Reviewerly/exHarmony huggingface dataset:
Similarity-based Deep Neural Network to Detect Imperceptible Adversarial Attacks

Deep neural networks (DNN's) have become es-sential for solving diverse complex problems and have achieved considerable success in tackling computer vision tasks. How-ever, DNN's are vulnerable to human-imperceptible adversarial distortion/noise patterns that can detrimentally impact safety-critical applications such as autonomous driving. In this paper, we introduce a novel robust-by-design deep learning approach, Sim-DNN, that is able to detect adversarial attacks through its inner defense mechanism that considers the degree of similarity between new data samples and autonomously chosen prototypes. The approach benefits from the abrupt drop of the similarity score to detect concept changes caused by distorted/noise data when comparing their similarities against the set of prototypes. Due to the feed-forward prototype-based architecture of Sim-DNN, no re-training or adversarial training is required. In order to evaluate the robustness of the proposed method, we considered the recently introduced ImageNet-R dataset and different adversarial attack methods such as FGSM, PGD, and DDN. Different DNN's methods were also considered in the analysis. Results have shown that the proposed Sim-DNN is able to detect adversarial attacks with better performance than its mainstream competitors. Moreover, as no adversarial training is required by Sim-DNN, its performance on clean and robust images is more stable than its competitors which require an external defense mechanism to improve their robustness.
Surrogate-assisted Parameter Re-initialization for Differential Evolution

With respect to parameter adaptation in meta-heuristic algorithms, a kind of feedback usually exists between parameter adaptation and individuals' survival. It is known that parameters in reproduction operator largely determine the quality of offspring. As feedback, successful survival of individuals can guide the direction in which parameters adapt. However, parameter adaptation only based on the preference of survivors would easily get premature convergence. To alleviate this issue, a parameter re-initialization strategy is proposed to regenerate more reasonable starting points at different stages for parameter adaptation. In this study, support vector regression with radial basis function is used to estimate potential parameter combinations for JADE. The IEEE CEC2014 test suite including 30 benchmark test functions is used to evaluate our proposed strategy. Obtained results are compared with six state-of-the-art optimization algorithms. Statistical analysis demonstrates the effectiveness of the proposed surrogate-assisted parameter re-initialization strategy.
Privacy-Aware Human Activity Classification using a Transformer-based Model

Fall detection in a bathroom requires privacy as an essential issue. Ultra-wideband sensors have the ability to protect human privacy because their output has only limited information. As a result, interpreting the output is a challenging task. This research implemented a transformer model that learned time-series signals from an ultra-wideband sensor in a bathroom. First, the signals were preprocessed into the two-dimensional range-time format. Second, the range-time data were passed into a convolutional neural network encoder before going into a transformer. Third, the basic movements of humans were used for training. Finally, the encoder and the Transformer were trained separately. The model achieved good accuracy on static postures but not good on transitions due to their overlapped similarity with the static postures.
Tagging Metaheuristics with Problem-Oriented Labels for Non-Expert Users

Represented by evolutionary computation and swarm intelligence, nature-inspired metaheuristics have shown superiority in solving complex optimization problems for decades. In spite of the source codes of state-of-the-art metaheuristics provided in existing libraries such as PlatEMO, it is still difficult for researchers unfamiliar with metaheuristics to select a suitable one for solving their problems. Since no metaheuristic can per-form the best on all problems, selecting a suitable metaheuristic is very critical for handling real-world applications. Therefore, this work suggests a set of problem-oriented labels for tagging metaheuristics, where metaheuristics are not simply classified into disjoint categories but tagged with multiple labels. By referring to the labels of a metaheuristic, researchers can easily know whether it is capable of solving a given type of problems. The proposed labels can highly facilitate the selection of metaheuristics.
Explainable Model-specific Algorithm Selection for Multi-Label Classification

Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes.MLC is increasingly gaining interest in different application domains such as text mining, computer vision, and bioinformatics.Several MLC algorithms have been proposed in the literature, resulting in a meta-optimization problem that the user needs to address: which MLC approach to select for a given dataset?To address this algorithm selection problem, we investigate in this work the quality of an automated approach that uses characteristics of the datasets -so-called features -and a trained algorithm selector to choose which algorithm to apply for a given task.For our empirical evaluation, we use a portfolio of 38 datasets.We consider eight MLC algorithms, whose quality we evaluate using six different performance metrics.We show that our automated algorithm selector outperforms any of the single MLC algorithms, and this is for all evaluated performance measures.Our selection approach is explainable, a characteristic that we exploit to investigate which meta-features have the largest influence on the decisions made by the algorithm selector.Finally, we also quantify the importance of the most significant metafeatures for various domains.
Co-evolving morphology and control of soft robots using a single genome

When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the “brain” and the “body” of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects “brain” and the “body” at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.
An Improvement of Loop Closure Detection Based on BoW for RatSLAM

The visual perception of the original RatSLAM algorithm works directly on the intensities of image pixels and thus is greatly affected by illuminations, weathers, and other factors. The algorithm may result in perception errors and mapping failures in some situations. Therefore, this paper proposes an image sequence matching algorithm based on a visual dictionary model termed as Bag of Words (BoW) and the dynamic island mechanism to realize fast and accurate scene recognition. The feature vector of scene image is generated by the ORB algorithm, and then the image matching is transformed into the calculation of the distance between image feature vectors, improving the efficiency of image matching. The dynamic island mechanism is used as a detection algorithm to realize the sequence matching of the environment, improving the accuracy of loop closure detection. Experiments are performed in different environments, showing that the visual processing performance and mapping performance of the proposed algorithm are significantly better than the existing RatSLAM algorithm.
A Qualitative Analysis of Interaction Techniques in a Virtual Reality Instruction Environment: Experiences From a Case Study

As they can immerse people into virtual worlds free from external distraction, virtual reality (VR) applications are an important factor in research today and can be useful in various scenarios such as intervention training, simulations and in learning environments. However, many people are not used to interacting and navigating in VR and may not have much prior technical experience, which can become a distracting factor to the content and can influence success negatively. Thus, benefits of VR-environments cannot be fully exploited. Aiming to counteract the problem that people cannot take advantage of the benefits of VR-applications, an instruction room for training representative interaction patterns, i.e. interaction and locomotion mechanics, was designed. Students (N = 12) were videotaped going through six stations of the instruction room with different interaction tasks. They were prompted to think aloud to capture their thoughts (e.g., intuitiveness, usability and expansion ideas) while interacting, and interviewed afterwards for an overall assessment. A qualitative content analysis helped to identify patterns. Interactions like localization field triggers for instructions and answering a quiz were regarded as highly intuitive, giving feedback with a drum keyboard was reported as most entertaining.
Log AdaBoost: Optimizing Polylog loss function to improve the generalization performance of AdaBoost

As an ensemble learning algorithm, the classic AdaBoost has achieved incredible success in both classification and regression problems, but its generalization ability is still unsatisfactory. A modified version of the Real AdaBoost which is called Gentle AdaBoost was proposed by Friedman. Gentle AdaBoost which is more robust than Real AdaBoost optimizes the exponential loss function by using the Newton-Raphson stepping. In this paper, a more moderate variant of AdaBoost, called Log AdaBoost, is proposed for training classification model. Firstly, to be more tolerant for outliers, we optimized polylog loss function instead of the exponential loss function by taking gradient descent method. Moreover, a new weight updating strategy is taken to find the weak classifier most relevant to the label under the current weight distribution. In other word, the weight is updated towards the negative gradient of the function to select the optimal weak classifier. The experimental results show that compared with Real AdaBoost and Gentle AdaBoost, the proposed method has better performance in generalization ability.
Path planning of mobile robot based on deep reinforcement learning with transfer learning strategy

Under complex environments, mobile robots can decision-making, autonomous learning, intelligent obstacle avoidance, and complete the task from start point to endpoint. This paper designed the mobile robot, excluding planners and unknown maps, which can successfully reach the target by autonomously learning and navigating in the unknown environment. By applying deep reinforcement learning to the path planning of mobile robots, the robot can collect data and conduct training on its own, and improve it autonomously without manual supervision. Consequently, it can complete the path planning task. The application of transfer learning improves the adaptive efficiency of the mobile robot to the environment. Finally, the results are verified by comparative experiments in three simulation environments.
Face Identity Detection and Recognition using Novel Convolutional Neural Network in Comparison with Haar Cascade to Improve Accuracy

The main aim of the project is to recognize faces using the Novel Convolutional Neural Network algorithm in comparison with the Haar Cascade algorithm for the Google AI images dataset. Materials and Methods: Recognition of face is performed using CNN Algorithm (N=10) and Haar Cascade algorithm (N=10). CNN algorithm is a supervised machine learning algorithm. The Haar Cascade algorithm is a simple approach mainly used for classifying. Google AI Image dataset is used for Recognition of face. These samples are divided into two types: training samples (n=52,000(75%)) and test samples (n=17500(25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> )). By the help of CNN. Accuracy is calculated for face recognition. Results: The accuracy of face recognition using CNN algorithm is 91.01 % and Haar Cascade algorithm is 85.02 %. There is a significant difference between Adaboost algorithm and Support Vector algorithm with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.04(\mathrm{P}\unicode{x00A1}{0.05})$</tex> ). Conclusion: CNN Algorithm appears to have better accuracy than the Haar Cascade algorithm in recognition face.
Analyzing the impact of COVID-19 on the grades of university education: A case study with economics students

COVID-19 has been one of the major incidents in the global university education system in recent years. Its influence and effects on education are still difficult to determine today. Both students and teachers have had to change their study and work routines and disciplines, in many cases lacking the necessary infrastructure to adapt to online learning. Students had to start a new academic year with a complete return to face-to-face teaching without having overcome, in many cases, the incidence of online learning. This study, through 167 responses to a survey addressed to economics students at the Universidad Politécnica de Madrid, aims to analyse the causes of an improvement or a worsening of the academic performance of university students in the return to normality after having gone through COVID-19's restrictions. The results obtained show that students, students who attend tutorials and those who have evaluated online teaching positively, are the ones who have most improved their performance in the return to face-to-face teaching. And those who have suffered the physical and psychological consequences of COVID and those with less infrastructure and income have worsened their results.
AerialWaste dataset for landfill discovery in aerial and satellite images

Illegal landfills are sites where garbage is dumped violating waste management laws. Aerial images enable the use of photo interpretation for territory scanning and landfill detection but this practice is hindered by the manual nature of this task which also requires expert knowledge. Deep Learning methods can help capture the analysts' expertise and build automated landfill discovery tools. However, this goal requires public high-quality datasets for model training and testing. At present no such datasets exist and this gap penalizes the research toward scalable and accurate landfill discovery methods. We present a dataset for landfill detection featuring airborne, WorldView-3, and GoogleEarth images annotated by professional photo interpreters. It comprises 3,478 positive and 6,956 negative examples. Most positive instances are characterized by metadata: the type of waste, its storage mode, the type of the site, and the evidence and severity of the illicit. The dataset has been technically validated by building an accurate landfill detector and is accompanied by a visualization and annotation tool.
Enhancing Multi-View Smoothness for Sequential Recommendation Models

Sequential recommendation models aim to predict the interested items to a user based on his historical behaviors. To train sequential recommenders, implicit feedback data is widely adopted since it is easier to obtain than explicit feedback data. In the setting of implicit feedback, a user’s historical behaviors can be characterized as a chronologically ordered sequence of interacted items. From a perspective of machine learning, the historical interaction sequence and the recommended items can be considered as context and label , respectively, which are usually in one-hot representations in the recommendation models. However, due to the discrete nature, one-hot representations are hard to sufficiently reflect the underlying user preference, and might also contain noise from implicit feedback that will mislead the model training. To solve these issues, we propose a general optimization framework, Multi-View Smoothness (MVS), to enhance the smoothness of sequential recommendation models in both data representations and model learning. Specifically, with the help of a complementary model, we smooth and enrich the one-hot representations of contexts and labels to better depict the underlying user preference (i.e., context smoothness and label smoothness), and devise a model regularization strategy to enforce the neighborhood smoothness of the model itself (i.e., model smoothness). Based on these strategies, we design three regularizers to constrain and improve the training of sequential recommendation models. Extensive experiments on five datasets show that our approach is able to improve the performance of various base models consistently and outperform other regularization training methods.
A Dense Representation Framework for Lexical and Semantic Matching

Lexical and semantic matching capture different successful approaches to text retrieval and the fusion of their results has proven to be more effective and robust than either alone. Prior work performs hybrid retrieval by conducting lexical and semantic matching using different systems (e.g., Lucene and Faiss, respectively) and then fusing their model outputs. In contrast, our work integrates lexical representations with dense semantic representations by densifying high-dimensional lexical representations into what we call low-dimensional dense lexical representations (DLRs). Our experiments show that DLRs can effectively approximate the original lexical representations, preserving effectiveness while improving query latency. Furthermore, we can combine dense lexical and semantic representations to generate dense hybrid representations (DHRs) that are more flexible and yield faster retrieval compared to existing hybrid techniques. In addition, we explore jointly training lexical and semantic representations in a single model and empirically show that the resulting DHRs are able to combine the advantages of the individual components. Our best DHR model is competitive with state-of-the-art single-vector and multi-vector dense retrievers in both in-domain and zero-shot evaluation settings. Furthermore, our model is both faster and requires smaller indexes, making our dense representation framework an attractive approach to text retrieval. Our code is available at https://github.com/castorini/dhr .
Augmented Reality-Assisted Quality Control Based on Asset Administration Shells for Concrete Elements

The implementation of the Digital Twin concept in the form of Asset Administration Shell (AAS) enables smart flow production even in fields that currently do not incorporate such methods, such as in the fabrication of precast concrete elements. The authors present an approach to implement architecture, engineering and construction-specific AAS submodels for flow production using Building Information Modeling (BIM) data and further enhance these with a new stepwise, task-based quality control process utilizing smart devices as a human machine interface to incorporate Augmented Reality visualization. The concept briefly describes the general production process for concrete wall elements, in which the detailed process of creating quality control tasks is embedded, as well as the meta-models for those components communicating using AAS. Utilizing both product type-specific and product instance-specific data, knowledge about the process, the product’s state in the process, and the physical setup enable a reliable and integrated quality control system with clear data flows, automatic documentation, and improved traceability. Finally, a prototypical implementation shows the integration between BIM-Software, the created AAS templates and submodels, and a prototypical quality control setup with smart device support in the proposed process.
Fusion of Artificial Intelligence and Blockchain in the Banking Industry: Current Application, Adoption, and Future Challenges

Digital transformation has made enormous changes in the banking domain, where FinTech is the salient driving force inside it. Especially in the banking industry, Artificial Intelligence (AI) and Blockchain technology act as the most innovative technologies. Similarly, Chatbots in commercial banking and Robo-advisors in investment banking has shifted banks into the entire virtual environment. In this context, the main objectives of this chapter are: to determine the current application of AI and blockchain in the banking industry, to identify the challenges faced by banks in adapting AI and blockchain technology, and to provide new insights on future banking in the industry 5.0 in this digital era. This chapter discusses the application of two robotic platforms widely used in banking, chatbots, and Robo-advisors. Chatbots are more like frontline employees of banks who are commonly engaged in customer relationship management, sales, and marketing. In contrast, Robo-advisors are a relatively advanced AI tool involved in investment and portfolio management. Blockchain will accelerate digital transformation where a decentralized digital ledger system is used for banking transactions. However, this is entirely the opposite of conventional centralized digital banking, and the adoption of these technologies is still in its infancy. Employment, performance, security, privacy and trust, cost, ethical and regulatory challenges are the most common challenges. To avoid the challenges, banks should concern with strategies like collaboration with robots, increasing the platforms' performance, etc. Finally, the chapter provides insights on banking at industry 5.0. In the future, banking customers can experience completely virtual and customer-oriented banking services. In this regard, the fusion of all technologies and collaborative human effort is essential.