Reviewerly/exHarmony huggingface dataset:
Wall following navigation algorithm for a disinfecting robot

This paper introduces the advanced wall following navigation using various distance sensors and navigation methods for a disinfecting robot. Based on distance data from the lidar sensor, we propose the virtual distance data so that a mobile robot can rotate reliably wall following navigation and robot can drive stably by preprocessing distance data and using the PID controller. In addition, we suggested a way for the mobile robot using active slams to return from start position to original position through pseudo code. Finally, through simulation and real-world experiments, we verified the stability of the mobile robot's wall following navigation in the simulation and real world.
A Novel Filter Pruning Algorithm for Vision Tasks based on Kernel Grouping

Although the size and the computation cost of the state of the art deep learning models are tremendously large, they run without any problem when implemented on computers thanks to the remarkable enhancements and advancements of computers. However, the problem is likely to be faced when the need for deploying them on mobile platforms arises. Model compression techniques such as filter pruning or knowledge distillation help to reduce the size of deep learning models. However the conventional methods contain sorting algorithms therefore they cannot be applied to models that have reshaping layers like involution. In this research, we revisit a model compression algorithm named Model Diet that can be both applied to involution and convolution models. Furthermore, we present its application on two different tasks, image segmentation and depth estimation.
Touchless Shared-Control for Wheelchair Navigation

We present a touchless shared-control approach merging wearable sensor-based control and autonomous safety control for wheelchair navigation in this study. To assist disabled people who have no or limited sensorimotor control with their fingers, a wearable armband sensor is used to control an electric wheelchair. Laser rangefinder based autonomous safety control of obstacle avoidance is also implemented through the learning from demonstration. The shared-control architecture for the wheelchair navigation is built up on the integration of the armband-based human control and autonomous safety control. A customized arbitration function was developed to make smooth transition of two control systems according to user’s preference and satisfaction. Our experiments demonstrate that the touchless shared-control can be applied for wheelchair navigation in the real world.
Redox dynamics of subduction revealed by arsenic in serpentinite

Redox dynamics of subduction processes remain poorly constrained owing to the lack of direct geochemical tracers.We studied, using X-ray absorption spectroscopy, the chemical and redox state of arsenic in the Tso Morari serpentinites that are witnesses of the Himalayan subduction.Our measurements reveal remarkably contrasting redox speciation, from arsenide (As -III ) to arsenite (As III ) and arsenate (As V ).Combined with physical-chemical constraints, these data enable reconstruction of the 'redox travel' of arsenic in the subduction process.Upon early serpentinisation of mantle peridotite, arsenic was scavenged from the fluid and dragged down as insoluble nickel arsenide.Partial deserpentinisation close to the peak metamorphism (550-650 °C) resulted in oxidative dissolution of arsenide to aqueous As III and As V and their non-specific intake by antigorite.The As V /As III ratios (∼0.1-10) analysed in the mineral are ∼10 4 times higher on average than predicted assuming bulk system thermodynamic equilibrium.These findings reflect a transient out-of-equilibrium release of highly oxidised fluids, with f O 2 reaching ∼10 log units above the fayalite-magnetite-quartz buffer (FMQþ10).Arsenic in serpentinite is thus a sensitive record of subduction redox dynamics inaccessible when using traditional equilibrium approaches applied to bulk fluid-mineral systems.
Efficient non-blind deconvolution method for large scale blurred image with hybrid regularizations

We propose to combine a variety of regularization terms and design a non-blind image deconvolution algorithmic framework which is based on the split-Bregman approach. With the proposed algorithm, the original problem can be decomposed into simple sub-problems and solved efficiently by variable splitting and penalty technology. Furthermore, we also propose an acceleration scheme for restoring large scale blurred image which is based on image segmentation and parallel computing of GPU and multi-core CPU. Experimental results show that our approach can achieve restored image of high quality which is comparable with some state of the art methods, and the efficiency of the algorithm can be significantly enhanced with the proposed acceleration scheme for restoring large scale blurred image.
A multi-birth metric learning framework based on binary constraints

Multi-metric learning plays a significant role in improving the generalization of algorithms related to distance metrics since using a single metric is sometimes insufficient to handle complex data. Metric learning can adjust automatically the distance between samples to make the intra-class samples compact while making the inter-class distance as far as possible. To implement this intention better,in this work, we propose a novel multi-metric learning framework based on the pair constraints instead of triple constraints to reduce computational burden. To solve effectively the problem, we first propose a multi-birth metric learning model (termed MBML), where for each class sample, the global metric and a local metric are jointly trained. Both global and local structural information are adapted to better depict sample information. Then two alternating iterative algorithms are developed to optimize the MBML. The convergence of the proposed algorithm and complexity are analyzed theoretically. Moreover, a fast diagonal multi-metric learning method is proposed based on binary constraints, and problem can be reformulated a linear programming, with fast training speed, low the computational burden and the global optimal solutions. Numerical experiments are carried out on different scales and different types of datasets including an artificial data, benchmark datasets and an image database from binary class and multi-class problems. Experiment results confirm the feasibility and effectiveness of the proposed methods.
A Natural and Intuitive Virtual Environment for Monitoring and Control of Electric Power Substations

Virtual Reality-based systems can provide intuitive and distinct three-dimensional user interfaces compared to traditional two-dimensional layouts. Such systems give the users physical and cognitive immersion, which results in a mental model that is quite consistent with field operation. In this work, we present the development aspects and a case study of a solution based on Virtual Reality to provide a more natural and intuitive environment for controlling electrical operation centers. The solution intends to minimize the issues caused by the operation of electric power substations due to the lack of spatial and functional information on the traditional operation interfaces. The research is being carried out with the collaboration between the energy electric company of Minas Gerais–Brazil (CEMIG) and the Federal University of Uberlândia. Besides operating generation and transmission actives, the environment presents great potential for the training of operators and other professionals of support and maintenance.
Blockchain Technology Applications: Enhancing Fashion Supply Chain Sustainability

The fashion industry is considered unsustainable as products are made and disposed quickly causing damage to the environment due to huge amounts of water consumption, pollution, and large carbon emissions. To fight this growing problem, more and more companies in the fashion industry are required to show proof of sustainability which can be a very difficult task as this industry needs global supply chains for their activities, and these are very fragmented and have serious transparency and traceability issues. Thus, there is very poor data quality control, disruptions are hard to communicate and searching for data is very difficult as it often gets lost. A potential solution to these issues is the application of an emerging technology known as Blockchain. Blockchain is a distributed shared ledger technology where all data can be stored in the same network in a secure and immutable way and be accessed by every stakeholder in the supply chain. This article seeks to explore how Blockchain technology can be implemented to improve the sustainability of the production processes of the fashion industry’s supply chain.
Two-Stage Spatio- Temporal Vision Transformer for the Detection of Violent Scenes

The rapid expansion and adoption of CCTV systems brings with itself a series of problems that, if remain unchecked, have the potential of hindering the advantages brought by such systems and reduce the effectiveness of this type of system in security surveillance scenarios. The possibly vast quantities of data associated with a CCTV system that covers a city or problematic areas of that city, venues, events, industrial sites or even smaller security perimeters can over-whelm the human operators and make it hard to distinguish important security events from the rest of the normal data. Therefore, the creation of automated systems that are able to provide operators with accurate alarms when certain events take place is of paramount importance, as this can heavily reduce their workload and improve the efficiency of the system. In this regard, we propose a Two-Stage Vision Transformer-based (2SViT) system for the detection of violent scenes. In this setup, the first stage handles frame-level processing, while the second stage processes temporal information by gathering frame-level features. We train and validate our proposed Transformer architecture on the popular XD- Violence dataset, while testing some size variations for the architecture, and show good results when compared with baseline scores.
Classification of Various Workout Motions Using Wearable Sensors

A wearable sensor system is worn at two different locations on the body to automatically classify different workout activities being performed by the trainees in the gym. The sensor provides raw acceleration data in the x, y, and z-axis, then imported into MATLAB. The classifier predicts the workout actions based on the time and frequency features extracted from the sensor data. The classifier used was a Quadratic kernel function for Support Vector Machine (SVM) using Bayesian optimization with 30 iterations. A training dataset with labels was used to train the SVM. The model was trained and tested using separate test data, and an average accuracy of 99% was obtained. Different sensor locations were compared and concluded that the wrist was the most preferred location for workout classification.
Applying Aspect-Oriented Design Methodology to Manage Time-Validity of Information in Internet-of-Things Systems

The Internet of Things (IoT) is becoming increasingly ubiquitous, typified in software by large-scale multi-agent systems of heterogeneous agents. IoT devices are constrained in terms of memory and processing power, limiting their capacity to hold large sets of information upon which decision-making logic must execute. IoT devices are also frequently deployed as distributed sensors constrained in terms of time, location and communications bandwidth. These constraints demand a level of multi-agent communication and co-ordination to provide accurate, up-to-date information on-demand to different intelligent agents in the system. In this paper, we propose a novel method by which Aspect-Oriented Software Design can be applied to managing the validity of time-constrained data in IoT systems while decoupling the application code from this concern.
Classification of Movie Success: A Comparison of Two Movie Datasets

This work presents a classification problem to classify a movie's success based on features of a given movie. Two movies' datasets along with features generated from web scraping are utilized to generate the training and testing datasets. Four Machine Learning classifiers are applied to these datasets: Stochastic Gradient Descent, Random Forests, LinearSVC and Extra Trees. This study compares the performance metrics for these Machine Learning models on these two movies datasets and draws conclusions based on the results.
On the Rényi Cross-Entropy

The Rényi cross-entropy measure between two distributions, a generalization of the Shannon cross-entropy, was recently used as a loss function for the improved design of deep learning generative adversarial networks. In this work, we examine the properties of this measure and derive closed-form expressions for it when one of the distributions is fixed and when both distributions belong to the exponential family. We also analytically determine a formula for the cross-entropy rate for stationary Gaussian processes and for finite-alphabet Markov sources.
Vector Quantization of Deep Convolutional Neural Networks With Learned Codebook

In this paper, we present a novel vector quantization technique, VQLC, for the compression of convolutional neural networks. Motivated by the hypothesis that better quantization codebooks exist beyond the neighbourhood of a pretrained model, we depart from the conventional strategy in which a codebook is constructed from a pretrained model and then fine tuned. In VQLC, the quantization codebook is randomly initialized and learned together with the training of the neural network. On several popular image classification benchmarks, we demonstrate the state-of-the-art performance of VQLC and show that VQLC is particularly effective on wide networks.
Modeling and Energy Analysis of Adversarial Perturbations in Deep Image Classification Security

Despite the great success of deep neural networks (DNNs) in computer vision, they are vulnerable to adversarial attacks. Given a well-trained DNN and an image <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> , a malicious and imperceptible perturbation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> can be easily crafted and added to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> to generate an adversarial example <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x^{\prime}$</tex> . The output of the DNN in response to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x^{\prime}$</tex> will be different from that of the DNN in response to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> • To shed light on how to defend DNNs against such adversarial attacks, in this paper, we apply statistical methods to model and analyze adversarial perturbations <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> crafted by FGSM, PGD, and CW attacks. It is shown statistically that (1) the adversarial perturbations <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> crafted by FGSM, PGD, and CW attacks can all be modelled in the Discrete Cosine Transform (DCT) domain by the Transparent Composite Model (TCM) based on generalized Gaussian (GGTCM); (2) CW attack puts more perturbation energy in the background of an image than in the object of the image, while there is no such distinction for FGSM and PGD attacks; and (3) the energy of adversarial perturbation in the case of CW attack is more concentrated on DC components than in the case of FGSM and PGD attacks.
Privacy-Preserving Data Falsification Detection in Smart Grids using Elliptic Curve Cryptography and Homomorphic Encryption

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers' fine-grained power consumption data; however, analyzing customers' private data violates the customers' privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme.
Knowledge Infusion for Context-Aware Sensor-Based Human Activity Recognition

Neuro-symbolic AI methods aim at integrating the capabilities of data-driven deep learning solutions with the ones of more traditional symbolic approaches. These techniques have been poorly explored in the sensor-based Human Activity Recognition (HAR) research field, even if they could lead to multiple benefits such as improving model interpretability and reducing the amount of labeled data that is necessary to reliably train the model. In this paper, we propose DUSTIN, a novel knowledge infusion approach for sensor-based HAR. DUSTIN concatenates the features automatically extracted by a CNN model from raw sensor data and high-level context data with the ones inferred by a knowledge-based reasoner. In particular, the symbolic features encode common-sense knowledge about the activities which are consistent with the context of the user, and they are infused within the model before the classification layer. We experimentally evaluated DUSTIN on a HAR dataset of mobile devices sensor data that includes 14 different activities performed by 26 users. Our results show that DUSTIN outperforms state-of-the-art neuro-symbolic approaches, with the advantage of requiring a limited amount of training data and training epochs to reach satisfying recognition rates.
A Federated Learning Approach for Distributed Human Activity Recognition

In recent years, the widespread diffusion of smart pervasive devices able to provide AI-based services has encouraged research in the definition of new distributed learning paradigms. Federated Learning (FL) is one of the most recent approaches which allows devices to collaborate to train AI-based models, whereas guarantying privacy and lower communication costs. Although different studies on FL have been conducted, a general and modular architecture capable of performing well in different scenarios is still missing. Following this direction, this paper proposes a general FL framework whose validity is assessed by considering a distributed activity recognition scenario in which users' personal devices are employed as the basis of the sensing infrastructure. Experimental analysis was performed to evaluate the effectiveness of the architecture as compared with a centralized approach, under different settings. Results demonstrate the versatility and functionality of the proposed solution.
Skadi: Heterogeneous Human-sensing System for Automotive IoT

Over the past years, cars' computing, sensing, and networking capabilities have rapidly increased, and the automotive development aims for autonomous driving. However, the driver is still the focal point for decision making. It has to be alert at all times to avoid traffic accidents due to human factors like tiredness, inattentiveness, and intoxication. Therefore, there is a need for a system that monitors the driver and intervenes before human failure can have a negative impact on traffic. A variety of commercially available wearable IoT devices, such as smartwatches, bracelets, and rings, are capable of monitoring human health conditions. However, those devices come with technological differences and manufacturer-specific implementations. This paper proposes a prototype for a human-sensing and health monitoring system based on wearable sensor devices. The aim is to find a solution that ignores the technological heterogeneity of IoT devices and generalises their implementation into the automotive system. Consequently, the data should be available to be analysed together with the data collected from the vehicular sensors. Our solution is compatible with open-source platforms Eclipse Hono and Kuksa.
Event Information Search Method from SNS Data considering Privacy of User's Location Information

In recent years, with the spread of social networking services (hereinafter referred to as SNS), information on events of various sizes, including local events and ongoing events, has been posted on SNS. Research has already begun to create systems that recommend events for users to attend at specific times and places based on a collection of event information and using user location data. However, for the user, there is a concern regarding privacy that the user's location information or the “like” reaction within SNS to certain events is transmitted to the server as is and used for information analysis. Therefore, in this work, we considered the user's privacy by converting the user's position information into a dummy position using regional mesh information and utilizing it for the inquiry to the server. We examined a method of acquiring event information from Twitter, which is a type of SNS, and searching for event information from a large amount of SNS data after satisfying the geographical constraints on the user side.
AmicroN: Framework for Generating Micro-Activity Annotations for Human Activity Recognition

In recent years, non-invasive human activity recognition (HAR) has gathered huge momentum using locomotive sensors. However, for effective HAR, there is a need for a significant volume of annotated data. Typically, the conventional practices for gathering HAR annotations have relied on human annotators. Nevertheless, the growing volume of data often leads to the collection of shallow annotations, which in most cases ignore the fine-grained micro-activities that constitute any complex activities of daily living (ADL). Understanding this, we, in this paper, try to develop the framework AmicroN that can automatically generate micro-activity annotations using locomotive signatures. To achieve this, in the backend, AmicroN applies change-point detection for the precise detection of activity boundaries followed by zero-shot learning with verb attributes to identify the unseen micro-activities without any external supervision. Rigorous evaluation on a publicly available Kitchen dataset shows that AmicroN can identify the micro-activities with a median F1-score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\geq \mathbf{0.75}$</tex> for all the subjects, which can help develop novel pervasive applications.