Reviewerly/exHarmony huggingface dataset:
Binary Kummer Line

The idea of the Kummer line was introduced by Gaudry and Lubicz [22]. Karati and Sarkar [31] proposed three efficient Kummer lines over prime fields, and [31, 40] show that they are faster than $$\textsf{Curve25519}$$ [4]. In this work, we explore the problem of secure and efficient scalar multiplications using the Kummer lines over binary fields compared to Koblitz curves, binary Edwards curves, and Weierstrass curves. In this article, we provide the first concrete proposal for binary Kummer line: $$\textsf{BKL}251$$ over the field $$\mathbb {F}_{2^{251}}$$ , and it offers 124.5-bit security that is the same as that of $$\textsf{BEd251}$$ [8] and $$\textsf{CURVE2251}$$ [51]. $$\textsf{BKL}251$$ has small curve parameters and a small base point. We implement $$\textsf{BKL}251$$ using the instruction $$\texttt{PCLMULQDQ}$$ of modern Intel processors and a software $$\textsf{BBK251}$$ for batch computation of scalar multiplications using the bitslicing technique. We also provide the first implementation of Edwards curve $$\textsf{BEd}251$$ [8] using the $$\texttt{PCLMULQDQ}$$ , best to our knowledge. Thus this work complements the works of [5, 8]. All the implemented software compute scalar multiplications in constant time using Montgomery ladders. For the right-to-left Montgomery ladder scalar multiplication, each ladder step of a binary Kummer line needs fewer field operations than an Edwards curve. In the case of the left-to-right Montgomery ladder, a Kummer line and an Edwards curve have almost the same number of field operations. Our experimental results show that left-to-right Montgomery scalar multiplications of $$\textsf{BKL}251$$ are $$9.63\%$$ and $$0.52\%$$ faster than those of $$\textsf{BEd}251$$ for fixed-base and variable-base, respectively. Left-to-right Montgomery scalar multiplication for the variable-base of $$\textsf{BKL}251$$ is $$39.74\%$$ , $$23.25\%$$ , and $$32.92\%$$ faster than those of the curves $$\textsf{CURVE2251}$$ , $$\mathsf {K-283}$$ , and $$\mathsf {B-283}$$ , respectively. Using the right-to-left Montgomery ladder with precomputation, $$\textsf{BKL}251$$ achieves a $$17.84\%$$ speedup over $$\textsf{BEd}251$$ for fixed-base scalar multiplication. For a batch computation, $$\textsf{BBK251}$$ performs comparatively the same (slightly faster) as the $$\textsf{BBE251}$$ and $$\textsf{sect283r1}$$ . Our experiments reveal that scalar multiplications on $$\textsf{BKL}251$$ and $$\textsf{BEd251}$$ are (approximately) 65% faster than one scalar multiplication (after scaling down) of batch software $$\textsf{BBK251}$$ and $$\textsf{BBE251}$$ .
PIE: p-adic Encoding for High-Precision Arithmetic in Homomorphic Encryption

A large part of current research in homomorphic encryption (HE) aims towards making HE practical for real-world applications. In any practical HE, an important issue is to convert the application data (type) to the data type suitable for the HE. The main purpose of this work is to investigate an efficient HE-compatible encoding method that is generic, and can be easily adapted to apply to the HE schemes over integers or polynomials. p-adic number theory provides a way to transform rationals to integers, which makes it a natural candidate for encoding rationals. Although one may use naive number-theoretic techniques to perform rational-to-integer transformations without reference to p-adic numbers, we contend that the theory of p-adic numbers is the proper lens to view such transformations. In this work we identify mathematical techniques (supported by p-adic number theory) as appropriate tools to construct a generic rational encoder which is compatible with HE. Based on these techniques, we propose a new encoding scheme $$\textsf{PIE}$$ that can be easily combined with both AGCD-based and RLWE-based HE to perform high precision arithmetic. After presenting an abstract version of $$\textsf{PIE}$$ , we show how it can be attached to two well-known HE schemes: the AGCD-based $$\textsf {IDGHV}$$ scheme and the RLWE-based (modified) Fan-Vercauteren scheme. We also discuss the advantages of our encoding scheme in comparison with previous works.
Formal Verification of Arithmetic Masking in Hardware and Software

Masking is a popular countermeasure to protect cryptographic implementations against physical attacks like differential power analysis. So far, research focused on Boolean masking for symmetric algorithms like AES and Keccak. With the advent of post-quantum cryptography (PQC), arithmetic masking has received increasing attention because many PQC algorithms require a combination of arithmetic and Boolean masking and respective conversion algorithms (A2B/B2A), which represent an interesting but very challenging research topic. While there already exist formal verification concepts for Boolean masked implementations, the same cannot be said about arithmetic masking and accompanying mask conversion algorithms. In this work, we demonstrate the first formal verification approach for (any-order) Boolean and arithmetic masking which can be applied to both hardware and software, while considering side-effects such as glitches and transitions. First, we show how a formal verification approach for Boolean masking can be used in the context of arithmetic masking such that we can verify A2B/B2A conversions for arbitrary masking orders. We investigate various conversion algorithms in hardware and software, and point out several new findings such as glitch-based issues for straightforward implementations of Coron et al.-A2B in hardware, transition-based leakage in Goubin-A2B in software, and more general implementation pitfalls when utilizing common optimization techniques in PQC. We provide the first formal analysis of table-based A2Bs from a probing security perspective and point out that they might not be easy to implement securely on processors that use of memory buffers or caches.
Skills in Demand for Blockchain Related Jobs

Technology development has a major impact on the economic systems, transforming all its components. For instance, the emergence of the blockchain technology changes the means of collaboration and transactions in society and economy, generating opportunities for financial markets, software companies, public and social services, to mention just a few. Furthermore, the challenges related to privacy, storage space, and networking are arising, creating new jobs. All over, different industries are looking for blockchain skills. This study aims to investigate the current requirements associated with blockchain-related jobs in the Romanian labor market, also examining the similarity to the worldwide situation. The importance of this topic lies in the fact that understanding employers’ requirements it is a major step for signaling current trends related to labor demand and skills mismatching. The findings are extracted using unsupervised text mining techniques on textual data sourced from the LinkedIn platform. The results present a snapshot of the current situation in Romania, revealing two main subsets of characteristics required for blockchain-related jobs. These define the two broad categories of actual vacancies: software engineers, which are expected to have a computer science background, programming skills, and product designers where marketing, communication, and design skills are essential.
Impact of Governance Indicators on Inclusive Growth and the Achievement of the Sustainable Development Goals in Africa

This study aims to assess the impact of good governance on inclusive growth in Africa. In a sustainable insight, this study explores the widely debated relationship between governance indicators and economic growth rate over time and from a sustainable and inclusive perception. Indeed, from a methodological perspective, the empirical material is compiled from a panel database of 49 African countries. After profiling the state of governance in Africa, a test of the exploratory relationship between governance indicators and the growth rate from 2007 to 2020. We choose the β-convergence test to assess the process of convergence and inclusion of economic growth in African countries. The results of this study indicate the positive effect of governance on economic growth in Africa. Then, the less developed countries of the continent are improving faster than the more developed countries because they have improved their governance rate over time. Therefore, promoting inclusive and sustainable growth in Africa requires a good governance foundation as a perfect stepping stone to achieving the Sustainable Development Goals.
DCNN-Based Transfer Learning Approaches for Gender Recognition

Gender recognition becomes a very critical task for security agencies whilst assessing protest activities. At present, with the advent of GPUs, high computing machines, deep convolution neural networks (DCCNs), and automated gender recognition are possible. In this research work, we explore the performance of various DCNN architectures using transfer learning approaches for gender recognition. We performed a detailed ablation study on different input sizes and on different architectures to see the trade-off between latency and the accuracy of the classification. The performance of models tested against standard dataset WIKI, UTKFace, and Adience. We explored VGG-16 and MobileNetV3 architectures for comparison against accuracy and latency parameters in order to select a model suitable for the embedded device considering their low processing and less storage capacity. Experiments conducted using standard architecture against the standard dataset by changing the resolution and fine-tuning it.
Study of the Need for Effective Cyber Security Trainings in India

With the availability of android phones and internet in every hand, the rate of cyber-crimes have increased tremendously. This increase in cyber-crimes has led to the need for effective cyber training in all fields. Awareness and training are strongly needed at this point of time. Cyber training is a much debated but the least worked on topic. Everybody talks about this, but very few take steps in this regard. The reason behind this is the lack of expertise and knowledge in the field. The issue of cyber training against social engineering attacks remain in the forefront of organisations. But when we enquire into the larger context of its implementation, then it is revealed that no substantial measure is being taken due to low human resource in the field. Even after so many trainings that are provided there is no valuable decrease in the number of cases of hacking. This paper aims to bring about the ongoing developments in this field and also the vulnerabilities in the organizational structure.
Video Object Detection with an Improved Classification Approach

Object detection is primary task in computer vision. The various CNN are majorly used by researchers to improve the classification and detection of objects present in video frames. Object detection is a prime task in self-driven cars, satellite images, robotics, etc. The proposed work is focused on improvement of object classification and detection in videos for video analytics. The key focus of work is identification and tuning of hyper-parameters in deep learning models. The deep learning-based object detection models are broadly classified into two categories, i.e., one-stage detector and two-stage detector. We have selected one-stage detector for experimentation. In this paper, a custom CNN model is given with hyper-parameter tuning and the results are compared with state of art models. It is found out that the hyper-parameter tuning on CNN models helps in improvement of object classification and detection accuracy of deep learning models.
InCReASE: A Dynamic Framework Towards Enhancing Situational Awareness in Cyber Incident Response

Protecting valuable IT assets is one of the most significant challenges that organizations face today. Cyber criminals operating beyond physical boundaries, are able to disrupt and destroy cyber infrastructure, deny organizations access to IT services, and steal sensitive data. In response, enterprises organize security operations centres at the heart of their entities with the purpose of employing socio-technical systems with capabilities to detect, analyze and respond to these threats. This exploratory study examines how such capabilities are operationalized in leading “Managed Security Service Providers” (MSSPs) providing cybersecurity operations and incident response, and looks at how situation awareness knowledge is constructed through the organizational levels of the enterprise detection and response. In this context, situational awareness span over different levels in the organization starting from team personnel, ending at top management. Our work contributes to situational awareness theory in the context of cybersecurity operations and incident response. Thus, we advance the understanding of the organizational capabilities of MSSPs to develop awareness of the cyber-threat landscape and the broader operational dynamics. By introducing InCReASE, a dynamic framework towards enhancing situation awareness in Security Operations Centers (SOC) operations and incident response; we extend existing situational awareness models, combining elements of the existing body of knowledge and our empirical findings. The presented work is a reflection on the best practices adopted by MSSPs organizations operating in Norway.
Trajectory‐BERT: Pre‐training and fine‐tuning bidirectional transformers for crowd trajectory enhancement

Abstract To address the issue of trajectory fragments and ID switches caused by occlusion in dense crowds, we propose a space‐time trajectory encoding method and a point‐line‐group division method to construct Trajectory‐BERT in this paper. Leveraging the spatiotemporal context‐dependent features of trajectories, we introduce pre‐training and fine‐tuning Trajectory‐BERT tasks to repair occluded trajectories. Experimental results show that data augmented with Trajectory‐BERT outperforms raw annotated data on the MOTA metric and reduces ID switches in raw labeled data, demonstrating the feasibility of our method.
Tamil NLP Technologies: Challenges, State of the Art, Trends and Future Scope

This paper aims to summarize the NLP-based technological development of the Tamil language. Tamil is one of the Dravidian languages that are serious about technological development. This phenomenon is reflected in its activities in developing language technology tools and the resources made for technological development. Tamil has successfully developed tools or systems for speech synthesis and recognition, grammatical analysis of grammar, semantics and social media text, along with machine translation. There are many types of research undertaken to orient towards this achievement. Similarly, many activities are developing resources to facilitate technological development. The activities include preparing text corpora for text including monolingual, parallel and lexical along with speech with lexical resources and grammar. What is needed now is to stock-take the achievement made so far and found out where Tamil is in the arena of technological development and looks forward further to its fast technological development. Computational linguistics in Tamil NLP is gaining more attraction, and various data sets available for research is highlighted in this work for further exploration.
Simulation of English part-of-speech classification based on artificial intelligence and additive logistic regression

English part-of-speech classification technology is a technology that can process text data, can effectively solve the problem of messy data in text information categories, make data structured and organized, and facilitate people to obtain effective information implicit in the text. This article transforms the original polynomial distribution into a generalized linear model and uses logistic regression algorithm for specific implementation. Moreover, the model proposed in this paper inherits the good explanatory characteristics of the decision tree, and it locally uses logistic regression to fit the data, which greatly improves the function space that logistic regression can fit. In addition, due to changes in the decision theory of logistic regression leaf nodes, the corresponding tree branch theory also needs to be changed accordingly. Finally, this paper designs experiments to study the performance of the model constructed in this paper. The research results show that the model constructed in this paper has high accuracy in the extraction and classification of English part-of-speech features.
Data Driven Smart Cities and Data Spaces

Abstract Smart Cities are places where traditional networks and services are made more efficient through digital solutions, benefiting residents and businesses. Moving to digital solutions basically means applying a technological process that transforms various aspects of social or individual life into data that are subsequently transformed into information endowed with new forms of value, including economic value. In addition to economic value, in the digital age, access to data makes it possible to have transparent and open government, build trust, fight corruption, address inequality, and create more resilient democracies. This chapter describes how, for these purposes, data management and sharing are changing with the adoption of Data Spaces , a new approach that moves away from data integration to data coexistence.
Bit‐level evaluation of piccolo block cipher by satisfiability problem solver

Abstract In the field of symmetric key cryptography, the security against distinguishing attacks is one of the crucial security requirements. With advancements in computing capabilities and cryptanalysis techniques in recent years, more efficient methods have been proposed for exploring distinguishers using Mixed‐Integer Linear Programing (MILP) or satisfiability problem (SAT), thereby updating the security bounds of various ciphers. Piccolo is a lightweight block cipher proposed at CHES in 2011, with support 80‐bit and 128‐bit keys. Designers have undergone a rough security evaluation against differential, impossible differential, and related‐key differential attacks, based on nibble‐wise estimations due to the limitation of computational resource. Here, the authors perform bit‐level evaluations on Piccolo block cipher against differential, integral and impossible differential attacks by leveraging SAT‐based approaches. For the first time, the authors succeed in identifying optimal differential distinguisher on 6 rounds in the single key setting, and on 10/12 rounds in the related‐key setting for 80‐bit and 128‐bit keys, respectively. For integral attacks, the authors find integral distinguisher up to 7 rounds. Although the number of attacked rounds is the same as that of the previous attack, the authors find the 56th ordered integral distinguisher, which enable reducing the data complexity for attacks from 2 63 to 2 56 . As a result, the authors find the 7‐round impossible differentials which is the same number of rounds as the previous nibble‐wise evaluation.
Autoencoders as a Characterization Technique and Aid in the Classification of Volcanic Earthquakes

Volcanic seismicity is one of the most relevant parameters for the evaluation of volcanic activity and consequently the prognosis of eruptions. Earthquakes of volcanic origin are of different classes, directly related to the physical process that generates them. The distribution of the data between classes of seismic-volcanic signals generally presents an unbalanced profile (imbalanced datasets), which can hinder the performance of the classification in machine learning models. Therefore, this research presents a characterization technique (feature extract) that, in addition to reducing the dimension of each seismic record, allows a representation of the signals with the most relevant and significant information. This work proposes the use of a Dual Feature Autoencoder (DAF), which is compared with conventional characterization techniques such as Linear Prediction Coefficients (LPC) and Principal Component Analysis (PCA). The training of the model was performed with a dataset containing volcano-tectonic earthquakes (VT), long period events (LP) and Tornillo-type events (Tor) of the Galeras volcano, one of the most active volcanoes in Colombia. The classification results reach 99% of the classification of the mentioned classes.
AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications

Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based communication paradigm that exploits feedback from the receiver. We present AttentionCode, a new class of feedback codes leveraging deep learning (DL) technologies. The underpinnings of AttentionCode are three architectural innovations: AttentionNet, input restructuring, and adaptation to fading channels, accompanied by several training methods, including large-batch training, distributed learning, look-ahead optimizer, training-test signal-to-noise ratio (SNR) mismatch, and curriculum learning. The training methods can potentially be generalized to other wireless communication applications with machine learning. Numerical experiments verify that AttentionCode establishes a new state of the art among all DL-based feedback codes in both additive white Gaussian noise (AWGN) channels and fading channels. In AWGN channels with noiseless feedback, for example, AttentionCode achieves a block error rate (BLER) of 10−7 when the forward channel SNR is 0 dB for a block size of 50 bits, demonstrating the potential of AttentionCode to provide ultra-reliable short-packet communications.
Learning When to Use Adaptive Adversarial Image Perturbations Against Autonomous Vehicles

Deep neural network (DNN) models are widely used in autonomous vehicles for object detection using camera images. However, these models are vulnerable to adversarial image perturbations. Existing methods for generating these perturbations use the image frame as the decision variable, resulting in a computationally expensive optimization process that starts over for each new image. Few approaches have been developed for attacking online image streams while considering the physical dynamics of autonomous vehicles, their mission, and the environment. To address these challenges, we propose a multi-level stochastic optimization framework that monitors the attacker's capability to generate adversarial perturbations. Our framework introduces a binary decision attack/not attack based on the attacker's capability level to enhance its effectiveness. We evaluate our proposed framework using simulations for vision-guided autonomous vehicles and actual tests with a small indoor drone in an office environment. Our results demonstrate that our method is capable of generating real-time image attacks while monitoring the attacker's proficiency given state estimates.