Reviewerly/exHarmony huggingface dataset:
Strengthening transferability of adversarial examples by adaptive inertia and amplitude spectrum dropout

Deep neural networks are sensitive to adversarial examples and would produce wrong results with high confidence. However, most existing attack methods exhibit weak transferability, especially for adversarially trained models and defense models. In this paper, two methods are proposed to generate highly transferable adversarial examples, namely Adaptive Inertia Iterative Fast Gradient Sign Method (AdaI2-FGSM) and Amplitude Spectrum Dropout Method (ASDM). Specifically, AdaI2-FGSM aims to integrate adaptive inertia into the gradient-based attack, and leverage the looking ahead property to search for a flatter maximum, which is essential to strengthen the transferability of adversarial examples. By introducing a loss-preserving transformation in the frequency domain, the proposed ASDM with the dropout invariance property can craft the copies of input images to overcome the poor generalization on the surrogate models. Furthermore, AdaI2-FGSM and ASDM can be naturally integrated as an efficient gradient-based attack method to yield more transferable adversarial examples. Extensive experimental results on the ImageNet-compatible dataset demonstrate that higher transferability is achieved by our method than some advanced gradient-based attacks.
Path-based multi-hop reasoning over knowledge graph for answering questions via adversarial reinforcement learning

Multi-hop knowledge graph question answering targets at pinpointing the answer entities by inferring across multiple triples in knowledge graphs. To enhance model interpretability, path-based methods are proposed. Specifically, with the advances of deep reinforcement learning (DRL), this paper explores to extend the line of RL-based approaches. However, existing solutions suffer from the issue of spurious paths. A major reason lies in that the agent takes an opportunistic way to explicitly pursue the predictive accuracy of answer entities instead of considering correct reasoning paths. To overcome this challenge, our idea is inspired by adversarial learning and we expect that a discriminator could effectively distinguish whether the reasoning chain is correct or not. To this end, we propose an interpretable reasoning method based on adversarial reinforcement learning for multi-hop KGQA, namely Adversarial Reinforcement Reasoning Network (AR2N). AR2N consists of two crucial components: an answer generator (i.e., policy network of RL) and a path discriminator. By alternately updating two components in an adversarial manner, the generator is able to infer answer entities by following the correct reasoning chain, the discriminator is capable of evaluating the plausibility of reasoning paths. Extensive experiments conducted over three benchmark datasets well demonstrate the effectiveness of our method.1
Knowledge graph relation reasoning with variational reinforcement network

Knowledge graphs typically suffer from incompleteness due to construction defects and therefore need to be complemented by reasoning methods to facilitate advanced applications. Although many approaches have been proposed to address knowledge graph reasoning, they are heuristic and limited by the search quality and quantity of paths between entities. Inspired by variational inference and reinforcement learning, this paper proposes a variational reinforcement network (termed VRNet), which aims to infer new relation by fusing the information found on the paths connecting a pair of entities to complete the knowledge graph. Specifically, we assume that the direct relation between two entities can be inferred by multiple paths, which are likely to be multi-hop and modeled by Markov chains. We introduce latent variables to bridge the paths and relation, and design a multi-class classifier and score functions to determine the relations. Instead of traversing all the paths, we use the variational approach combined with reinforcement learning to search necessary paths with relational discrimination information. Experimental results on multiple real-world datasets indicate that VRNet integrates information from different paths and has achieved competitive performances in relation reasoning tasks.
Design and Implementation of a Robust 6-DOF Quadrotor Controller Based on Kalman Filter for Position Control

The objective of this chapter is to develop quadcopter flight control algorithms using a PID controller enhanced by a Kalman Filter (KF) using an experimental approach to extract the physical and aerodynamic settings of the quadcopter. It is first necessary to present the current state of the quadcopter analytical dynamics model in order to achieve an effective design. A second step involves the development of the quadcopter’s hardware and software, as well as the development of a full thrust test rig to extract the parameters of the propulsion system and the linearisation approximations between the different variables. Using the quadcopter’s 6-DOF analytical dynamic model, the controller’s control parameters are determined using a PID design enhanced with KF. Test results were assessed using dynamic response curves and 3D Matlab visualisations. In order to evaluate the performance of the PID controllers, we measured the time response, overshoot, and settling time with and without the KF. After the SIMULINK model’s results for the drone were accepted, a C++ code was produced. Uploading the generated code into the Pixhawk autopilot was accomplished through a Simulink application in the autopilot firmware. Based on the Pixhawk autopilot, we present a quick and real-time test solution for drone controllers. Further enhancements are provided by near-real-time tuning of the control settings. This research uses the Embedded Coder Tool to develop SIMULINK-generated code for the Pixhawk autopilot board.
New Technologies and Safety in Agriculture: SAFETY AR

New technologies, such as augmented reality or artificial intelligence, are able to offer contributions to delicate sectors of our society, such as the world of safety at work. Suffice it to say that in 2021 in Italy accidents at work increased significantly and fatal cases were 1,270, 181 more than the 1,089 in 2019 (+16.6%). Against a reduction in deaths of about 30.1%, those occurring at work have in fact increased by over a third (+34.9%). Important numbers that make us reflect on the importance of correct training and information on safety at work as required by the European directives implemented by Legislative Decree 81/2008 in Italy. One of the main problems in the world of agriculture appears to be the total negligence of employers in respecting the training and information obligations towards workers. For this reason, the University of Tuscia has developed, at the end of intense research, an augmented reality system called Safety AR, through which workers and also employers, pursuant to the very recent d.l. taxation and safety (146/2021), can train and inform themselves about the risks present in the workplace and avoid injuries or, in the most serious cases, white deaths. Safety AR is an innovative multifunctional tool, which identifies the real needs of every single production process with an interactive and functional approach. Safety AR is accessible thanks to one of the most innovative technologies of the moment, recognized by the national development plan among the enabling tools of industry 4.0 because it increases real-world interactions without replacing it. The Safety AR allows you to reproduce the safety pictograms on “markers”, that is aluminum signs, which once framed by the workers through a phone / tablet and an application, will be animated in augmented reality explaining the meaning of the pictogram and the various risks and dangers. The data extracted from the Safety AR will be treated with the utmost respect for the confidentiality of the worker. Through this system, an important decrease in accidents at work is estimated.
Smart Glove: Development and Testing of a Wearable RFID Reader Connected to Mixed Reality Smart Glasses

The evolution of Precision livestock farming has given the possibility to the farmer to obtain a large amount of information. However, to access the database, the operator often is forced to use PC or mobile devices, losing time during on-fields activities. In this context, the use of mixed reality smart glasses (MRSG), such as HoloLens (Microsoft, USA), represents an interesting tool to consult the information in real-time leaving the operator hands-free. The aim of the study was to decrease the technological gap between the animal’s electronic identification to all the information linked to a specific animal. Specifically, the research focused on developing a smart wearable system composed of a prototype, called SmartGlove (SG), capable to link the RFID tag and MRSG, via Bluetooth connection. The MRSG displays all the information related to the animal stored in an online or offline database thanks to the development of a dedicated software. The use of the SG system allows the operator to visualize, monitor, and modify the information related to the animal during on-field activities. From the preliminary experiments, the system shows promising in reducing the required workforce and improving productivity in farm management. The SmartGlove system allow farmers to activate both ear tags and rumen bolus at a distance up to 5 cm, and to visualize animal data in a variable timeframe of 4.3 s for ear tag and 3.8 s for the rumen bolus. Future works will aim to improve the reading capability of the SmartGlove, improving the antenna performances, expand the battery life, and to upgrade the MRSG software.
On the Impact of Noisy Labels on Supervised Classification Models

The amount of data generated daily grows tremendously in virtually all domains of science and industry, and its efficient storage, processing and analysis pose significant practical challenges nowadays. To automate the process of extracting useful insights from raw data, numerous supervised machine learning algorithms have been researched so far. They benefit from annotated training sets which are fed to the training routine which elaborates a model that is further deployed for a specific task. The process of capturing real-world data may lead to acquring noisy observations, ultimately affecting the models trained from such data. The impact of the label noise is, however, under-researched, and the robustness of classic learners against such noise remains unclear. We tackle this research gap and not only thoroughly investigate the classification capabilities of an array of widely-adopted machine learning models over a variety of contamination scenarios, but also suggest new metrics that could be utilized to quantify such models’ robustness. Our extensive computational experiments shed more light on the impact of training set contamination on the operational behavior of supervised learners.
Real-Time Power System Event Detection: A Novel Instance Selection Approach

This study presents a novel adaptation of the Hoeffding Adaptive Tree (HAT) classifier with an instance selection algorithm that detects and identifies cyber and non-cyber contingencies in real time to enhance the situational awareness of cyber-physical power systems (CPPS).Wide-area monitoring, protection, and control (WAMPAC) systems allow system operators to operate CPPS more efficiently and reliably.WAMPAC systems use intelligent devices such as phasor measurement units (PMUs) to monitor the CPPS state.However, such devices produce continuous and unbounded data streams, posing challenges for data handling and storage.Moreover, WAMPAC devices and the communication links connecting them are vulnerable to cybersecurity risks.In this study, we consider several cyber and non-cyber contingencies affecting the physics and monitoring infrastructure of CPPS.Our proposed classifier distinguishes disturbances from cyberattacks using a novel instance selection algorithm with three algorithmic stages to ease data management.A cost and complexity analysis of the algorithm is discussed.With reduced computational effort, the classifier can handle high-velocity, high-volume, and evolving data streams from the PMUs.Six case studies with extensive simulation results corroborate the merits of the proposed classifier, which outperforms state-of-the-art classifiers.Moreover, the classifier demonstrated a high performance using a dataset outside the contingency detection domain.Finally, the real-time applicability of the proposed methodology is assessed, and its limitations are discussed.
Audio Anti-Spoofing Based on Audio Feature Fusion

The rapid development of speech synthesis technology has significantly improved the naturalness and human-likeness of synthetic speech. As the technical barriers for speech synthesis are rapidly lowering, the number of illegal activities such as fraud and extortion is increasing, posing a significant threat to authentication systems, such as automatic speaker verification. This paper proposes an end-to-end speech synthesis detection model based on audio feature fusion in response to the constantly evolving synthesis techniques and to improve the accuracy of detecting synthetic speech. The model uses a pre-trained wav2vec2 model to extract features from raw waveforms and utilizes an audio feature fusion module for back-end classification. The audio feature fusion module aims to improve the model accuracy by adequately utilizing the audio features extracted from the front end and fusing the information from timeframes and feature dimensions. Data augmentation techniques are also used to enhance the performance generalization of the model. The model is trained on the training and development sets of the logical access (LA) dataset of the ASVspoof 2019 Challenge, an international standard, and is tested on the logical access (LA) and deep-fake (DF) evaluation datasets of the ASVspoof 2021 Challenge. The equal error rate (EER) on ASVspoof 2021 LA and ASVspoof 2021 DF are 1.18% and 2.62%, respectively, achieving the best results on the DF dataset.
Developing Analytical Tools for Arabic Sentiment Analysis of COVID-19 Data

Due to the widespread distribution of coronavirus and the existence of a massive quantity of data on social networking sites, particularly Twitter, there was an urgent need to develop a model that evaluates users’ emotions and determines how they feel about the pandemic. However, the absence of resources to assist Sentiment Analysis (SA) in Arabic hampered the completion of this endeavor. This work presents the ArSentiCOVID lexicon, the first and largest Arabic SA lexicon for COVID-19 that handles negation and emojis. We design a lexicon-based sentiment analyzer tool that depends mainly on the ArSentiCOVID lexicon to perform a three-way classification. Furthermore, we employ the sentiment analyzer to automatically assemble 42K annotated Arabic tweets for COVID-19. We conduct two experiments. First, we test the effect of applying negation and emoji rules to the created lexicon. The results indicate that after applying the emoji, negation, and both rules, the F-score improved by 2.13%, 4.13%, and 6.13%, respectively. Second, we applied an ensemble method that combines four feature groups (n-grams, negation, polarity, and emojis) as input features for eight Machine Learning (ML) classifiers. The results reveal that Random Forest (RF) and Support Vector Machine (SVM) classifiers work best, and that the four feature groups combined are best for representing features produced the maximum accuracy of (92.21%), precision (92.23%), recall (92.21%), and F-score (92.23%) with 3.2% improvement over the base model.
Text to Causal Knowledge Graph: A Framework to Synthesize Knowledge from Unstructured Business Texts into Causal Graphs

This article presents a state-of-the-art system to extract and synthesize causal statements from company reports into a directed causal graph. The extracted information is organized by its relevance to different stakeholder group benefits (customers, employees, investors, and the community/environment). The presented method of synthesizing extracted data into a knowledge graph comprises a framework that can be used for similar tasks in other domains, e.g., medical information. The current work addresses the problem of finding, organizing, and synthesizing a view of the cause-and-effect relationships based on textual data in order to inform and even prescribe the best actions that may affect target business outcomes related to the benefits for different stakeholders (customers, employees, investors, and the community/environment).
Planning on the Verge of AI, or AI on the Verge of Planning

The urban planning process is complex, involving social, economic, environmental, and political systems. Knowledge of how these systems interact is the domain of professional planners. Advances in artificial intelligence (AI) present planners with a ripe opportunity to critically assess their approaches and explore how new data collection, analysis, and methods can augment the understanding of places as they seek to anticipate futures with improved quality of life. AI can offer access to more and better information about travel patterns, energy consumption, land utilization, and environmental impacts, while also helping to better integrate these systems, which is what planners do. The adoption process will likely be gradual and involve significant time and resources. This article highlights several topics and issues that should be considered during this process. It is argued that planners will be well-served by approaching AI tools in a strategic manner that involves the topics discussed here.
Empirical Instances Knowledge Mining Method for Ship Design Domain

Knowledge mining based on ship instances information has an important support role for ship design.In order to obtain easy to use knowledge from ship design instances composed of natural language text, this paper proposes an empirical case knowledge mining method for ship design domain.The method uses natural language processing techniques to structure the empirical instances according to the characteristics of the ship design domain and the structural characteristics of the empirical instances; and then combines knowledge clustering and variable precision rough set theory to obtain ship design rules from the instances.The experimental analysis of the data set provided by shipyards shows the feasibility and effectiveness of this method to obtain ship design knowledge rules.
Multitarget Intelligent Recognition of Petrographic Thin Section Images Based on Faster RCNN

The optical features of mineral composition and texture in petrographic thin sections are an important basis for rock identification and rock evolution analysis. However, the efficiency and accuracy of human visual interpretation of petrographic thin section images have depended on the experience of experts for a long time. The application of image-based computer vision and deep-learning algorithms to the intelligent analysis of the optical properties of mineral composition and texture in petrographic thin section images (in plane polarizing light) has the potential to significantly improve the efficiency and accuracy of rock identification and classification. This study completed the transition from simple petrographic thin image classification to multitarget detection, to address more complex research tasks and more refined research scales that contain more abundant information, such as spatial, quantitative and category target information. Oolitic texture is an important paleoenvironmental indicator that widely exists in sedimentary records and is related to shallow water hydraulic conditions. We used transfer learning and image data augmentation in this paper to identify the oolitic texture of petrographic thin section images based on the faster region-based convolutional neural network (Faster RCNN) method. In this study, we evaluated the performance of Faster RCNN, a two-stage object detection algorithm, using VGG16 and ResNet50 as backbones for image feature extraction. Our findings indicate that ResNet50 outperformed VGG16 in this regard. Specifically, the Faster RCNN model with ResNet50 as the backbone achieved an average precision (AP) of 92.25% for the ooids test set, demonstrating the accuracy and reliability of this approach for detecting ooids. The experimental results also showed that the uneven distribution of training sample images and the complexity of images both significantly affect detection performance; however, the uneven distribution of training sample images has a greater impact. Our work is preliminary for intelligent recognition of multiple mineral texture targets in petrographic thin section images. We hope that it will inspire further research in this field.
Weight of Evidence and Information Value on Support Vector Machine Classifier

In building a classification model, variables containing low predictive information are sometimes used.This can increase the bias on classification.Weight of Evidence (WoE) and Information Value (IV) provide a good theoretical foundation to explore, filtering, and transforming variables in binary classification.The value of IV can help measure the predictive power possessed by a variable in separating binary classes.This research implements this framework to screen 24 predictor variables that will be used in the svm classification model to improve the evaluation of the food insecure household classification model.We use the National Socioeconomic Survey by the Indonesian Central Bureau of Statistics in 2020 for West Java Province and 2021 for East Java Province to produce a classification model.The results of this study showed that WOE was able to improve the model evaluation value from the AUC value of 0.81 to 0.83 for West Java Province and the AUC value of 0.58 to 0.66 for East Java Province.