Reviewerly/exHarmony huggingface dataset:
Tactics, Techniques and Procedures of Cybercrime: A Methodology and Tool for Cybercrime Investigation Process

Individuals, organizations and nation-states are increasingly falling victim to cyberattacks. A lot of research on how to understand the modus operandi of a cyber attacker through the MITRE ATT&CK framework, CAPEC enumeration and various cyber kill chain frameworks has been available for cyber incident responders for both pre-attack defensive posture preparation as well as post-attack mapping of the indicators. Cybercrime, on the other hand, has a very different set of motives, majorly financial, but also impersonation, harassment and hate crimes, which are targeted mostly towards individuals who are vulnerable. Investigative officers in cyber cells around the world use various investigative processes in their attempt to solve a cybercrime incident, detect the criminal(s), recover the lost money, shut down the accounts of perpetrators, or bring down the cyber-criminal gangs involved in reported cybercrime incidents. However, to the best of our knowledge, there is no framework to map the cybercrime incident narratives to the tactics, techniques and procedures (TTPs) used by cybercriminals, nor are the processes used by cybercrime investigating officers mapped to such TTPs to create a systematic modus operandi for the investigators to solve a cybercrime, cluster multiple crime incidents as attributable to a specific cybercrime operator, or systematically map a set of evidence collected to a pattern of TTPs. In this paper, we present a TTP-based framework for classified cybercrimes to formulate a comprehensive strategy for cybercrime interpretation and action recommendations for law enforcement officers, which can be used as a reference point to establish targeted threat models and incident response methodologies for cybercrime investigations.
Securing the Flow: Security and Privacy Tools for Flow-based Programming

This paper presents a comprehensive collection of reusable artifacts for addressing security and privacy issues in the context of flow-based programming in Function-as-a-Service (FaaS) environments. With the rapid adoption of FaaS platforms, it becomes important to guarantee the security and privacy of applications. The presented artifacts incorporate a wide variety of nodes and techniques into the popular Node-RED architecture. They intend to improve the security and privacy of applications by addressing critical aspects such as secure data flow management, code authenticity and validation, access control mechanisms, and runtime monitoring and anomaly detection. Using these artifacts, developers can construct more robust and resilient applications in FaaS environments while mitigating potential security and privacy risks.
Towards Grammatical Tagging for the Legal Language of Cybersecurity

Legal language can be understood as the language typically used by those engaged in the legal profession and, as such, it may come both in spoken or written form. Recent legislation on cybersecurity obviously uses legal language in writing, thus inheriting all its interpretative complications due to the typical abundance of cases and sub-cases as well as to the general richness in detail. This paper faces the challenge of the essential interpretation of the legal language of cybersecurity, namely of the extraction of the essential Parts of Speech (POS) from the legal documents concerning cybersecurity.
Enabling Efficient Threshold Signature Computation via Java Card API

Threshold signatures are becoming an increasingly popular method of signing key protection, primarily due to their ability to produce signatures that require the cooperation of multiple parties yet appear indistinguishable from a regular signature. This unique feature allows for their easy integration with existing systems, making them highly desirable in applications like national identity systems and transaction authorization, where they are being gradually deployed; their growing importance is further attested by NIST’s recently initiated efforts to standardize threshold schemes [19].
Beware the Doppelgänger: Attacks against Adaptive Thresholds in Facial Recognition Systems

Biometric recognition systems typically use a fixed threshold to differentiate between legitimate users and imposters. Yet, this method can be problematic due to differences in individual user performance, whereas some users are more easily recognizable than others. Furthermore, fixed thresholds require extensive tuning on a large test set a priori to determine an optimal threshold value. Adaptive thresholds address these shortcomings by adjusting threshold values based on population characteristics. However, our research demonstrates that adaptive thresholds suffer from a significant weakness as they inadvertently increase the attack surface against face recognition systems. We do so by introducing a novel attack, the doppelgänger attack, where a malicious actor inserts adversarial examples that mimic legitimate users and increase the false rejection rate for these legitimate users by 70%.
Accelerated deep-learning-based process monitoring of microfluidic inkjet printing

The growing demand for multi-nozzle inkjet printing has created a need for automated status monitoring algorithms. This study proposes an efficient and accelerated deep learning model for inkjet printing monitoring, which utilizes a residual neural network with a translation-insensitive, linear transform for input size reduction. Three representative ejected droplet statuses – none, non-spherical, and spherical – are classified by this model. The condition of the jetting is inspected by analyzing each frame of the process video in sequence. Thanks to the reduced input size, both training and inference speeds are improved by 4.4-fold and 2.4-fold, while it preserves more features than simple interpolation methods. The model also achieves an inference accuracy of approximately 96% within 5 epochs, whereas the uncompressed model can only obtain ∼92% of accuracy. In addition, a total of 125 consecutive frames acquired from realistic inkjet printing are examined by this model, and the comparison with the ground truth results in 97% of accuracy. The resulting inference speed was 14-fold faster than the recording speed (25 fps) of CCD cameras, highlighting its real-time application capability. To extend the applications, a multi-class droplet categorization was also provided, which can more precisely sort geometries based on tail length. The present study will help bridge the gap between deep learning algorithms and industry by reducing computational costs.
Real-time continuous EOG-based gaze angle estimation with baseline drift compensation under stationary head conditions

This work aims to propose a novel method to estimate the gaze from electrooculography (EOG) signals while compensating for the baseline drift, and which intrinsically detects fixations, saccades and blinks. In contrast to existing baseline drift mitigation techniques, the proposed framework is real-time-implementable, and does not require the average point of gaze to lie at the primary gaze position, nor does it disrupt the overall ocular pose-induced EOG signal DC characteristics. The EOG data used to validate the proposed method is also being made publicly available. The proposed method is based on the dual Kalman filter, which estimates the gaze angles (GAs) and the baseline concurrently, taking into consideration the EOG signal's non-stationary and temporally-multimodal characteristics. In fact, it is a multiple-model technique based on a battery model of the eye wherein fixations, saccades and blinks are modelled separately. When applied to short EOG data segments, a horizontal and vertical GA estimation error of 1.64 ± 0.82°and 1.97 ± 0.34°, respectively, was obtained, which compared well with the corresponding results obtained using the state-of-the-art linear regression models. Conversely, for longer data segments, the proposed framework yielded superior GA estimation performance when compared to the state-of-the-art techniques. Eye movement detection and labelling F-scores exceeding 90% were achieved. The proposed method yields reliable gaze estimation performance, and accurately detects fixations, saccades and blinks. This work proposes an integrated method to simultaneously estimate the GAs and address the baseline drift issue without the limitations of existing baseline drift mitigation techniques.
Different realities: a comparison of augmented and virtual reality for the sensemaking process

Analysts perform sensemaking on large complex multimedia datasets in order to extract concepts, themes, and other kinds of insights from them. Immersive analytics, in particular, puts users in virtual environments that allow them to explore data in a unique way where they can interact and move through the data. Previous research using virtual reality immersive analytics tools found users wanting to refer to real-world objects or understand the physical world around them while continuing to perform their analysis. Therefore, we designed and ran a comparative study looking at the tradeoffs between virtual and augmented reality for our immersive analytics approach: Immersive Space to Think. Through two mixed-methods studies we found that virtual reality affords users a space where users can focus more on their task, but augmented reality allows them to use various real-world tools that can increase user satisfaction. In future immersive analytics tools, we recommend a blend of the two—augmented virtuality—with pass-through portals which allow users to see various real-world tools, such as whiteboards or desks and keyboards, while still giving themselves a space to focus.
Blockchain-based Electricity Market Agent-based Modelling&amp;Simulation

The use of distributed energy generation through business and residential photovoltaic (PV) applications creates new energy markets that blur the traditional line between energy providers and users. This new market dynamic results in the emergence of energy prosumers, whose role is to produce and consume energy. Blockchain technology automates direct energy exchanges within a distributed system architecture that relies on encryption hashing and general agreement verification. This technology provides prosumers, consumers, energy providers, and utilities with an affordable, safe, and unique energy-trading alternative. The Education City Community Housing (ECCH) in Qatar is the focus of this project, which aims to develop and implement an accurate Agent-Based Modeling (ABM) model and a Geographic Information System (GIS) to facilitate energy exchange in a real estate market. The ABM model simulates the spatiotemporal aspects of trading in a small market and collects and analyzes a large amount of data about daily energy usage. These simulations can help to better understand the structure of a trading market and to develop a decentralized system for trading energy. The findings of this study demonstrate that the peculiarities of transactions carried out in a community-based housing market can be easily researched using GIS data combined with an agent-based design by simply changing the settings. For large-scale simulation models with numerous stakeholders, high-performance computing will be used to improve the model’s performance and to provide a scalable environment for analyzing an energy blockchain community for the technological, financial, and social sectors of Qatar.
Vehicle Counting on Vietnamese Street

Object counting is the process of determining the count of objects in images using computer vision techniques. In this paper, we employ several state-of-the-art object detection and tracking algorithms to solve the object counting problem in image regions of interest (ROI) on Vietnamese streets. Specifically, we propose video-based methods for counting vehicles in various weather conditions and low-light environments, a new dataset for Vietnamese streets, and retrain the scratch model on the new dataset. A video is processed in three phases, including object detection with YOLO (You Only Look Once), tracking with StrongSORT, and vehicle counting in ROI. The experimental analysis of real-world video footage demonstrates that the proposed method can accurately detect, monitor, and count vehicles. In addition, by using our collected dataset, the proposed method performs significantly better than the pretrained YOLO model.
Resource-Efficient Federated Learning Robust to Communication Errors

The effectiveness of federated learning (FL) in leveraging distributed datasets is highly contingent upon the accuracy of model exchanges between clients and servers. Communication errors caused by noisy links can negatively impact learning accuracy. To address this issue, we present an FL algorithm that is robust to communication errors while reducing the communication load on clients. To derive the proposed algorithm, we consider a weighted least-squares regression problem as a motivating example. We cast the considered problem as a distributed optimization problem over a federated network, which employs random scheduling to enhance communication efficiency, and solve it using the alternating direction method of multipliers. To improve robustness, we eliminate the local dual parameters and reduce the number of global model exchanges via a change of variable. We analyze the mean convergence of our proposed algorithm and demonstrate its effectiveness compared with related existing algorithms via simulations.
Markov Decision Process Design for Imitation of Optimal Task Schedulers

Due to the generally prohibitive computational requirements of optimal task schedulers, much of the field of task scheduling focuses on designing fast suboptimal algorithms. Since the tree search commonly used by sequencing algorithms such as Branch-and-Bound can naturally be framed as a Markov decision process, designing schedulers using imitation and reinforcement learning is a promising and active area of research. This paper demonstrates how polices can be trained on previously solved scheduling problems and successfully generalize to novel ones. Instead of focusing on policy design, however, this work focuses on designing the Markov decision process observation and reward functions to make learning as effective and efficient as possible. This can be of critical importance when training data is limited or when only simple, fast policies are practical. Various Markov decision process designs are introduced and simulation examples demonstrate the resultant increases in policy performance, even without integration into search algorithms.
A novel technique for optimizing the filter size of CNNs without backpropagation

Image filters play a crucial role in the performance of convolutional neural networks (CNNs). Yet, the optimisation of those filters tends to focus solely on optimising their weights. As a result, the machine learning practitioner either uses the standard 3×3 filter size or has to select the filter size empirically, as the optimal filter size depends on the application at hand. There has been a lack of serious attempts to address this issue in CNNs. To this end, we propose a novel technique to learn the filter size without depending on either gradient descent or backpropagation. We compare our technique with the only serious attempt existing and show that not only our method performs better, but also converges to an optimal solution at a much smaller number of training iterations.
Learning Directed Graphs From Data Under Structural Constraints

For real-world graph signals, the relationships between two nodes may not always be symmetric. Hence, a directed graph would be more flexible to characterize such relationships between signals. In this paper, we propose a two-stage algorithm to learn directed graphs from the observed data, i.e., designing the graph frequency components and afterward estimating the graph shift matrix. The graph frequency components are designed to improve the sparsity of graph signals in graph frequency domain, and the estimation of directed shift matrix is thereafter modelled as a convex problem, where the structural constraints of graph signals could be taken into account. Such a directed graph shift matrix would greatly facilitate further processing of the associated graph signals such as sampling and graph filtering in frequency domain since the graph frequency components are specifically designed and the signals over the graph are sparse. Numerical results demonstrate the effectiveness of the proposed method.
In-context Cross-Density Adaptation on Noisy Mammogram Abnormalities Detection

This paper investigates the impact of breast density distribution on the generalization performance of deep-learning models on mammography images using the VinDr-Mammo dataset. We explore the use of domain adaptation techniques, specifically Domain Adaptive Object Detection (DAOD) with the Noise Latent Transferability Exploration (NLTE) framework, to improve model performance across breast densities under noisy labeling circumstances. We propose a robust augmentation framework to bridge the domain gap between the source and target inside a dataset. Our results show that DAOD-based methods, along with the proposed augmentation framework, can improve the generalization performance of deep-learning models (+5% overall mAP improvement approximately in our experimental results compared to commonly used detection models). This paper highlights the importance of domain adaptation techniques in medical imaging, particularly in the context of breast density distribution, which is critical in mammography screening.
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms

Image augmentation techniques have been widely investigated to improve the performance of deep learning (DL) algorithms on mammography classification tasks. Recent methods have proved the efficiency of image augmentation on data deficiency or data imbalance issues. In this paper, we propose a novel transparency strategy to boost the Breast Imaging Reporting and Data System (BI-RADS) scores of mammogram classifiers. The proposed approach utilizes the Region of Interest (ROI) information to generate more high-risk training examples for breast cancer (BI-RADS 3, 4, 5) from original images. Our extensive experiments on three different datasets show that the proposed approach significantly improves the mammogram classification performance and surpasses a state-of-the-art data augmentation technique called CutMix. This study also highlights that our transparency method is more effective than other augmentation strategies for BI-RADS classification and can be widely applied to other computer vision tasks.
Assessing Critical Data Types for Deep Leaming-Based PV Generation Forecasting

Solar power generation is playing an increasingly important role for sustainable power systems. Reliable forecasting of solar power generation is one of the fundamental challenges faced by network operators nowadays, especially under drastic weather changes. In this paper, two artificial neural network models, Long Short-Term Memory (LSTM) network and Gated Recurrent Unit (GRU) network, are used to predict solar power generation day ahead, and the hyperparameters in the neural network are optimized by the Gaussian process. Considering various input variables affecting the forecast, such as active power, wind speed, temperature, humidity, global horizontal radiation, etc., the dominant factors for solar power generation forecast are analysed. To identify the critical data types for higher prediction accuracy, three models are designed to compare the influence of different levels of use of day ahead weather data on solar power forecasting, and the testing is carried out for the model input of 12 months, 6 months and 1 month.
The Voiceless Assistant for The Specially Abled

A novel system aimed at bridging the communication gap between deaf and blind individuals and those who are not familiar with sign language or Braille is proposed. Google’s Speech Recognition library is used to convert speech to text and the pyttsx3 library is used to convert text to speech. The system is designed to interconvert between Indian sign language, Braille, and English text, creating a unified platform for individuals with different disabilities to communicate effectively. The sign language recognition component of the system was trained with a training accuracy of 97.73 percent and test accuracy of 98 percent. The Braille recognition component was trained with a training accuracy of 97 percent and test accuracy of 95.5 percent. The integration of sign language and Braille into one platform is a unique feature that sets this system apart from existing solutions.
Design &amp; Implementation of Novel AI Based Hand Gestured Smart Wheelchair

Disability is a significant health concern in India, according to research of disabled people carried by NSSO. Cerebral palsy, dwarfism, muscular dystrophy, as well as other conditions can cause disability, although the majority of these disorders are acquired at birth or accidentally. The wheelchairs are the ideal mobility support for those with physical impairments. The purpose of this proposed work is to create a programmable wheelchair which would aid individuals with physical impairments in their daily activities and fulfilment of some routine life. Individuals who have physical abnormalities lack the ability to move around just like regular people. They are indeed forced to rely on others for all of their daily necessities. They feel discouraged due to their dependence on others. An AI-based hand gestured smart wheelchair is a type of wheelchair that uses artificial intelligence technology to interpret hand gestures and translate them into wheelchair movements. This type of wheelchair is designed to provide greater mobility and independence to people with disabilities, particularly those who may have limited mobility in their arms or hands. The system works by using sensors and cameras to detect and interpret the user’s hand gestures, which are then transmitted to the wheelchair’s control system. The control system processes these gestures and translates them into specific commands, such as moving the wheelchair forward, turning left or right, or stopping. One of the key benefits of an AI-based hand gestured smart wheelchair is that it allows for a more intuitive and natural control mechanism, as users can simply gesture in the direction, they want to go rather than having to manipulate joysticks or other controls. Additionally, this type of wheelchair can be adapted to individual users, allowing them to customize their gestures to better suit their needs.
Improved YOLO X with Bilateral Attention for Small Object Detection

Currently, general object detection networks exhibit low accuracy and instability in small object scenes. To address this issue, we propose an improved YOLO X algorithm for small object detection based on bilateral attention. This algorithm introduces a bilateral attention module that integrates attention feature maps from both spatial and channel dimensions, adaptively capturing refined features of the target object. This enriches the information of small objects on feature maps of different sizes and enhances communication of potential semantic information. Additionally, a relative position labeling strategy is used to enhance the accuracy of target location information and expand the detection range. Experimental results demonstrate that our method is more effective than the YOLO X model, particularly in handling small objects in remote sensing images, with a 1.1% improvement in mAP0.5.