Reviewerly/exHarmony huggingface dataset:
Upper bounds on the numbers of binary plateaued and bent functions

Abstract We prove that the logarithm of the number of binary n-variable bent functions is asymptotically less than (11/ 32) 2 n as n → ∞. We also prove an asymptotic upper bound on the number of s-plateaued functions.
Annals of AIESEP Conference 2023

Annals of AIESEP Conference 2023&#x0D; Pontificia Universidad Católica de Chile&#x0D; July 4 - 7, 2023
Robust excitation of C-band quantum dots for quantum communication

Building a quantum internet requires efficient and reliable quantum hardware, from photonic sources to quantum repeaters and detectors, ideally operating at telecommunication wavelengths. Thanks to their high brightness and single-photon purity, quantum dot (QD) sources hold the promise to achieve high communication rates for quantum-secured network applications. Furthermore, it was recently shown that excitation schemes such as longitudinal acoustic phonon-assisted (LA) pumping provide security benefits by scrambling the coherence between the emitted photon-number states. In this work, we investigate further advantages of LA-pumped quantum dots with emission in the telecom C-band as a core hardware component of the quantum internet. We experimentally demonstrate how varying the pump power and spectral detuning with respect to the excitonic transition can improve quantum-secured communication rates and provide stable emission statistics regardless of network-environment fluctuations. These findings have significant implications for general implementations of QD single-photon sources in practical quantum communication networks.
Attacking Modulation Recognition With Adversarial Federated Learning in Cognitive Radio-Enabled IoT

Internet of Things (IoT) based on cognitive radio (CR) exhibits strong dynamic sensing and intelligent decision-making capabilities by effectively utilizing spectrum resources. The federal learning (FL) framework based modulation recognition (MR) is an essential component, but its use of uninterpretable deep learning (DL) introduces security risks. This paper combines traditional signal interference methods and data poisoning in FL to propose a new adversarial attack approach. The poisoning attack in distributed frameworks manipulates the global model by controlling malicious users, which is not only covert but also highly impactful. The carefully designed pseudo-noise in MR is also extremely difficult to detect. The combination of these two techniques can generate a greater security threat. We have further advanced our proposal with the introduction of the new adversarial attack method called "Chaotic Poisoning Attack" to reduce the recognition accuracy of the FL-based MR system. We establish effective attack conditions, and simulation results demonstrate that our method can cause a decrease of approximately 80% in the accuracy of the local model under weak perturbations and a decrease of around 20% in the accuracy of the global model. Compared to white-box attack methods, our method exhibits superior performance and transferability.
Controllable Inversion of Black-Box Face Recognition Models via Diffusion

Face recognition models embed a face image into a low-dimensional identity vector containing abstract encodings of identity-specific facial features that allow individuals to be distinguished from one another. We tackle the challenging task of inverting the latent space of pre-trained face recognition models without full model access (i.e. black-box setting). A variety of methods have been proposed in literature for this task, but they have serious shortcomings such as a lack of realistic outputs and strong requirements for the data set and accessibility of the face recognition model. By analyzing the black-box inversion problem, we show that the conditional diffusion model loss naturally emerges and that we can effectively sample from the inverse distribution even without an identity-specific loss. Our method, named identity denoising diffusion probabilistic model (ID3PM), leverages the stochastic nature of the denoising diffusion process to produce high-quality, identity-preserving face images with various backgrounds, lighting, poses, and expressions. We demonstrate state-of-the-art performance in terms of identity preservation and diversity both qualitatively and quantitatively, and our method is the first black-box face recognition model inversion method that offers intuitive control over the generation process.
FORMATION OF LEADERSHIP QUALITIES IN THE STUDENTS OF SOCIAL SPECIALTIES: PEDAGOGICAL APPROACHES AND CHALLENGES

Abstract: this article delves into essential pedagogical approaches employed in cultivating leadership qualities among higher education students. Each approach is meticulously characterized, and a comprehensive analysis of pedagogical challenges encountered during the process of nurturing leadership qualities in students is presented. Building upon the examination of these approaches and challenges, distinct features of leadership qualities in professionals within the social sphere are identified. Furthermore, the article provides strategic recommendations for the effective development of these qualities.
Content-based filtering using cosine similarity algorithm for alternative selection on training programs

The large selection of training programs provided by the Ministry of Manpower of the Republic of Indonesia makes it difficult for prospective trainees to choose a training program that suits their interests and needs. The purpose of this research is to support the selection process so that an appropriate method is needed to recommend the selection of training programs that match the interests and needs of users. One of the selection methods that can be used is the Content-Based Filtering method with similarity measurement using Cosine Similarity. The content-based filtering method is a content-based filtering method, which recommends training programs based on the suitability between the description of the training program and the interests of prospective trainees using the cosine similarity distance measurement. The test results using the Content-Based Filtering method were able to achieve an average precision value of 88%, indicating the ability of the system to provide training program recommendations that are very relevant and in accordance with the interests and needs of the trainees.
The Quantum House Of Cards

Quantum computers have been proposed to solve a number of important problems such as discovering new drugs, new catalysts for fertilizer production, breaking encryption protocols, optimizing financial portfolios, or implementing new artificial intelligence applications. Yet, to date, a simple task such as multiplying 3 by 5 is beyond existing quantum hardware. This article examines the difficulties that would need to be solved for quantum computers to live up to their promises. I discuss the whole stack of technologies that has been envisioned to build a quantum computer from the top layers (the actual algorithms and associated applications) down to the very bottom ones (the quantum hardware, its control electronics, cryogeny, etc.) while not forgetting the crucial intermediate layer of quantum error correction.
Protection Motivation Theory in Information Security Behavior Research: Reconsidering the Fundamentals

Scholars commonly use protection motivation theory (PMT) by Rogers to examine information systems (IS) security behaviors and behavioral intentions. A recent influential paper by Boss, Galletta, Lowry, Moody, and Polak (2015; hereafter BGLMP) in MIS Quarterly outlines correct and incorrect uses of PMT in Information Security behavior research. In this paper, we review some of BGLMP’s key recommendations, such as the claim that all IS behavior studies that apply PMT should always use the model of the full theory, contain and measure fear, and measure actual behaviors. We defend an interpretation of Rogers (1975, 1983) that differs from the interpretation that BGLMP propose. We present evidence that Rogers’ PMT and the empirical evidence do not adequately support many of BGLMP’s suggestions and that these suggestions contradict good scientific practices (e.g., restricting the use of the method of isolation) that the philosophy of science and the original literature on PMT uphold. As a result, if reviewers and editors continue to embrace these recommendations, they could hinder the progress of IS behavior research by not allowing isolation or the combination of different theoretical components. In contrast to BGLMP’s paper, we argue that further PMT research can focus on isolated PMT components and combine them with other theories. Some of our ideas (e.g., isolation) are not PMT-specific and could be useful for IS research in general. In summary, we contest BGLMP’s recommendations and offer revised recommendations in return.
COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec

We introduce COOL-CHIC, a Coordinate-based Low Complexity Hierarchical Image Codec. It is a learned alternative to autoencoders with 629 parameters and 680 multiplications per decoded pixel. COOL-CHIC offers compression performance close to modern conventional MPEG codecs such as HEVC and is competitive with popular autoencoder-based systems. This method is inspired by Coordinate-based Neural Representations, where an image is represented as a learned function which maps pixel coordinates to RGB values. The parameters of the mapping function are then sent using entropy coding. At the receiver side, the compressed image is obtained by evaluating the mapping function for all pixel coordinates. COOL-CHIC implementation is made open-source <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model

The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data. Although most existing works are established on the premise of learning from scratch, growing efforts have been devoted to incorporating the benefits of pre-training. However, how to adaptively exploit the pre-trained knowledge for each incremental task while maintaining its generalizability remains an open question. In this work, we present an extensive analysis for continual learning on a pre-trained model (CLPM), and attribute the key challenge to a progressive overfitting problem. Observing that selectively reducing the learning rate can almost resolve this issue in the representation layer, we propose a simple but extremely effective approach named Slow Learner with Classifier Alignment (SLCA), which further improves the classification layer by modeling the class-wise distributions and aligning the classification layers in a post-hoc fashion. Across a variety of scenarios, our proposal provides substantial improvements for CLPM (e.g., up to 49.76%, 50.05%, 44.69% and 40.16% on Split CIFAR-100, Split ImageNet-R, Split CUB-200 and Split Cars-196, respectively), and thus outperforms state-of-the-art approaches by a large margin. Based on such a strong baseline, critical factors and promising directions are analyzed in-depth to facilitate subsequent research. Code has been made available at: https://github.com/GengDavid/SLCA.
금융 지표와 파라미터 최적화를 통한 로보어드바이저 전략 도출 사례

Facing the 4th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine...
Incorporating Fuzzy Membership Functions into Evolutionary Probabilistic Neural Networks

In this contribution a new supervised classification model is proposed, namely the Fuzzy Evolutionary Probabilistic Neural Network (FEPNN). The proposed model in- corporates a fuzzy class membership function into the recently proposed Evolutionary Probabilistic Neural Network (EPNN). EPNN employs an evolutionary algorithm, namely the Particle Swarm Optimization (PSO), for the selection of the spread parameters and prior probabilities of Probabilistic Neural Networks. FEPNN combines ecient and eec- tive evolutionary algorithms as well as techniques from fuzzy set theory. This combination provides an adequate model that achieves similar and superior performance than the well known and widely used Feed Forward Neural Networks (FNNs). FEPNN is applied to a credit card approval task with promising results.
Face Recognition using combined Binary particle swarm optimization and Hidden layer of Artificial Neural Network

Face recognition is one of the challenging domains. We have seen artificial neural network perform very well in both detection and recognition. In this paper, we propose a novel method of feature extraction where features obtained at the end of hidden layer of neural network is utilised. This hidden layer output is our first level of features. On these features, we apply binary particle swarm optimisation (BPSO) to remove the redundancy, the few hidden units in the network. BPSO over hidden layer outputs can be implemented in two ways: 1) to apply BPSO over hidden layer in the training stage so the network is better optimised; 2) to directly use the BPSO on an optimised neural network's hidden layer output. Both the techniques performed well over traditional neural network and conventional BPSO. Experiments on FERET and LFW datasets show promising results.
Reversible data hiding methods in Integer Wavelet Transform

Reversible data hiding is art of concealing secret information such that cover media and secret information are both recovered without any information loss. In this paper high frequency sub-bands of integer wavelet transform are used for data embedding. All coefficients are used for embedding and to improve the security the embedding is carried out in frequency domain using spiral, sequential and random embedding method. The main objective of this research is to hide the maximum data with minimal distortion and to attain reversible hiding phenomenon both in cover and secret image. The experimental result shows the improved capacity, imperceptibility and complete reversibility attained on standard and medical images. The parameter of robustness has not been vastly studied for reversible data hiding and an attempt is made to check the same for basic attacks and results shows that it can withstand geometrical attack.
Impact of Clustering on quality of Recommendation in Cluster based Collaborative Filtering: an Empirical Study

In memory nearest neighbour computation is a typical approach for collaborative filtering (CF) due to its high recommendation accuracy. However, this approach fails on scalability; which is the declined performance of the same due to the rapid increase in the number of users and items in archetypal merchandising applications. One of the popular techniques to attenuate scalability issue is cluster-based collaborative filtering (CBCF), which uses clustering approach to group most similar users/items from complete dataset. In this work we present a detailed analysis of the impact of clustering in CF approach. Specifically, we study how the extent of clustering impacts collaborative filtering systems in terms of quality of predictions, quality of recommendations, throughput and coverage. Based on the empirical results obtained from two datasets, Movielens100K and Jester; we conclude that with increasing number of clusters the quality of predictions, the quality of recommendations and the throughput are enhanced but the coverage provided by clustered subsystems declines.
Fast Causal Division for Supporting Robust Causal Discovery

Discovering the causal relationship from the observational data is a key problem in many scientific research fields. However, it is not easy to detect the causal relationship by using general causal discovery methods among large scale data, due to the curse of the dimension. Although some causal dividing frameworks are proposed to alleviate these problems, they are, in fact, also faced with high dimensional problems. In this work, we propose a split-and-merge method for causal discovery. The original dataset is firstly divided into two smaller subsets by using low-order CI tests, and then the subsets are further divided into a set of smaller subsets. For each subset, we employ the existing causal learning method to discovery the corresponding structures, by combined all these structures, we finally obtain the complete causal structure. Various experiments are conducted to verify that compared with other methods, it returns more reliable results and has strong applicability.
Compensation System and Job Satisfaction: The Application of the Job Descriptive Index in the Public-Sector of Jordan

The current study examined the effect of compensation system in the prediction of employee job satisfaction within the public-sector of Jordan through using the job-descriptive-index (JDI). Data was collected through questionnaire across (314) respondents. Structural-equation-modelling applied and following the completion of the analysis, direct-monetary compensation was found to be partially related to JDI dimensions, whilst, in contrary to expectations indirect-monetary compensation were seen to have no role in achieving job satisfaction, further, non-monetary compensation significantly influenced JDI dimensions of satisfaction, in addition to that, demographic characteristics were found to be significant in affecting job satisfaction. Such results indicate an issue with the provided compensation system that need to be addressed by the public-sector administrative. Further works need to be carried out in order to explore how provided compensation could be effective booster for employee satisfaction especially in the context of Jordan.
The effect of knowledge-based information technology tools on the new product development processes in software companies

Software development is a knowledge intensive process and its success depends on the management of knowledge effectively. Knowledge management (KM) tools play a critical role in this challenge. The main purpose of the study is to investigate the effect of knowledge-based information technology tools on the new product development processes in software companies. The domain of the research covers knowledge-based software companies. The data of the research were collected by a questionnaire based on five-spectrum Likert from 138 experts who have worked in the field of production, development and establishment and support of software. Findings shown that there is a meaningful correlation between knowledge-based IT tools and the NPD processes in a way that IT tools have significant effect on the implementation and to support phases of the NPD.
Implicit feature identification for opinion mining

In opinion mining area, mining consumer reviews can give a finer-grained understanding of consumer needs, which can efficiently help companies and merchants to improve the quality of their products and services. However, identifying features on which consumers express their opinions and sentiments is not always a simple task. Some existing approaches that attempt to extract implicit features using opinion words as clues or co-occurrence techniques lead to unsatisfactory results, and that due to the ambiguity caused by common opinion words which are often expressed on various features. In this paper, we propose an approach based on Association Rule Mining (ARM) and classification techniques. The first step consists of creating from a corpus, a set of association rules regrouping explicit feature-opinion pairs. The second step consists to use this set to build a classification model able to predict for each given set of opinion words the appropriate target. Tested on many classifiers, experimental results show that our approach performs better when incorporating many opinion words rather than using single ones.
Assessing agile project management practices: the case of Palestinian software development companies

This paper analyses the readiness of agile project management (APM) implementation in software development companies in the context of Palestine; by assessing changes required for transforming projects into agile and challenges and risks involved in the transformation, in addition to exploring APM success factors. A case study methodology using mixed methods technique was adopted. Semi-structured interviews and questionnaire data from 67 different companies were used. Although data analysis shows that there is a high degree of implementation of APM practices, the results show that software development companies have a moderate level of ability to deal with challenges and risks of the APM processes. A managerial framework was developed to enhance software development companies' working in the context of a Middle East country to properly adopt the practices of APM. The framework identifies determinants of a successful APM process realisation that will help software development companies maintain a sustainable growth.
Gender Classification based on similarity features through SURF and SVM

The recognisable proof of people in view of their biometric body parts, for example, face, fingerprint, walk, iris, and voice, assumes an imperative part in electronic applications and has turned into a prominent territory of research in image pre-processing. It is likewise a standout amongst the best utilisations of computer-human interaction and understanding. Out of all the previously mentioned body parts, the face is one of most well known qualities in view of its extraordinary feature. In reality, people can process a face in an assortment of approaches to characterise it by its personality, alongside various different attributes. In this paper, we proposed a new algorithm to extract the facial features using SURF algorithm, features are invariant to extract affine transformations are extracted from each face using speeded up robust features (SURF) method (Morteza and Yousefi, 2011) and shows best accuracy on real-time face images compared with different licence datasets like ORL database and FGNet database and with different training ratios by using SVM algorithm (Rahman et al., 2013; Moghaddam and Yang; 2000; Swaminathan, 2000).
Visualizing Semantic Structures of Sequential Data by Learning Temporal Dependencies.

While conventional methods for sequential learning focus on interaction between consecutive inputs, we suggest a new method which captures composite semantic flows with variable-length dependencies. In addition, the semantic structures within given sequential data can be interpreted by visualizing temporal dependencies learned from the method. The proposed method, called Temporal Dependency Network (TDN), represents a video as a temporal graph whose node represents a frame of the video and whose edge represents the temporal dependency between two frames of a variable distance. The temporal dependency structure of semantic is discovered by learning parameterized kernels of graph convolutional methods. We evaluate the proposed method on the large-scale video dataset, Youtube-8M. By visualizing the temporal dependency structures as experimental results, we show that the suggested method can find the temporal dependency structures of video semantic.
Reconfigurable design and implementation of nonlinear Boolean function for cloud computing security platform

Nonlinear Boolean function plays a pivotal role in the stream cipher algorithms and cloud computing security platforms. Based on the analysis of multiple algorithms, this paper proposes a hardware structure of reconfigurable nonlinear Boolean function. This structure can realise the number of variables and AND terms less than 80 arbitrary nonlinear Boolean function in stream cipher algorithms. The entire architecture is verified on the FPGA platform and synthesised under the 0.18 μm CMOS technology, the clock frequency reaches 248.7 MHz, the result proves that the design is propitious to carry out the most nonlinear Boolean functions in stream ciphers which have been published, compared with other designs, the structure can achieve relatively high flexibility, and it has an obvious advantage in the area of circuits and processing speed.