Reviewerly/exHarmony huggingface dataset:
Analysis of the Textual Information Extracted from News Portals

The primary means of informing the population in modern society is through news portals. This paper analyses the characteristics and effects that such way of communication creates. The influence was studied in particular on an example of current global phenomenon “vaccination” (hr. cijepljenje). The research method follows the CRISP-DM process adapted to the digitalized form of textual data. The analysed corpus, in the form of natural spoken language, was scraped from Croatian news portals. The subsequent processing extracts information from unstructured textual sources and provides valuable insights, like how much a particular topic is represented in the article. Modeling is based on the application of multiple text mining algorithms, like Words Cloud, Topic Modelling, Concordance and Sentiment Analysis. The implemented model produces indicators for objective information interpretation. The findings suggest that the portals associated the notion of vaccination with the COVID-19 pandemic. Furthermore, this term was often used in a political context. The words used and predominantly negative character of texts dealing with vaccination has led to the transmission of negative emotions to readers. A significant aspect of the study is the fact that it was conducted on the corpus of texts written in Croatian - a relatively small and morphologically complex language.
Siamese Network for Content-Based Image Retrieval: Detection of Alzheimer's Disease from neuroimaging data

In recent years deep-learning methods have demon-strated impressive results in various domains of computer vision, including medical imaging. This paper examines the possibility of leveraging deep-learning concepts in designing a computer system that could help clinicians make accurate Alzheimer disease (AD) diagnosis by retrieving the most similar archived brain scans of patients with already known diagnoses. We implement a siamese network with ResNet-50 twin subnetworks and train it on the MRI data obtained from ADNI (Alzheimer's Disease Neu-roimaging Initiative) dataset. Four different approaches for slice extraction from MRI volume are considered: using the three slices from the same plane (axial, coronal or sagittal) and combining one slice from each plane. The final performance of the CBIR system on new patient's data based only on MR neuroimaging modality shows limited and comparable performance with all four approaches and leaves space for further enhancements, including complementing neuroimaging MRI data with other data modalities relevant for AD detection.
Augmented Reality Based Sensor Data Visualization for Plant Growth Monitoring

Augmented reality (AR) provides an interface for sensor data visualization thus allowing user to receive context aware information about the environment. In this paper we develop a prototype of a system that allows users to access important information (temperature, humidity) from monitored plants. The AR information is initialized with position markers and the data from sensors organized within the IoT paradigm is displayed in real-time. A description of the end-to-end solution is given which includes the Android app based on the Unity platform for data visualization, the AWS server for data storage and the plant models based on the NodeMCU ESP32 platform for data collection and retrieval. The results confirm that the presented framework is able to synchronize the transmission of data, and visualize and accurately map relevant plant growth information, which is important for successful horticulture.
Telecom Fraud Detection with Machine Learning on Imbalanced Dataset

This paper compares the performance of different ML algorithms to a fraud dataset. Telecom operators have long history and experience against fraud activities. During the time and changing the role of telecom operators, from service and infrastructure carriers to communication service providers handling data, voice, and content transfer. There is an urgent need to develop efficient and adaptive algorithms for early fraud detection and prevention. In this paper, we used machine learning techniques as an effective method to detect fraudsters in mobile communications. Fraud datasets are derived from the real telco operator's environment. Different experiments with a few popular machine learning classification algorithms were performed to evaluate the performance of the model. The used dataset, characterized by highly imbalanced distribution, is oversampled with SMOTE method to generate synthetic minority class instances and provide experiments. Performance metrics are evaluated and future research directives are proposed.
Innovations for Intelligent Edge

Massive amounts of data will continue to be generated when everything becomes intelligent and connected, and where data-driven decision making becomes a necessity. Data sources are also becoming increasingly distributed and mobile. These trends have resulted in the rise of the Intelligent Edge, where compute is brought closer to the data source to address scale and latency requirements. The opportunity is to leverage the emerging confluence of compute, communication, and AI at the Edge to wisely partition complex workloads between Edge and Cloud, and to transform the way human and machines interact with the world. Consequently, industry and academia need to innovate faster than ever in key technologies to keep up with this rapidly evolving market. This keynote will cover the required technology innovations and challenges in both 1) monolithic/heterogeneous integration on the technology side, as well as (2) design and architecture on the circuits/systems side, spanning both compute and connectivity domains for a digitized world where everything can be made intelligent and connected.
A 4 to 40V Wide Input Range and Energy Re-Cycling High Power LiDAR Driver for 5% Efficiency Enhancement and 300m Long-distance Object Detection

In automotive applications, the battery voltage may drop to a low voltage of 4V when a sudden power-on occurs, and immediately rise to a high voltage of 40V during a loading pump. Thus, the paper presents a 4 to 40V wide input range high power LiDAR driver based on an enhancement Gallium Nitride (eGaN) switch. In addition, in the case of high di/dt, the proposed protection re-cycling (PRC) technique is used to re-direct the charge at the gate of the eGaN switch to a storage capacitor, which not only protects the GaN switch but also improves the efficiency by 5%. Experimental results show that the resultant long-distance object detection can be up to 300 meters.
Instant Messaging Services Network Traffic Packets Selection Method in the Information Security Incidents Investigation

The article describes a method for network traffic packets selection based on clustering. This method allows encrypted network traffic packets marking for subsequent classification in the task of detecting potential data leakage initiated through unauthorized instant messaging services usage. To start, a feature space dimension reduction method is proposed. As first step result, dimension is reduced from 1460 to 17 features. On the next step, application of popular cluster analysis algorithms is presented. There are some issues in existing algorithmic methods of input parameters determination described. Another method called "minimum description length" principle is applied to count evaluation value of dataset, that describes encrypted network traffic with instant messaging services packets. This principle is tested on standard and commonly used datasets (blobs, noisy moons, iris) and its acceptable results are received. Finally, "minimum description length" principle is applied to dataset consists of encrypted network traffic with instant messaging services packets. The method for optimal clusterization parameters determination is proposed that allows to group packets for subsequent marking during "training set" creation procedure. "Training set" allows to reveal potential data leakage initiated through unauthorized instant messaging services usage in information security incidents investigation.
Configurable Energy-Efficient Lattice-Based Post-Quantum Cryptography Processor for IoT Devices

This work presents a configurable lattice-based post-quantum cryptography processor suitable for lightweight edge devices. To reduce hardware cost and energy consumption, it employs a look-up-table-based modular multiplication for the number-theoretic transform and a real-time processing for polynomial sampling. Implementation in 28nm CMOS shows 15.4x and 14.5x reductions of gate count and on-chip memory size, respectively, compared to the previous state-of-the-art implementation at the cost of only 54% in energy.
Hardware Design and Implementation of Classic McEliece Post-Quantum Cryptosystem Based on FPGA

With the development of information age, the security of data transmission has attracted more attention. In addition, quantum computers pose a great threat to widely used cryptography algorithms. Therefore, Classic McEliece al-gorithm is a post-uantum algorithm, which has high security and stands firm in all kinds of attacks for decades. The wide application of the cryptosystem is inseparable from its hard-ware implementation scheme. So this paper proposes a Classic McEliece implementation scheme based on FPGA platform. To achieve the balance between resources and speed, a variety of methods to implement the scheme are adopted. First, using the characteristics of random access in the RAM, the clock cycle consumption of the error vector generating module is reduced by 95.1%. Second, multiple computing units are employed inside the module for parallel computing and which reduces the number of computing cycles by about 22.4%. Finally, this thesis proposes a multiplexing syndrome decoding module, and compared to the non-multiplexing scheme, the LUT resource consumption of this thesis is reduced by about 24.2%, and the FF resource consumption of this thesis is reduced by about 15.4%.
Hardware Design and Implementation of Post-Quantum Cryptography Kyber

In order to resist quantum attacks, post-quantum cryptographic algorithms have become the focus of cryptog-raphy research. As a lattice-based key algorithm, the Kyber protocol has great advantages in the selection of post-quantum algorithms. This paper proposes an efficient hardware design scheme for Kyber512 whose security level is Ll. This paper first design a general hash module to reuse computing cores to improve resource utilization. A ping-pong RAM and a pipeline structure is used to design a general-purpose NTT processor to support all operations on polynomial multiplication. Finally, the inter-module cooperation and data scheduling are compactly designed to shorten the working cycle. In this paper, the top-level key generation, public key encryption and private key decryption modules are implemented on Artix 7 FPGA with 204MHz frequency. The times of the corresponding modules are 11.5s, 17.3s, and 23.5s, respectively. Compared with the leading hardware implementation, the design in this paper reduces the area-delay product by 10.2 %, achieving an effective balance between resources and area.
Node Correlation Effects on Learning Dynamics in Networked Multiagent Reinforcement Learning

Systems of intelligent interacting agents demonstrate high complexity of learning process due to complexity of single agent learning combined with their communication. Agent interactions are aimed at enhancing speed, quality, and complexity characterictics, nevetheless, each interaction may worse single agent results as well as enhance them. Therefore, building effective communication patterns is of high interest for learning process of intelligent systems. As an applied task, we consider project execution dynamics, where single tasks are assigned to employees having several conflicting parameters, while an intelligent system consists of multiple intelligent agents, learned by reinforcement algorithms. Different patterns of interaction according to agent similarities are explored as a factor affecting learning process. The condition of two agents connection is there similarity value, greater than some determined threshold; similarity function is determined for five static and dynamics parameters, and their influence is regulated by the corresponding five multipliers. The experiment shows there are significant parameters, showing more effect of connection on learning dynamics. This can be seen via effect of parameters, regulating neighbours contribution.
Towards Third Generation AI: Explainable and Interpretable AI

Today, artificial intelligence-based systems, especially machine learning and deep neural network algorithms, make decisions that directly affect people's lives, from health to autonomous vehicles and even to the defense sector. And yet, algorithms like deep neural networks that are high in performance accuracy but low in explainability and trust bring problems in ethics and interpretability. Explainable Artificial Intelligence (XAJD) is a branch of artificial intelligence that produces highquality interpretability, end-user-oriented understanding and explainable tools, techniques and algorithms. In this study, general information about XAI is given.
Morphological Annotation System in The Corpus of Internet Information Texts in The Uzbek Language

Morphological and syntactic annotation is one of the most important types of annotation at the current stage of development of text corpora because it is clearly applied in areas such as lexical and grammatical development. Morphological annotation of language units in Uzbek was done manually and semi-automatically. We offered special tags for the language corpus, if we identify the most common affixes of combinations speech parts and suffixes in words in Uzbek and analyze them with marked tags, it can serve as a foundation for the further development of language corpora. More than 70 special tags were selected for morphological annotation, and speech units were annotated in the corpus. Search and analysis results are displayed in 3 different ways in the user interface: 1) only by word search: in this case, any word is written in the search line. If the analyzed word is tagged in the corpus, it will appear with all its grammatical markers. 2) word+tag search: at this stage, the morphological or semantic feature of a word in a certain word group can be analyzed. For example, if a word with a specific morphological indicator is analyzed, a word is written in the search line, and a tag is selected from the list of morphological tags. At the time of choosing a morphological indicator, a word group must be specified. 3) search by tag only: this option is used when the researcher needs speech units belonging to a certain morphological index or a semantic group in a certain word group.
Linguistic-based Data Augmentation Approach for Offensive Language Detection

The massive amount of data generated by social media possess a great deal of toxic content that lead to serious content filtering problems including hate speech, cyberbullying and insulting. Offensive content even without profanity may result in psychological and physical harms to, particularly children and sensitive people. As of 2022, Turkey houses 7th largest Twitter community among all countries in terms of the active user size exceeding 16 million users, which represents a high diversity of people considering its population. That said, there is a growing need for a comprehensive and high-quality dataset in Turkish that can be utilized in development of NLP models for robust detection of offensive language usage in social media. Related studies in literature have mostly focused on small, synthetic and label-imbalanced datasets. Machine learning models trained on such datasets tend to favor majority class for accuracy and possess generalizability issues. However, it is challenging to create an unbiased dataset containing hate speech without offensive words, and build an accurate detection model to identify the actual hate speech Tweets. The models may lack sufficient context due to the absence of swear words. Therefore, we propose a data augmentation approach based on data mining methods utilizing the linguistic features of Turkish that can help enhance the generalizability of machine learning models without further human interaction. Furthermore, we evaluated the impact of our comprehensive dataset in detection of offensive language in social media. The NLP models training using the augmented dataset improved the macro average detection accuracy by 7.60% in comparison to the baseline approach.
Personal Identification Based Automatic Face Annotation in Multi-View Systems

We propose a method for automatic face annotation in a closed multi-view environment. In such an environment faces are automatically detected and images are collected from several sources. Then, for the purpose of real-time labelling, a model is incrementally trained with every new person who enters the environment. The incremental learning model is validated on the Pandora dataset, which is split into different classes for each incremental step. Active learning is used to determine the type of images needed for training based on a predefined budget.
Fusion of Deep Features for Differential Face Morphing Attack Detection at Automatic Border Control Gates

Face recognition systems (FRS) are showing increasing accuracy in an uncontrolled real world, leading to their usage in automated border control (ABC) gates. However, both automatic and manual FRS are prone to Face Morphing Attacks (FMA), which can be generated by linearly blending face images from two contributory data subjects. Differential Morphing Attack Detection (D-MAD), which compares the face image in an electronic Machine Readable Travel Document (eMRTD) with a trusted live capture from an ABC gate, is thus a significant problem to address. This paper presents Robust D-MAD (RD-MAD), which performs pair-wise pose normalization on the bona fide and probe face images based on global affine alignment. The proposed D-MAD technique is based on the comparison score level fusion of deep features extracted using off-the-shelves pre-trained deep networks such as AlexNet and ResNet. The deep features are extracted corresponding to both enroled and probe face images independently from AlexNet and ResNet. Then the signed difference of the features is computed between enroled and probe face images independently on both AlexNet and ResNet. Two linear SVMs are trained on the signed difference features corresponding to deep networks whose comparison scores are fused using the weighted sum rule to make the final decision. Extensive experiments are performed on a challenging dataset having lighting, face pose, expression, illumination and print-scan variations. Obtained results outperform the state-of-the-art (SOTA) as we achieve an EER=2.1% compared to the SOTA with an EER=4.6%.
Representing linear equations and pulse propagation within waveguide junctions using Petri-nets

In this work, we explore and demonstrate how Petri-nets (PNs) can be exploited to represent linear equations. Such feature is then applied to graphically model the propagation of electromagnetic (EM) pulses within waveguide junctions. PNs have been used in a wide range of research and industrial scenarios to study dynamic systems such as designing asynchronous circuits and chemical engineering, here we exploit features of PNs to represent TEM square pulses in waveguide junctions. In this realm, as new approaches to computing with photonics may require modelling large systems that may need computationally intensive techniques, we discuss how PNs can provide a fast, efficient, and visual modelling of EM pulse propagation within a network of interconnected waveguides. This work represents a step towards allowing individuals from varying areas of expertise to contribute via a common visual language to the future of photonic computing systems and control.
FaultMorse: An automated controlled-channel attack via longest recurring sequence

With the rise of the concept of Trusted Execution Environments (TEEs), such as Intel Software Guard Extensions (SGX), researchers are prompted to constantly verify its effectiveness. Controlled-channel attacks are proposed to construct side channels against the shielding systems by intentionally provoking page faults. So far, various powerful and noise-free controlled-channel attacks have been introduced. However, there are some challenges encountered in the actual practice of these attacks, e.g., extensive manual effort is always required to analyze the target binary and identify conditional control-flow patterns. In this paper, we present FaultMorse, an automated controlled-channel attack. We adopt a global perspective to analyze the page fault sequence and find a specific recurring pattern that corresponds to some specific instructions in the program. Most of the secret bits can be automatically deduced by analyzing the locations of the recurring pattern in the page fault sequence. Compared to previous works, FaultMorse can reduce the complexity of analysis. We propose a method to control page fault counts to improve the attack performance. We implement our FaultMorse attack on a physical machine and evaluate its effectiveness, universality, and page-fault rate. The experimental results show that for some known vulnerable algorithms, FaultMorse can automatically deduce more than 99% of the secret bits.
Remote AFIS: development and validation of low-cost remote tower concepts for uncontrolled aerodromes

Abstract Remote tower systems are widely established as a means to provide efficient air traffic control (ATC) from a remote location. However, even these cost-effective systems cause procurement, implementation, and maintenance costs, which make them unaffordable for non-ATC aerodromes with low revenues, often only offering an aerodrome flight information service (AFIS) or UNICOM information service. In this paper, two more inexpensive concepts enabling remote control at these aerodromes are presented. They are based on a simplified camera set-up comprising a pan-tilt-zoom-camera (PTZ-camera) and a simple panoramic camera. A virtual reality-headset (VR-headset) is used to display the video streams and to control the PTZ-camera. The results of a validation study with nine ATC and AFIS officers using live traffic at the Braunschweig-Wolfsburg aerodrome (BWE) are presented. They are discussed with respect to perceived usability, virtual reality induced cybersickness, and operator acceptance. The system’s cost is compared to that of a conventional remote tower set-up. Furthermore, measured objective data in the form of angular head rotation velocities are presented and requirements for the camera set-up are deduced. In conclusion, the developed concept utilizing both the panorama camera and PTZ-camera was found to be sufficiently usable and accepted by the validation participants. In contrast, the concept based only on the PTZ-camera suffered from jerky and delayed camera movements leading to considerable cybersickness and making it barely usable.