Reviewerly/exHarmony huggingface dataset:
ObScene database: Semantic congruency norms for 898 pairs of object-scene pictures

Research on the interaction between object and scene processing has a long history in the fields of perception and visual memory. Most databases have established norms for pictures where the object is embedded in the scene. In this study, we provide a diverse and controlled stimulus set comprising real-world pictures of 375 objects (e.g., suitcase), 245 scenes (e.g., airport), and 898 object-scene pairs (e.g., suitcase-airport), with object and scene presented separately. Our goal was twofold. First, to create a database of object and scene pictures, normed for the same variables to have comparable measures for both types of pictures. Second, to acquire normative data for the semantic relationships between objects and scenes presented separately, which offers more flexibility in the use of the pictures and allows disentangling the processing of the object and its context (the scene). Along three experiments, participants evaluated each object or scene picture on name agreement, familiarity, and visual complexity, and rated object-scene pairs on semantic congruency. A total of 125 septuplets of one scene and six objects (three congruent, three incongruent), and 120 triplets of one object and two scenes (in congruent and incongruent pairings) were built. In future studies, these objects and scenes can be used separately or combined, while controlling for their key features. Additionally, as object-scene pairs received semantic congruency ratings along the entire scale, researchers may select among a wide range of congruency values. ObScene is a comprehensive and ecologically valid database, useful for psychology and neuroscience studies of visual object and scene processing.
A Tale of Resilience: On the Practical Security of Masked Software Implementations

Masking constitutes a provably-secure approach against side-channel attacks. However, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recombination effects</i> (e.g., transitions) severely reduce the proven security. Concerning the software domain, CPU microarchitectures encompass techniques improving the execution performances. Several studies show that such techniques induce recombination effects. Furthermore, these techniques implicitly induce some form of parallelism, and the potential associated threat has never been investigated. In addition, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">practical</i> security of masking relies on the chosen masking scheme. Few works analysed the security of software protected by different masking schemes, and none considered the parallelism threat. Thus, literature lacks of a more comprehensive investigation on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">practical</i> security of software implementations relying on various masking schemes in presence of micro-architecture-induced recombination effects and parallelism. This work performs a first step to fill this gap. Specifically, we evaluate the practical security offered by first-order <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">boolean</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">arithmetic-sum</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inner-product</i> masking against transitions and parallelism in software. We firstly assess the presence of transition and parallel-induced leakages in software. Secondly, we evaluate the security of the encodings of the selected masking schemes with respect to each leakage source via micro-benchmarks. Thirdly, we assess the practical security of different AES-128 software implementations, one for each selected masking scheme. We lead the investigation on the STM32F215 and STM32F303 micro-controllers. We show that (1) CPU’s parallel features allow successful attacks against transition-resistant masked implementations; (2) implementation choices (e.g., finite field multiplication) impact on the practical security of masked software implementations in presence of recombination effects.
PG-CAS: Pro-Active EM-SCA Probe Detection Using Switched-Capacitor-Based Patterned-Ground Co-Planar Capacitive Asymmetry Sensing

This paper presents the design and analysis of a pro-active strategy to detect the presence of an electromagnetic (EM) side-channel analysis (SCA) attack, using Patterned-Ground co-planar Capacitive Asymmetry Sensing (PG-CAS) system. The PG-CAS system senses the asymmetry created in the plate-ground capacitance and turns on a SCA countermeasure in presence of an EM probe. The proposed PG-CAS system for approaching probe consists of the EM SCA detection sensor plate and circuits. The EM SCA detection sensor is implemented as a grid of four metal plates of the same dimensions using the top metal layer along with a patterned-ground plane at the immediate lower metal layer. The EM SCA detection system consists of a proximity to capacitance conversion circuit, digital synchronization logic circuit to detect and alarm the IC, and an EM SCA countermeasure. When an attack is detected, the countermeasure is turned on based on the deviation of the symmetry of the plate-ground capacitance pairs. The PG-CAS system-level post-layout simulation results using TSMC 65nm technology and Ansys Maxwell show a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;5\times $ </tex-math></inline-formula> improvement in the detection range and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 29\times $ </tex-math></inline-formula> improvement in power consumption over existing inductive sensing methods for attack detection.
YOLOv3-based human detection and heuristically modified-LSTM for abnormal human activities detection in ATM machine

In existing works, accurately analyzing human activities is a complicated problem in public places. Consequently, the detection of human activities becomes challenging the computer vision technology. The major scope of the research is to develop an abnormal Human Activity Recognition (HAR) model using deep structured architectures for detecting the suspicious activities of humans in the ATM using the video surveillance system. The classification phase utilizes the enhanced deep learning approach named improved Long Short-Term Memory (LSTM) by optimizing certain parameters in LSTM by hybrid optimization algorithm for accurately classifying the normal and abnormal activities of humans. This hybrid optimization algorithm is developed and termed Hybrid Spider Monkey-Chicken Swarm Optimization (HSM-CSO) for achieving the effective performance of the deep learning-based classification. Hence, the designed HAR model in ATM is proven that it helps to improve the system performance and also give relief from prohibited activities or crimes and false alarms for humans.
Deep Learning-Based Earthquake Prediction Technique Using Seismic Data

Earthquakes are natural disasters that can cause severe damage to human life and infrastructure. Therefore, accurate earthquake prediction is crucial for disaster prepared-ness and risk reduction. Recently, machine learning techniques have shown promise in earthquake prediction. In this paper, we present a comprehensive study on the application of machine learning techniques for earthquake prediction. We first review the existing literature on earthquake prediction using machine learning techniques, including neural networks. We then propose a machine learning approach for earthquake prediction, based on analyzing seismic data. The proposed approach uses a convolutional neural network to extract relevant features from the seismic data, and a long short-term memory network to predict the probability of an earthquake. We evaluate the performance of the proposed approach on earthquake datasets from different regions and demonstrate its high accuracy in earthquake prediction. Our study provides a new perspective on earthquake prediction using machine learning techniques and highlights the potential of deep learning approaches for improving earthquake prediction. The proposed approach can be used in conjunction with existing earthquake prediction methods to provide more accurate and reliable predictions, which can help mitigate the potential impact of earthquakes on human life and infrastructure.
GNSGA: A Decentralized Data Replication Algorithm for Big Science Data

Domain science applications in fields such as Genomics and High-Energy Particle Physics use geographically distributed data federations for publishing and accessing datasets. Data is typically replicated among data federation nodes to improve efficiency and fault tolerance. While replication strategies are well documented in distributed database instances (e.g., Apache Cassandra), replication among distributed data storage nodes can be ad-hoc. Replication over wide area networks can also require global coordination (or global shared state) which is not ideal when multiple organizations are involved. In this paper, we introduce GNSGA, which stands for Greedy Non-dominated Sorting Genetic Algorithm II. It is an optimization algorithm that combines greedy and non-dominated sorting genetic algorithms to solve multi-objective optimization problems. The “greedy” aspect of the algorithm refers to the use of a greedy strategy in the selection of nodes, while the “Non-dominated Sorting Genetic Algorithm II (NSGA-II)” is a fast non-dominated multi-objective optimization algorithm with an elite retention strategy. Replication decisions in GNSGA are based on the local properties and resource availability of the data storage nodes. By incorporating Greedy and NSGA-II algorithms, GNSGA optimizes multiple conflicting objectives to satisfy replica placement constraints such as cost, time, and storage capacity. We compared GNSGA with popular replica placement strategies, such as closest node replication, shortest transfer time, and a Particle Swarm Optimization (PSO)-based replication algorithm. We performed simulations and an actual deployment on the NSF's FABRIC testbed for evaluation. The results demonstrate that GNSGA consistently selects nodes to reduce replication time by 5.8%-15.4% while satisfying replication constraints (i.e., cost, time, and storage). We also show that GNSGA is beneficial for replicating large files over wide area networks.
Multi-Label Classification of Emotions in Arabic Tweets From Different Perspectives

Sentiment analysis has been studied widely in the literature. Despite these many works, there is a lack of works that concentrates on finding the basic emotions behind these sentiments. This problem becomes more challenging in under-resourced languages, such as Arabic. Furthermore, to our knowl-edge, no works have studied this problem from the reader's versus the writer's perspectives. In this work, we study sentiment analysis and basic emotions extraction from the writer's perspec-tive, which is the person who wrote the text, to complement an earlier work of ours focusing on the reader perceptive. Using a dataset of Arabic tweets, we compare the two perspectives. Since each tweet may contain multiple emotions, we use Multi-Label Classification (MLC) techniques. Three classifiers are compared: Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Prior results showed that the top performing classifier in the case of the reader dataset was RF. For this work focusing on the writer dataset, RF is not a clear winner for all performance metrics under consideration as DT produces competitive results.
A cheap and accurate delay-based IP Geolocation method using Machine Learning and Looking Glass

Predicting the geographical location of an IP host is a fundamental and valuable but long-standing challenge in the field of network research. Although delay-based methods have relatively high coverage and low time consumption, currently this type of method is not accurate enough and requires a large number of vantage points, making its cost high. In this paper, we propose a novel delay-based framework to make IP geolocation more accurate and cheap. Firstly, we collect 373 Looking Glass with known geographical addresses and overcome the high cost problem by using them as vantage points. Secondly, we make the prediction of geographical coordinates more accurate by using the machine learning algorithm and regional information of the target IP. Finally, we propose a method based on machine learning to supplement missing values in the delay data and improve the accuracy of geolocation successfully. Our experiment results validate the feasibility and improvement of our method. Using our method, we have an average error of 69.49 km for the geolocation of our test set, which is approximately 160 km less than the state-of-art work.
MetaIoT: Few Shot Malicious Traffic Detection in Internet of Things Networks Based on HIN

Identification of abnormal and malicious traffic in the Internet-of-Things (IoT) network is critical for IoT security. However, it is worth noting that the majority of recent efforts demand a large amount of tagged traffic to train a machine-learning model. In this paper, we develop MetaIoT, an intelligent approach for identifying malicious traffic. MetaIoT is more accurate and more difficult for attackers to circumvent by taking into account both the local attributes of each traffic source and their global relationships. In MetaIoT, we begin by considering the heterogeneous and dynamic nature of traffic. Then, we introduce a heterogeneous graph (HG) to model the relationships between traffic and employ a relation-based heterogeneous graph attention network to learn node (i.e., traffic) representations over the built HG. Alternatively, MetaIoT addresses the issue of needing enough data for model training through the meta-learning technique. After conducting a comprehensive comparison with the baseline through experiments, our model demonstrated superior performance in few-shot learning scenarios, obtaining an accuracy score of 91.65% and an F1 score of 90.33%. When compared with current state-of-the-art IoT traffic detection models, our model showed the best results.
FSTC: Dynamic Category Adaptation for Encrypted Network Traffic Classification

With the advancement in security and privacy on the Internet, network traffic has become increasingly difficult to classify. Current deep learning (DL)-based encrypted network traffic classification approaches rely on protocol-specific features (e.g., TLS headers) and/or assume that the classification categories (i.e., applications) remain constant over time. However, both the encryption protocols and applications continue to evolve. Therefore, DL models must be retrained from scratch for newer encryption protocols or applications, which makes existing approaches intractable in practice. In this paper, we propose novel Transfer Learning (TL) approaches for introducing new traffic classes to DL models without retraining them from scratch. We also propose a framework named FSTC, which leverages Active Learning (AL) to achieve human-assisted TL for new traffic classes and minimizes the labeled data needed for encrypted network traffic classification. We evaluate our TL and AL approaches using protocol-agnostic features from the publicly available ISCXVPN2016 and QUIC datasets. To the best of our knowledge, neither proposal has been explored before in the existing literature.
Dragonfly: In-Flight CCA Identification

We introduce the Dragonfly system, which is designed to classify on the fly the congestion control algorithm of any flow that crosses a given router, starting at any time, and quickly reach a reasonable accuracy. To do so, we discuss the unique challenges of real-time congestion control classification. We explain how the number of bytes of the flow within the shared router queue contains an intrinsic memory that significantly helps real-time classification. However, we show that this number of bytes is not straightforward to compute in real time, and introduce ways to do so. We further design an eBPF-based scalable traffic-collection system that helps dynamically filter specific flows at high rates. Finally, we evaluate our Dragonfly system using a variety of platforms, and show that it clearly outperforms state-of-the-art algorithms.
Full Exploitation of Limited Memory in Quantum Entanglement Switching

We study the problem of operating a quantum switch with memory constraints. In particular, the switch has to allocate quantum memories to clients to generate link-level entanglements (LLEs), and then use these to serve end-to-end entanglements requests. The paper's main contributions are (i) to characterize the switch's capacity region, and (ii) to propose a memory allocation policy (MEW) that is throughput optimal. The worst-case time complexity of MEW is exponential on the system parameters. However, when the requests are bipartite and the LLE attempts are always successful, we propose a variant of MEW (MEW2) that has polynomial time complexity. We evaluate the proposed policies numerically and illustrate their performance depending on the requests arrivals characteristics and the time available to obtain a memory allocation.
Behavioral Biometrics for Mobile User Authentication: Benefits and Limitations

User authentication serves as the primary defense, also referred to as first line of defense, by verifying the identity of a mobile user, often as a requirement for accessing resources on a mobile device. For many years, user authentication relied on “something that the user knows,” also known as knowledge-based user authentication. However, recent research indicates that knowledge-based user authentication is no longer considered secure or convenient for mobile users because it imposes several limitations. These limitations highlight the need for more secure and user-friendly user authentication methods. One promising solution is user authentication based on “something that the user is,” which includes authentication methods that use physical characteristics of the mobile user (i.e., physiological biometrics) or their involuntary actions (i.e., behavioral biometrics). Although physiological biometrics have been successfully deployed for mobile user authentication over the last years, recent studies suggest that they show several weaknesses (e.g., vulnerable to various attacks such as impersonation). Consequently, experts in the security field are now focusing more on user authentication based on behavioral biometrics. Therefore, the aim of this work is to investigate the benefits, as well as the limitations of behavioral biometrics for mobile user authentication in order to provide a foundation for organizing research efforts toward the design and development of proper user authentication solutions based on behavioral biometrics for mobile devices.
Optimizing Service Selection and Load Balancing in Multi-Cluster Microservice Systems with MCOSS

With the advent of cloud and container technologies, enterprises develop applications using a microservices architecture, managed by orchestration systems (e.g. Kubernetes), that group the microservices into clusters. As the number of application setups across multiple clusters and different clouds is increasing, technologies that enable communication and service discovery between the clusters are emerging (mainly as part of the Cloud Native ecosystem). In such a multi-cluster setting, copies of the same microservice may be deployed in different geo-locations, each with different cost and latency penalties. Yet, current service selection and load balancing mechanisms do not take into account these locations and corresponding penalties. We present MCOSS, a novel solution for optimizing the service selection, given a certain microservice deployment among clouds and clusters in the system. Our solution is agnostic to the different multi-cluster networking layers, cloud vendors, and discovery mechanisms used by the operators. Our simulations show a reduction in outbound traffic cost by up to 72% and response time by up to 64%, compared to the currently-deployed service selection mechanisms.
Mobile App Fingerprinting through Automata Learning and Machine Learning

Application fingerprinting is crucial in network management and security to provide the best Quality of Service (QoS). To generate fingerprints for applications, we use an automata learning algorithm to observe the temporal order among destination-related features of network traffic and create a language as a fingerprint. We label fingerprints through machine learning classifiers. We propose our approach in a framework called ML-NetLang for fingerprinting mobile applications from encrypted network traffic. Our evaluation achieves an average accuracy of 95% for Android and iOS applications. ML-NetLang outperforms comparable state-of-the-art techniques using behavioral-based, correlation-based, and machine-learning solutions.
Towards Standardized AI-Ready Data: A Proposed Framework for the UAE

Artificial Intelligence (AI) is a vital component of sustainable development, and the United Arab Emirates (UAE) aims to become a global leader in AI. However, to achieve this goal, there is a need to create a data-sharing framework that provides shared open and standardized AI-ready data collected through a consistent data standard. This research paper proposes a framework for creating such a data-sharing infrastructure in the UAE. The framework addresses challenges such as the lack of standardized data, limited access to data, and inadequate infrastructure, and presents solutions for data standardization, data collection, and data sharing. The paper also highlights the importance of investing in AI infrastructure development, enhancing the skills and capabilities of AI experts and professionals, and developing ethical and legal frameworks for responsible and transparent AI usage. The proposed framework can help the UAE leverage the power of AI to achieve sustainable development goals, improve the quality of life for its citizens, and enhance the overall competitiveness of its economy.
An Enhanced Machine Learning Approach to Identify Noise and Detect Relevant Structures for Predictive Modeling

The era of big data and social networking platforms have provided great repositories of the data for mining useful information for the real-world industry. However, along with this benefit comes the noise in the data. Generally, noise is the data-set that are redundant, false, bad, and/or outliers. Data cleaning, outlier identification, feature engineering, data slicing, etc. are few of many techniques used traditionally. End goal remains ensuring good data (signal) is not lost in bad data (noise) and less processing cost are incurred to extract useful knowledge out of given big data. This paper presents a follow up progress on existing work of the author in relevance of machine learning algorithms, academic and career data predictions and personality computing. All of that have been initially inspired by potential of useful relationships and data points in unstructured data and thus Noise becomes very relevant and may appear Signal in other contexts and predictors in goal. This proposed model is collectively titled as ‘Noise Removal and Structured Data Detection’ based on inherited parallel processing and unique n-Dimensional training approach. Personality features can be quantified into talent traits, matrix indicating the max/min for relevance factors in the academics/career of nD. The engine internals examine and train the algorithm that it minimizes the x,y co-ordinates and maximizes the z co-ordinate. It records and compares the engine internal metrics and reports it back to engine to further optimize the machine learning process until the optimum results are obtained or do not improve any further.