Reviewerly/exHarmony huggingface dataset:
PentaGOD

Secure multiparty computation (MPC) is increasingly being used to address privacy issues in various applications. The recent work of Alon et al. (CRYPTO'20) identified the shortcomings of traditional MPC and defined a Friends-and-Foes (FaF) security notion to address the same. We showcase the need for FaF security in real-world applications such as dark pools. This subsequently necessitates designing concretely efficient FaF-secure protocols. Towards this, keeping efficiency at the center stage, we design ring-based FaF-secure MPC protocols in the small-party honest-majority setting. Specifically, we provide (1,1)-FaF secure 5 party computation protocols (5PC) that consider one malicious and one semi-honest corruption and constitutes the optimal setting for attaining honest-majority. At the heart of it lies the multiplication protocol that requires a single round of communication with 8 ring elements (amortized). To facilitate having FaF-secure variants for several applications, we design a variety of building blocks optimized for our FaF setting. The practicality of the designed (1,1)-FaF secure 5PC framework is showcased by benchmarking dark pools. In the process, we also improve the efficiency and security of the dark pool protocols over the existing traditionally secure ones. This improvement is witnessed as a gain of up to 62x in throughput compared to the existing ones. Finally, to demonstrate the versatility of our framework, we also benchmark popular deep neural networks.
Data Leakage in Business and FinTech

With the advent of the internet and day-by-day advancement in technology, traditional services started to utilize technology to offer better, digitized, cutting-edge services. Financial services called FinTech are one such example of the adoption of cutting-edge technologies. On the other hand, with the adoption of technologies comes to the cybersecurity risks associated with them. Data leakage is one big issue in FinTech and business-related applications and services. The main aim of this chapter is to identify different ways and methods by which data leakage occurs and its adverse effects on organizations providing FinTech services. Furthermore, this chapter explores the various solutions and challenges in their implementation. It is concluded that no single solution can handle data leakage from all perspectives. Multiple solutions need to be combined and utilized to handle all scenarios.
Image Security Using Visual Cryptography

The use of digital image in a variety of disciplines has skyrocketed, particularly in multimedia, medicine, and social media. The integrity and confidentiality of digital images communicated over the internet can be jeopardized by a variety of security threats. As a result, the content of these digital images must be preserved at all costs for a variety of domain-specific applications. Visual cryptography scheme (VCs) is an image-based approach to encrypt the secret image in such a way, and human visual system (HVS) can be used to decrypt the image. The core concepts of visual cryptography and applications of several visual cryptography schemes are covered in this chapter. The authors describe a unique reversible data hiding method for protecting secret shares using a covering subset and recovering the secret image using an overlaying technique. This chapter also covers a comparative analysis of 2-out-of-n and 3-out-of-n visual cryptography schemes.
Privacy-Preserving Computing via Homomorphic Encryption

In the era of IoT and big data, an enormous amount of data being generated by various sensors and handheld devices and for sectors not limited to healthcare, commerce, smart driving, smart grids, and fintech requires privacy and security. Although security can be ensured once the data is in transit or at rest, for certain application domains need to ensure privacy computations over encrypted data. Homomorphic encryption (HE) is one mechanism that allows parties to compute any arbitrary functions in an encrypted domain. Homomorphic encryption schemes have been employed in various applied sectors for privacy preservation; however, the limiting factor of these schemes is the computational and communication overhead and associated security. This chapter reviews the types of HE schemes, the application domains, and the associated costs for privacy preserving computing and discusses the underlying mathematical hardness problems, security in the classical and post quantum era, and challenges and recommendations for tradeoff in applied domains.
Credit Card Fraud Detection Using Various Machine Learning and Deep Learning Approaches

It is evident that the evolution in technology has surpassed expectations and reached different heights in a shorter span of time and with evolving technology; a lot of changes have been introduced in our lives, and one such change is the replacement of traditional payment methods with the credit card system. Credit card use increases the most during online shopping. With the huge demand for credit cards worldwide, credit card fraud cases to are increasing rapidly. In this paper, four machine learning algorithms that are decision tree, random forest, logistic regression, and Naïve Bayes have been used for training the models. Also, deep neural networks have been implemented for model training which is giving more promising results compared to the machine learning algorithms. The accuracy of each algorithm used in the implementation of the credit card fraud detection has been compared and analyzed.
An Exploration of Machine Learning and Deep Learning Techniques for Offensive Text Detection in Social Media—A Systematic Review

The increasing popularity of usage of social media platforms such as Facebook, Twitter, and What's App has also given a potential to spread hatred or to cause harassment or inconvenience by using offensive and abusive texts on these platforms. It has been identified that offensive language is a significant problem for the safety of both social platforms and their users. The circulation of offensive or abusive language to the online community undermines its reputation, scares away users and also directly affects their mental growth. Offensive or abusive text just not only affects users but also affects stakeholders such as governments, autonomous organizations, and social media platforms. Every day such stakeholders have to spend long hours to remove such content manually from these platforms. So, there arises the need to detect offensive and abusive text in user's posts, messages, comments, blogs, etc., automatically. To address this issue, detection of offensive/abusive text in user's message, posts, comments, blogs, etc., has become a crucial task in recent times. There are various machine-learning and deep learning approaches existing in literature to identify such abusive texts. We have followed a systematic review process, in which we aim to explore the various machine learning or deep learning approaches adopted by various researchers to detect and the offensive/abusive speech in user's textual posts, messages, comments, blogs, etc. This systematic review will help to strengthen the design and implementation of a new and efficient approach for automatic detection and removal of abusive or offensive text in user's message or post. This deep exploration of the existing techniques will further have strong benefit to people, society, government, and social platforms in order to avoid spreading of hatefulness, harassment through social media.
A Secure Distributed System for the Electronic Voting System Using Blockchain Technology

Traditionally, electronic voting has relied on a centralized method of administration. The database and the system are both under the jurisdiction of the system's central administration, which oversees the voting process. As a result, issues like database manipulation and duplicate voting may arise, whether accidentally or purposefully. Permissionless blockchain technology has helped overcome many of these issues; however, since the basic consensus technique of such blockchains demands particular computer resources for each voting operation, they are not ideal for new voting systems. Power consumption, efficiency, and system latency all suffer as a result. These issues may be alleviated in part if electronic voting technologies are used. By using corporate blockchain technology, this research presents an electronic voting system that is very reliable and secures the secret vote. It also discusses some of the frequent security and dependability challenges of electronic voting system solutions, such as a flexible network setup.
Study of Neural Machine Translation With Long Short-Term Memory Techniques

The growing demand for having a conversation amongst people who come from different areas, across the globe, resulting from globalization, has led to the development of systems like machine translations. There are techniques like statistical models, Bayesian models, etc. that were used earlier for machine translations. However, with growing expectations towards better accuracies, neural networks aided systems for translations termed as neural machine translations (NMT) have come up. Models have been proposed by several organizations like Google NMT (G-NMT) that are widely accepted and implemented. Several machine translations are also based on RNN models. This work studies neural machine translations with respect to long short-term memory (LSTM) network and compares them on the basis of several widely accepted accuracy metrics like BLEU score, precision, recall, and F1 score. Further, a combination of two LSTM models is implemented for better accuracy. This work analyzes the various LSTM models on the basis of these metrics.
Abstractive Turkish Text Summarization and Cross-Lingual Summarization Using Transformer

Abstractive summarization aims to comprehend texts semantically and reconstruct them briefly and concisely where the summary may consist of words that do not exist in the original text. This chapter studies the abstractive Turkish text summarization problem by a transformer attention-based mechanism. Moreover, this study examines the differences between transformer architecture and other architectures as well as the attention block, which is the heart of this architecture, in detail. Three summarization datasets were generated from the available text data on various news websites for training abstractive summarization models. It is shown that the trained model has higher or comparable ROUGE scores than existing studies, and the summaries generated by models have better structural properties. English-to-Turkish translation model has been created and used in a cross-lingual summarization model which has a ROUGE score that is comparable to the existing studies. The summarization structure proposed in this study is the first example of cross-lingual English-to-Turkish text summarization.
Humanities in the Age of Blockchain Technology and Web 3.0

Although there has been a call for the transformation of the world from the Eurocentric paradigms to a decolonised and decentralised world, the role of technology is still embedded and continues to be at the centre of everything in society. Over the past few years, particularly from 2019 to 2022, since the discovery of the COVID-19 pandemic, the technological interdependency intensified by moving contact spaces to online platforms, private resources to open resources, including the pedagogical space. Despite advances in technology and pedagogy and their relation to education, and the focus on the local and global, the new internet and teaching and learning including its policy still lack serious engagement with blockchain technology and the metaverse, as well as how to effectively utilise it. This chapter address this gap by using Howard Gardner's theory of ‘multiple intelligences' to consider the role of blockchain technology and the metaverse as new options for the facilitation of open-ended teaching and learning that has emerged in the age of Web 3.0.
Sign Language Recognition for Daily Activites Using Deep Learning

Sign language recognition has become a critical research in the field of computer vision as the need of disability solutions grow. Sign language acts as a bridge to reduce the communication gap between normal people and deaf and dumb people. Current sign language identification systems, on the other hand, lack essential characteristics such as accessibility and cost, which are critical for people with speech disabilities to interact with their daily settings. The successful attractive solution is to initiate the sign languages in terms of words and common expressions for daily activities. This will interact the deaf and dumb people by connecting to the outside world more quickly and easily. The sign gestures obtained are processed through popular machine learning and deep learning models for classification accuracy. This chapter discusses the word sign recognition, image processing algorithms for separating the signs from the background, machine learning algorithms, and the complete model set up for sign recognition.
Meta-transfer-adjustment learning for few-shot learning

Deep neural network models with strong feature extraction capacity are prone to overfitting and fail to adapt quickly to new tasks with few samples. Gradient-based meta-learning approaches can minimize overfitting and adapt to new tasks fast, but they frequently use shallow neural networks with limited feature extraction capacity. We present a simple and effective approach called Meta-Transfer-Adjustment learning (MTA) in this paper, which enables deep neural networks with powerful feature extraction capabilities to be applied to few-shot scenarios while avoiding overfitting and gaining the capacity for quickly adapting to new tasks via training on numerous tasks. Our presented approach is classified into two major parts, the Feature Adjustment (FA) module, and the Task Adjustment (TA) module. The feature adjustment module (FA) helps the model to make better use of the deep network to improve feature extraction, while the task adjustment module (TA) is utilized for further improve the model’s fast response and generalization capabilities. The proposed model delivers good classification results on the benchmark small sample datasets MiniImageNet and Fewshot-CIFAR100, as proved experimentally.
Efficient hardware implementations of lightweight Simeck Cipher for resource-constrained applications

Due to the vigorous and ever-evolving demands of the IoT field, resource-constrained devices are being deployed in such areas and several new challenges like sequestration and surveillance, have come into the picture. Multiple lightweight designs have been recommended to overcome the security issues for resource-constrained devices. In applications requiring data to be transmitted fast between various devices, loop unrolling and subsequent pipelining architectures are extremely efficient. In this paper, several efficient hardware implementations of Simeck cipher such as loop unrolling architecture and outer round pipelining technique have been proposed in order to achieve high throughput. This paper has also proposed a bit serialized architecture of the Simeck cipher that reduces the area footprint and used in less resource applications. Our Simeck Serial architecture is 29.5 times more efficient than LiCi cipher and occupies only 7% of the area occupied by LiCi cipher. Similarly, our proposed serial architecture occupies only 13% and 25% of total slices occupied by X192 and XTEA respectively while still performs 184% and 161% more efficiently. All three architectures are evaluated and compared on the basis of throughput, area utilization, and power consumption for their implementations in different FPGA platforms.
Mathematical characterizations and computational complexity of anti-slide puzzles

For a given set of pieces and a frame, an anti-slide puzzle asks us to arrange the pieces so that none of the pieces can slide in the frame. Since the first anti-slide puzzle that consists of dozens of cuboid pieces in 3D was invented, tons of anti-slide puzzles using pentominoes have been proposed. Some of them are not in a frame, which we call that interlock puzzles. In this paper, we investigate computational complexity of anti-slide puzzles and interlock puzzles in 2D. In previous work in theoretical computer science, a few models have been proposed for dealing with the notion of anti-slide, however, there exist gaps between these models and real puzzles. We first give mathematical characterizations of anti-slide puzzles and show the relationship between the previous work. Using a mathematical characterization, we give a polynomial time algorithm for determining if a given arrangement of polyominoes is anti-slide or not in a model. Next, we prove that the decision problem whether a given set of polyominoes can be arranged to be anti-slide or not is strongly NP-complete even if every piece is x-monotone. On the other hand, a set of pieces cannot be arranged to be interlocked if all pieces are convex polygons.
Image outpainting guided by prior structure information

Deep learning-based image outpainting infers the missing region by using the known parts of images. However, due to lack of fully exploring the known image structure or texture information, most existing methods always produce blurry contents and distorted structures. To generate more natural outpainting results, we propose a two-stage outpainting method guided by prior structure information. It consists of both structure outpainting and texture outpainting, which allows the model to complete structure outpainting and refines the generated image. In Stage-I, we build the structure outpainting network to infer the structural information of the missing regions by utilizing that of the known structure. This could fully explore the global structure information and produce complete structure images. Stage-II builds upon Stage-I results and utilizes both inferred image-level and aggregated multi-scale feature-level structure information to refine results with more authentic and natural texture. Moreover, a multi-level dilated convolution block is presented to significantly enlarge the receptive field of texture outpainting, promoting it to extract more useful feature information for producing finer texture. Compared with the existing state-of-the-art (SOTA) methods, the experimental results on both Places2 and Paris StreetView datasets illustrate that our method exhibits better in terms of qualitative and quantitative comparisons.
Assessing attitudes towards evidence-based software engineering in a government agency

Evidence-based practice (EBP) has allowed several disciplines to become more mature by emphasizing the use of evidence from well-designed and well-conducted research in decision-making. Its application in SE, Evidence-based software engineering (EBSE) can help to bridge the gap between academia and industry by bringing together academic rigor and research of practical relevance. To achieve this, it seems necessary to improve its adoption. We sought both to study the attitudes towards EBSE of stakeholders working in a government agency (GA) and to assess whether knowledge of EBSE would impact their working practices. We conducted a multi-stage field investigation in an Uruguayan national GA that is responsible for digital policies. First, we organized an EBSE awareness lecture and we collected and analyzed participants’ perceptions of the value and limitations of EBSE. Sixteen months later, in a second stage, we contacted the agency and asked participants whether they had made use of the information about EBSE we presented to them. Initially, participants reported that EBSE seemed useful for tackling challenging problems and, in particular, considered its use appropriate given the agency’s responsibilities. Perceived barriers to EBSE adoption were the need for institutional support, the lack of government practice reports, inadequate skills or motivation, the cost of conducting systematic reviews, and the lack of evidence about emerging issues. In the follow-up survey, although the participants were not undertaking systematic reviews themselves, many reported improvements in how they searched for and evaluated information to support their work. Our study presents some insights to better understand EBSE adoption. With the exception of GA-specific issues, perceived value and barriers to adoption were consistent with those reported in software engineering and other disciplines. Our follow-up study confirms the potential value of evidence in the context of IT regulatory and government bodies.
The discrimination algorithms for overlapped MPSK and MQAM modulations using higher-order cumulants

Automatic Modulation Classification (AMC) is responsible for detecting the correct modulation types in the intelligent receivers. AMC performance degrades when the signal-to-noise ratio (SNR) decreases because of the overlapping among the digital modulation types’ features, and this performance worsens under fading channel conditions. This paper proposes two new algorithms that improve the AMC performance accuracy of the overlapped digital modulations in feature space by improving their discrimination. These algorithms are named temporal Fisher discriminant analysis (TFDA) and supervised Fisher discriminant analysis (SFDA). The simulation results show that TFDA improves AMC performance accuracy up to 19.01% compared with the reference paper (Ge et al., 2021) and up to 38.15% compared with the reference paper (Teng et al., 2018). In contrast, SFDA improves AMC performance accuracy up to 23.12 % compared with the reference paper (Ge et al., 2021) and up to 49.025% compared with the reference paper (Teng et al., 2018).
I recognize you by your steps: Privacy impact of pedometer data

In recent years, with the popularity of fitness bands and smartwatches, there has been a surge in the collection of personal physiological data. Tracking activities and fitness levels has become an intrinsic part of many people’s lives. While the deep insight it provides into an individual’s health has multiple benefits, this lifestyle choice comes with the risk of endangering the user’s privacy. Data from these devices has previously been proven capable to identify a person, much like a fingerprint. Most existing privacy attacks rely on fine-grained multi-sensor data. Through our observations we can demonstrate that pedometer data, collected per default by iPhones, even when aggregated to a 15-minute resolution, can be used to identify a user with good precision if sufficient training data is used to create the fingerprints. We also demonstrate that the participant’s data has such a strong individual signal that they can be clustered with high accuracy if they are divided into days.
Operator-centric joystick mapping for intuitive manual operation of differential drive robots

Intuitive joystick-based mobile robot operations are critical in terms of work efficiency in tasks where humans and robots work closely, such as in smart factories or electric wheelchairs. An operator-centric joystick mapping algorithm, based on a questionnaire study of 101 respondents, was proposed to determine the expected movement direction of a differential drive robot according to the joystick direction of the operator. The proposed approach considers the kinematic constraints of differential drive robots and maps the operator’s joystick control input to the robot velocity command by efficiently conveying the intention of the operator. This method generates a smooth robot velocity command from the continuous movement of the joystick without a sudden change in acceleration. The proposed algorithm was evaluated by 22 test subjects maneuvering a two-dimensional joystick module to control a virtual robot in a simulated environment visiting waypoints, and demonstrated superior performance when compared with two standard algorithms. The proposed algorithm was also implemented as a hardware system with a physical differential drive robot for testing in a real environment.