Reviewerly/exHarmony huggingface dataset:
An Efficient Method for Localization of Magnetic Field Sources That Produce High-Frequency Side-Channel Emanations

Presented is a new, low-cost system for locating the strongest sources of high-frequency EM side-channel emanations on PCBs. These sources indicate the best locations to monitor or mitigate the leakage. The challenges inherent in both side-channel and high-frequency measurements are addressed through careful design of the measurement and localization system. The system is time efficient, requiring only measurements taken around the edge of the device. Instead of testing specific cryptographic programs, this system focuses on identifying and then measuring the side-channel sources of the basic instructions that are commonly used by a multitude of programs on the device. The accuracy of the measurement setup was verified by comparing measurements with simulated results. The setup was then used to locate the instruction-dependent sources at 1 GHz on an field-programable gate array (FPGA) development board and an Internet-of-Things device. The 1 GHz sources are compared to previously identified sources on the same devices taken at significantly lower frequencies. The results demonstrate that the sources of the EM side channel can vary not only with the executed instruction, but also with the frequency of the side-channel signal.
Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions

Federated Learning (FL) is quickly becoming a goto distributed training paradigm for users to jointly train a global model without physically sharing their data. Users can indirectly contribute to, and directly benefit from a much larger aggregate data corpus used to train the global model. However, literature on successful application of FL in real-world problem settings is somewhat sparse. In this paper, we describe our experience applying a FL based solution to the Named Entity Recognition (NER) task for an adverse event detection application in the context of mass scale vaccination programs. We present a comprehensive empirical analysis of various dimensions of benefits gained with FL based training. Furthermore, we investigate effects of tighter Differential Privacy (DP) constraints in highly sensitive settings where federation users must enforce DP to ensure strict privacy guarantees. We show that DP can severely cripple the global model’s prediction accuracy, thus disincentivizing users from participating in the federation. In response, we demonstrate how recent innovation in personalization methods can help significantly recover the lost accuracy.
Building up End-to-end Mask Optimization Framework with Self-training

With the continuous shrinkage of device technology node, the tremendously increasing demands for resolution enhancement technologies (RETs) have created severe concerns over the balance between computational affordability and model accuracy. Having realized the analogies between computational lithography tasks and deep learning-based computer vision applications (e.g., medical image analysis), both industry and academia start gradually migrating various RETs to deep learning-enabled platforms. In this paper, we propose a unified self-training paradigm for building up an end-to-end mask optimization framework from undisclosable layout patterns. Our proposed flow comprises (1) a learning-based pattern generation stage to massively synthesize diverse and realistic layout patterns following the distribution of the undisclosable target layouts, while keeping these confidential layouts blind for any successive training stage, and (2) a complete self-training stage for building up an end-to-end on-neural-network mask optimization framework from scratch, which only requires the aforementioned generated patterns and a compact lithography simulation model as the inputs. Quantitative results demonstrate that our proposed flow achieves comparable state-of-the-art (SOTA) performance in terms of both mask printability and mask correction time while reducing 66% of the turn around time for flow construction.
Mobile-Assisted Language Learning

Learning a second language is challenging for anyone worldwide. When trying to learn a second language in a remote area of the world or under circumstances that do not allow for face-to-face learning, it becomes even more challenging. With the invention and development of mobile devices such as phones and tablets, it becomes much easier to accomplish through mobile-assisted language learning (MALL). Mobile-assisted language learning allows for a user to learn a new language using their mobile device, which is a ubiquitous form of learning. Mobile-assisted language learning is not without risk or vulnerabilities, however. It is imperative that users receive security awareness training, so they can operate their mobile phone in a secure manner. It is also critical that the mobile phones, wireless networks, learning management systems, and computer networks are also secured against various types of viruses, malware, and attacks. Without certain security measures being installed and configured on these devices and systems, the potential for security breaches present themselves.
Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models

While Radio Frequency Machine Learning (RFML) is expected to be a key enabler of future wireless standards, a significant challenge to the widespread adoption of RFML techniques is the lack of explainability in deep learning models. This work investigates the use of Concept Bottleneck (CB) models as a means to provide inherent decision explanations in the context of deep learning (DL)-based Automatic Modulation Classification (AMC). Results show that the proposed approach not only meets the performance of single-network DL-based AMC algorithms, but provides the desired model explainability and shows potential for classifying modulation schemes not seen during training (i.e. zero-shot learning).
Lightweight Residual Network for Person Re-identification

Abstract In this paper, we investigated several lightweight convolutional neural network (CNN) classifiers for person re-identification problems. Person re-identification can be described as finding a person by performing a query with a specific person image on some defined gallery of people images. We construct the lightweight CNN classifier by utilizing the lightweight residual network architectures originally used for the CIFAR dataset. The goal is to design a lightweight CNN classifier with a maximum of 3 million number of parameters that produce good accuracy on person re-identification problems. The last layer of the residual network is detached and changed by two fully-connected layers, one for learning the discriminant features, and the other fully-connected layer for calculated the final classification score in the training process. Experiment results show that the ensemble of lightweight residual networks achieved a good performance on Market-1501 (rank-1 accuracy of 89.46% with mAP of 84.48% for single-query and rank-1 accuracy of 92.23% with mAP of 88.07% for multi-query). Although the lightweight residual network does not achieve state-of-the-art performance, our analysis shows that the lightweight residual network has higher information density comparing with other state-of-the-art models. More information density means that the classifier is more efficient in terms of performance with the number of parameters in the classifier. Implementation of these experiments are available at https://github.com/rezafuad/ person-reid-lightweight-residualnet.
Optimized load balancing in high‐performance computing for big data analytics

Summary New generation application problems in big data and high‐performance computing (HPC) areas claim very diverse operational properties. The convergence requires the dynamic behavior of system components. Load balancing is a critical issue in response to the highly unpredictable, dynamic, and data‐oriented behavior of the system. Possible practical constraints such as communication and load transfer delays play an essential role in designing a dynamic load balancer. On the other hand, according to most of the new platforms' distributed nature, the load balancer should be able to perform in a fully distributed manner. In this research, we consider practical issues, including different processing power, storage capability, communication, load transfer delays, and propose two distributed and optimized load balancing methods in HPC for Big Data processing. We model the constraints and present an argument named compensating factor for the optimized load balancer. We try to minimize the task execution time by reducing the nodes' idle time. We evaluate the proposed methods in different scenarios by using Monte Carlo. Evaluations results show that proposed methods decrease idle time significantly while being scalable to network size and applicable in heterogeneous networks with dynamic resources and configuration.
Upscaling factor estimation on pre-JPEG compressed images based on difference histogram of spectral peaks

Image is one of the most widely used information carrier exchanged in the Internet, which raises a problem of privacy leakage. Private images are vulnerable to be intercepted and altered by an attacker, violating the owner’s privacy. When an image is tampered maliciously, it is often necessary to perform geometric transformations such as scaling to hide the traces of tampering, introducing resampling traces. In the last two decades, spectral analysis is the most commonly used method for resampling detection. However, since JPEG compression severely interferes the statistical characteristics of resampled images and introduces blocking artifacts, the robustness is really poor for most classical spectrum-based methods in the presence of JPEG compression. In this paper, we propose a method to estimate the upscaling factors of upscaled images in the presence of JPEG compression. A comprehensive analysis in spectrum of scaled images is given. We find that both the location and their difference of spectral peaks in the spectrum of the upscaled pre-JPEG images are related to the upscaling factor. Hence, we adopt the difference histogram of spectral peaks to screen candidate upscaling factors and obtain the final estimation by additional verification step according to the location of the spectral peaks. The experimental results demonstrate the effectiveness of the proposed method.
Enriques involutions on pencils of K3 surfaces

The three pencils of K3 surfaces of minimal discriminant whose general element covers at least one Enriques surface are Kond\={o}'s pencils I and II, and the Ap\'ery--Fermi pencil. We enumerate and investigate all Enriques surfaces covered by their general elements.
Variable Fraunhofer MEVIS RegLib Comprehensively Applied to Learn2Reg Challenge

In this paper, we present our contribution to the learn2reg challenge. We applied the Fraunhofer MEVIS registration library RegLib comprehensively to all 4 tasks of the challenge. For tasks 1–3, we used a classic iterative registration method with NGF distance measure, second order curvature regularizer, and a multi-level optimization scheme. For task 4, a deep learning approach with a weakly supervised trained U-Net was applied using the same cost function as in the iterative approach.
Hadith Arabic Text Classification Using Convolutional Neural Network and Support Vector Machine

There are a lot of work has been implemented to solve the problem of text classification but There is only few researchers doing Arabic text classification because of the difficulties in text preprocessing. Convolution Neural network and support vector machine is two different algorithm that can be applied on text classification. CNN seems to be good in extracting the feature from input and SVM is good for classify the class. This study is to introduce Hadith text classification using Convolutional Neural Network and Support Vector Machine. In order to get preliminary result, we used BBC news article (English language) and Arabic tweet sentiment (Arabic language) as dataset for CNN with SVM model. There are 4 methods to evaluate the model which are f1-score, precision and recall and accuracy and error rate probability. We evaluate the model using accuracy and loss using different learning rate. The model accuracy and loss for preliminary result of BBC news article (English language) and Arabic tweet sentiment(Arabic language) are 0.857 accuracy, 0.245 loss and 0.884 accuracy, 0.344 loss. This shows that the proposed model has potential for Hadith text classification.
Real Time Implementation of Square Path Tracing by Autonomous Mobile Robot

Abstract Since last 30 years, autonomous mobile robot is one of the booming fields as well as in near future this field will be top research areas among various scientists and researchers since lot of recent technology have been changing frequently. In this paper, a small work has been executed by mobile robot for path tracing by using online robot simulator Robotsetup 1.0.13071. The simulation results have been displayed by tracing perfect square path by autonomous mobile robot which is advantageous to dense and cluttered environment by using this online simulator.
Review the Utilization of Big Data and K-Means Algorithm in Supporting The Determination of Village Status As Support To The Ministry of Village PDTT

Abstract Big data refers to big data, fast data processing, diversity of data structures, and data values so that it is not possible to be processed with outdated methods. Big data technology is used in various industrial sectors. Big data technology is the whole technology that can handle Big Data. Some of the uses of big data are based on the largest significant data traffic sources such as social media, financial transactions, public data, sensor data, and corporate data. The same is the case with the status of so many villages in Indonesia, so it is better to use big data for the classification of village status based on the village index build by involving an algorithm process. This research aims to produce a description of the role of big data in supporting activities in a grouping. The method used is a qualitative approach related to data collection based on scientific work with the source of data information needed to study literature techniques from various studies that have been published in national and international journals. A decision is made that big data has been widely used in different circles to facilitate performance and speed up the decision-making process.
Enhanced data narratives

Data narratives are an emerging form of communication that employs enhanced media for effective knowledge transfer of complex information. Researchers in the fields of data visualization and artificial intelligence have begun to pioneer new structures of communication to improve the efficiency of construction and the retention of information provided by the knowledge transfer experience. In this paper, we report the results of an empirical study conducted to compare the performance of various narrative communication techniques including frame based narrative visualization, documentary narrative visualization, computer generated text narratives and human generated text narratives. We assess the knowledge transfer performance for each of these data driven narrative structures. Across all conditions, an identical set of knowledge retention questions assessed participants’ recall of details from their assigned narrative communication. Statistical analysis on group performance answering the knowledge retention questions revealed that some narrative communication techniques perform better with general audiences.
Requirements for Augmented Reality Solutions for Safety-Critical Services – The Case of Water Depth Management in a Maritime Logistics Hub

Augmented reality (AR) is widely acknowledged to be beneficial for services that have exceptionally high requirements regarding knowledge and simultaneous tasks to be performed and are safety-critical. In such services, AR enables to augment service provision by delivering seamless integration of information in the field of view while enabling hands-free usage in the case of head-mounted displays. This study explores the user-centered requirements for AR solutions in the operations of a large European maritime logistics hub. Specifically, it deals with the process of soil sounding. Based on eleven think-aloud sessions during service delivery, two expert interviews, and two expert workshops, we derived five core requirements for AR in soil sounding. Thus, we present the first study on the applicability and feasibility of AR in the maritime industry and identify requirements that impact further research on AR use in safety-critical environments.
Ensemble Recurrent Neural Networks for Complex Time Series Prediction with Integration Methods

In this paper the development of the general architecture of a method to design recurrent ensemble neural networks for time series prediction is presented. Therefore, experiments are shown for the ensemble recurrent neural network, as well as the integration of the responses of the ensemble recurrent neural network, with integration by average, weighted average integration, type-1, type-2 and Generalized Type-2 fuzzy systems. The time series used to test the proposed architecture is that of petroleum. The simulation results show the effectiveness of the proposed method.
Investigating cyber alerts with graph-based analytics and narrative visualization

In real-world situations, several threat alerts are being investigated by the specialised staff. In order to prompt response to serve incidents or ignore false alarms, alerts are prioritised and analysed. Security professionals rely on information provided in the alert message. Insufficient information in alert messages raises challenges for security analysts that require them to keep track of all internal and external sources to identify the relevant information. In this paper, a Narrative Analytics-Assisted System (NAAS) is proposed, and a knowledge graph is used in the proposed system to present the relationships. The knowledge graph is proposed to capture the complex relationships between the alert and relevant information from the Internal and External knowledge bases to reduce the cognitive effort in information digestion and to understand a wealth of security data. To enable cooperation in the cyber risk management process, it is an inevitable necessity to generate the knowledge graph and interpret it in a human-friendly format. The current machine-friendly formats for reporting incidents from alerts are complex and of an extensive nature. These characteristics hamper the readability and contribution, therefore preventing humans from understanding and being up to date about the incident. NAAS contains four life cycles to assist an analyst to have a better perception of the elements of the environment by involving more staff in the risk management: (1) Analyses the alert, (2) designs the knowledge graph with the natural language sentences, (3) automatically implements the incident report in natural language by applying novel storytelling techniques from the knowledge graph, and (4) maintains it with the contribution of different levels of expertise. The performance of various NAAS's cycles is demonstrated in a case study with an example scenario from the Security Operations Centre (SOC) at an educational institution, highlighting its useability.
A Visual Interactive Analytics Interface for Complex Event Processing and Machine Learning Processing of Financial Market Data

Applying machine learning techniques over streaming data is notoriously difficult as it involves the interplay of several technologies that need to be judiciously put together by IT experts. This has motivated the need to provide intuitive and easy to use interactive interfaces for financial experts to be able to leverage both Complex Event Processing (CEP) and Machine Learning (ML) technologies. The solution proposed in this paper is based on an open architecture that uses CEP engines as a pre-processing function for downstream ML training and prediction computations. We demonstrate this approach using a few scenarios involving the analysis of financial market data streams.
Australian Copyright Law Impedes the Development of Artificial Intelligence: What Are the Options?

Artificial Intelligence (AI) is an emerging technology that has a huge potential in contributing to the Australian economy and addressing economic and social problems in society. However, Australian copyright laws are likely to impede the development of AI, and machine learning in particular, by requiring authorisation every time copyrighted content is used in machine learning processes. This puts Australian AI industries at a competitive disadvantage, since other AI-focused jurisdictions, such as the US, EU, UK and Japan, allow such use under copyright exceptions. After analysing the scope of copyright in relation to machine learning and licensing options, this paper examines different copyright exceptions (fair use, fair dealing and an EU-style specific TDM exception) as potential solutions for Australia.
Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence.
Building Linked Open Date Entities for Historical Research

Time is a focal point for historical research. Although existing Linked Open Data (LOD) resources hold time entities, they are often limited to modern period and year-month precision at most. Therefore, researchers are currently unable to execute co-reference resolution through entity linking to integrate different datasets which contain information on the day level or remote past. This paper aims to build an RDF model and lookup service for historical time at the lowest granularity level of a single day at a specific point in time, for the duration of 6000 years. The project, Linked Open Date Entities (LODE), generates stable URIs for over 2.2 million entities, which include essential information and links to other LOD resources. The value of date entities is discussed in a couple of use cases with existing datasets. LODE facilitates improved access and connectivity to unlock the potential for the data integration in interdisciplinary research.