Reviewerly/exHarmony huggingface dataset:
Tainted Love: A Systematic Review of Online Romance Fraud

Romance fraud involves cybercriminals engineering a romantic relationship on online dating platforms. It is a cruel form of cybercrime whereby victims are left heartbroken, often facing financial ruin. We characterise the literary landscape on romance fraud, advancing the understanding of researchers and practitioners by systematically reviewing and synthesising contemporary qualitative and quantitative evidence. The systematic review provides an overview of the field by establishing influencing factors of victimhood and exploring countermeasures for mitigating romance scams. We searched ten scholarly databases and websites using terms related to romance fraud. Studies identified were screened, and high-level metadata and findings were extracted, synthesised, and contrasted. The methodology followed the PRISMA guidelines: a total of 232 papers were screened. Eighty-two papers were assessed for eligibility, and 44 were included in the final analysis. Three main contributions were identified: profiles of romance scams, countermeasures for mitigating romance scams, and factors that predispose an individual to become a scammer or a victim. Despite a growing corpus of literature, the total number of empirical or experimental examinations remained limited. The paper concludes with avenues for future research and victimhood intervention strategies for practitioners, law enforcement, and industry.
Q-Map: Quantum Circuit Implementation of Boolean Functions

Quantum computing has gained attention in recent years due to the significant progress in quantum computing technology. Today many companies like IBM, Google and Microsoft have developed quantum computers and simulators for research and commercial use. The development of quantum techniques and algorithms is essential to exploit the full power of quantum computers. In this paper we propose a simple visual technique (we call Q-Map) for quantum realisation of classical Boolean logic circuits. The proposed method utilises concepts from Boolean algebra to produce a quantum circuit with minimal number of quantum gates.
Optimization and Amplification of Cache Side Channel Signals

In cache-based side channel attacks, an attacker infers information about the victim based on the presence, or lack thereof, of one or more cachelines. Determining a cacheline's presence, which we refer to as "reading the signal", typically requires testing the access time of the line using a suitably high precision timer. In this paper we introduce novel gadgets which leverage CPU speculation to enable modification of these signals, before they are read, for a variety of purposes. First, these gadgets enable an attacker to optimize cache-based side channel attacks by evaluating arbitrary logic functions on cacheline signals prior to their measurement. Second, we demonstrate amplification techniques that enable an attacker to read a signal even if no high precision timer is available. Combined, these techniques can be used to improve existing side channel attacks even if timer access is limited. We evaluate the effectiveness of these techniques on a modern x86 CPU and demonstrate that when properly tuned, cache side channel signals can be reliably modified with near 100% accuracy and are able to be read with a timer as coarse as 100ms or more.
Texture-Based Input Feature Selection for Action Recognition

The performance of video action recognition has been significantly boosted by using motion representations within a two-stream Convolutional Neural Network (CNN) architecture. However, there are a few challenging problems in action recognition in real scenarios, e.g., the variations in viewpoints and poses, and the changes in backgrounds. The domain discrepancy between the training data and the test data causes the performance drop. To improve the model robustness, we propose a novel method to determine the task-irrelevant content in inputs which increases the domain discrepancy. The method is based on a human parsing model (HP model) which jointly conducts dense correspondence labelling and semantic part segmentation. The predictions from the HP model also function as re-rendering the human regions in each video using the same set of textures to make humans appearances in all classes be the same. A revised dataset is generated for training and testing and makes the action recognition model exhibit invariance to the irrelevant content in the inputs. Moreover, the predictions from the HP model are used to enrich the inputs to the AR model during both training and testing. Experimental results show that our proposed model is superior to existing models for action recognition on the HMDB-51 dataset and the Penn Action dataset.
HyScale-GNN: A Scalable Hybrid GNN Training System on Single-Node Heterogeneous Architecture

Graph Neural Networks (GNNs) have shown success in many real-world applications that involve graph-structured data. Most of the existing single-node GNN training systems are capable of training medium-scale graphs with tens of millions of edges; however, scaling them to large-scale graphs with billions of edges remains challenging. In addition, it is challenging to map GNN training algorithms onto a computation node as state-of-the-art machines feature heterogeneous architecture consisting of multiple processors and a variety of accelerators. We propose HyScale-GNN, a novel system to train GNN models on a single-node heterogeneous architecture. HyScale- GNN performs hybrid training which utilizes both the processors and the accelerators to train a model collaboratively. Our system design overcomes the memory size limitation of existing works and is optimized for training GNNs on large-scale graphs. We propose a two-stage data pre-fetching scheme to reduce the communication overhead during GNN training. To improve task mapping efficiency, we propose a dynamic resource management mechanism, which adjusts the workload assignment and resource allocation during runtime. We evaluate HyScale-GNN on a CPU-GPU and a CPU-FPGA heterogeneous architecture. Using several large-scale datasets and two widely-used GNN models, we compare the performance of our design with a multi-GPU baseline implemented in PyTorch-Geometric. The CPU-GPU design and the CPU-FPGA design achieve up to 2.08x speedup and 12.6x speedup, respectively. Compared with the state-of-the-art large-scale multi-node GNN training systems such as P3 and DistDGL, our CPU-FPGA design achieves up to 5.27x speedup using a single node.
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation

Nash Q-learning may be considered one of the first and most known algorithms in multi-agent reinforcement learning (MARL) for learning policies that constitute a Nash equilibrium of an underlying general-sum Markov game. Its original proof provided asymptotic guarantees and was for the tabular case. Recently, finite-sample guarantees have been provided using more modern RL techniques for the tabular case. Our work analyzes Nash Q-learning using linear function approximation -- a representation regime introduced when the state space is large or continuous -- and provides finite-sample guarantees that indicate its sample efficiency. We find that the obtained performance nearly matches an existing efficient result for single-agent RL under the same representation and has a polynomial gap when compared to the best-known result for the tabular case.
BP-NTT: Fast and Compact in-SRAM Number Theoretic Transform with Bit-Parallel Modular Multiplication

Number Theoretic Transform (NTT) is an essential mathematical tool for computing polynomial multiplication in promising lattice-based cryptography. However, costly division operations and complex data dependencies make efficient and flexible hardware design to be challenging, especially on resource-constrained edge devices. Existing approaches either focus on only limited parameter settings or impose substantial hardware overhead. In this paper, we introduce a hardware-algorithm methodology to efficiently accelerate NTT in various settings using in-cache computing. By leveraging an optimized bit-parallel modular multiplication and introducing costless shift operations, our proposed solution provides up to 29x higher throughput-per-area and 2.8-100x better throughput-per-area-per-joule compared to the state-of-the-art.
Feature Extraction Matters More: Universal Deepfake Disruption through Attacking Ensemble Feature Extractors

Adversarial example is a rising way of protecting facial privacy security from deepfake modification. To prevent massive facial images from being illegally modified by various deepfake models, it is essential to design a universal deepfake disruptor. However, existing works treat deepfake disruption as an End-to-End process, ignoring the functional difference between feature extraction and image reconstruction, which makes it difficult to generate a cross-model universal disruptor. In this work, we propose a novel Feature-Output ensemble UNiversal Disruptor (FOUND) against deepfake networks, which explores a new opinion that considers attacking feature extractors as the more critical and general task in deepfake disruption. We conduct an effective two-stage disruption process. We first disrupt multi-model feature extractors through multi-feature aggregation and individual-feature maintenance, and then develop a gradient-ensemble algorithm to enhance the disruption effect by simplifying the complex optimization problem of disrupting multiple End-to-End models. Extensive experiments demonstrate that FOUND can significantly boost the disruption effect against ensemble deepfake benchmark models. Besides, our method can fast obtain a cross-attribute, cross-image, and cross-model universal deepfake disruptor with only a few training images, surpassing state-of-the-art universal disruptors in both success rate and efficiency.
Convolutional Visual Prompt for Robust Visual Perception

Vision models are often vulnerable to out-of-distribution (OOD) samples without adapting. While visual prompts offer a lightweight method of input-space adaptation for large-scale vision models, they rely on a high-dimensional additive vector and labeled data. This leads to overfitting when adapting models in a self-supervised test-time setting without labels. We introduce convolutional visual prompts (CVP) for label-free test-time adaptation for robust visual perception. The structured nature of CVP demands fewer trainable parameters, less than 1\% compared to standard visual prompts, combating overfitting. Extensive experiments and analysis on a wide variety of OOD visual perception tasks show that our approach is effective, improving robustness by up to 5.87% over several large-scale models.
SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective

Researchers have proposed various methods for visually interpreting the Convolutional Neural Network (CNN) via saliency maps, which include Class-Activation-Map (CAM) based approaches as a leading family. However, in terms of the internal design logic, existing CAM-based approaches often overlook the causal perspective that answers the core "why" question to help humans understand the explanation. Additionally, current CNN explanations lack the consideration of both necessity and sufficiency, two complementary sides of a desirable explanation. This paper presents a causality-driven framework, SUNY, designed to rationalize the explanations toward better human understanding. Using the CNN model's input features or internal filters as hypothetical causes, SUNY generates explanations by bi-directional quantifications on both the necessary and sufficient perspectives. Extensive evaluations justify that SUNY not only produces more informative and convincing explanations from the angles of necessity and sufficiency, but also achieves performances competitive to other approaches across different CNN architectures over large-scale datasets, including ILSVRC2012 and CUB-200-2011.
Improved Quantum Query Complexity on Easier Inputs

Quantum span program algorithms for function evaluation sometimes have reduced query complexity when promised that the input has a certain structure. We design a modified span program algorithm to show these improvements persist even without a promise ahead of time, and we extend this approach to the more general problem of state conversion. As an application, we prove exponential and superpolynomial quantum advantages in average query complexity for several search problems, generalizing Montanaro's Search with Advice [Montanaro, TQC 2010].
Speeding Up EfficientNet: Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer Learning

The performance of convolutional neural networks (CNN) depends heavily on their architectures. Transfer learning performance of a CNN relies quite strongly on selection of its trainable layers. Selecting the most effective update layers for a certain target dataset often requires expert knowledge on CNN architecture which many practitioners do not posses. General users prefer to use an available architecture (e.g. GoogleNet, ResNet, EfficientNet etc.) that is developed by domain experts. With the ever-growing number of layers, it is increasingly becoming quite difficult and cumbersome to handpick the update layers. Therefore, in this paper we explore the application of genetic algorithm to mitigate this problem. The convolutional layers of popular pretrained networks are often grouped into modules that constitute their building blocks. We devise a genetic algorithm to select blocks of layers for updating the parameters. By experimenting with EfficientNetB0 pre-trained on ImageNet and using Food-101, CIFAR-100 and MangoLeafBD as target datasets, we show that our algorithm yields similar or better results than the baseline in terms of accuracy, and requires lower training and evaluation time due to learning less number of parameters. We also devise a metric called block importance to measure efficacy of each block as update block and analyze the importance of the blocks selected by our algorithm.
Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems

We introduce two new classes of exact Markov chain Monte Carlo (MCMC) samplers for inference in latent dynamical models. The first one, which we coin auxiliary Kalman samplers, relies on finding a linear Gaussian state-space model approximation around the running trajectory corresponding to the state of the Markov chain. The second, that we name auxiliary particle Gibbs samplers corresponds to deriving good local proposals in an auxiliary Feynman--Kac model for use in particle Gibbs. Both samplers are controlled by augmenting the target distribution with auxiliary observations, resulting in an efficient Gibbs sampling routine. We discuss the relative statistical and computational performance of the samplers introduced, and show how to parallelise the auxiliary samplers along the time dimension. We illustrate the respective benefits and drawbacks of the resulting algorithms on classical examples from the particle filtering literature.
An improved point-line incidence bound over arbitrary finite fields via the VC-dimension theory

The main purpose of this paper is to prove that the point-line incidence bound due to Vinh (2011) over arbitrary finite fields can be improved in certain ranges by using tools from the VC-dimension theory. As consequences, a number of applications will be discussed in detail.
Architecture for enduring knowledge-extraction from online social networks

Nowadays social networks and media play significant role in daily life. All our life in the real world is recorded in the digital space as well. Scientists have enormous potential in researching issues such as social influence ontop news and top news influence on society. Its impact on daily life spans such diverse areas as digital marketing, publicopinion analysis, political monitoring and disaster notification. Any task of processing such a large data stream needs acoherent architecture that will fit the analyzed resource. In the presented work, we set ourselves the task of creating ahighly loaded, fault-tolerant, scalable system for extracting and processing data from various social networks and analyzing data in real time. The solution is architecture in the form of a set of modules. Modules have their own characteristics depending on the work performed, from collecting textual data to direct processing and extraction of knowledge.
Evaluations of the service robot OSONO referring to Joruri puppets and a remote evaluation system with operation and directing elements

One of the important requirements for service robots is attracting people by attrac-tiveness and to be able to exchange messages with people. With reference to the tradi-tional Japanese puppet show, Ningyo Joruri, we have independently developed OSONO, which is a physical robot, with high-quality choreography, we have exhibited it through large-scale exhibitions and evaluated it by the ordinary person. In this paper, we report questionnaire evaluations on OSONO targeting a puppeteer expert group and compare them with existing results targeting the ordinary person. This shows that the method of creating OSONO and its choreography is effective in widely general. Additionally, it is verified that the same result can be obtained in the remote evaluation with the robot operation. In this paper, we evaluate the effect by adding the technique of Ningyo- Joruri to the operation and sound effects of OSONO, and analyze the influence of the quality of the real-time image transferring quality. Through these results, we propose effective system design guidelines for displaying robots online.
Rule Generation from Several Types of Table Data Sets and Its Application: Decision-Making with Transparency and an Improved Execution Environment

This paper copes with rule generation from table data sets and applies the obtained rules to decision support. Here, two types of table data sets are considered. One type of them is specified as a Deterministic Information System (DIS). The other type is specified as a Non-deterministic Information System (NIS) for dealing with incomplete information. Two rule generation algorithms are refined and newly implemented in Python. Every obtained rule is applied as evidence of decision-making. Therefore, the reasoning process preserves its transparency, which will be an essential characteristic for Explainable AI. The decision support environment is strengthened due to some described improvements and is also brushed up in Python. Some running videos of Python are available on the web page. This framework applies to almost any table data set, and we can generate rules from them. This framework based on discrete data will complement statistical data analysis based on numerical data.
constrained recursion algorithm for tree-structured LSTM with mini-batch SGD

Tree-structured LSTM is a promising concept to consider long-distance interaction over hierarchies with syntactic information. Besides, compared with chain-structured one, tree-structured LSTM has better modularity of the learning process. However, there still remains the challenge concerning hyperparameter tuning in tree-structured LSTM. Mainly, hyperparameter of mini-batch SGD (Stochastic Gradient Descent) is one of the most important factors which decides the quality of the prediction of LSTM. For more sophisticated hyperparameter tuning of mini-batch SGD, we propose a constrained recursion algorithm of tree-structured LSTM. Our algorithm enables the program to generate an LSTM tree for each batch. By doing this, we can evaluate the tuning of hyperparameter of mini-batch size more correctly compared with chain-structured one. Besides, our constrained recursion algorithm can traverse the LSTM and update the weights over several LSTM tree with a breadth-first search. In the experiment, we have measured the validation loss and elapsed time in changing the size of mini-batch. We have succeeded in measuring the learning process’s stability with small batch size and the instability of overfitting with large batch size more precisely than chain-structured LSTM.
FORMATION OF MOTIVATION OF STUDENTS FOR PROFESSIONAL SELF-IMPROVEMENT IN THE CONDITIONS OF DISTANCE LEARNING

The article is devoted to the formation of motivation of students to professional development.The effectiveness of the educational process is directly related to the motivation and incentive to master the future profession.The urgency of this problem is exacerbated in the current crisis.The main factor that affects the motivation to study students in distance learning is the acquisition of knowledge, skills and abilities that will further form the basis of their professional activities.Increasing the motivation of educational activities is facilitated by: teaching methods, ways of learning the educational material, its content; the use of problematic motivations and tasks at each stage of the lesson; the use of interactive and communication technologies.Motivation of students in distance learning and its support throughout the educational process is a problem that teachers (teachers, researchers) and administration of educational institutions should pay attention to.In today's conditions, despite the fact that there are many approaches and developed models to increase and maintain motivation; involvement in educational processes, it is clear that there is not enough work and effective, efficient strategies that could be applied in distance learning.The authors conducted several surveys using Google form, which were conducted on the basis of Vinnytsia National Agrarian University, Uzhhorod National University and the Municipal Higher Educational Institution "Rivne Medical Academy".The survey was attended by 90 applicants for the first (bachelor's) level of education.In the first survey, students had to evaluate different categories of motivational factors on a 5-point scale.The second part of the survey was aimed at determining the possibility of professional self-improvement in the conditions of distance learning for bachelor's degree students.The authors found that learning is more effective when it is motivated, and the activities of students coincide in orientation and internal motives.As a result, the professional training of a graduate of an educational institution becomes successful.The motivation of students for professional self-improvement in the process of distance learning should be focused on the active activity of students, extensive practical training, the involvement of information and communication technologies, stimulation by scholarships and the promotion of positive emotions -then motivation is maintained, and the volume and strength of the knowledge gained reaches the maximum level.
METODE CALISNA HIFAL MENINGKATKAN KEMAMPUAN MEMBACA ANAK USIA DINI DI ERA PANDEMI COVID-19

Children are successors on earth, every child born has various abilities. The impact of the Covid-19 pandemic has resulted in parents not optimally assisting their children in learning, especially learning to read. Reading is an important component thatmust be developed for early childhood. The need for an effective method to trigger talent, interest in early childhood development in reading and understanding their environment. For this reason, the calisna hifal method creates a leraning pattern that is interactive, active and collaborative. This study uses a descriptive qualitative research method by examining 30 children aged 0-8 years for 10 months and the results of the analysis conclude that the calisna hifal method can effectively improve early childhood reading skills in the Covid-19 pandemic era.