Reviewerly/exHarmony huggingface dataset:
THE EFFECTS OF ENVIRONMENTAL QUALITY, TRADE OPENNESS, AND ECONOMIC GROWTH ON FINANCIAL DEVELOPMENT IN ALGERIA: A DIKS AND PANCHENKO APPROACH.

The current paper's purpose is to examine the effect of economic growth on Algeria's financial development and the position of trade openness. The paper uses evidence from the time series spanning 1970 to 2018. Gregory and Hansen (1996) co-integration approach, Non-linear ARDL as the elasticity estimator, and Diks and Panchenko (2006) have been applied to the causality test. The outcome of co-integration indicates a long-term relationship between variables. Moreover, the findings confirm that financial development asymmetry related to economic growth. The analysis further reveals the presence of non-linear unidirectional causality between economic growth and financial performance. Besides, the result shows that financial development contributes to environmental degradation in Algeria. It also documents a one-way causal link between trade openness that leads to financial development. It suggests that there exists an overall positive and significant effect of economic growth and trade openness on financial development. The current study advises that an increase in the performance of the real sector and the more openness to trade as it will result in a beneficial spillover effect on the growth of the Algerian financial sector.
Real-time Human Action Detection for Elderly Monitoring System

As the safety of elderly people living alone receives more and more attention, some works start focusing on the monitoring system for abnormal behaviors of the elderly. In this paper, we propose a simple and effective realtime human action detection method based on model fine-tuning for elderly monitoring system. At first, we built our own action dataset, where falling down is the most important abnormal behavior. We also utilize data augmentation technique to generate diverse training samples and prevent severe over-fitting. Then, we use our own action dataset to fine tune the improved ECO model. Through the part of the existing model parameters as the initial parameters of the new model, greatly accelerated the convergence rate of the model training. Finally, we apply the trained model to real-time human action detection. The experimental results prove that the proposed approach can accurately detect the abnormal behavior of falling down while ensuring the online real-time requirement.
A Function Space Analysis of Finite Neural Networks With Insights From Sampling Theory

This work suggests using sampling theory to analyze the function space represented by interpolating mappings. While the analysis in this paper is general, we focus it on neural networks with bounded weights that are known for their ability to interpolate (fit) the training data. First, we show, under the assumption of a finite input domain, which is the common case in training neural networks, that the function space generated by multi-layer networks with bounded weights, and non-expansive activation functions are smooth. This extends over previous works that show results for the case of infinite width ReLU networks. Then, under the assumption that the input is band-limited, we provide novel error bounds for univariate neural networks. We analyze both deterministic uniform and random sampling showing the advantage of the former.
Pervasive Health Systems: Convergence through Artificial Intelligence and Blockchain Technologies

Proposed research aims to investigate the convergence of A.I. and Blockchain in innovating applications for the healthcare industry. Healthcare is one of the most critical aspects of human life; however, in recent years, it has been plagued by a problem of data access. Due to stakeholders using different Information systems, it is not ideal for processing healthcare data in realtime. To tackle this problem, Artificial Intelligence and Blockchain technologies can help present Healthcare systems in a Pervasive form. The convergence of technologies can realize the ‘democratization of data’ to provide healthrelated information with greater possibilities.
Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.
Fault diagnosis of Combined Cycle Power Plant Using ELM

In this study, fault detection and fault diagnosis in the high-pressure tubes of a combined cycle power plant's high pressure steam generator was investigated. Identification and prevention from fault propagation in combined cycle power plants plays the main role in improving the reliability and safety of these systems. In this work, leakage faults detection in the internal, inlet and outlet tubes and their effects on operating parameters of a heat recovery steam generator (HRSG) were studied. In order to determine the system's behaviors, residual generation based on nonlinear autoregressive exogenous networks have been proposed, and appropriate features based on statistical feature generation have been extracted. To achieve more accurate classifying of system's faults, an extreme learning method technique was employed for the training of extracted features. Extreme learning machine method's excellent prediction capabilities are the main advantages of represented method, which could be used for improving the performance and troubleshooting of the power plant's equipment. The performances of the proposed fault diagnosis system were assessed at different leakage fault conditions by performing simulation experiments on an accurate model of power plant. The obtained results show the accuracy and reliability of this method.
Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics

Studies are constantly developing and improving speech recognition systems, especially their accuracy. This study developed an isolated word recognition system by using the syllable number characteristics of speech signals that will be recognized. First, the syllable number of speech signals to be recognized was detected, and then, the detection results were used to call one of the database groups that matched the syllable number characteristics. This method was designed to reduce the error possibility through a matching process between test data features and database features. This study used Mel frequency cepstral coefficients (MFCC) for feature extraction and the K-nearest neighbor (KNN) method for classification. Three versions of the proposed method were designed. The results showed that version three increased the accuracy by 4% compared to the conventional recognition system. Version three had the fastest computational time compared to the other methods. The addition of syllable detection algorithms in version three increased the computational time by only 0.151 s compared to the conventional MFCC method. The data cut length and threshold value for the filter also influenced the speech recognition system accuracy.
Omnidirectional mobile robot design with height and width adaptation

This paper aims to present an innovative design in order to skip obstacles with dimensions adjustments. These adjustments are carried out by width and height adaptations. The capability of overcoming the obstacles with the minimum amount of energy consumption is considered to be superiority. In some applications, bypassing the obstacles is impossible or inefficient. The width adjustment of the robot is fulfilled by a special mode of wheels rotation configuration and without an extra actuator. A linear actuator is mounted on the platform to make the height adjustment possible. In additions, the kinematic equations have been derived and also compared with simulation in ADAMS software. A practical application for overcoming an obstacle is simulated to prove the presented design is able to function in the environment effectively. The obtained results from Adams show 6% difference with the kinematic analysis.
Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable (Dagstuhl Seminar 19452).

This report documents the program and the outcomes of Dagstuhl Seminar 19452 Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable.
Addressing Cold Start Challenges In Recommender Systems: Towards A New Hybrid Approach

Along with the rapid expansion of data in information systems, managing and searching for personalized information has become a tedious task. Users are overwhelmed by a wide range of available choices. It then becomes necessary to have access to effective tools and techniques to filter data and make it usable for everyday operations. Recommender systems have been considered as key tools for assisting the user and providing her with more effective access to information through personalized recommendations based on prior feedback. However, this becomes difficult when the rating or purchase history is not available. This is known as the cold start problem. To fill these gaps, this paper proposes a new hybrid approach considering three main modules and relying on the combination of semantic recommendation, demographic recommendation, and Singular Value Decomposition-based collaborative recommendation.
"Unsex me here": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples.

To effectively tackle sexism online, research has focused on automated methods for detecting sexism. In this paper, we use items from psychological scales and adversarial sample generation to 1) provide a codebook for different types of sexism in theory-driven scales and in social media text; 2) test the performance of different sexism detection methods across multiple data sets; 3) provide an overview of strategies employed by humans to remove sexism through minimal changes. Results highlight that current methods seem inadequate in detecting all but the most blatant forms of sexism and do not generalize well to out-of-domain examples. By providing a scale-based codebook for sexism and insights into what makes a statement sexist, we hope to contribute to the development of better and broader models for sexism detection, including reflections on theory-driven approaches to data collection.
Quantitative assessment of concept maps for conceptualizing domain ontologies: a case of Quran

The use of graphical knowledge representation formalisms with a representational vocabulary agreement of terms of conceptualization of the universe of discourse is a new high potential approach in the ontology engineering and knowledge management context. Initially, concept maps were used in the fields of education and learning. After that, it became popular in other areas due to its flexible and intuitive nature. It was also proven as a useful tool to improve communication in corporate environment. In the field of ontologies, concept maps were explored to be used to facilitate different aspects of ontology development. An essential reason behind this motivation is the structural resemblance of concept maps with the hierarchical structure of ontologies. This research aims to demonstrate quantitative evaluation of 4 different hypotheses related to the effectiveness of using concept maps for ontology conceptualization. The domain of Quran was selected for the purpose of this study and it was conducted in collaboration with the experts from the Centre of Quranic Research, Universiti Malaya, Kuala Lumpur, Malaysia. The results of the hypotheses demonstrated that concept mapping was easy to learn and implement for the majority of the participants. Most of them experienced improvement in domain knowledge regarding the vocabularies used to refer to the structure of organization of the Quran, namely Juz, Surah, Ayats, tafsir, Malay translation, English translation, and relationships among these entities. Therefore, concept maps instilled the element of learning through the conceptualization process and provided a platform for participants to resolve conflicting opinions and ambiguities of terms used immediately.
Attention-guided cascaded networks for improved face detection and landmark localization under low-light conditions

Often, state-of-the-art techniques for face detection exhibit suboptimal performance under poor illumination conditions. For instance, the absence of ambient light effectively challenges a model’s capacity to extract relevant global and local features needed to localize faces in images. This paper proposes a technique based on generative adversarial networks to improve the visual quality of images captured in low lighting conditions to significantly improve face detection and landmark localization in real-world images. An attention-guided cascaded residual generator network (AG-CRN) along with a Markovian discriminator is trained in an adversarial manner to synthesize enhanced and properly illuminated images given corresponding low-light pairs. Extensive experiments on several datasets, including the DARK FACE dataset, demonstrate that AG-CRN is capable of producing visually pleasing images, and also significantly improves the performance of state-of-the-art models for face detection and landmark localization under very low-lighting conditions.
Extending and Analyzing Self-Supervised Learning Across Domains

Self-supervised representation learning has achieved impressive results in recent years, with experiments primarily coming on ImageNet or other similarly large internet imagery datasets. There has been little to no work with these methods on other smaller domains, such as satellite, textural, or biological imagery. We experiment with several popular methods on an unprecedented variety of domains. We discover, among other findings, that Rotation is by far the most semantically meaningful task, with much of the performance of Jigsaw and Instance Discrimination being attributable to the nature of their induced distribution rather than semantic understanding. Additionally, there are several areas, such as fine-grain classification, where all tasks underperform. We quantitatively and qualitatively diagnose the reasons for these failures and successes via novel experiments studying pretext generalization, random labelings, and implicit dimensionality. Code and models are available at this https URL.
A Hybrid Method for Text-Based Sentiment Analysis

Text-based sentiment analysis is an automated process of analyzing natural language text data and classifying opinions as negative, positive or neutral using computational methods. It can assist in search engines, recommender systems, marketing research, and particularly with online business. With the growth of the internet, numerous business websites have been deployed to support online shopping products, booking services as well as to allow online reviewing and commenting the services in forms of either business forums or social networks. Use of sentiment analysis for automatically mining opinion using the feedbacks from such emerging internet platforms is not only useful for customers seeking for advice, but also necessary for business to study customers' attitudes toward brands, products, services, or events, and has become an increasingly dominant trend in business strategic management. Current state-of-the-art approaches for sentiment analysis include lexicon based and machine learning based methods. In this research, we proposed a hybrid method that utilizes lexicon based approach with machine learning. We showed that our method outperformed the state-of-the-art lexicon based methods.
A nonlinear invariant attack on T-310 with the original Boolean function

There are numerous results on nonlinear invariant attacks on T-310. In all such attacks found so far, both the Boolean functions and the cipher wiring were contrived and chosen by the attacker. In this article, we show how to construct an invariant attack with the original Boolean function that was used to encrypt government communications in the 1980s.
Research on QR 2-D Code Graphics Correction Algorithms Based on Morphological Expansion Closure and Edge Detection

As a kind of widely used form of two-dimensional codes, the QR codes have been widely applied in various aspects of our life, but in the process of the use of the QR codes, the QR code images captured often suffer from deformity because the QR code label is attached onto the object irregular surface (for example, a curved surface) or the camera, and the QR code label is different in position. This paper improves the definition of images and thus, effectively improves the recognition rate by adopting the morphological closure and edge detection algorithm and effectively improving the classic correction algorithm for QR code image based on shape function as for QR code image with surface distortion.
Data retrieval service on poverty blockchain-based on regional government

The distribution of assistance to disadvantaged people in an area is always synonymous with problems especially, in the duplication of assistance and manipulation of family data that actually has no right to obtain assistance data for underprivileged families. To ensure the integrity of the data taken is not duplicated and non-repudiation, the blockchain-based poverty data collection plan is one solution. The purpose of this research is to ensure that the data retrieved cannot be modified after data retrieval and the database cannot validly deny that the data has been provided as a result of a particular request. The feasibility of the proposed service using the blockchain method is a request from the poverty database in the form of low-income family data and assistance type data that are placed in the blockchain series and tested with hash values that have been created from the blockchain
Monocular depth estimation for vision-based vehicles based on a self-supervised learning method

Unsupervised depth estimation methods that allow training on monocular image data have emerged as a promising tool for monocular vision-based vehicles that are not equipped with a stereo camera or a LIDAR. Predicted depths from single images could be used, for example, to avoid obstacles in autonomous navigation, or to improve in-vehicle change detection. We employ a self-supervised depth estimation network to predict depth in monocular image sequences acquired by a military vehicle and a UGV. We trained the models on the KITTI dataset, and performed a fine-tuning on monocular image data for each vehicle. The results illustrate that the estimated depths are visually plausible for on-road as well as for off-road environments. We also provide an example application by using the predicted depths for computing stixels, a medium-level representation of traffic scenes for self-driving vehicles.
Can an Algorithm Prepare Students for Tasks without Knowing What the Tasks Are?

we report on two consecutive randomized controlled studies that tested the implementation of a state-of-the-art neural network-based algorithm for personalizing the sequencing of content to learners based on predictive subjective difficulty level. Performance of the students who followed the algorithm recommendations were first compared to those of students who followed an expert teacher-based recommendations (study 1); then, based on the findings, we compared the impact of the algorithm recommendations to that of a baseline (non-personalized) sequence set-up by human experts (study 2). In the second study, the algorithm was successful in preparing the students to the post-test tasks equally well as the human experts were, however without knowing what these tasks were. We highlight the advantages and the limitations of the expert teacher, as well as the algorithm's ability to do no worse than the human experts.
Artificial intelligence and inventorship: an Australian perspective

The proliferation of artificial intelligence (AI) in recent years has been described by the World Economic Forum as the ‘Fourth Industrial Revolution’.1 Sophisticated machine learning has already transformed the fields of literature, food, art, medicine, music and even law.2 Some have gone so far as to suggest that AI will ultimately be responsible for, or at least contribute to, the vast majority of future inventions.3 However, the dawn of AI is not without complications. According to Alan Turing's Polite Convention: if a machine acts as intelligently as a human being, then it is as intelligent as a human being.4 Indeed, AI is acting so intelligently that it is capable of generating new material without human input, and this has created concern around whether the existing legal framework is adequate to cater for this new development. In the context of copyright, there have been calls for...
Ring-LWE Based Face Encryption and Decryption System on a GPU

This paper presents a novel method to implement ring learning with errors (ring-LWE) cryptography for video-based face encryption and decryption on a graphics processing unit (GPU). By conducting ring arithmetic operations in parallel on a GPU, the processing time of these operations is significantly reduced. Consequently, ring-LWE encryption and decryption operations are remarkably improved. The simulation results conducted on GPU and CPU platforms using CUDA C++ show that the ring-LWE based face encryption and decryption operations implemented on a GPU are approximately 100 times faster than that implemented on a CPU.
L0-norm Constrained Autoencoders for Unsupervised Outlier Detection

Unsupervised outlier detection is commonly performed using reconstruction-based methods such as Principal Component Analysis. A recent problem in this field is the learning of low-dimensional nonlinear manifolds under L0-norm constraints for error terms. Despite significant efforts, no method that consistently treats such features exists. We propose a novel unsupervised outlier detection method, L0-norm Constrained Autoencoders (L0-AE), based on an autoencoder-based detector with L0-norm constraints for error terms. Unlike existing methods, the proposed optimization procedure of L0-AE provably guarantees the convergence of the objective function under a mild condition, while neither the relaxation of the L0-norm constraint nor the linearity of the latent manifold is enforced. Experimental results show that the proposed L0-AE is more robust and accurate than other reconstruction-based methods, as well as conventional methods such as Isolation Forest.
Possibilities of using practical limitations of an eavesdropper in quantum cryptography

Abstract An important advantage of quantum cryptography over classical cryptography consists in the fact that the security of the transmitted keys is not related to the assumptions about the eavesdropper capabilities and is guaranteed by the laws of nature. Nevertheless, in some situations it makes sense to consider some reasonable assumptions about the eavesdropper capabilities, which can increase the secret key distribution rate. Methods are proposed for legitimate users to employ some practical limitations, and attacks are constructed that the eavesdropper can apply under the conditions of these limitations.