Reviewerly/exHarmony huggingface dataset:
Integrating Real-Time Health Status into Everyday Objects: A Design Case Study on Enhancing Diabetic Health Monitoring with Artistic Creations

How can everyday objects interact or change based on a person’s real-time health status? In the following design case study, we bring together the fields of health and technology with art in the context of diabetic health monitoring. Specifically, we combine smart sock wearable diabetic monitoring technology with home decor (dynamic artwork) as well as artistically designed changing smartwatch faces. The aim is to allow for health awareness for both diabetic patients and their caregivers/relatives in an ambient living context. By creating subtly changing artworks as well as changing smartwatch faces that are based on health status, we can allow for gentle non-emergent notifications to the diabetic and their caregivers/relatives to create health status awareness in order to positively impact the quality of their health and home-life experience.
The European and national legal frameworks for combating the digital dimension of gender-based violence

The development of technology and the Internet has made virtual space a place for the exchange of various information and data. In addition to benefits it provides to users, there are unwanted side effects, including digital violence. The main problem is the establishment of a legal framework for the fight against digital violence directed against individuals or groups, because there are no temporal or geographical boundaries in virtual space, which makes it difficult to define national jurisdictions, as well as a great number of types of this violence. The subject of the research in this paper is the prevention of gender-based digital violence due to the importance and impact it has on society, and also the fact that violence which begins as digital one often culminates in physical violence and leads to serious injuries or death of persons affected by it. Successful combating this type of violence requires the cooperation of various actors in order to show that violence against women and girls is not acceptable. The Council of Europe and the bodies of the European Union simultaneously fight against digital violence in Europe by adopting various documents that are mandatory for member states. The Republic of Serbia harmonises its legislation with European acts. The method of content analysis and historical method have been used in writing this paper, and the objective has been to answer the following question: which international documents prohibit digital violence and what mechanisms are available to state authorities to fight against it. It has been concluded that it is necessary to ensure the implementation of adopted documents and the cooperation of state authorities and bodies with companies that are the owners of the platforms and applications used for conducting violence, as well as the constant improvement of the services and officers who fight against this type of violence.
DESIGNING AN AUXILIARY AUTOMATED MANAGEMENT DECISION MAKING SYSTEM BASED ON INFORMATION SECURITY LEVEL ANALYSIS

The aim of the scientific work is to create a methodology for designing an auxiliary automated management decision making system based on analysing information security level of intellectual resources of a large industrial enterprise. The methodology is based on the principle of comprehensive information security. The article is devoted to solving the problem of creating a convenient auxiliary tool for auditing information security events. The novelty of the work is the proposed creative concept of using the maximum amount of data on information security events to make a management decision (including a personnel management decision). The study results are recommendations for building an auxiliary automated system for auditing and analysing the information security level of intellectual resources of a large industrial enterprise.
Design and development of a nano drone – A real time UAV

One of the most widespread applications of drones or unmanned aerial vehicles is in aerial photography and videography. The type of drones that accomplish this job are called camera drones that are unmanned aerial vehicles in quadrotor configuration. Even though there are camera drones available in the market, most of them are not easy to acquire, setup and operate legally without registration and permission from authority for every flight. This is mainly due to the reason most camera drones in the market that are capable exceed 250 g which is the maximum limit for any drone in India to operate legally without registration and permission. This can be very restricting for consumers but mainly photographers and content creators. The objective of this project is to design and develop a camera drone drive and control system platform that is essentially a quadrotor for aerial photography that weighs within 35 g excess of the weight falling under Nano category according to Directorate General of Civil Aviation (DGCA). Despite this weight and space restriction, the functionality of the drone shall not be greatly sacrificed.
Several classes of linear codes with few weights over finite fields

Linear codes with few weights have applications in secret sharing schemes, authentication codes, association schemes and strongly regular graphs. In this paper, we introduce a new flexible defining set, and consequently, we obtain several classes of t-weight linear codes over Fp, where t=4,5,6 and p is an odd prime. The parameters and weight distributions of the generated linear codes are fully determined using quadratic Gauss sums over finite fields. Moreover, two classes of optimal two-weight codes meeting the Griesmer bound are obtained from our constructions. Our results are heavily based on evaluating some exponential sums over finite fields. Computer experiments using MAGMA programs show that many (almost) optimal codes can be derived from our results.
MeaningBERT: assessing meaning preservation between sentences

In the field of automatic text simplification, assessing whether or not the meaning of the original text has been preserved during simplification is of paramount importance. Metrics relying on n-gram overlap assessment may struggle to deal with simplifications which replace complex phrases with their simpler paraphrases. Current evaluation metrics for meaning preservation based on large language models (LLMs), such as BertScore in machine translation or QuestEval in summarization, have been proposed. However, none has a strong correlation with human judgment of meaning preservation. Moreover, such metrics have not been assessed in the context of text simplification research. In this study, we present a meta-evaluation of several metrics we apply to measure content similarity in text simplification. We also show that the metrics are unable to pass two trivial, inexpensive content preservation tests. Another contribution of this study is MeaningBERT (https://github.com/GRAAL-Research/MeaningBERT), a new trainable metric designed to assess meaning preservation between two sentences in text simplification, showing how it correlates with human judgment. To demonstrate its quality and versatility, we will also present a compilation of datasets used to assess meaning preservation and benchmark our study against a large selection of popular metrics.
Towards Indirect Assessment of Surface Anomalies on Wind Turbine Rotor Blades

We present results from novel field, lab and computer studies, that pave the way towards non-invasive classification of localised surface defects on running wind turbine rotors using infrared thermography (IRT). In particular, we first parametrise the problem from a fluid dynamical point of view using the roughness Reynolds number ( $$R\!e_{k}$$ ) and demonstrate how the parameter regime relevant for modern wind turbines translate to parameter values that are currently feasible in typical wind tunnel and computer experiments. Second, we discuss preparatory wind tunnel and field measurements, that demonstrate a promising degree of sensitivity of the recorded IRT data w.r.t. the key control parameter ( $$R\!e_{k}$$ ), which is a minimum requirement for the proposed classification technique to work. Third, we introduce and validate a local domain ansatz for future computer experiments, that enables well-resolved Navier–Stokes simulations for the target parameter regime at reasonable computational costs.
An Approach to Automated Detection of Sessile Droplets in Mixed Convection

A method for the measurement of position and size of sessile water droplets is presented. Droplets originate from condensation on a plane vertical surface in a vented cuboidal cavity with a mixed convective flow with humid air as the working fluid. Condensation is observed through a transparent cooling device coated with polyvinyl chloride with an average contact angle of 80.0(3) $$^\circ $$ . The implemented detection algorithm is based on the circle Hough Transform together with sophisticated pre- and post-processing steps, which are detailed in this work. Validation experiments yield a detection of over 97% of the area covered by droplets by detecting a minimal radius of $${13.8}\,\upmu \textrm{m}$$ . Additionally, first experimental results of droplet size distributions are presented.
Linguistic Features Distinguishing Students’ Writing Ability Aligned with CEFR Levels

Abstract A substantive body of research has been revolving around the linguistic features that distinguish different levels of students’ writing samples (e.g. Crossley and McNamara 2012; McNamara et al. 2015; Lu 2017). Nevertheless, it is somewhat difficult to generalize the findings across various empirical studies, given that different criteria were adopted to measure language learners’ proficiency levels (Chen and Baker 2016). Some researchers suggested using the Common European Framework of Reference for Languages (CEFR) (Council of Europe 2001) as the common standard of evaluating and describing students’ proficiency levels. Therefore, the current research intends to identify the linguistic features that distinguish students’ writing samples across CEFR levels by adopting a machine-learning method, decision tree, which provides the direct visualization of decisions made in each step of the classification procedure. The linguistic features that emerged as predicative of CEFR levels could be employed to (i) inform L2 writing instruction, (ii) track long-term development of writing ability, and (iii) facilitate experts’ judgment in the practice of aligning writing tests/samples with CEFR.
FeatsFlow: Traceable representation learning based on normalizing flows

This paper studies effective traceable feature representation learning in the view of distribution transformation, termed FeatsFlow, by proposing a distribution-aware learning framework combining the discriminating model with a normalizing flow-based model. The process can be regarded as a series of feature distribution transformations, from the input images to the expected results. Focusing on the learned representation of the target model, we take full advantage of the invertible nature of normalizing flows and learn the practical and traceable feature representation for target goals. Considering that it is difficult to model the traceable process for feature extraction, we propose an effective model by combining a general discriminating model with normalizing flows for traceable feature extraction. The normalizing flows module is added to the original model in a plug-in mode, which is convenient to make it available for effective and traceable feature learning. Thus we can obtain an effective and traceable representation distribution. Extensive experiments are conducted on our proposed representation learning model for the image classification task, and the experimental results illustrate that our proposed model is adequate for traceable representation learning. The most important is that we present a distribution-aware representation learning approach, which makes it possible to conduct and understand feature representation learning at the feature level.
Target Extraction Based on Cross-Domain Alignment and Self-Correlation Mechanism With Weak-Labeled SAR Data

Target extraction is a significant task in Synthetic Aperture Radar (SAR) image processing. Recently, SAR target extraction with weak labels has attracted great attention due to the low labeling cost. However, weak-labeled SAR data brings great challenges to the data-driven methods: 1) location and structural information of the targets are lost in weak labels; 2) discrepancy of heterogeneous SAR images restricts the training efficiency of the model. In this paper, a novel Cross-domain Self-correlation Aware Network (CSANet) for SAR target extraction based on image-level weak labels is proposed to address such challenges. Firstly, the Cross-domain Representation Alignment (CRA) strategy is proposed to learn transferrable knowledge from heterogeneous SAR datasets. Through cross-domain alignment, invariant feature space is constructed to bridge the heterogeneous SAR data and improve the generalization performance of the model. Then, we propose a self-correlation aware extraction module with image-level weak labels, which only indicate whether the images contain the targets or not. Self-Correlation Module (SCM) is designed to preserve the context dependency of SAR pixels and compensate for the gap between weak labels and dense prediction. Finally, Affinity-Guided Optimization (AGO) is introduced to learn the inner-pixel affinity and refine the coarse extraction maps with clear boundaries. Comparison with state-of-the-arts and the ablation experiments demonstrate the efficiency of our method.
Differential Private Discrete Noise-Adding Mechanism: Conditions, Properties, and Optimization

Differential privacy is a widely used framework for evaluating privacy loss in data anonymization. While the continuous noise-adding mechanism has been extensively studied, there is a dearth of research on discrete random mechanisms for discretely distributed data. This study addresses this gap by examining the primary differential privacy conditions and properties for general discrete random mechanisms, and investigating the trade-off between data privacy and data utility. We establish sufficient and necessary conditions for discrete <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</i> -differential privacy and sufficient conditions for discrete ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</i> )-differential privacy, with closed-form expressions for differential privacy parameters. These conditions can be applied to evaluate the differential privacy properties of discrete noise-adding mechanisms with various types of noise. Moreover, we propose an optimal discrete <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</i> -differential private noise-adding mechanism under the utility-maximization framework. Here, the utility is characterized by the similarity of the statistical properties between the mechanism’s input and output. Our findings suggest that the optimal class of discrete noise probability distributions in the mechanism is staircase-shaped.
CMNet: a novel model and design rationale based on comparison studies and synergy of CNN and MetaFormer

Abstract Convolutional- and Transformer-based backbone architecture are two dominant, widely accepted, models in computer vision. Nevertheless, it is still a challenge, thus a focus of research, to decide which backbone architecture performs better, and under which circumstances. In this paper, we conduct an in-depth investigation into the differences of the macroscopic backbone design of the CNN and Transformer models with the ultimate purpose of developing new models to combine the strengths of both types of architectures for effective image classification. Specifically, we first analyze the model structures of both models and identified four main differences, then we design four sets of ablation experiments using the ImageNet-1K dataset with an image classification problem as an example to study the impacts of these four differences on model performance. Based on the experimental results, we derive four observations as rules of thumb for designing a vision model backbone architecture. Informed by the experiment findings, we then conceive a novel model called CMNet which marries the experiment-proved best design practices of CNN and Transformer architectures. Finally, we carry out extensive experiments on CMNet using the same dataset against baseline classifiers. Initial results prove CMNet achieves the highest top-1 accuracy of 80.08% on the ImageNet-1K validation set, this is a very competitive value compared to previous classical models with similar computational complexity. Details of the implementation, algorithms and codes, are publicly available on Github: https://github.com/Arwin-Yu/CMNet .
Hybrid Navigation Decision Control Mechanism for Intelligent Wheel-Chair

The continuous rising of the elderly/disabled population has created a requirement for assistive robotics devices to counter the lack of trustworthy servants. Intelligent Wheelchairs are developed for that particular purpose. Intelligent Wheelchairs differ depending on the interactive modality and most commonly found modalities are speech-controlled. Since these are assistive devices that need to act as human companions, it is necessary to have a dialogue between the device and the user. Even though the wheelchair is fully automated, the user should have control over it at some point. However, this exchange of control should be intelligent and transitions need to be executed in order to safeguard the user. Therefore the purpose of this paper is to propose an intelligent system that would navigate an intelligent voice-controlled wheelchair facilitating the intelligent exchange of control between the user and the wheelchair. This control is not simultaneous and one can override the other only when navigation could lead to collisions. In the proposed method, users can control the wheelchair using fixed vocal commands, and execution of those commands will be performed using the spatial and control parameters. Control of the wheelchair will be exchanged between the user and the wheelchair itself considering specific parameters such as obstacle distance, collision time, the velocity of the wheelchair among others. User control mode has 5 definite vocal commands with classifiers to identify any navigation command into the command model and considers uncertain terms such as ‘little’ and ‘hard’ for ‘Turn’ commands. Command classification had produced a Cohen’s Kappa value of 0.9462 and the classifier for the uncertain terms had produced a Cohen’s Kappa value of 0.7325. Both were acceptable values for those particular classifications. As per the experiment results, the proposed system reduced the vocal command frequency and risk of collisions through proper control of the velocity levels and intelligent exchange of control at given locations.
Photovoltaic sizing using machine learning

Renewable energy is the future of energy in the world. Solar energy is a major renewable energy source which addresses the energy deficiency problem in various countries. This endless energy source is captured using photovoltaic systems. However, in order to make the best out of photovoltaic systems, the size of the system must be optimized. Sizing has always been a critical issue that leads to huge losses once not properly designed. Existing methods consider sizing problem as a case dependent optimization problem that requires data from the place where the photovoltaic system is to be installed. This study introduces machine learning algorithms into the photovoltaic systems’ sizing problem for the first time. The proposed case-independent model automates the sizing process and is generalized to be applied at any location. Linear regression, polynomial regression, neural network, random forest, and decision tree were implemented and compared in terms of accuracy. Results show that random forest is the best suited algorithm for this application.
A Lightweight Stochastic Blockchain for IoT Data Integrity in Wireless Channels

Trustworthy validators selection is crucial as validators determine whether a block should be added to its chain. In this paper, we proposed a novel confidence score-based lightweight stochastic blockchain for wireless Internet of Things (IoT) systems. The received signal strength is used to define the trust level of a wireless IoT node to facilitate the selection of more trustworthy validator nodes, thereby reducing the possibility of selecting malicious validators. We used a lightweight authentication protocol called Burrows-Abadi-Needham (BAN) logic to prevent unauthorised information leakage. A formal security analysis of BAN logic is provided for proving secure and fresh data storage. Our analysis and simulations reveal that the probability of successful defense against data integrity attacks (i.e., the probability that the majority of the validator nodes are not compromised) can be improved up to two times higher than that associated with the closest competitive scheme of random selection in stochastic blockchains. Our results further reveal that the probability of successful defense depends on the total number of nodes in the network and the number of validator nodes. The proposed blockchain concept can be easily implemented in various wireless IoT environments to enhance the successful defense of the system for maintaining IoT data integrity.
Optimizations of Privacy-Preserving DNN for Low-Latency Inference on Encrypted Data

Homomorphic encryption (HE) based on the CKKS scheme is a promising candidate for implementing privacy-preserving deep neural networks (PP-DNN) by performing operations directly on the encrypted data. However, due to the computational complexity of HE operation, even simple PP-DNNs require a huge amount of processing time. In order to reduce the processing time of PP-DNN, in this paper, we present an innovative, low-latency model optimization solution for PP-DNNs. Our proposed low-latency model optimization solution exploits second-order polynomials that approximate original activation functions, ensuring low-latency and accurate DNN performance. To further reduce the processing latency of PP-DNNs, we introduce the coefficient absorbing technique and a masking convolution for convolutional layers. The experimental results show that the proposed solution constructs bootstrapping-free PP-DNN and reduces the inference latency of CKKS-based ResNet-34 by 35% in the CIFAR-100 dataset and ResNet-32 by 77% in the CIFAR-10 dataset compared to previous approaches while maintaining the same level of inference accuracy. Moreover, through the layer-wise latency analysis, we show the efficacy of our approaches, and through validation in various scenarios, we demonstrate the generality of our methods.