Reviewerly/exHarmony huggingface dataset:
Discussion, Conclusion, and Recommendations

In this final chapter, we integrate the ideas discussed in earlier chapters regarding blockchain’s potential to bring economic, political, and social transformations in the Global South. We consider the potentially transformative impacts of blockchain in diverse industries and markets in the Global South. The chapter also analyzes the barriers and challenges in implementing blockchain projects in the Global South. Finally, it looks into the future of blockchain in the Global South.
Polarization Fingerprint-Based LoRaWAN Physical Layer Authentication

Currently, radio frequency fingerprint (RFF), which describes the physical layer features of wireless signals in time-frequency domain, has been intensively studied. However, the research of polarization fingerprint (PF), which describes the physical layer features of wireless signals in the polarization domain, has just started. In this paper, PF is deeply studied, and a PF-based LoRaWAN physical layer authentication solution is designed under the actual application scenario. Firstly, the physical layer features in polarization are analyzed from the full path of polarization formation, propagation and reception, and the mathematical model of PF is constructed. There exist two main properties in PF: frequency characteristic and spatial characteristic. The spatial characteristic is the unique property of PF compared with RFF, which significantly improves the fingerprint discrimination of the same type of device. Subsequently, the solution has two authentication mechanisms according to different deployment strategies of LoRa devices: polarization fingerprint identification (PFI) based authentication and PF tracking-based authentication. PFI is implemented based on ensemble convolutional neural network (CNN), which assigns different weights to parts of PF with different discrimination to improve identification accuracy. PF tracking-based authentication solves the impact of LoRa device movement on authentication. Finally, the authentication performance and robustness of the solution are evaluated by experiments, and the ability of the solution to deal with typical attacks is analyzed. In summary, the PF-based LoRaWAN physical layer authentication solution provides considerable application potential.
An Exploratory Study on Artifacts for Cyber Attack Attribution Considering False Flag: Using Delphi and AHP Methods

When a security incident by an attacker occurs in the cyber world, an analyst analyzes the artifacts collected in the incident area. The findings from the analysis of this incident utilize to track hackers or create security plans for the organization. However, if an analyst analyzes fabricated traces from an incident, he or she is not only fooled by the attacker’s false flags but also makes it difficult to track the attacker. As a result, inappropriate responses can lead to a waste of limited resources and financial damage to the organization. Considering the incorporation of false flag operations, the collection of artifacts from intrusion incidents and their development into new Indicators of Compromise (IOCs) or Indicators of Attack (IOAs) can significantly enhance the accuracy of entity identification. This study is an exploratory research that aims to uncover valuable artifacts for false flag operations based on qualitative research targeting cybersecurity experts who have direct experience or extensive knowledge in the field. Specifically, through the participation of researchers knowledgeable about both defensive and offensive techniques, this study employed Delphi and AHP analyses to apply experts’ knowledge and experience. Ultimately, the goal is to select artifacts related to the attacker’s false flag operations and utilize the identified indicators in the analysis of intrusion incidents stemming from false flag tactics.
Tag Generation Using Chaotic Sequences for Physical-Layer Authentication

We consider in this work a physical layer authentication method in which a message authentication code, referred to as a tag, is transmitted along with the data message to provide a robust authentication method. This work diverges from previous work in the area when it comes to the tag generation method. While the previous works use methods based on cryptographic hash functions our system employs unidimensional chaotic maps to generate these tags. Due to the loss of information about the initial condition of chaotic maps, we show that they are strong candidates for the tag generation process. We employ an information-theoretic approach to show that chaotic tags provide a positive lower bound on the unconditional security of the system even in a noiseless environment. To the best of our knowledge this is the first work where unconditional security is obtained independently of the noise power. Additionally, we calculate the probability of success for two active attacks to the authentication system: impersonation, substitution.
Toward Practical Deep Blind Watermarking for Traitor Tracing

Traitor tracing via blind watermarking is a promising solution to protect copyright. Recently, it was demonstrated that deep learning-based blind watermarking methods could outperform traditional watermarking methods in terms of robustness against distortions. However, we found they could sacrifice the imperceptibility as a trade-off. To handle this challenge, we propose a novel method consisting of a deep blind model and watermarking strategy. For the purpose, we first investigate the fundamental components of the basic deep blind watermarking model, and empirically show how the performance changes when each component is modified with respect to the robustness. Based on it, we construct a deep blind watermarking encoder, CFC+CONCAT, which can encode watermarks in a robust way against distortions without imperceptibility degradation. We then propose a watermarking strategy to make deep blind watermarking robust by increasing watermarking capacity (by splitting a large image into small patches), and using the effect of distortion types on the robustness we found. According to the experiments, our method achieved 5.46 higher PSNR on average than the baseline methods with comparable robustness under various distortions when watermarking in the 3×256×256 image. Also, the training time and VRAM usage are reduced by less than 1/4, demonstrating our method can mitigate the trade-off between robustness and imperceptibility, and achieve lightweight training.
Coarse-to-Fine Stereo Matching Network Based on Multi-Scale Structural Information Filtrating

Recent studies have shown that stereo matching can be achieved by supervised learning. In this paper, we propose a coarse-to-fine stereo matching framework based on structural information filtering to obtain an accurate disparity map. Our framework addresses the challenge of disparity estimation in ill-posed areas such as texture-less and reflective surfaces. We introduce a contextual attention feature extraction module to extract multi-scale features and contextual information to decompose complex scenes. Additionally, we design a structural attention weight generation module to add structural information to the cost volume to deal with ill-posed regions. Furthermore, we regularize the initial cost volume using a two-stage cost aggregation module that can sufficiently aggregate deep information. Finally, the dense disparity map is obtained by a disparity refine module that optimizes disparity boundaries. Our ablation experiments on various variants of the coarse-to-fine stereo matching network based on structural information filtering (Cs-Net) demonstrate the effectiveness of each module for handling ill-posed regions and complex scenes. Experiments on Scene Flow, KITTI, and DriveStereo datasets show that our method achieves remarkable accuracy.
FIDO2 Passwordless Authentication for Remote Devices

Today, passwords are a very popular mode of authentication in the modern web and applications. But as we have known for a long time, passwords are not secure. To improve its security, OTP and other mechanisms are used. One of the newer methods is the WebAuthn protocol, developed by the FIDO alliance. However, this is not widely applicable for all devices due to hardware limitations. This paper provides a methodology for remote WebAuthn authentication using QR and smartphone based authentication. With this method of passwordless authentication, we can ensure an additional layer of security that is easily accessible even to remote devices like TVs.
Sorting the Quality Criteria of an ServiceDesk in a State Public Court of Brazil Using Macbeth

Understanding the dimensions and criteria of quality in Information Technology (IT) care is one of the key factors in achieving competitive advantage. Therefore, the expectations associated with providing IT services are very challenging. In this context, there was a need to identify the criteria that permeate a successful service and its association with the SERVQUAL model, in order to monitor the quality of this service. The objective of this article is to organize the criteria and dimensions of the IT service, using a multicriteria decision model. We contextualize the IT service process, the available frameworks, the use of the brainstorm tool, the cognitive mapping and the application of the multicriteria model to carry out the ordering, making it possible to identify the most relevant criteria that affect the quality of service.
Comparative Analysis of Face Detection Techniques on Videos of Low Vs High Quality

In today's age, there is a lot of visual information that is generated in various fields, ranging from daily routine to specialized applications. Processing all these types of information efficiently is a valid concern and needs focused research. Most face detection algorithms have to deal with low quality data in videos, since they're mainly focused on surveillance applications, whose information capturing devices capture less information each frame. This paper reviews some state-of-the-art face detection algorithms and compares their processing efficiency on low and high quality videos. The comparative analysis reveals that these recent and modern algorithms do not work as effectively on high quality videos as they do on lower quality videos. This paper defines the need to focus research on analysis of high quality information in videos in an efficient manner, so as to keep up the pace of their analysis with the information that is generated.
Game-Aided Blockchain Twin for Incentive and Relay-Free Model Sharing in Heterogeneous Chain-Driven Swarm Learning

Swarm learning (SL) is a novel decentralized machine learning paradigm that provides a privacy-preserving approach based on permissioned blockchain without the need for a centralized coordinator. However, the various architectures and design characteristics of blockchains make it difficult to employ applications on heterogeneous blockchains, which limits the scalability, efficiency, and interoperability of blockchains ecology and restricts the application of SL. To solve this problem, first, we propose a Blockchain Twin mechanism consisting of multichains to enable model sharing between heterogeneous blockchains without single central relay-chain. Next, to encourage roles in Blockchain Twin to actively and honestly participate in consensus phase, we design a multileader multifollower Stackelberg game-based incentive mechanism. Additionally, we prove that a unique Stackelberg equilibrium exists in the game and propose an alternating direction method of multipliers (ADMM)-based algorithm to obtain the optimal solution. Finally, we evaluate the performance of twin-chain interactions regarding average delay and throughput. We also conduct numerical simulation on the proposed incentive mechanism, and the results show that our mechanism can jointly maximize the reward of every participant roles in Blockchain Twin.
Named Entity Recognition for Weather Domain in the Marathi Language

A vast volume of content is being published on the internet every second as a consequence of the advancement of digital media and the growing breadth of journalism. Computationally, this unstructured data is opaque. Named Entity Recognition (NER) is a two-step process of finding and then classifying crucial textual data. Most Named Entity Recognition work has already been addressed in English literature compared to other Indian languages. There are restrictions on research in the Marathi language because of scarce resources, and regional and morphologically complicated language. This work is concerned with the Weather Entity Recognition framework in the Marathi language. For predicting the sequences, Conditional Random fields(CRFs) and bidirectional LSTM (BiLSTM) models are used. To represent the word vectors, fastText and Transformer based mBERT embeddings are used. The result observed in the Bi-LSTM CRF model with FastText word embedding has an accuracy of 97.45%.
Using Design Science Research to Iteratively Enhance Information Security Research Artefacts

A report published in 2020 by the FBI on Internet Crime revealed that organizations lost USD$4.2 billion as a result of cybercrime. This was a 200% increase since last analyzed three years earlier. Cyber vulnerability is one of the most critical risk areas. Inaction to review, understand and react to cyber threat is simply too great for any organisation to ignore. Companies need to be confident that information security is managed effectively so that the organizations can be protected against data breaches and malicious cyber-attacks. Breaches negatively affect organizations in that time-to-recover is long and expensive. The reputational impact is also detrimental to organizations especially when strategic or personal information is stolen and/or disseminated. Design Science Research (DSR) is a research paradigm that allows for the development and iterative enhancement of practical applicative information security artefacts that can help organizations improve their security position.
High Speed Enhanced Pipeline Architecture of LEA Light Weight Cipher for Resource Limited Applications

Due to advancement in computing power and technology specifically in the cyber-security domain, need for data security has increased. For such requirements cryptography plays an important role in securely transferring data on the internet. But when it comes to IoT devices, this need for data security increases further. For this lightweight cryptography provides means for resource-limited devices as most IoT devices are resource-constrained. These devices inherently bring additional limitations of trade-off between area, security, and speed of operation. In many applications, area increase can be acceptable if increases in frequency of operation is needed. This paper proposes such a design that provides higher speed. The LEA cipher when compared to other LWCs has inherently smaller code size, smaller memory-footprint and spend less power. LEA is a lightweight crypto-algorithm that takes in 128bit of data and 128/192/256 bit of key for encryption of the data. A pipelined architecture is proposed that reduces the critical path delay to increase the frequency of operation. Pipelining registers reduce the critical path delay. This design gives better results than existing 192-bit key pipelined architectures of LEA and other lightweight cipher with some area tradeoff.
Review Of The Recent Methods For Classification Of Colorectal Polyps Using Computer Aided Diagnosis

Colorectal cancer is one of the prevalent forms of fatal cancer. The presence of tumors inside the colon is identified through colonoscopy which helps to determine whether the polyp is benign or malignant. Prediction of the polyp histology during colonoscopy itself will help to reduce the number of unnecessary biopsies due to false diagnosis. Hence, accurate classification of the polyp is critical for effective treatment and improved patient outcomes. Over the years, lots of work has been done to improve the performance of colorectal cancer classification systems using computer aided diagnosis. This paper presents the various approaches used for colorectal cancer classification while also addressing the existing limitations associated with different methodologies. Although substantial progress has been made in this field in the past few years, several important obstacles must still be overcome to achieve an accurate and reliable classification of polyps. This article will also help academicians and practitioners in this field to familiarize with state-of-the-art techniques.
Fonte: Finding Bug Inducing Commits from Failures

A Bug Inducing Commit (BIC) is a commit that introduces a software bug into the codebase. Knowing the relevant BIC for a given bug can provide valuable information for debugging as well as bug triaging. However, existing BIC identification techniques are either too expensive (because they require the failing tests to be executed against previous versions for bisection) or inapplicable at the debugging time (because they require post hoc artefacts such as bug reports or bug fixes). We propose Fonte, an efficient and accurate BIC identification technique that only requires test coverage. Fonte combines Fault Localisation (FL) with BIC identification and ranks commits based on the suspiciousness of the code elements that they modified. Fonte reduces the search space of BICs using failure coverage as well as a filter that detects commits that are merely style changes. Our empirical evaluation using 130 real-world BICs shows that Fonte significantly outperforms state-of-the-art BIC identification techniques based on Information Retrieval as well as neural code embedding models, achieving at least 39% higher MRR. We also report that the ranking scores produced by Fonte can be used to perform weighted bisection, further reducing the cost of BIC identification. Finally, we apply Fonte to a large-scale industry project with over 10M lines of code, and show that it can rank the actual BIC within the top five commits for 87% of the studied real batch-testing failures, and save the BIC inspection cost by 32% on average.
Reversible Data Hiding for Interpolated Images using CNN based Nearest Neighbour Interpolation

Interpolation based reversible data hiding (RDH) has gained plenty of research interest because to its ability to increase large amount of secret information in an image. However, one of the issues is that it involves approximating pixel values, which can result in a loss of image detail or in other words deteriorated image quality. To resolve this issue, this paper defines an innovative RDH method for interpolated images using convolution neural network based nearest neighbor interpolation (CNN-NNI). The CNN-NNI generates high-resolution image that closely matches the original image by processing through a series of convolutional layers. On the other hand, the strategy for embedding data involves using both interpolated pixels as well as reference pixels for data insertion in each block. Experimental findings indicate that the suggested approach beats recently developed and comparable techniques not only on final visual quality yet also at the maximum embedding ability i.e., higher than 1.75 bpp including an average PSNR value higher than 30 dB.
When to Say What: Learning to Find Condition-Message Inconsistencies

Programs often emit natural language messages, e.g., in logging statements or exceptions raised on unexpected paths. To be meaningful to users and developers, the message, i.e., what to say, must be consistent with the condition under which it gets triggered, i.e., when to say it. However, checking for inconsistencies between conditions and messages is challenging because the conditions are expressed in the logic of the programming language, while messages are informally expressed in natural language. This paper presents CMI-Finder, an approach for detecting condition-message inconsistencies. CMI-Finder is based on a neural model that takes a condition and a message as its input and then predicts whether the two are consistent. To address the problem of obtaining realistic, diverse, and large-scale training data, we present six techniques to generate large numbers of inconsistent examples to learn from automatically. Moreover, we describe and compare three neural models, which are based on binary classification, triplet loss, and fine-tuning, respectively. Our evaluation applies the approach to 300K condition-message statements extracted from 42 million lines of Python code. The best model achieves a precision of 78% at a recall of 72% on a dataset of past bug fixes. Applying the approach to the newest versions of popular open-source projects reveals 50 previously unknown bugs, 19 of which have been confirmed by the developers so far.
High-Precision Flatness Measurement for Cryogenic Mosaic Focal Plane Arrays

Mosaic CCD or CMOS cameras are crucial for giant telescopes, especially those with a large field of view. One of the critical determinants of image quality is the flatness of the Mosaic Focal Plane Arrays (MFPAs). The MFPAs can only be assembled at room temperature and operated under cryogenic cooling with a vacuum chamber to reduce image noise. However, changes and inhomogeneities in temperature can deform the focal plane and affect the flatness. Under cryogenic conditions, the measurement must be taken through a flat dewar window which will reduce the measurement precision. To correctly measure the MFPAs under cryogenic conditions, we developed a method called Differential Triangulation Measurement (DTM) using the method of laser triangulation and differential measurement. In this study, the feasibility of the laser triangulation measurement through a flat window using the oblique mode is demonstrated. Furthermore, a differential measurement method using two laser triangulation sensors is adopted to improve the precision of measurement. One sensor is used to scan a precision optical flat as a reference, and the other is used to scan the MFPAs. A flatness calculation method based on the Progressive Sample Consensus algorithm (PROSAC) is used to optimize data processing. A measurement system based on DTM (DTMS) is designed and implemented for the flatness measurement of MFPAs in a vacuum chamber. The measurement precision of the system was verified by measuring an ultrahigh precision gauge step block. The test results show that the measurement error through the flat dewar window is less than 0.5%. Finally, the DTMS is applied in the MFPA flatness measurement of the prime-focus camera of the Wide Field Sky Survey Telescope (WFST).