Reviewerly/exHarmony huggingface dataset:
Image Segmentation for Radar Signal Deinterleaving Using Deep Learning

Passive systems, such as electronic intelligence and electronic support measures systems, aim to extract necessary information from the received radar signals for situational awareness. To achieve this, the system must first deinterleave the radar signals simultaneously coming from different emitters, so that the pulse repetition interval (PRI) patterns will be revealed for further analysis and identification purposes. PRI transform is a well-known deinterleaving method that utilizes the complex autocorrelation function. There are two main versions of the method. The initial version detects only constant PRI schemes, while the second modified version is capable of detecting varying PRI schemes as well. Miss detection of varying PRI patterns is the drawback for the first version, while producing harmonics, especially at high PRI levels, is the disadvantage of the second one. To alleviate these problems, we propose an image segmentation method based on deep learning. The developed preprocessing step uses both versions of the PRI transform outputs to generate 2-D time–PRI images of the collected radar emissions, so that constant and varying PRI patterns are revealed. The images are concatenated and fed to the proposed network, which uses a practicable U-Net structure. The output of the network directly estimates the PRI levels of the existing radars and the time duration of the transmission jointly. In addition to qualitative and quantitative experiments on the synthetic datasets, qualitative experiments are conducted on real measurements, in which we demonstrate that the proposed method effectively utilizes PRI transform in the preprocessing step and outperforms both versions of the PRI transform in terms of accuracy, Jaccard index, structural similarity, and PRI estimation error metrics.
Nonlinear Gaussian Filtering With Network-Induced Delay in Measurements

This article designs an advanced Gaussian filtering algorithm for improving accuracy in the presence of time-delay in measurements. The proposed method uses a Bernoulli random variable and a geometric random variable to reformulate the delay modeling strategy. Subsequently, the traditional Gaussian filtering method for the modified measurement model is rederived. The proposed method precludes two major drawbacks of the existing delay filtering methods, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of many delay probabilities and an ambiguous selection of an upper bound of delay. Thus, the proposed method outperforms the existing delay filtering methods and the same is validated from the simulation results. The proposed method is a general modification of the traditional Gaussian filtering and applies to all conventionally popular Gaussian filters.
Layer Normalization for TSK Fuzzy System Optimization in Regression Problems

Recently, mini-batch gradient descent (MBGD)-based optimization has become popular in Takagi–Sugeno–Kang (TSK) fuzzy system optimization. However, it suffers from some challenges, including the curse of dimensionality and the sensitivity to the choice of the optimizer. The former has been alleviated by our previously proposed high-dimensional TSK (HTSK) algorithm. In this article, we point out that the latter is caused by the gradient vanishing problem on the rule consequent parameters, which in turn is caused by the small magnitude of the normalized rule firing levels, especially when the number of rules is large. Thus, the rule consequents are easily trapped into a bad local minimum with poor generalization performance. We propose to use first layer normalization (LN) to amplify the small firing levels, and then rectified linear unit (ReLU) to discard rules far away from the current training sample. We evaluated our proposed HTSK-LN and HTSK-LN-ReLU on twelve regression datasets with various sizes and dimensionalities. Experiments demonstrated that they can significantly improve the generalization performance, regardless of the training set size, feature dimensionality, choice of the optimizer, and rulebase size.
Collaborative edge computing for distributed CNN inference acceleration using receptive field-based segmentation

This paper studies inference acceleration using distributed CNNs in collaborative edge computing network. To ensure no inference accuracy loss in task partitioning, we propose receptive field-based segmentation. To reduce the computation time and communication overhead, we propose a novel collaborative edge computing using fused-layer parallelization to partition a CNN model into multiple blocks. To find the optimal partition of a CNN model, we use dynamic programming, named as DPFP. To address computation heterogeneity of edge servers (ESs), we design a low-complexity search algorithm which can select the optimal subset of collaborative ESs for inference. The experimental results show that DPFP can accelerate inference up to 71% for ResNet-50 and 73% for VGG-16 compared to running the pre-trained models, which outperforms the existing works MoDNN and DeepSlicing. Moreover, we propose an analytical method to estimate the speedup ratio of different GPU platforms by using FLOPs and effective computing capacity. Furthermore, we evaluate the service failure probability under time-variant channel and variation of image sizes, which shows that DPFP is effective to ensure high service reliability with strict service deadline.
AI-powered information and Big Data: current regulations and ways forward in IFRS reporting

Purpose With the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining economic benefits. AI-powered information and Big Data (simply data henceforth) have quickly become some of the most important strategic resources in the global economy. However, their value is not (yet) formally recognized in financial statements, which leads to a growing gap between book and market values and thus limited decision usefulness of the underlying financial statements. The objective of this paper is to identify ways in which the value of data can be reported to improve decision usefulness. Design/methodology/approach Based on the authors' experience as both long-term practitioners and theoretical accounting scholars, the authors conceptualize and draw up a potential data value chain and show the transformation from raw Big Data to business-relevant AI-powered information during its process. Findings Analyzing current International Financial Reporting Standards (IFRS) regulations and their applicability, the authors show that current regulations are insufficient to provide useful information on the value of data. Following this, the authors propose a Framework for AI-powered Information and Big Data (FAIIBD) Reporting. This framework also provides insights on the (good) governance of data with the purpose of increasing decision usefulness and connecting to existing frameworks even further. In the conclusion, the authors raise questions concerning this framework that may be worthy of discussion in the scholarly community. Research limitations/implications Scholars and practitioners alike are invited to follow up on the conceptual framework from many perspectives. Practical implications The framework can serve as a guide towards a better understanding of how to recognize and report AI-powered information and by that (a) limit the valuation gap between book and market value and (b) enhance decision usefulness of financial reporting. Originality/value This article proposes a conceptual framework in IFRS to regulators to better deal with the value of AI-powered information and improve the good governance of (Big)data.
Contract Automata Library

Contract automata facilitate the specification, composition, and synthesis of behavioural contracts, comprehending modalities and configurations. Contract automata are supported by a software API called Contract Automata Library. This paper accompanies the software artefact by discussing its architecture, showing some usage examples and presenting recent improvements of the software in terms of quality, availability, usability, and documentation.
Extraction of Step Performed in Use Case Description as a Reference for Conformity of Sequence Diagrams Using Text Mining (Case Study: SRS APTU)

Extraction is an essential part of processing a document to ensure the success of the text mining process. In this study, the example of the SRS document used is the Integrated Service Application (APTU) KPKNL Bandung, an application to manage the process of submitting service tickets at the State Property and Auction Service Office. There is a difference in interpreting the activities that exist in the Use Case Description artifact with a Sequence Diagram that provides an overview of the functionality of a process to show the involvement of an activity related to the Use Case Description. This study aims to perform step extraction on the Use Case description. The results of this extraction are compared for their suitability with the sequence diagram using the concept of text mining. There are core results from this research activity. First, the highest similarity between documents is in the SP01 and SD01 documents, with the similarity value being 0.69108792. Second, the highest similarity between words is found in words “list” and “menu,” with the similarity value being 0.9412. Third, the Kappa Score from Gwet's AC1 formula using the Python programming language is 0.12362, which means “Slight Agreement,” while the Kappa Score value using a questionnaire filled in by the expert is 0.97464, which means “Almost perfect.
Measurement of Similarity Between Requirement Elicitation and Requirement Specification Using Text Pre-Processing in the Cinemaloka Application

There are differences in perceptions between Clients and Developers regarding software requirements specifications therefore, research is needed to determine the perceived similarity between software requirements specifications and requirements elicitation results. The SRS document used in this study is called the Cinemaloka application. This document contains business processes and Requirements Specifications related to website-based cinema ticket reservations. This study aims to measure the suitability of perceptions between developers and clients regarding the specification of software requirements that will be or are being built. There are methods that are combined in this research, namely: determining the similarity of the requirement specification with the elicitation of requirements, analyzing the text contained in the elicitation results, Text Pre-processing, and validation through Gwet's AC1. The results of the measurement of the similarity between the elicitation of requirements and the requirement specification carried out in this study resulted in a match between the applications made and the wishes of potential users/clients. Through stages such as CountVectorizer, PorterStemmer, and Cosine Similarity, resulting in a match of 0.717144. Kappa Score from Gwet's AC1 formula using Python is -0.2222, which means “Less than Chance-agreement,” while the Kappa Score value using a questionnaire filled out by the Expert is 0.5378, which means “Moderate Aggregation.”.
Anonymous Trading on the Dark Online Marketplace

Advancement in technology provides numerous solutions to not only legitimate businesses but to illegal trades as well. Selling substances, drugs, and prohibited merchandise and goods on the internet comes under illegal trading. The internet we surf is merely a thin layer of this deeply rooted miraculous mechanism of connecting the world. The dark web is the part of the deep web that utilizes the internet to flourish the illicit intentions of trading illegal items, thereby fostering the ongoing societal devastation. This chapter is exploring anonymous trading on the dark online marketplace using the Silkroad 2.0 dataset. This work aims to analyze the various aspects of dark e-commerce trading and highlight different themes used for trading illicit drugs on Twitter by performing the thematic analysis using Latent Dirichlet Allocation unsupervised machine learning with a 0.44 coherence score. The findings have shown that developed countries are participating in illegal trading, and teenage schoolgoers can be victims of social media drug trading.
Ensemble Deep Learning for Sustainable Multimodal UAV Classification

Unmanned aerial vehicles (UAVs) have increasingly shown to be useful in civilian applications (such as agriculture, public safety, surveillance) and mission critical military applications. Despite the growth in popularity and applications, UAVs have also been used for malicious purposes. In such instances, their timely detection and identification has garnished rising interest from government, industry and academia. While much work has been done for detecting UAVs, there still exist limitations related to the impact of extreme environmental conditions and big dataset requirements. This paper proposes a novel ensemble deep learning framework that has hybrid synthetic and deep features to detect unauthorized or malicious UAVs by using acoustic, image/video and wireless radio frequency (RF) signals for robust UAV detection and classification. We present the performance evaluation of the proposed approach using numerical results obtained from experiments using acoustic, image/video and wireless RF signals. The proposed approach outperforms the existing related approaches for detecting malicious UAVs.
Exploring Query Processing on CPU-GPU Integrated Edge Device

Huge amounts of data have been generated on edge devices every day, which requires efficient data analytics and management. However, due to the limited computing capacity of these edge devices, query processing at the edge faces tremendous pressure. Fortunately, in recent years, hardware vendors have integrated heterogeneous coprocessors, such as GPUs, into the edge device, which can provide much more computing power. Furthermore, the CPU-GPU integrated edge device has shown significant benefits in a variety of situations. Therefore, the exploration of query processing on such CPU-GPU integrated edge devices becomes an urgent need. In this article, we develop a fine-grained query processing engine, called FineQuery, which can perform efficient query processing on CPU-GPU integrated edge devices. Particularly, FineQuery can take advantage of both architectural features of edge devices and query characteristics by performing fine-grained workload scheduling between the CPU and the GPU. Experiments show that on TPC-H workloads, FineQuery reduces 42.81% latency and improves 2.39× bandwidth utilization on average compared to the implementation of using only GPU or CPU. Furthermore, query processing at the edge can bring significant performance-per-cost benefits and energy efficiency. On average, FineQuery at the edge brings 21× performance-per-cost ratio and 4× energy efficiency compared with processing the data on a discrete GPU platform.
Factual and Counterfactual Explanations in Fuzzy Classification Trees

Classification algorithms have recently acquired great popularity due to their efficiency to generate models capable of solving high complexity problems. Specifically, black box models are the ones that offer the best results, since they greatly benefit from the enormous amount of data available to learn models in an increasingly accurate way. However, their main disadvantage compared to other simpler algorithms, e.g., decision trees, is the loss of interpretability for both the model and the individual classifications, which may become a major drawback because of the increasing number of applications where it is advisable and even compulsory to provide an explanation. A well-accepted practice is to build an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explainable</i> model that can mimic the behavior of the (more complex) classifier in the neighborhood of the instance to be explained. Nonetheless, the generation of explanations in such white box models is not trivial either, which has generated intense research. It is common to generate two types of explanations, factual explanations and counterfactual explanations, which complement each other to justify why the instance has been classified into a certain class or category. In this work, we propose the definition of factual and counterfactual explanations in the frame of fuzzy decision trees, where multiple branches can be fired at once. Our proposal is centered around the definition of factual explanations that can contain more than a single rule, in contrast to the current standard that is limited to considering a single rule as a factual explanation. Moreover, we introduce the idea of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">robust</i> factual explanation. Finally, we provide procedures to obtain counterfactual explanations from the instance and also from a factual explanation.
Secure Non-interactive Reduction and Spectral Analysis of Correlations

Correlated pairs of random variables are a central concept in information-theoretically secure cryptography. Secure reductions between different correlations have been studied, and completeness results are known. Further, the complexity of such reductions is intimately connected with circuit complexity and efficiency of locally decodable codes. As such, making progress on these complexity questions faces strong barriers. Motivated by this, in this work, we study a restricted form of secure reductions—namely, Secure Non-Interactive Reductions (SNIR)—which is still closely related to the original problem, and establish several fundamental results and relevant techniques for it. We uncover striking connections between SNIR and linear algebraic properties of correlations. Specifically, we define the spectrum of a correlation, and show that a target correlation has a SNIR to a source correlation only if the spectrum of the latter contains the entire spectrum of the former. We also establish a "mirroring lemma" that shows an unexpected symmetry between the two parties in a SNIR, when viewed through the lens of spectral analysis. We also use cryptographic insights and elementary linear algebraic analysis to fully characterize the role of common randomness as well as local randomness in SNIRs. We employ these results to resolve several fundamental questions about SNIRs, and to define future directions.
A Highly Secure FPGA-Based Dual-Hiding Asynchronous-Logic AES Accelerator Against Side-Channel Attacks

Encryption in field-programmable gate array (FPGA) often provides a good security solution to protect data privacy in Internet-of-Things systems, but this security solution can be compromised by side-channel attacks (SCAs). In this article, we present an FPGA-based dual-hiding asynchronous-logic (async-logic) advanced encryption standard (AES) accelerator, which is highly resistant against SCAs and yet low area/energy overheads. The proposed AES accelerator achieves vertical (amplitude) SCA hiding via an area-efficient dual-rail mapping approach and a zero-value (ZV) compensated substitution-box (S-Box), while enhancing the horizontal (temporal) SCA hiding of async-logic operations via a timing-boundary-free input arrival-time randomizer and a skewed-delay controller. A comprehensive SCA evaluation is performed with 11 SCA models, and we show that our proposed design can offer a strong SCA resistance with measurement-to-disclosure (MTD) of >20 million traces. To our best knowledge, our design is the most secure AES design evaluated with the largest number of traces in FPGA. To compare the design overheads for security, we quantify the figure of merit as normalized (Area <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> Energy/MTD(All) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 10^{6}$ </tex-math></inline-formula> ). The figure of merit of our proposed design is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$403\times $ </tex-math></inline-formula> smaller than the benchmark dual-rail synchronous-logic design and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$95\times $ </tex-math></inline-formula> smaller than a reported async-logic design.
Learning Social Relationship From Videos via Pre-Trained Multimodal Transformer

As a crucial task for video analysis, social relation recognition from characters provides intelligent applications with great potential to better understand the behaviors or emotions of human beings. Most existing methods mainly focus on training models from a large amount of labeled data. However, labeling social relations in videos is time-consuming. To solve this problem, we propose a Pre-trained Multimodal Feature Learning (PMFL) framework for self-supervised learning from unlabeled video data, and then transfer the pre-trained PMFL to downstream social relationship recognition task. First, the space-time interaction between visual instances, and cross-modal interaction between visual and textual information provide important cues for social relation understanding. To incorporate these cues, we design a Multimodal Instance Interaction Transformer (MIIT), which consists of two Transformers to capture intra-modal and cross-modal information interaction, respectively. Second, to better endow PMFL with the capability of learning visual and textual semantic features, we pre-train it via two tasks: Masked Action Feature Regression (MAFR) and Masked Object Label Classification (MOLC). These tasks can help learn both intra-modal and cross-modal semantic information. After fine-tuning PMFL from pre-trained parameters, it achieves the state-of-the-art results on a public benchmark.
A Novel Fusion Method With Thermal and RGB-D Sensor Data for Human Detection

Human detection methods are widely used in various fields such as autonomous vehicles, video surveillance, and rescue systems. To provide a more effective detection system, different types of sensor data (i.e. optics, thermal, and depth data) may be used together as hybrid information. Fortifying object detection, based on optical data and additional sensor data, such as depth and thermal data, also represents information regarding the distance and temperature of classified objects that can be used for video surveillance, rescue systems, and various applications. In this study, a simple and effective method is introduced to fuse RGB-D and thermal sensor data to achieve a more accurate form of human detection. To accurately combine the sensors, they are physically fixed to each other, and the relationship between them is determined using a novel method. The feature points on the optical and thermal images are extracted and matched successfully using computer vision. The proposed method is completely brand-free, easy to implement, and can be used in real-time applications. Using both thermal and optical data, humans are classified as benefiting from a widely used object detection method. The performance of the presented method is tested with a newly generated dataset. The proposed method boosts human detection accuracy by 5% when compared to the use of only optical data and by 37% when compared to the use of thermal data with COCO Dataset upon YOLOv4 neural network weights. After training with the newly generated dataset, the detection accuracy increases by 18% compared with the best results of single sensor usage.