Reviewerly/exHarmony huggingface dataset:
Comparison and Evaluation of Machine Learning-Based Classification of Hand Gestures Captured by Inertial Sensors

Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms.
Data Driven Fuzzy Modeling Using Level Sets

The paper looks at the structure of fuzzy rule-based models from the point of view of a function relating membership grades of inputs with rule outputs. This view in turn is generalized by an approach that produces the output functions of the fuzzy rules using input and output data. In this view, a formulation to compute the output of the model consists of estimating the parameters of the output functions. Essentially, the paper suggests an alternative method for fuzzy modeling based on output functions constructed from level sets and input and output data. The data driven method provides an easy and efficient way to develop and process fuzzy models. Examples of function estimation problems show that the data driven level set method is very effective when compared with alternative modeling techniques.
How to Quantify the Degree of Explainability: Experiments and Practical Implications

Explainable AI was born as a pathway to allow humans to explore and understand the inner working of complex systems. Though, establishing what is an explanation and objectively evaluating explainability, are not trivial tasks. With this paper, we present a new model-agnostic metric to measure the Degree of Explainability of (correct) information in an objective way, exploiting a specific theoretical model from Ordinary Language Philosophy called the Achinstein’s Theory of Explanations, implemented with an algorithm relying on deep language models for knowledge graph extraction and information retrieval. In order to understand whether this metric is actually behaving as explainability is expected to, we have devised an experiment on two realistic Explainable AI-based systems for healthcare and finance, using famous AI technology including Artificial Neural Networks and TreeSHAP. The results we obtained suggest that our proposed metric for measuring the Degree of Explainability is robust on several scenarios.
A Fuzzy-Logic Based Multi-Dimensional Analysis of Traffic Incident Data

In the current era, big data can be generated and collected anywhere. These include different types of data, which can be mined by data mining. Many interesting important data are multi-dimensional. Many of them capture uncertain or imprecise data, which include traffic incident data. Multi-dimensional data analysis and mining helps reveal factors associating with, or contributing to, traffic accidents. In this paper, we present a fuzzy-logic based multi-dimensional analysis of traffic incident data. Fuzzy logic helps capture uncertainty and vagueness of users. Evaluation on real-life traffic data from a Canadian city demonstrates the practicality and effectiveness of using our fuzzy-logic based multi-dimensional data mining and analysis approach within the domain of traffic incident data.
Can Post-hoc Explanations Effectively Detect Out-of-Distribution Samples?

Today there is consensus around the importance of explainability as a mandatory feature in practical deployments of Artificial Intelligence (AI) models. Most research activity reported so far in the eXplainable AI (XAI) research arena has stressed on proposing new techniques for eliciting such explanations, together with different approaches for measuring their effectivity to increase the trustworthiness of the audience for which explanations are furnished. However, alternative uses of explanations beyond their original purpose have been very scarcely explored. In this work we investigate whether local explanations can be utilized for detecting Out-of-Distribution (OoD) test samples in machine learning classifiers, i.e., to identify whether query examples of an already trained classification model can be thought to belong to the distribution of the training data. To this end, we devise and assess the performance of a clustering-based OoD detection approach that exemplifies how heatmaps produced by well-established local explanation methods can be of further use than explaining individual predictions issued by the model under analysis. The overarching purpose of this work is not only to expose the benefits and the limits of the proposed XAI-based OoD approach, but also to point out the enormous potential of post-hoc explanations beyond easing the interpretability of black-box models themselves.
A Fuzzy-Cognitive-Maps Approach to Decision-Making in Medical Ethics

Although machine intelligence is increasingly employed in healthcare, the realm of decision-making in medical ethics remains largely unexplored from a technical perspective. We propose an approach based on fuzzy cognitive maps (FCMs), which builds on Beauchamp and Childress’ prima-facie principles. The FCM’s weights are optimized using a genetic algorithm to provide recommendations regarding the initiation, continuation, or withdrawal of medical treatment. The resulting model approximates the answers provided by our team of medical ethicists fairly well and offers a high degree of interpretability. Possible applications of such a system include informal guidance on medical ethics dilemmas as well as educational purposes.
Project FMEA for Recognizing Difficulties in Machine Learning Application System Development

Digital Transformation (DX) is spreading across all industries. AI, especially machine learning, is inevitable for effective use of data collected and stored in DX, and systems that utilize machine learning have been developed in various industries and companies. The development of machine learning application systems (MLASs) has many difficulties different from the traditional IT system development. Therefore, software engineering (especially project management) for MLASs becomes one of the most important issues in these days. We classified the difficulties of MLAS development based on various documents and interviews, and created a difficulty map consisting of 12 categories. Unique features of this difficulty map include introduction of relationship between difficulties and the dual MLAS development process (implementation process and exploitation process). Then, we propose a method of expressing and sharing these difficulties among stakeholders based on MLAS Project FMEA (Failure Mode Effect Analysis). The proposed method is evaluated using two illustrative MLA examples.
Smart Policing: Ethical Issues &amp; Technology Management of Robocops

The killing of George Floyd in the United States has drawn attention to police brutality worldwide because it was caught on video. The frequency of incidents of police brutality has resulted in mutual distrust and fear between police and citizens. Repeated stories of police violence suggest policing needs a reimagined overhaul that addresses human rights. New ubiquitous technologies point to a need for greater smart policing research, development and technology management. Surveillance cameras and robotics are considered forms of smart policing. Robocops are currently out on patrol in many jurisdictions around the world. Smart technologies have the potential to improve policing and ethical outcomes through technological objectivity. Smart policing can potentially alleviate racial bias through technology management.Many reported incidents of police brutality in the United states involve traffic stops. While there is likely a need for traditional policing in cases such as rape and murder, violent—often fatal— incidents stemming from community policing interactions should be examined. This systemic review finds that there are few studies that address this gap. The “defund the police” movement in relation to minor crimes includes the reallocation of funds toward community services and community policing. This research focuses on how police funds can be better allocated toward social services, community policing and smart policing that results in much needed police reforms.
Emerging Technologies, Evolving Threats: Next-Generation Security Challenges

Security is a fundamental human requirement. We desire the security of our person against injury, security of our capability to provide for our families, security of income linked to needs (food, water, clothing, and shelter), and much more. Most also hope for security of a way of life that is fulfilling and pleasant and peaceful <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . In 2003, Alkire <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> defined “human security” as: “[t]he objective … to safeguard the vital core of all human lives from critical pervasive threats, in a way that is consistent with long-term human fulfillment.” Today most of the world’s population is highly dependent, even for basic needs, on large technological systems. According to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Oxford Dictionary</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dependence</i> can be defined as: “the state of relying on or being controlled by someone or something else.” In the context of technological systems, dependence may imply an unwanted consequence of nonavailability. Dependence may also be deliberately misapplied to create some level of coercion toward some other action <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> . For instance, in drug abuse, we can say that there is an overreliance on a dependency that leads to addiction. In the realm of technology systems, as they are increasingly becoming a part of everyday life, dependencies can have major consequences. Thus, an emphasis on “security” seems highly relevant for a journal devoted to considering the effects of technology on society in all its facets.
An efficient SIFT-based matching algorithm for optical remote sensing images

Scale-invariant feature transform (SIFT) and its improved versions have been widely used to match the remote-sensing optical images. However, it is still a challenging task to get enough correct matching pairs by using SIFT-based methods. In this letter, an efficient matching scheme is proposed to increase the number of correct matches. The proposed matching scheme uses scale, orientation, and translation differences between the input images to remove the outliers and to retain the correct matches. At first, the initial matching candidates are obtained by a SIFT-based algorithm. Then, our proposed matching scheme is used to select the correct matching pairs. The proposed method can obtain more correct matches than the state-of-the-art methods. Experiments are performed on five pairs of optical images to verify the performance of the proposed method.
Multilabel Recognition Algorithm With Multigraph Structure

In multi-label recognition tasks, convolutional neural networks (CNNs) can extract rich features of images in Euclidean space. However, CNNs struggle to handle dependencies between multi-labels in non-Euclidean space. Graph Convolutional Networks (GCNs) have natural advantages in processing the data in non-Euclidean space. Therefore, many excellent algorithms used GCN to capture the deep dependencies among multi-labels. However, these algorithms directly applied GCN for label feature fusion, leading to nodes with background features affecting the quality of the representation of their neighbor nodes. For example, during label feature smoothing, the representation of nodes with weak edge information tends to converge to nodes with background features. We propose a multi-label image recognition algorithm with a multi-graph structure to address the impact of background features on recognition performance. In brief, when smoothing the dependencies among labels using GCN, we transform the shared adjacency matrix into a combination of multiple adjacency matrices. Unlike node features sharing an adjacency matrix, the multi-graph structure can calculate the performance of each feature carried by the nodes separately during feature fusion and convey more appropriate information rather than in a generalized manner. We have conducted extensive experiments on two multi-label image recognition datasets and have achieved the state-of-the-art.
Cryptography in the Quantum Era

Recent advances in quantum computing and quantum information theory represent a severe threat to the current state of the art of data protection. In this context, new quantum-safe techniques have emerged in recent decades, which fall into post-quantum and unconditionally secure cryptographic schemes. The firsts rely on computational problems supposed to be hard also for quantum computers. In contrast, the seconds do not depend on the difficulty of a computational problem and are therefore immune to quantum power. In particular, unconditionally secure techniques include Quantum Key Distribution (QKD) protocols for transmitting secret keys thanks to the quantum properties of light. In this work, we discuss QKD networks and post-quantum algorithms, considering their opportunities and limitations and showing that reconciliation between these two directions of cryptography is feasible and necessary for the quantum era.This work is part of the activities of the PON project “Development of quantum systems and technologies for IT security in communication networks” (QUANCOM) which aims to the realization of a metropolitan quantum communication network through the collaboration between universities, research centers and companies operating in the communication market area.
Reliability-Aware Contrastive Self-ensembling for Semi-supervised Medical Image Classification

Self-ensembling framework has proven to be a powerful paradigm for semi-supervised medical image classification by leveraging abundant unlabeled data. However, the unlabeled data used in most of self-ensembling methods are equally weighted, which adversely affects the classification performance of models when difference exists among unlabeled data acquired from different populations, equipment and environments. To address this issue, we propose a novel reliability-aware contrastive self-ensembling framework, which can leverage the reliable unlabeled data selectively. Concretely, we introduce a weight function to the mean teacher paradigm for mapping the probability predictions of unlabeled data to corresponding weights that reflect their reliability. Hence, we can safely leverage the predictions of related unlabeled data under different perturbations to construct a reliable consistency loss. Besides, we further design a novel reliable contrastive loss to achieve better intra-class compactness and inter-class separability for the normalized embeddings derived from related unlabeled data. As a result, our reliability-aware scheme enables the contrastive self-ensembling framework concurrently capture both the reliable data-level and data-structure-level information, thereby improving the robustness and generalization power of the model. Experiments on two publicly available medical image datasets demonstrate the superiority of the proposed method. Our model is available at https://github.com/Mwnic/RAC-MT .
A Study on the Channel Expansion VAE for Content-Based Image Retrieval

Content-based image retrieval (CBIR) focuses on video searching with fine-tuning of pre-trained off-the-shelf features. CBIR is an intuitive method for image retrieval, although it still requires labeled datasets for fine-tuning due to the inefficiency caused by annotation. Therefore, we explored an unsupervised model for feature extraction of image contents. We used a variational auto-encoder (VAE) expanding channel of neural networks and studied the activation of layer outputs. In this study, the channel expansion method boosted the capability of image retrieval by exploring more kernels and selecting a layer of comparatively activated object region. The experiment included a comparison of channel expansion and visualization of each layer in the encoder network. The proposed model achieved (52.7%) mAP, which outperformed (36.5%) the existing VAE on the MNIST dataset.
Efficient Batch Authentication Scheme Based on Edge Computing in IIoT

In the industrial Internet of Things (IIoT) environment (e.g., a smart factory), smart devices with limited computing power can bring large amounts of privacy-sensitive data into insecure networks when they interact. If a network attacker intercepts and tampers with this data, it may cause chaos in production and even paralyze the entire IIoT system. Therefore, to ensure the regular operation of intelligent production, data receivers must authenticate the data before using them. However, existing message authentication schemes in the IIoT environment authenticate each message individually, which creates many redundant operations. Hence, to ensure data security among smart devices and reduce the computational overhead of data processing, we propose a batch authentication scheme based on edge computing in IIoT. Specifically, we design a lightweight batch authentication algorithm and use edge servers to assist smart devices in authenticating data, thus reducing the computational burden on smart devices and improving the efficiency of message authentication. The security analysis shows that the proposed scheme is secure in the random oracle model and meets the series of security requirements of the IIoT. In addition, we illustrate the efficiency of the scheme through experiments.
Gaussian Control Barrier Functions: Non-Parametric Paradigm to Safety

Inspired by the success of control barrier functions (CBFs) in addressing safety, and the rise of data-driven techniques for modeling functions, we propose a non-parametric approach for online synthesis of CBFs using Gaussian Processes (GPs). A dynamical system is defined to be safe if a subset of its states remains within the prescribed set, also called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safe set</i> . CBFs achieve safety by designing a candidate function a priori. However, designing such a function can be challenging. Consider designing a CBF in a disaster recovery scenario where safe and navigable regions need to be determined. The decision boundary for safety here is unknown and cannot be designed a priori. Moreover, CBFs employ a parametric design approach and cannot handle arbitrary changes to the safe set in practice. In our approach, we work with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safety samples</i> to construct the CBF online by assuming a flexible GP prior on these samples, and term our formulation as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gaussian</i> CBF. GPs have favorable properties such as analytical tractability and robust uncertainty estimation. This allows realizing the posterior with high safety guarantees while also computing associated partial derivatives analytically for safe control. Moreover, Gaussian CBFs can change the safe set arbitrarily based on sampled data, thus allowing non-convex safe sets. We validated experimentally on a quadrotor by demonstrating safe control for 1) arbitrary safe sets, 2) collision avoidance with online safe set synthesis, 3) and juxtaposed Gaussian CBFs with CBFs in the presence of noisy states. The experiment video link is: https://youtu.be/HX6uokvCiGk.
Code-Based Cryptography With Generalized Concatenated Codes for Restricted Error Values

Code-based cryptosystems are promising candidates for post-quantum cryptography. Recently, generalized concatenated codes over Gaussian and Eisenstein integers were proposed for those systems. For a channel model with errors of restricted weight, those q-ary codes lead to high error correction capabilities. Hence, these codes achieve high work factors for information set decoding attacks. In this work, we adapt this concept to codes for the weight-one error channel, i.e., a binary channel model where at most one bit-error occurs in each block of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> bits. We also propose a low complexity decoding algorithm for the proposed codes. Compared to codes over Gaussian and Eisenstein integers, these codes achieve higher minimum Hamming distances for the dual codes of the inner component codes. This property increases the work factor for a structural attack on concatenated codes leading to higher overall security. For comparable security, the key size for the proposed code construction is significantly smaller than for the classic McEliece scheme based on Goppa codes.