Reviewerly/exHarmony huggingface dataset:
Teaching a Modeling Process: Reflections From An Online Course

The outbreak of the COVID-19 pandemic in 2020 posed unique challenges for academic and professional education, while at the same time offering opportunities related to the mass switching of the delivery of courses to the online mode. In this paper, we share the experience of organizing and delivering an online doctoral-level course on Agent-Based Modeling for Social Research. Our aim was to teach interdisciplinary content on various elements of the modeling process in a coherent and practical way. In the paper, we offer a critical assessment of different aspects of the course, related to content as well as organization and delivery. By looking at the course in the light of the current knowledge on good teaching and learning practices from the educational and psychological literature, and reflecting on the lessons learned, we offer a blueprint for designing and running complex, multi-thread simulation courses in an efficient way.
Machine Learning Based Smart Assistive Device for Differently Abled People-SADDAP

Recently, Human Activity Recognition (HAR) has turned into an energetic exploration region, particularly because of the spread of electronic gadgets, for example, cell phones, digital watches and camcorders present in our everyday lives. Likewise fall accidents are one of the primary sources of passing’s because of cracks brought about by clash with the ground or hard obstructions. The failure to see objects around oneself is the most tragic things that could happen to an individual. This likewise annihilates the capacity of the individual to move around without anyone else in known regions and conditions. Over the range of the last decade, a few sorts of gadgets have been intended to help the differently abled individuals to move around in various types of conditions be that as it may, in any case need to have every one of the components for diversely abled individuals or senior’s in one gadget. The current paper is the augmentation of our past work to add to say research with a way for offering help to differently abled or elder’s people. The proposed smart assistive activity recognition device for differently abled people that perceive the movement of environment around them and assist them with living. It likewise recognizes snags and sound on their route to caution them so they don’t get into an accident. The current paper adds to help differently abled individuals or elder’s by permitting them to realize the encompassing objects and safe them from getting into any mishap by fall detection and obstacle detection. Activity discovery is turning into an indispensable piece of numerous portable applications. The proposed system utilizes the procedures of machine learning to make an application that would help the differently abled individuals or elders and give them a freshly discovered expectation for cherishing the lavishness and difficulty of the world, meanwhile effectively explore in it.
Stochastic Approximation with Gaussian Process Regression

Stochastic approximation (SA) is attractive due to its curse-of-dimensionality-free convergence rate, but its finite-sample performance is not always satisfying. In addition to improving SA solely, it is also a promising direction to combine SA with other simulation optimization methods together for better performance. In this paper we propose to integrate the original SA with Gaussian process (GP) regression, and call this algorithm SAwGP. The GP regression serves as a surrogate model and it uses all the past sampling information to guide the SA iteration, which tends to be beneficial especially in the early stage. We theoretically prove that integrating the surrogate model does not ruin the local convergence of SA, and numerically demonstrate that the finite-sample performance of SAwGP is better than the original SA while the rate of convergence does not deteriorate and is even enhanced.
Improving the Transferability of Adversarial Examples by Using Generative Adversarial Networks and Data Enhancement

Abstract Deep neural networks (DNNs) can be attacked by adversarial examples that are undetectable by humans. Generation-based approaches have recently gained popularity because they directly translate the input distribution to the distribution of adversarial instances, making them more effective and efficient. However, existing techniques are susceptible to overfitting on the substitute model, limiting the transferability of adversarial examples. In this paper, we introduce data augmentation into AdvGAN, called AdvGAN-DE, which can dramatically improve the transferability of adversarial examples. Experiments demonstrate that we enhance the success rate by 31.09% for defense models and 10.23% for typically trained models on average when compared to AdvGAN.
Intelligent Systems for Optimal Production Control of Unique Products

The article proposes and discusses new approaches to the development and effective use of intelligent systems for optimal control of the production processes of unique technological products, that is, such products that are able to satisfy unmet demand and have no analogues in the world. The main results of this research are a conceptual model of an intelligent control system formation for the development and production of unique products.
Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper. The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm's exploration and exploitation capability. A comprehensive test suite of standard benchmark problems with different natures, i.e., unimodal/multimodal and separable/non-separable, was used to test the convergence power of the proposed CTbADE algorithm. Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness, solution quality, and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms, such as jDE, CoDE, and EPSDE algorithms. This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computational models with a high number of parameters to adjust during the problem-solving process.
A Boundary-Information-Based Oversampling Approach to Improve Learning Performance for Imbalanced Datasets

Oversampling is the most popular data preprocessing technique. It makes traditional classifiers available for learning from imbalanced data. Through an overall review of oversampling techniques (oversamplers), we find that some of them can be regarded as danger-information-based oversamplers (DIBOs) that create samples near danger areas to make it possible for these positive examples to be correctly classified, and others are safe-information-based oversamplers (SIBOs) that create samples near safe areas to increase the correct rate of predicted positive values. However, DIBOs cause misclassification of too many negative examples in the overlapped areas, and SIBOs cause incorrect classification of too many borderline positive examples. Based on their advantages and disadvantages, a boundary-information-based oversampler (BIBO) is proposed. First, a concept of boundary information that considers safe information and dangerous information at the same time is proposed that makes created samples near decision boundaries. The experimental results show that DIBOs and BIBO perform better than SIBOs on the basic metrics of recall and negative class precision; SIBOs and BIBO perform better than DIBOs on the basic metrics for specificity and positive class precision, and BIBO is better than both of DIBOs and SIBOs in terms of integrated metrics.
Large-Scale Commodity Knowledge Organization and Intelligent Query Optimization

The Internet is facing the era of knowledge interconnection Web3.0, and its goal is to realize a more intelligent network that can be understood by both humans and machines. In this environment, various types of knowledge graphs have emerged. Because of the heterogeneity of knowledge, commodity knowledge makes its management more challenging. A large-scale product knowledge organization framework is designed, objective product classification knowledge is combined with subjective user perspectives in the framework, a neural network-based learning index technology is proposed to improve query efficiency. According to the properties of the knowledge structure and the characteristics of query requirements, a connection strategy is realized based on sub-variable combination.The experimental results show that, compared with the existing knowledge management system, the proposed method has a significant improvement in the retrieval efficiency of large-scale commodity knowledge.
Measurement Data Preprocessing Based on Word Length Manipulation to Improve Data Compression Procedure Efficiency

The article proposes a procedure for changing the word length of the measurement data, which is carried out before the data compression operation and leads to an increase in the compression ratio. The results of the study of the procedure for choosing the bit depth are presented. Studies were carried out both on sequences of samples obtained from one sensor and on frames sequences are consisting of samples acquired from several sensors. The data obtained show that the proposed procedure is promising. The article also outlines the main directions for further research. The issues considered are related to increasing the efficiency of the data compression subsystems of information-measuring systems working with complex technical objects.
An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

The Deep Neural Networks (DNN) training process is widely affected by backdoor attacks. The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers. The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger. To overcome this problem, in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies, and in order to achieve this objective, we are developing an improved Convolutional Neural Network (ICNN) model optimized using a Gradient-based Optimization (GBO)(ICNN-GBO) algorithm. In the ICNN-GBO model, we are injecting the triggers via a steganography and regularization technique. We are generating triggers using a single-pixel, irregular shape, and different sizes. The performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate, stealthiness, pollution index, anomaly index, entropy index, and functionality. When the CNN-GBO model is trained with the poisoned dataset, it will map the malicious code to the target label. The proposed scheme's effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 and MSCELEB 1M dataset. The results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse.
Complexity of the interpretability logics <b>IL</b>W and <b>IL</b>P

Abstract The interpretability logic ILP is the interpretability logic of all sufficiently strong $\varSigma _1$-sound finitely axiomatised theories, such as the Gödel-Bernays set theory. The interpretability logic IL is a strict subset of the intersection of the interpretability logics of all so-called reasonable theories, IL(All). It is known that both ILP and ILW are decidable, however their complexity has not been resolved previously. In [10] it was shown that the basic interpretability logic IL is PSPACE-complete. Here we prove the same for ILP and ILW.
Neural Topic Model Training with the REBAR Gradient Estimator

Topic modelling is an important approach of unsupervised machine learning that allows automatically extracting the main “topics” from large collections of documents. In addition, topic modelling is able to identify the topic proportions of each individual document, which can be helpful for organizing the collections. Many topic modelling algorithms have been proposed to date, including several that leverage advanced techniques such as variational inference and deep autoencoders. However, to date topic modelling has made limited use of reinforcement learning, a framework that has obtained vast success in many other unsupervised learning tasks. For this reason, in this article we propose training a neural topic model using a reinforcement learning objective and minimizing the objective with the recently-proposed REBAR gradient estimator. Experiments performed over two probing datasets have shown that the proposed model has achieved improvements over all the compared models in terms of both model perplexity and topic coherence, and produced topics that appear qualitatively informative and consistent.
Effective Frameworks Based on Infinite Mixture Model for Real-World Applications

Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications. In particular, recognizing human activities with accurate results have become a topic of high interest. Although the current tools have reached remarkable successes, it is still a challenging problem due to various uncontrolled environments and conditions. In this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some real-world applications. In particular, two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are developed. These models are then applied to address the problem of modelling grouped data where observations are organized into groups and these groups are statistically linked by sharing mixture components. The choice of the Gamma mixtures is motivated by its flexibility for modelling heavy-tailed distributions. In addition, deploying the Dirichlet process prior is justified by its advantage of automatically finding the right number of components and providing nice properties. Moreover, a learning step via variational Bayesian setting is presented in a flexible way. The priors over the parameters are selected appropriately and the posteriors are approximated effectively in a closed form. Experimental results based on a real-life applications that concerns texture classification and human actions recognition show the capabilities and effectiveness of the proposed framework.
Mahalanobis distance with an adapted within-author covariance matrix: An authorship verification experiment

Abstract The rotated delta, which is argued to be a theoretically better-grounded distance measure, has failed to receive any empirical support for its superiority. This study revisits the rotated delta—which is more commonly known as the Mahalanobis distance in other areas—with two different covariance matrices that are estimated from training data. The first covariance matrix represents the between-author variability, and the second the within-author variability. A series of likelihood ratio-based authorship verification experiments was carried out with some different distance measures. The experiments made use of the documents arranged from a large database of text messages that allowed for a total of 2,160 same-author and 4,663,440 different-author comparisons. The Mahalanobis distance with the between-author covariance matrix performed far worse compared to the other distance measures, whereas the Mahalanobis distance with the within-author covariance matrix performed better than the other measures. However, superior performance relative to the cosine distance is subject to word lengths and/or the order of the feature vector. The result of follow-up experiments further illustrated that the covariance matrix representing the within-author variability needs to be trained using a good amount of data to perform better than the cosine distance: the higher the order of the vector, the more data are required for training. The quantitative results also infer that the two sources of variabilities—notably within- and between-author variabilities—are independent of each other to the extent that the latter cannot accurately approximate the former.
Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding

Abstract Most current knowledge base question answering models mainly use RNN and its various derivative versions such as BiLSTM to model the problem, which limits Parallel computing capabilities of the model. In response to this problem, we try to use TransformerEncoder instead of BiLSTM to model and encode the problem, and hope to improve the parallel computing efficiency of the model. At the same time, to solve the problem of insufficient relative position information obtained by using absolute position coding in TransformerEncoder, it is proposed to use relative position coding instead of absolute position coding. According to the experimental results, our model effectively reduces a certain amount of training time and has achieved certain results on the WebQuestions benchmark data set.
Video as a didactic resource in a hybrid learning environment

The teaching-learning process had a drastic change with the pandemic, first it changed from face-to-face to virtual and later it changed to a hybrid modality. In the case of the Universidad Politécnica del Estado de Morelos, the hybrid modality consists of synchronous sessions with students in the classroom and online, this increases the complexity of the design of the appropriate learning environment. The objective of this research was to obtain information that would allow us to see if the use of video as an educational resource has a positive impact on the teaching-learning process in current days. To verify this, the methodology used consisted of five stages: Selection of technological tools, design of the action process using video for each of the four spaces of a hybrid learning environment, execution of the action process, answering surveys and analysis of results. With this study, it was possible to provide teachers with a strategy that allows them to expand their personal toolbox during the teaching-learning process in hybrid environments.
Alternating Diatonic Qualities in Etude 15, White on White

As mentioned in the introduction, the perhaps defining feature of the etudes of Book 3 is the restricting of the gamut of PCs used at any one time, as opposed to the freer chromatic saturation of his previous piano etudes. There is perhaps no more radical example of this than Etude 15, White on White, which for the majority of its duration does not stray at all from the white-note set—and the few black notes that do appear towards the end of the piece come across as specks of pepper in a sea of salt.
Variational Bayesian Based IMM Robust GPS Navigation Filter

This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.
CYBER (RE-)INSURANCE POLICY WRITING IS NP-HARD IN IOT SOCIETIES

The last decade has witnessed steadily growing markets for cyber (re-)insurance products to mitigate residual cyber-risk. In this introductory effort, we prove that underwriting simple cyber re-insurance policies can be worst case computationally hard, i.e., NP-Hard, especially for upcoming IoT societies. More specifically, let alone human underwriters, even a computer cannot compute an optimal cyber re-insurance policy in a reasonable amount of time in worst case scenarios. Here, optimality of a contract is judged based on the extent of information asymmetry induced negative externalities it mitigates between a re-insurance seller and a buyer. Our result does not challenge the existence of cyber re-insurance markets that we feel will be a necessity in the IoT age, but only rationalizes why their growth might be slow, and would subsequently need regulatory intervention. As a direct applicability of our methodology, we argue that optimal traditional cyber-insurance underwriting in IoT societies is also NP-Hard.
Hardware/Software Co-Design of a Low-Power IoT Fall Detection Device

Falls can cause severe injuries to elder people. Many studies have been conducted in order to develop devices that improve the recognition of fall events, while very few have demonstrated approaches for the minimization of their power consumption. We propose a hardware/software co-design of an Internet of Things (IoT) fall detection device that takes advantage of accelerometer’s embedded functionalities and enables its interrupt-driven operation while the rest of the circuit is in shutdown mode. In contrast with most low power fall detection devices, the proposed device supports Wi-Fi connectivity that enables ubiquitous use and real-time remote fall events monitoring through cloud services, without the need of external equipment or a local server. The device integrates a vibration motor and a cancelation button, which notify the detection of a fall event and waits for Patient’s response, while the implementation of battery monitoring mechanism enables continuous device use.