Reviewerly/exHarmony huggingface dataset:
Exploring Adversarial Graph Autoencoders to Manipulate Federated Learning in The Internet of Things

Mobile edge computing (MEC) enables the Internet of Things (IoT) with seamless integration of multiple application services. Federated learning is increasingly considered to improve training accuracy in MEC-IoT while circumventing the disclosure of private data, where the IoT nodes collaboratively train a machine learning model without disclosing their private data. In this paper, we propose a new cyber-epidemic attack that progressively manipulates federated learning and reduces the training accuracy of the benign MEC-IoT. The proposed cyber-epidemic attack explores adversarial graph autoencoders (GACE) to generate malicious local model updates that extract correlated features with the benign local and global models. The proposed GACE attack epidemically infects all the benign IoT nodes along with the training iterations in federated learning, while highly enhancing concealment of the attack.
A Human-in-the-loop Workflow for Multi-Factorial Sensitivity Analysis of Algorithmic Rankers

Algorithmic rankers are ubiquitously applied in automated decision systems such as hiring, admission, and loan-approval systems. Without appropriate explanations, decision-makers often cannot audit or trust algorithmic rankers' outcomes. In recent years, XAI (explainable AI) methods have focused on classification models, but there for algorithmic rankers, we are yet to develop state-of-the-art explanation methods. Moreover, explanations are also sensitive to changes in data and ranker properties, and decision-makers need transparent model diagnostics for calibrating the degree and impact of ranker sensitivity. To fulfill these needs, we take a dual approach of: i) designing explanations by transforming Shapley values for the simple form of a ranker based on linear weighted summation and ii) designing a human-in-the-loop sensitivity analysis workflow by simulating data whose attributes follow user-specified statistical distributions and correlations. We leverage a visualization interface to validate the transformed Shapley values and draw inferences from them by leveraging multi-factorial simulations, including data distributions, ranker parameters, and rank ranges.
Visualizing a Tabular Data Repository to Facilitate Descriptive Tag Augmentation for New Tables

Many online tabular datasets are maintained in centralized repositories and annotated with descriptive tags. These tags are helpful for data practitioners to search and understand tables. However, manually annotating descriptive tags for new tables added to a large repository is expensive and may be inconsistent. In this extended abstract, we propose tag inference methods and implement an interactive visual explainer prototype to visualize a table repository with respect to a new table and to help a human user examine whether a recommended tag is suitable for the new table.
Digitalization in Education and Learning Activities

The article deals with the content of the process of formation of noospheric-aesthetic images in younger schoolchildren. The possibilities of using digital technologies in their formation are analyzed, the features of the use of mind maps and quests in primary school are shown. The article is intended for primary school teachers, students, educators.
Statistical Approach in Relation to the Study of Compatibility

The extraction of collocations is one of the urgent tasks in the processing natural language, the results of which are important and in demand in various fields of applied linguistics. Our study compares three statistical measures carried out on a subset of The English Trends Corpus. The corpus has a huge methodological potential, so its use in foreign language lessons will allow students to cope with the solution of many educational and scientific problems, significantly expand their understanding of language as a changing phenomenon. Automatically extracted verb collocations in which the semantically main component was the search query word take were considered. During experiments studied the differences between statistical metrics, and also an attempt was made to find the most effective of them. The Pearson rank correlation coefficient between the measures was calculated. The results obtained show that some measures show similarity and relative interchangeability.
Entaglement and communication in digital qubits using FPGAs

Abstract This paper focuses on usage of digital qubits on a digital quantum computing platform implemented on FPGAs. Modifications have been made to existing digital qubit standards to account for complex probability amplitudes and not only real ones. This paper introduces an improved transient effect ring-oscillator based TRNG used in the scope of this project. The objective of this was to simulate the addition of environmental noise in a digital setting. Using the newly designed digital qubits, a few quantum logic gates have been designed to work on the FPGA platform using Verilog. These logic gates have then been used to implement entanglement on a digital hardware platform. Furthermore, this paper implements communication between two digital quantum computers over both wired and wireless media by transmitting alphabetical messages as qubits and compares the same with transmission involving classical digital bits only.
Analysis of Flight Performance and Efficiency according to the Number of Consecutive Flight of Navy Pilots

In the case of the Navy, if some of the co-pilots are included in the long-term promotion process due to the limited number of co-pilots, operational flight and administrative tasks will be added to the co-pilots not included in the rest of the Pilot in commander process. Therefore, to solve this problem, the co-pilot who has passed the PQS step-by-step process minimizes the personnel gap in the flight operation unit through a system that evaluates whether it is possible to perform its duties as a co-pilot through actual flight after entering the school. The advantage of the PQS course is that you can control flight plans on your own and minimize gaps in flight and ground work while carrying out the curriculum, but you can
Poetry Writing Skills for Grade IV SDIT ALIF Students Using the Acrostic Technique

Background: The ability to write is a human effort to express thoughts and emotions contained in written language. Writing poetry is a productive activity and the ability to write poetry can be achieved through systematic teaching and intensive training. Learning poetry provides many benefits for students. Students can express themselves and expand their vocabulary. In addition, students know how to use language to understand, develop, and convey ideas and information to readers. Purpose: The purpose of this study was to determine the poetry writing skills of fourth-grade students at SDIT ALIF after using the acrostic technique. Design and methods: This research was compiled using a descriptive method based on analysis guidelines through a qualitative approach. Qualitative descriptive is the term used in qualitative research for descriptive research. The researcher's analysis technique uses observation and document analysis. Results: This study shows that the results of the analysis of the poetry writing skills of fourth grade students at SDIT ALIF with the acrostic technique are students who match the skills of writing acrostic poetry by 75%. Students who do not fit the skills of writing acrostic poetry 25%. Conclusion (1) From the results of the analysis of the Poetry Writing Skills of Grade IV Students at SDIT ALIF using the acrostic technique, it was found that 75% of the 61 samples fit the skills of writing acrostic poetry. In the activity of writing poetry, class IV SDIT ALIF students using the acrostic technique carried out quite well, but there were some students who were still lacking in writing acrostic poetry, such as the discrepancy between the word patterns written vertically and the suitability of the content. and title and unable to express ideas. his thoughts into poetry. (2) This research was said to be quite successful because 75% of the 60 samples of poetry written by children were able to express their ideas, experiences, thoughts, expressions, and imagination into well-structured writing so as to give a good impression. readers' ideas about the meaning or message of the posts that have been written
IMPACT ON THE MARKETCAPITALIZATION OF BLOCKCHAIN-BASED CRYPTOCURRENCIES

This paper presents an advanced econometric model specifically designed to analyze the intricate relationship between blockchain technology and various economic variables. The model serves as a robust framework for comprehending the impact of blockchain on investment patterns, adoption rates, and market trends. By quantifying these relationships, the model enables predictions regarding future trends in the blockchain industry and facilitates the identification of factors influencing growth or hindering adoption. With its wide-ranging applicability, the model offers profound insights for policymakers, investors, entrepreneurs, and researchers, shedding light on the economic implications of this rapidly evolving technology.The findings of this study reveal a multitude of significant insights regarding the economic implications of blockchain technology. The econometric model demonstrates a strong positive relationship between blockchain investment and adoption rates, indicating that increased investment leads to higher adoption levels. Moreover, the model identifies specific market trends and factors that influence the growth and adoption of blockchain technology. By highlighting these factors, stakeholders can make informed decisions and strategize accordingly.The econometric model forblockchain technology offers numerous applications and implications for various stakeholders. Policymakers can leverage the model's insights to develop regulatory frameworks that foster blockchain innovation while mitigating risks. Investors can utilize the model to make data-driven investment decisions and identify lucrative opportunities within the blockchain industry. Entrepreneurs can gain valuable insights into the factors driving adoption and tailor their business strategies accordingly. Additionally, researchers can expand their understanding of the relationship between technology and economic variables, contributing to the development of new theories and frameworks.
Automatic Graphical User Interface aesthetic evaluation tool using the UIED segmentation algorithm

In this work, we deal with the issue of automating the evaluation of the aesthetics of the user's applications interfaces. The aesthetics of the user interface is a sub-factor of software quality. The aesthetics of the interface provides several advantages to the users, such as the attraction of the interface, an impact on the usability of the interface, a great pleasure of the interface's use, etc. That is why we deal with the evaluation of the aesthetics interfaces application. Our goal is to suggest a tool for quantitative evaluation of user interfaces using metrics. That is why we have developed a tool for evaluating the aesthetics of application interfaces that takes screenshots of mobile and desktop applications. This tool also generates metrics as output. For the segmentation of the different components of the interfaces we used the UIED algorithm which is an algorithm for the automatic segmentation of images. Our tool uses 10 of the 14 metrics of Ngo et al [1]. We generated the values with our tool by taking the images of a site. We used a total of 351 images. The validation of our tool was done by comparing the data calculated by our tool to those of QUESTIM. We used Pearson correlation test to perform the comparison and we obtained a correlation rate of 0.91. This means that our tool and QUESTIM evaluate the interfaces in almost the same way.
Occluded Person Re-Identification using Region-based Convolutional Neural Networks

The ability to continuously follow a target person in a dynamically changing environment remains a major challenge that indoor companion robots confront. Ongoing human following is complicated by close similarity matching and target occlusions. Person Re-Identification (re-ID) is a computer vision task that can help with the problem of intentional or unintentional occlusion of the subject or person of interest. Numerous research has been conducted on re-ID where the basic assumption is that the gallery set will only have non-occluded person images. This paper examines the problem from a human detection perspective and surveys the state-of-the-art of person re-ID, their achievement and shortcomings. A region-based convolutional neural network (R-CNN) Re-ID is proposed in this research. The findings from the prototyping and evaluation demonstrate that the proposed person Re-ID model is reliable for indoor re-ID situations. Thus, future works in this direction include investigating other factors, such as the number of persons in a scene and varying viewing angles of the probed target person, which can impact the performance of the proposed re-ID model.
Multi-Scale Dynamic Fixed-Point Quantization and Training for Deep Neural Networks

State-of-the-art deep neural networks often require extremely high computational power which results in the deployment of deep neural networks on embedded devices being impractical. Therefore, model quantization is important for the deployment of deep neural networks on edge devices. The purpose of this paper is to quantize the deep neural networks from high-precision to low-precision (e.g. INT8) dynamic fixed-point format at the layer-by-layer level quantization. In addition, we further improve the uniform dynamic fixed-point quantization to multi-scale dynamic fixed-point quantization for lower quantization loss. The proposed multi-scale dynamic fixed-point quantization scheme divides the quantization ranges into two regions, and each region is assigned different quantization levels and quantization parameters to better approximate the bell-shaped distributions. The proposed quantization pipeline is composed of post-training quantization followed by model fine-tuning which can keep the accuracy drop of the quantized model within 1% mean average precision (mAP). Furthermore, the proposed quantization and fine-tuning method can be combined with model pruning to obtain a compact and accurate deep neural network with low bit-width.
iWavePro: An improved framework for iWave++

End-to-end image coding methods based on wavelet-like transform have made great progress in recent years. The most advanced one is iWave++, which adopts multi-level lifting schemes based on convolutional neural networks. However, iWave++ still has many unresolved problems. First, the independent entropy coding of each component makes it impossible to use the correlation between components better. Secondly, additive wavelet transform limits the nonlinear ability of learnable wavelet transform. Moreover, the offline training strategy makes the iWave++ unable to adjust according to the content. In this paper, we propose an improved framework for iWave++ called iWave-Pro. iWavePro is designed with several techniques to overcome the problems mentioned above. These techniques are the joint multi-component Gaussian mixture entropy coding, the affine wavelet-like transform, and the online training. Experimental results show that our method can save 10.73% bit rate compared with iWave++ at the same quality.
NeuralPV: A Neural Network Algorithm for PV Power Forecasting

Photovoltaic (PV) forecasting plays a major role in residential and industrial PV installation as well as penetration with the grid. An inaccurate PV power forecasting may result in increased monetary and energy losses. This study proposes a metaheuristic-based strategy for accurate PV power forecasting using a heuristic-based data-driven PV model. The proposed algorithm integrates a dense explorative strategy with the existing PV equation knowledge by a multilayer perceptron (MLP) network with Sigmoid activation functions to predict the best coefficients for the inputs of the data-driven PV model. The proposed method is compared to a recently proposed metaheuristic algorithm, the artificial hummingbird optimizer algorithm (AHOA). The comparison is performed for inside distribution (ID) and out-of-distribution (OOD) irradiance datasets and with varying temperatures. The results prove that the proposed NN-based algorithm achieves higher accuracy in PV power parameter prediction and hence forecasting.
Real-time Traffic Classification in Encrypted Wireless Communication Network

Classification of traffic service types is a valuable function for wireless communication networks. Even though some progress has been made, the recognition of the type of the traffic services cannot be done in real time. In this paper, we propose a novel method for classifying traffic series in real time based on transfer learning techniques. We pre-train a deep learning model with long traffic series and fine-tune the model with short traffic series. In this way, the developed model achieves the capability of recognising traffic services in real time. In other words, the model can recognize traffic services by using short traffic series. We collect Downlink Control Information (DCI) from commercial LTE networks when using five common types of traffic services. Then we use the dataset to validate our method. Our experimental results show that, by using proposed method, LSTM accuracy rates will increase to 80% and 88.5% when the length of the traffic series is 5 seconds and 10 seconds respectively, which is higher than the baseline. The strategy is also suitable for one dimension convolution neural network (1D-CNN).
Neighborhood Learning from Noisy Labels for Cross-Modal Retrieval

Cross-modal retrieval methods are developed to retrieve relevant data across different modalities. Usually, super-vised cross-modal retrieval methods can achieve higher accuracy than unsupervised methods because they can utilize the semantic information provided by clean labels. However, training data with noisy labels will lead to the performance degradation of supervised cross-modal retrieval methods. In this work, we present a novel framework called Neighborhood Learning for Cross-Modal Retrieval (NLCMR) that is robust against noisy labels by exploiting the information contained in the neighbor-hood. Our NLCMR contains two main components: Clustering with Neighborhood Alignment and Neighborhood Contrastive Learning. The first component focuses on reducing the impact of noisy labels and improving clustering robustness, and the second component learns from noisy data by exploring pairwise and neighborhood information. Extensive experiments are conducted on three multi-modal datasets to demonstrate the effectiveness of NLCMR.
An Adaptive Quantization Method for CNN Activations

The post-training compression based on affine quantization is a common technology to improve the efficiency of embedded neural network accelerators. Current state-of-the-art quantization schemes for CNN activations usually rely on calibration dataset with better data representation to reduce the possibility of quantization overflow in inference, which is not always effective due to the uncertainty of the inference input data in practice. This paper proposes an adaptive quantization method for activations and its hardware-friendly design to directly address the quantization overflow in inference. This method monitors the quantization overflow of activations on-the-fly, adaptively updates the quantization parameters, and re-quantizes the activations when the overflow degree is over a tunable threshold. We evaluate the proposed method on VGG16 and MobileNetV2 and experiment results show that 11% improvement in inference accuracy at severe quantization overflow is achieved with the cost of 3% increase in runtime, and tuning the threshold enables trade-off between inference accuracy and speed.
Tunable $LC$ resonator for multiplexed multi-qubit readout

This paper proposes the use of a tunable <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$LC$</tex> resonator to read an array of qubits in a multiplexed fashion, by making the dispersive shift of the targeted qubit dominant. Cavity and circuit electrodynamics (QED) theory is shown to support this idea. The tunable capacitor array, in parallel with a superconducting inductance, is designed to maximize the quality factor by frequency range product, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q\cdot\Delta\omega$</tex> . This approach only requires one RF signal to measure multiple qubits, which can facilitate quantum computing scaling.
O<sup>3</sup>NMS: An Out-Of-Order-Based Low-Latency Accelerator for Non-Maximum Suppression

Non-maximum suppression (NMS) is an important post-processing method to eliminate overlapping bounding boxes in object detection neural networks. Suffering from quadratic computational complexity and frequent memory access, NMS has become a bottleneck of detection latency. To deal with this problem, we propose out-of-order NMS (O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> NMS), a hardware- software co-optimization approach to reduce latency as well as area overhead of NMS accelerator. In order to reduce startup latency, we devise the O3NMS algorithm that removes pre-sort operation. To efficiently support O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> NMS algorithm, we design a specialized hardware accelerator. Our design has been implemented in both Xilinx FPGA and SIMC 40nm technology. Experiments demonstrate O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> NMS accelerator achieves 2.51 x speedup as well as 37 % reduction in FPGA source utilization compared with the state-of-the-art (SOTA) NMS accelerator.
A Scalable Annealing Processing Architecture for Fully-Connected Ising Models

Combinational Optimization Problems (COPs) are prevalent in many different fields. Most of these problems are NP-hard and challenging for computers with conventional Von-Neumann architecture. Ising machines with numerous spins have the potential to solve these problems by emulating the natural annealing process of solid matter. Recent research has explored the hardware implementation of Ising machines to accelerate the convergence process of such problems at room temperature. However, most of them are suffering from low scalability and low parallel processing capability due to the huge hardware cost and high complexity. In this paper, a scalable annealing processing architecture for Ising processor is described to address these issues with a NoC computing paradigm, a distributed storage scheme, and a fully pipelined structure design. The prototype is synthesized using FPGA with the maximum operation frequency of 270MHz, achieving about 32 times faster than conventional simulated annealing method when solving the max-cut problem.