Reviewerly/exHarmony huggingface dataset:
New Versions of Miller-loop Secured Against Side-Channel Attacks

In this paper, we propose two new versions of Miller algorithm in order to secure pairing computations against existing side-channel attacks (SCA). We have chosen to use the co-Z arithmetic on elliptic curves from which we derive two methods for pairing computations: one based on Euclidean addition chains and one based on Zeckendorf representation. We show that our propositions are resistant to existing side-channel attacks against pairing-based cryptography. We consider differential power analysis and fault attacks. The complexities of our solutions are compared with state-of-the-art one. We demonstrate that our new proposed versions are more efficient by 17%.
Linear Complexity of Generalized Cyclotomic Sequences with Period $$p^{n}q^{m}$$

Linear complexity is a very important merit factor for measuring the unpredictability of pseudo-random sequences for applications. The higher the linear complexity, the better the unpredictability of a sequence. In this paper, we continue the investigation of generalized cyclotomic sequences constructed by new generalized cyclotomy presented by Zeng et al. In detail, we consider the new generalized cyclotomic sequence with period $$p^nq^m$$ where p, q are odd distinct primes and n, m are natural numbers. It is shown that these sequences have high linear complexity. Finally, we also give some examples to illustrate the correctness of our results.
Optimal Possibly Nonlinear 3-PIR Codes of Small Size

First, we state a generalization of the minimum-distance bound for PIR codes. Then we describe a construction for linear PIR codes using packing designs and use it to construct some new 5-PIR codes. Finally, we show that no encoder (linear or nonlinear) for the binary r-th order Hamming code produces a 3-PIR code except when $$r=2$$ . We use these results to determine the smallest length of a binary (possibly nonlinear) 3-PIR code of combinatorial dimension up to 6. A binary 3-PIR code of length 11 and size $$2^7$$ is necessarily nonlinear (as a PIR code), and we pose the existence of such a code as an open problem.
The Projective General Linear Group PGL(2, $$5^m$$) and Linear Codes of Length $$5^m+1$$

The importance of interactions between groups, linear codes and t-designs has been well recognized for decades. Linear codes that are invariant under groups acting on the set of code coordinates have found important applications for the construction of combinatorial t-designs. Examples of such codes are the Golay codes, the quadratic-residue codes, and the affine-invariant codes. Let $$q=5^m$$ . The projective general linear group PGL(2, q) acts as a 3-transitive permutation group on the set of points of the projective line. This paper is to present two infinite families of cyclic codes over GF $$(5^m)$$ such that the set of the supports of all codewords of any fixed nonzero weight is invariant under PGL(2, q), therefore, the codewords of any nonzero weight support a 3-design. A code from the first family has parameters $$[q+1,4,q-5]_q$$ , where $$q=5^m$$ , and $$ m\ge 2$$ . A code from the second family has parameters $$[q+1,q-3,4]_q$$ , $$q=5^m,~m\ge 2$$ . This paper also points out that the set of the support of all codewords of these two kinds of codes with any nonzero weight is invariant under $$\textrm{Stab}_{U_{q+1}}$$ , thus the corresponding incidence structure supports 3-design.
On a Conjecture on Irreducible Polynomials over Finite Fields with Restricted Coefficients

Let q be a prime power, $$\mathbb {F}_q$$ be the finite field of order q and let n, d be positive integers. Munemasa and Nakamura conjectured at WAIFI 2016 that there exist $$f\in \mathbb {F}_q[x]$$ of degree n and $$\alpha \in \mathbb {F}_{q^d}$$ not lying in any proper subfield such that $$f-\alpha $$ is irreducible in $$\mathbb {F}_{q^d}[x]$$ . In this paper, we prove that the conjecture holds true for every triple (q, n, d) such that d is larger than a constant that depends only on n. As a subproduct of our proofs we deduce that if $$F\in \mathbb {F}_q[x]$$ is a polynomial such that $$F-t_0$$ has a certain special factorization pattern for some $$t_0\in \mathbb {F}_q$$ , then the statistics of all the factorization patterns of $$F-t_1$$ , where $$t_1$$ ranges in $$\mathbb {F}_{q^d}$$ , are entirely determined up to an explicit error term independent of the size of the base field. At the end of the paper we provide some experimental results to show how sharp our statistics are.
Explicit Non-malleable Codes from Bipartite Graphs

Non-malleable codes are introduced to protect the communication against adversarial tampering of data, as a relaxation of the error-correcting codes and error-detecting codes. To explicitly construct non-malleable codes is a central and challenging problem which has drawn considerable attention and been extensively studied in the past few years. Recently, Rasmussen and Sahai built an interesting connection between non-malleable codes and (non-bipartite) expander graphs, which is the first explicit construction of non-malleable codes based on graph theory other than the typically exploited extractors. So far, there is no other graph-based construction for non-malleable codes yet. In this paper, we aim to explore more connections between non-malleable codes and graph theory. Specifically, we first extend the Rasmussen-Sahai construction to bipartite expander graphs. Accordingly, we establish several explicit constructions for non-malleable codes based on Lubotzky-Phillips-Sarnak Ramanujan graphs and generalized quadrangles, respectively. It is shown that the resulting codes can either work for a more flexible split-state model or have better code rate in comparison with the existing results.
Decomposition of Dillon’s APN Permutation with Efficient Hardware Implementation

Modern block ciphers incorporate a vectorial Boolean function (S-box) as their only nonlinear component. Almost Perfect Nonlinear (APN) functions exhibit optimal resistance to differential cryptanalysis and thus present ideal security properties as S-boxes. These optimal cryptographic properties have the side effect of making the function harder to represent and implement. As the number of variables of the function grows, lookup-table representations become less feasible, and so from a practical point of view, it is crucial to develop a good understanding of how cryptographically strong functions can be represented in hardware. This paper focuses on one of the most important APN functions, namely Dillon’s permutation in dimension 6. This is the only known APN permutation in an even number of variables. It is thus an ideal candidate for studying the efficiency of different representations since it combines at least two very important cryptographic properties, and since the number of variables is not large enough to make its computational investigation intractable. In this paper, we give a new description of Dillon’s permutation as a composition of two functions and compare it with its classic univariate polynomial representation. We give hardware architectures for both representations, and we report on the results obtained from their FPGA implementations. From the experimental results, the implementation of the new decomposed Dillon’s permutation presents reductions in the number of 2-input XOR gates of up to 27.3% and in the Area $$\times $$ Delay metrics of up to 27.4% with respect to the implementation of the corresponding univariate representation. Therefore, the new decomposed Dillon’s permutation representation is more efficient than the univariate polynomial one when reconfigurable devices are used for the hardware implementation. This indicates that by representing APN functions as a composition of simpler functions, significant reductions in the complexity of the implementation can be achieved.
Framework for a Computer-Aided Treatment Prediction (CATP) System for Breast Cancer

This study offers a framework for a breast cancer computer-aided treatment prediction (CATP) system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagnosis and frequent screening.Mammography has been the most utilized breast imaging technique to date.Radiologists have begun to use computer-aided detection and diagnosis (CAD) systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence (AI) in the medical field, this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model, data from 336,525 patients from the SEER dataset were split into training (80%), and testing (20%) sets.Decision Trees, Random Forest, XGBoost, and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve (AUC) achieved was 0.91 using Random Forest on the SEER dataset.
Use of the CANTOR System for Collaborative Learning in Medical Visual Object Recognition

This paper reports from a user requirement, design and evaluation study on supporting collaborative learning by visual perception in the medical education domain. The CANTOR (Converging Agreement by Networking Telematics for Object Recognition) system can briefly be described as a tool that support collaborative consensus making when classifying sets of medical images or objects in medical images An evaluation experiment showed that using CANTOR seems to give a better learning effect than by using traditional methods.
Efficient Remote Memory Paging for Disaggregated Memory Systems

Memory disaggregation has attracted increasing attention in recent years because it is a cost-efficient approach to scale memory capacity for applications in a data center. However, the latency of remote memory access is a major concern in disaggregated memory systems. This paper presents VANDI, a virtual memory paging mechanism that allows applications to use remote memory pools transparently. VANDI enables effective data caching and prefetching mechanisms to address the problem of high access latency in disaggregated memory systems. VANDI exploits a low-complexity cache replacement strategy to optimize the asynchronous staging queue so that the remote write latency can be significantly reduced. VANDI can also prefetch data in multi-granularity from a remote memory pool in an adaptive manner, and thus further improves the hit rate of the local cache to reduce the read latency of remote memory. Our extensive experiments using micro-benchmarks show that VANDI can improve the performance of typical remote paging system–Infiniswap by up to 15 $$\times $$ –102 $$\times $$ . VANDI can also improve the performance of state-of-the-art disaggregated memory system–Valet by 1.2 $$\times $$ –2.7 $$\times $$ . For typical machine learning workloads, VANDI can achieve 20% to 80% performance improvement compared with the state-of-the-art Valet.
Building Marathi SentiWordNet

Recently, many websites have offered users a platform to post their reviews and feelings around goods, service quality, strategies, and current affairs in their native languages. The extensive English lexical resources, like part-of-speech taggers, parsers, and polarity lexicons, have helped develop sophisticated sentiment analysis applications for the English language. However, the sentiment lexicon falls short for the resource-poor language, such as Marathi, in the Sentiment Analysis (SA) area. In spite of the fact that there have been attempts to create polarity lexicons in Indian dialects, they suffer from many deficiencies, such as a lack of publicly available sentiment lexicons with a proper scoring mechanism of opinion words. This research work presents a word-level translation scheme to create the first comprehensive Marathi polarity resource: “Marathi SentiWordNet” using a merger of existing linguistic resources: English SentiWordNet, Hindi SentiWordNet, Marathi WordNet, and IndoWordNet. The two polarity scores, positive and negative, are assigned to each Marathi opinion word. To increase the word coverage, the lexicon is systematically expanded using the Marathi synsets of IndoWordNet. Manual annotators have evaluated the resource resulting in a substantial agreement score (0.76 Kappa score). The Comparison of the proposed Marathi SentiWordNet approach and state-of-art work building SentiWordNet for Indian languages has been presented. The generated Marathi SentiWordNet would serve as a tool for sentiment analysis-related tasks concerning Marathi text.
Data Poisoning Attack by Label Flipping on SplitFed Learning

In the distributed machine learning scenario, we have Split Learning (SL) and Federated Learning (FL) as the popular techniques. In SL, the model is split between the clients and the server for sequential training of clients, whereas in FL, clients train parallelly. The model splitting in SL provides better overall privacy than FL. SplitFed learning (SFL) combines these two popular techniques to incorporate the model splitting approach from SL to improve privacy and utilize the generic FL approach for faster training. Despite the advantages, the distributed nature of SFL makes it vulnerable to data poisoning attacks by malicious participants. This vulnerability prompted us to study the robustness of SFL under such attacks. The outcomes of this study would provide valuable insights to organizations and researchers who wish to deploy or study SFL. In this paper, we conduct three experiments. Our first experiment demonstrates that data poisoning attacks seriously threaten SFL systems. Even the presence of 10% malicious participants can cause a drastic drop in the accuracy of the global model. We further perform a second experiment to study the robustness of two variants of SFL under the category of targeted data poisoning attacks. The results of experiment two demonstrate that SFLV1 is more robust than SFLV2 the majority of times. In our third experiment, we studied untargeted data poisoning attacks on SFL. We found that untargeted attacks cause a more significant loss in the global model’s accuracy than targeted attacks.
Reconstruction of the Instantaneous Images Distorted by Surface Waves via Helmholtz–Hodge Decomposition

Imaging through water waves will cause complex geometric distortions and motion blur, which seriously affect the correct identification of an airborne scene. The current methods main rely on high-resolution video streams or a template image, which limits their applicability in real-time observation scenarios. In this paper, a novel recovery method for the instantaneous images distorted by surface waves is proposed. The method first actively projects an adaptive and adjustable structured light pattern onto the water surface for which random fluctuation will cause the image to degrade. Then, the displacement field of the feature points in the structured light image is used to estimate the motion vector field of the corresponding sampling points in the scene image. Finally, from the perspective of fluid mechanics, the distortion-free scene image is reconstructed based on the Helmholtz-Hodge Decomposition (HHD) theory. Experimental results show that our method not only effectively reduces the distortion to the image, but also significantly outperforms state-of-the-art methods in terms of computational efficiency. Moreover, we tested the real-scene sequences of a certain length to verify the stability of the algorithm.
Application of Web Data Mining Algorithms in Information Management Education

How to quickly retrieve the required information from the vast data information needs web data mining technology. Its algorithm and its application in information management are worthy of research. Nowadays, web data mining plays a very important role in the field of information management such as scientific and technological literature retrieval. Based on the concept and characteristics of data mining technology, this paper briefly describes the various application fields and important significance of data mining technology in information management, and focuses on the innovative multi field applications of Web Data Mining in optimizing data resources and providing customers with personalized intelligent automation services.
Choosing shape parameters for regression in reproducing kernel Hilbert space and variable selection

The Gaussian radial basis function (RBF) is a widely used kernel function in kernel-based methods. The parameter in RBF, referred to as the shape parameter, plays an essential role in model fitting. In this paper, we propose a method to select the shape parameters for the general Gaussian RBF kernel. It can simultaneously serve for variable selection and regression function estimation. For the former, asymptotic consistency is established; for the latter, the estimation is as efficient as if the true or optimal shape parameters are known. Simulations and real examples are used to illustrate the method's performance of prediction by comparing it with other popular methods.
Cluster- and PCA-based gamma-ray spectrometry data processing algorithm for hydrothermal alteration haloes mapping

In the present paper a high-resolution gamma-spectrometry data processing algorithm aimed at locating hydrothermal alteration haloes is discussed. On the first stage a cluster model of spatial variation of radioactive background is created using information on natural radioactive elements (NRE) concentrations and numerical geomorphological characteristics. This model allows for good terrain differentiation in terms of its lithological and landscape variability. Mathematical procedure for removal of NRE distribution variance, related to variability of lithology and elementary landscapes, improved radiometry data for mapping zones of hydrothermal alterations. On the second stage we integrated the “corrected” gamma-ray spectrometry data using principal component analysis (PCA). Comparing the acquired factor model with a priori radioactive models of hydrothermal alterations allowed us to differentiate quartz-hydromica and quartz-pyrite-sericite alteration haloes.
A Hybrid System for Copy-Move Forgery Detection

Copy-move forgery detection in images is an attractive topic recently dealt with by many researchers. It is still challenging to identify the location of intentionally or incidentally induced changes in the image and to determine if the image is original or not, despite a large number of both new algorithms and improved existing ones that have been proposed to this aim, achieving results with a high degree of precision. We contribute to this area of research with a proposal of a hybrid copy-move forgery detection system based on applying fuzzy S-metrics and metaheuristics to clustering. We developed the system in the programming language Python. To validate the proposed model, we compared the obtained results with those obtained by two other relevant algorithms (on the same images).
Designing a Smart Speaker for Emergent Users: Human Plus AI Response

This paper reports on the development of a smart speaker for the home setting of ‘emergent’ users – those whose technology experience and resource availability are low. Earlier research has shown that AI (Artificial Intelligence) powered smart speakers struggled in recognising many emergent users requests. On the other hand, smart speakers powered by human responses were more accurate but slower. In this study, we began by determining, given a choice, emergent users prefer a smart speaker enabled by a human response or an AI response, and what are their preference criteria. We found that they were not completely inclined towards either of those choices. Rather they preferred a smart speaker based on three factors: first, the language of the request, second, the length and complexity of the request, and third, the urgency of response. We developed an integrated version of the smart speaker and evaluated it with emergent user families. From our analysis, it was evident that, when combined, AI and human responses complement each other and provide an elaborate and richer response for emergent users.
A Virtual Reality System for Fruit Processing Line Personnel Training: A Usability Study

Labour in most agro-industrial plants accounts for a significant part of the cost structure. Even though fully automated processing facilities can be built, a lower initial cost solution involves training the personnel to increase their efficiency in the plant. Advances in VR technologies enables the creation of immersive virtual environments that simulates different contexts. A VR experience is proposed in order to simulate the work environment for fruit processing line personnel. In this manner, the workers can be trained without using the physical resources of the processing plant. A usability test was performed, showing that participants felt a high degree of immersion and realism. They were able to complete the assigned tasks, indicating that the software meets the objective of instructing the participants in the selection process of IQF berries.
Adversarial Search Algorithms Performance in the Yote Game

The performance of several adversarial search algorithms is very dependent on the move generation and ordering procedure, as well as on the transposition table characteristics. In this work we compare Alpha-beta, MTD-f and NegaScout performance in the presence and absence of the following features: transposition table, Hash Move heuristic and move ordering using the evaluation function. We found that NegaScout did not yield major improvements over Alpha-Beta, which is likely due to the move ordering performed using a weak evaluation function. In most cases, MTD-f examines fewer nodes, and completes fixed depth searches in less time than Alpha-beta and NegaScout. This is consistent with the literature when using coarse grained evaluation functions. Finally, we found that enabling move ordering leads to fewer nodes examined than enabling the Hash Move heuristic, but, as sorting is time consuming, search is slower. Best results overall are found using MTD-f with all enhancements enabled. These results, in a previously unexplored game, are consistent with existing literature. This enables us to make predictions of algorithm and heuristics’ performance in novel games, based on an analysis of the games’ features, without having to implement all techniques.
Identification of Ethical Issues in the Phases of the Software Development Life Cycle: A Preliminary Secondary Study

The principles of ethics are not a new topic in the area of Software Engineering; this term emerged in the 90s after the rise of computers due to incorrect practices in software development. This research aims to classify the ethical problems arising in the software development life cycle, for which a preliminary secondary study (PSS) was conducted. As a result of the PSS, 27 primary studies were obtained. These studies propose methodologies and solutions for some ethical issues in a specific phase. Therefore, we classified the ethical issues, but not all adversities can be included due to the variety of problems. We conclude that no research papers classify ethical issues arising in the phases of software development.