of the major challenges that limit DNN adoption in real-world applications. Recent works have explored different parallelism strategies (i.e., data parallelism and model parallelism) and used multi-GPUs in datacenters to accelerate the training process. However, naively adopting data parallelism and model parallelism across multiple GPUs can lead to sub-optimal executions. The major reasons are i) the large amount of data movement that prevents the system from feeding the GPUs with the required data in a timely manner (for data parallelism); and ii) low GPU utilization caused by data dependency between layers that placed on different devices (for model parallelism).
Fuzzy Multioutput Transfer Learning for Regression

Multioutput regression aims to predict multiple continuous outputs simultaneously using the common set of input variables. The significant challenge arises from modeling relevance between inputs and outputs. Moreover, the shortage of labeled multioutput data and the divergence of data are other factors that impede the development of multioutput regression problems. The recent emergence of transfer learning techniques, which have the ability of leveraging previously acquired knowledge from a similar domain, provide a solution to the above issues. In this article, a novel fuzzy transfer learning method is proposed to tackle the multioutput regression problems in homogeneous and heterogeneous scenarios. By considering output–input dependencies and inter-output correlations, fuzzy rules are extracted to reflect the shared characteristics of different outputs and capture their uniqueness. For a homogeneous scenario, fuzzy rules are first accumulated in a related domain (called the source domain), which has a sufficient amount of training data. Based on different transform strategies, the fuzzy rules are then transferred to improve the new but similar regression tasks in the current domain (called the target domain), where only a few data have multiple responses. On this basis, we handle a more complex heterogeneous scenario by learning a latent input space to reduce the disagreement of variables between domains. The experiment results on thirteen real-world datasets with multiple outputs illustrate the effectiveness of our method. The impact of core coefficients on performance is also analyzed.
Identity-Based Linkable Ring Signatures From Lattices

Linkable ring signatures is a useful cryptographic tool for constructing applications such as ones relative to electronic voting (e-voting), digital cashes (e-cashes) as well as cloud computing.Equipped with linkable ring signatures, e-voting, e-cash systems can simultaneously enjoy the privacy and the unreusability properties thanks to the anonymity and the linkability of linkable ring signatures.Likewise, cloud servers can enjoy a privacy-preserving ability, a flexible access control and an efficient security management with linkable ring signatures.Moreover, linkable ring signatures built in the identity-based setting would help to remove the expense of using the conventional public key infrastructure and also could be applied to the user management.This primitive hence would be suitable for huge-scale applications.In this paper, we present the first identity-based linkable ring signatures (IdLRS) in both integer lattice and ideal lattice setting.The proposed IdLRS is proved secure in the random oracle model and based on the hardness of the short integer solution and ring short integer solution assumption.We also implement the proposed idLRS as a proof of concept and then do some experiments to evaluate the running times and the sizes.
Some aspects of fractional-order circular moments for image analysis

In this paper, we briefly review the fractional-order circular moments, such as fractional-order Zernike moments, fractional-order Fourier–Mellin moments, fractional-order Legendre–Fourier moments, and fractional-order Chebyshev–Fourier moments, which can characterize, analyze, and manipulate the information contained in an image with minimal redundancy. Also, they depend on an α parameter for better feature extraction. Therefore, we propose a procedure to find the optimal α in terms of image reconstruction error and classification. We validate the search for the best rotation-invariant features using the MNIST and MNIST-R datasets. Finally, we present the study results and conclusions.
Exploiting Category-Level Multiple Characteristics for POI Recommendation

Point of interest (POI) recommendation has received significant attention in recent years, most existing studies exploit multiple auxiliary information to alleviate the problem of data sparsity, and consider the sequential features of user mobility. However, few studies consider the category-level characteristics derived from each user's historical check-in frequencies, and the characteristics between different latent factors. In this paper, we exploit category-level multiple characteristics to generate recommendation. First, we obtain the frequency characteristics by in-depth analysis of the historical check-in frequencies for different categories by each user, and propose a scheme including KL-divergence and text analysis algorithm. Then we propose a category-level sequential- and non-sequential influence-aware probabilistic generative model (CSNS), which models the characteristics (correlation and indeterminate decisiveness) between user latent behavior topics and latent sequence patterns. We design two stages to generate recommendations. In the first stage, CSNS and frequencies characteristics are exploited jointly to recommend the POI categories that users may visit. In the second stage, we depend on user profiles and poi features, and sort the candidate POI sets by combining the POI categories provided in the first stage. Comprehensive experiments on two real-world datasets demonstrate that our method outperforms the existing state-of-the-art POI recommendation models.
A Comparative Study Using LZW with Wavelet or DCT for Compressing Color Images

As a result of the significance of image compression in reducing the volume of data, the requirement for this compression permanently necessary; therefore, will be transferred more quickly using the communication channels and kept in less space in memory. In this study, an efficient compression system is suggested; it depends on using transform coding (Discrete Cosine Transform or bi-orthogonal (tap-9/7) wavelet transform) and LZW compression technique. The suggested scheme was applied to color and gray models then the transform coding is applied to decompose each color and gray sub-band individually. The quantization process is performed followed by LZW coding to compress the images. The suggested system was applied on a set of seven standard images; the achieved compression results showed good efficiency in increasing the compression while keeping the fidelity level with the acceptable level. The acquired compression ratios are 20 for color Lena and 12.4 for gray Lena, both are 32 dB of PSNR.
How Experience Impacts Practitioners' Perception of Causes and Effects of Technical Debt

Context: The technical debt (TD) metaphor helps to conceptualize the pending issues and trade-offs made during software development. Knowing TD causes can support in defining preventive actions and having information about effects aids in the prioritization of TD payment. Goal: To investigate the impact of the experience level on how practitioners perceive the most likely causes that lead to TD and the effects of TD that have the highest impacts on software projects. Method: We approach this topic by surveying 227 practitioners. Results: While experienced software developers focus on human factors as TD causes and external quality attributes as TD effects, low experienced developers seem to concentrate on technical issues as causes and internal quality issues and increased project effort as effects. Missing any of these types of causes could lead a team to miss the identification of important TD, or miss opportunities to preempt TD. On the other hand, missing important effects could hamper effective planning or erode the effectiveness of decisions about prioritizing TD items. Conclusion: Having software development teams composed of practitioners with a homogeneous experience level can erode the team's ability to effectively manage TD.
Open Vocabulary Recognition of Offline Arabic Handwriting Text Based on Deep Learning

The offline Arabic text recognition is a substantial problem that has several important applications. It has attracted special emphasis and has become one of the challenging areas of research in the field of computer vision. Deep Neural Networks (DNN) algorithms provide the great performance improvement in problems of sequence recognition such as speech and handwriting recognition. This paper interests on recent Arabic handwriting text recognition researches based on DNN. Our contribution in this work is based on CRNN model with CTC beam search decoder that is used for the first time for handwriting Arabic recognition. The proposed system is an Open-Vocabulary approach that based on character-model recognition.
CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System

Detecting anomalies in large complex systems is a critical and challenging task. The difficulties arise from several aspects. First, collecting ground truth labels or prior knowledge for anomalies is hard in real-world systems, which often lead to limited or no anomaly labels in the dataset. Second, anomalies in large systems usually occur in a collective manner due to the underlying dependency structure among devices or sensors. Lastly, real-time anomaly detection for high-dimensional data requires efficient algorithms that are capable of handling different types of data (i.e. continuous and discrete). We propose a correlation structure-based collective anomaly detection (CSCAD) model for high-dimensional anomaly detection problem in large systems, which is also generalizable to semi-supervised or supervised settings. Our framework utilize graph convolutional network combining a variational autoencoder to jointly exploit the feature space correlation and reconstruction deficiency of samples to perform anomaly detection. We propose an extended mutual information (EMI) metric to mine the internal correlation structure among different data features, which enhances the data reconstruction capability of CSCAD. The reconstruction loss and latent standard deviation vector of a sample obtained from reconstruction network can be perceived as two natural anomalous degree measures. An anomaly discriminating network can then be trained using low anomalous degree samples as positive samples, and high anomalous degree samples as negative samples. Experimental results on five public datasets demonstrate that our approach consistently outperforms all the competing baselines.
Design of a String Encoder-and-IMU-Based 6D Pose Measurement System for a Teaching Tool and Its Application in Teleoperation of a Robot Manipulator

Potassium permanganate is mostly used for lightening denim in the textile industry. It is an inorganic chemical compound and an oxidizing agent. PP is sprayed to denim with a spray gun as microparticles. During the spraying process, these microparticles can be inhaled by workers causing lung diseases in the long term. PP spray is one of the most dangerous practices in the textile industry for the health and safety of workers. Robots can carry out PP spray instead of humans to protect workers’ health. Also, robots offer less labor cost, no labor mistake, fast production, and standardization in every product. The use of robots requires the teaching of tasks to robots. For this teaching procedure, it is necessary to record the teaching tool’s movements with high precision and accuracy. In this study, it is aimed at measuring the position and orientation, i.e. the pose, of the teaching tool in six dimensions using an inertial measurement unit (IMU) and six string encoders. In addition to this, the measured pose information is transferred to a robot manipulator for tracking the recorded trajectory. In that way, the operator’s movements can be copied and then used by robots to regenerate the best product while protecting the health of workers. The pose measurement system was designed and experimentally tested. The obtained results show that the system satisfies accuracy and precision expectations for the target application.
On the performance of deep learning for numerical optimization: An application to protein structure prediction

Deep neural networks have recently drawn considerable attention to build and evaluate artificial learning models for perceptual tasks. On the other hand, optimization is the problem of selecting a set of element to find an optimal/near optimal criterion. Here, we present a study on the performance of the deep learning models to deal with global optimization problems. More precisely, we would like to learn how to optimize the problems by using the machine learning techniques. Different from proposing very large networks with GPU computational burden and long training time, we focus on searching for lightweight implementations to find the best architecture. The performance of NAS is first analyzed through empirical experiments on CEC 2017 benchmark suite. Thereafter, it is applied to a set of protein structure prediction (PSP) problems. The experiments reveal that the generated learning models can achieve competitive results when compared to hand-designed algorithms; given enough computational budget.
Origami Store: UC-Secure Foldable Datachains for the Quantum Era

Traditional blockchains preserve everything, if verification is to avoid trusted third parties. The accumulation of outdated but undeletable data makes traditional blockchains non-scalable for practical applications. Stateless blockchains mitigate this problem via history deletion; however, existing stateless blockchains such as Mimblewimble are not quantum resistant as they rely on specific discrete-log-based cryptography to achieve history deletion. In this paper, we introduce the “Origami Store” (or O-store for short), as a general new approach to distributed data storage with a generic history deletion mechanism called “folding.” O-store is built using a hash function and an arbitrary signature scheme. Since the signature scheme is decoupled from the framework, O-stores are compatible with any quantum-resistant signature scheme by design. More importantly, O-store only needs to preserve one signature and one verification key per account (or owner) for any number of transactions (or updates), thereby mitigating the inefficiency of large quantum-safe signatures and verification keys. We prove the security of the basic O-store using the framework of Universal Composability (UC); in particular, O-store's universal security is shown to hold for a generic consensus mechanism, to facilitate implementation from a wide range of decentralized consensus protocols. The Origami store is thus the first UC-secure efficient distributed data storage framework that supports plug-and-play consensus mechanisms for fully decentralized trustless operation. We demonstrate working implementations of Origami stores with three post-quantum signature libraries-qTESLA, Dilithium, and Picnic-to illustrate its scalability and practicality for real-world applications.