Reviewerly/exHarmony huggingface dataset:
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion

In federated learning (FL), the communication constraint between the remote clients and the Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be compressed so as to minimize the loss in accuracy resulting from the communication constraint. This paper proposes “M- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">magnitude weighted L<sub>2</sub> distortion</i> + 2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">degrees of freedom</i> ” (M22) algorithm, a rate-distortion inspired approach to gradient compression for federated training of deep neural networks (DNNs). In particular, we propose a family of distortion measures between the original gradient and the reconstruction we referred to as “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -magnitude weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ” distortion, and we assume that gradient updates follow an i.i.d. distribution – generalized normal or Weibull, which have two degrees of freedom. In both the distortion measure and the gradient distribution, there is one free parameter for each that can be fitted as a function of the iteration number. Given a choice of gradient distribution and distortion measure, we design the quantizer to minimize the expected distortion in gradient reconstruction. To measure the gradient compression performance under a communication constraint, we define the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">per-bit accuracy</i> as the optimal improvement in accuracy that one bit of communication brings to the centralized model over the training period. Using this performance measure, we systematically benchmark the choice of gradient distribution and distortion measure. We provide substantial insights on the role of these choices and argue that significant performance improvements can be attained using such a rate-distortion inspired compressor.
Human–Object Interaction detection via Global Context and Pairwise-level Fusion Features Integration

Recent two-stage detector-based methods show superiority in Human-Object Interaction (HOI) detection along with the successful application of transformer. However, these methods are limited to extracting the global contextual features through instance-level attention without considering the perspective of human-object interaction pairs, and the fusion enhancement of interaction pair features lacks further exploration. The human-object interaction pairs guiding global context extraction relative to instance guiding global context extraction more fully utilize the semantics between human-object pairs, which helps HOI recognition. To this end, we propose a two-stage Global Context and Pairwise-level Fusion Features Integration Network (GFIN) for HOI detection. Specifically, the first stage employs an object detector for instance feature extraction. The second stage aims to capture the semantic-rich visual information through the proposed three modules, Global Contextual Feature Extraction Encoder (GCE), Pairwise Interaction Query Decoder (PID), and Human-Object Pairwise-level Attention Fusion Module (HOF). The GCE module intends to extract the global context memory by the proposed crossover-residual mechanism and then integrate it with the local instance memory from the DETR object detector. HOF utilizes the proposed pairwise-level attention mechanism to fuse and enhance the first stage’s multi-layer feature. PID outputs multi-label interaction recognition results with the input of the query sequence from HOF and the memory from GCE. Finally, comprehensive experiments conducted on HICO-DET and V-COCO datasets demonstrate that the proposed GFIN significantly outperforms the state-of-the-art methods.
Real-time mask-wearing detection in video streams using deep convolutional neural networks for face recognition

This research aims to develop a real-time mask-wearing detection system using deep convolutional neural networks (CNNs). This is crucial in the coronavirus disease 2019 (COVID-19) pandemic to alert individuals who are not wearing masks early on, thereby reducing the spread of the virus. Since COVID-19 primarily spreads through respiratory droplets and mask-wearing is recommended, our proposed study utilizes computer vision techniques, specifically image processing, to detect masked and unmasked faces. We employ a customized CNN architecture consisting of five convolutional layers, followed by max-pooling layers and fully connected (FC) layers. The final output layer utilizes softmax activation for classification. The model is updated with optimized layer configurations and parameter values. We are developing an application that uses a digital camera as an input device. The application utilizes a dataset comprising 11,792 image samples, which are used for training and testing purposes with the 80:20 ratio. Real-time testing is conducted using human subjects captured by the camera. The experimental results demonstrate that the CNN method achieves a classification accuracy of 99% on the training data and 98.83% during real-time video testing. These findings suggest that the real-time mask detection system using CNN performs effectively.
Unavoidable social contagion of false memory from robots to humans.

Many of us interact with voice- or text-based conversational agents daily, but these conversational agents may unintentionally retrieve misinformation from human knowledge databases, confabulate responses on their own, or purposefully spread disinformation for political purposes. Does such misinformation or disinformation become part of our memory to further misguide our decisions? If so, can we prevent humans from suffering such social contagion of false memory? Using a social contagion of memory paradigm, here, we precisely controlled a social robot as an example of these emerging conversational agents. In a series of two experiments (ΣN = 120), the social robot occasionally misinformed participants prior to a recognition memory task. We found that the robot was as powerful as humans at influencing others. Despite the supplied misinformation being emotion- and value-neutral and hence not intrinsically contagious and memorable, 77% of the socially misinformed words became the participants' false memory. To mitigate such social contagion of false memory, the robot also forewarned the participants about its reservation toward the misinformation. However, one-time forewarnings failed to reduce false memory contagion. Even relatively frequent, item-specific forewarnings could not prevent warned items from becoming false memory, although such forewarnings helped increase the participants' overall cautiousness. Therefore, we recommend designing conversational agents to, at best, avoid providing uncertain information or, at least, provide frequent forewarnings about potentially false information. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
ACNPU: A 4.75TOPS/W 1080P@30FPS Super Resolution Accelerator With Decoupled Asymmetric Convolution

Deep learning-driven superresolution (SR) outperforms traditional techniques but also faces the challenge of high complexity and memory bandwidth. This challenge leads many accelerators to opt for simpler and shallow models like FSRCNN, compromising performance for real-time needs, especially for resource-limited edge devices. This paper proposes an energy-efficient SR accelerator, ACNPU, to tackle this challenge. The ACNPU enhances image quality by 0.34dB with a 27-layer model, but needs 36% less complexity than FSRCNN, while maintaining a similar model size, with the decoupled asymmetric convolution and split-bypass structure. The hardware-friendly 17K-parameter model enables holistic model fusion instead of localized layer fusion to remove external DRAM access of intermediate feature maps. The on-chip memory bandwidth is further reduced with the input stationary flow and parallel-layer execution to reduce power consumption. Hardware is regular and easy to control to support different layers by processing elements (PEs) clusters with reconfigurable input and uniform data flow. The implementation in the 40 nm CMOS process consumes 2333 K gate counts and 198Â KB SRAMs. The ACNPU achieves 31.7 FPS and 124.4 FPS for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 2$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula> scales Full-HD generation, respectively, which attains 4.75 TOPS/W energy efficiency.
Mixed-Frequency Signal Processing Implemented by a Multi-Terminal Electrolyte-Gated Transistor-Based Physical Reservoir

Constructing highly compact and low hardware consuming multi-timescale physical reservoir is one of challenges in the hardware implementation of reservoir computing (RC) systems. However, state-of-the-art memristor-based reservoir layer based on ionic diffusion mechanisms suffers from relatively narrow temporal dynamic modulation window, limiting the multi-timescale feature extraction performance. In this letter, a proton-driven multi-terminal electrolyte-gated transistor (MTEGT) is proposed to obtain abundant reservoir states by ion-electron coupling through the multi-terminal modulation. The relaxation time of MTEGT ranges nearly two orders of magnitude (0.03 s to 1.0 s) within a 2.0 V bias voltage variation window (−1.2 V to 0.8 V). The MTEGT-based reservoir successfully modulates the amplitude of input signals at different frequencies. Moreover, a multiple superimposed oscillator task (MSO5 task) with five different frequencies is demonstrated with an ultralow normalized mean square error (NMSE) of 0.0009. This paves the way for the development of multi-timescale ion-controlled physical reservoirs with low hardware consumption and integration complexities.
High-Speed Nonlinear Circuit Macromodeling Using Hybrid-Module Clockwork Recurrent Neural Network

In the computer-aided design (CAD) area, the recurrent neural network (RNN) has shown notable functionality in generating fast and high-performance models rather than the models in simulation tools. Predicting time sequences is a pervasive and challenging problem that may require identifying the dependencies between sequences that RNN is capable of performing. Despite all its features, conventional RNN still faces challenges such as limited accuracy and a large number of parameters. Therefore, we propose new macromodeling methods for nonlinear circuits called the Clockwork-RNN (CWRNN) and its hybrid version which is a more powerful but simpler implementation of a conventional RNN architecture with relatively little model complexity. In addition, CWRNN inherently models complex dependencies without the need for a large number of parameters. As a result, the computational cost is less than conventional RNN. Moreover, understanding and implementing the CWRNN is relatively simple and provides great flexibility in architectural configuration by introducing modules with several clock rates of exponents of 2. In addition to the above new modeling technique, we proposed the Hybrid-Module CWRNN as another new modeling method that utilizes modules of various exponents of different numbers resulting in further accuracy improvement of the CWRNN. Furthermore, the models obtained from the proposed techniques required much smaller simulation times compared to the current models used in simulation tools. Three nonlinear high-frequency examples have been utilized to verify the benefits of the proposed modeling methods.
Distributed Semi-Supervised Learning With Consensus Consistency on Edge Devices

Distributed learning has been increasingly studied in edge computing, enabling edge devices to learn a model collaboratively without exchanging their private data. However, existing approaches assume the private data owned by edge devices are all labeled while the reality is that massive private data are unlabeled and remain to be utilized, which leads to suboptimal performance. To overcome this limitation, we study a new practical problem, Distributed Semi-Supervised Learning (DSSL), to learn models collaboratively with mixed private labeled and unlabeled data on each device. We also propose a novel method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DistMatch</i> that exploits private unlabeled data by self-training on each device with the help of models from neighboring devices. DistMatch generates pseudo-labels for unlabeled data by properly averaging the predictions of these received models. Furthermore, to avoid self-training with wrong pseudo-labels, DistMatch proposes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consensus consistency</i> loss to filter pseudo-labels with high consensus and force the output of the trained model to be consistent with these pseudo-labels. Extensive evaluation results via our self-developed testbed indicate the proposed method outperforms all baselines on commonly used image classification benchmark datasets.
An improvement for CAST-128 encryption based on magic square and matrix inversion

This paper presents two image encryption methods which aim to improve the CAST 128-bit algorithm by increasing the security level of encrypted images. The first improvement uses a magic square of order three, while the second improvement uses a 2×2 matrix over GF(P). Both modifications are used in each round of the CAST algorithm, in place of a standard algorithm which uses XOR to increase the correlation between the plaintext and ciphertext. Simulations are carried out in order to evaluate the image encryption system with regard to complexity, time consumption, histogram, information entropy, differential attacks, noise evaluation, adjacent pixels’ correlation index, National Institute of Standards and Technology (NIST) analyses, mean absolute error, and average difference. The experimental results demonstrate that the encryption and decryption time when using the proposed CAST 128-bit algorithm with magic square is less than the time required for the CAST 128-bit algorithm with the matrix. Conversely, the proposed CAST with the matrix is higher than the CAST with the magic square. Both theoretical analysis and experimental results confirm that the two proposed enhancements to CAST perform effectively with sufficient security levels.
Improving sentiment analysis using text network features within different machine learning algorithms

Sentiment analysis poses a significant challenge due to the inherent subjectivity of natural language and the prevalence of unstandardized dialects in social networks. Regrettably, existing literature lacks a dedicated focus on network representation learning for sentiment classification. This paper addresses this gap by investigating ten machine learning algorithms, including support vector machine (SVM), random forest (RF), logistic regression (LR), and Naive Bayes (NB). Our approach integrates text network analysis and sentiment analysis to propose a comprehensive solution. We begin by applying text preprocessing techniques and converting a text corpus into a text network using word co-occurrence. Subsequently, we employ network analysis techniques to extract features based on network topology and node attributes. These network-derived features serve as inputs for sentiment prediction on Yelp reviews. Through the incorporation of diverse text network features and various machine learning algorithms, we achieve significant enhancements in sentiment classification performance. Our evaluation demonstrates an improved area under curve (AUC) of 83% on the Yelp reviews corpus, underscoring the efficacy of integrating network features to enhance sentiment classifiers. This research underscores the critical role of network representation and its potential impact on sentiment analysis, highlighting the prospect of harnessing network features for sentiment classification tasks.
A deep learning-based intelligent decision-making model for tumor and cancer cell identification

In the current era, the prevalence of common ailments is leading to an increasing number of fatalities. Various infections, viruses, and other pathogens can cause these illnesses. Some illnesses can give rise to tumors that seriously threaten human health. Distinct forms of tumors exist, including benign, premalignant, and malignant, with cancer being present only in malignant forms. Deep learning (DL) algorithms have emerged as one of the most promising methods for detecting cancers within the human body. However, existing models face criticism for their limitations, such as lack of support for large datasets, and reliance on a limited number of attributes from input images. To address these limitations and enable efficient cancer detection throughout the human body, an intelligent decision-making approach model (IDMA) is proposed. The IDMA is combined with the pre-trained VGG19 for improved training. The IDMA analyses convolutional neural network (CNN) layer images for signs of malignancy and rules out false positives. Various performance indicators, like sensitivity, precision, recall, and F1-score, are used to assess the system's performance. The suggested system has been evaluated and proven to outperform similar current systems, achieving an impressive 98.67% accuracy in detecting cancer cells.
Evaluation of domain sulfur industry for DIA translator using bilingual evaluation understudy method

Evaluation is important part of our system development cycle; it also contributes to improving new machine translation (MT) technology optimum via comparing them with the traditional systems available to determine the weaknesses and the effectiveness to be improved in the proposed MT system. This work aiming to make a study that evaluate the performance and effectivness of the domain sulfur industry (DSI) for English-Arabic DIA translator quality. The recent study has conducted evaluating by making a comparison between this programme with the prominent Google translator through applying a rendering of 1,200 English sentences in bilingual evaluation understudy (BLUE) method. The obtain results show that the efficiency of Google translator is about 30.325%, while DIA translator efficiency in domain sulfur industry is about 73.325% and it’s more effective and give a better translation accuracy. The BLUE method efficiency is about (90.478%) compared with the human expert evaluator.
S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking With Adaptive Spatio-Temporal Appearance Representations

Following the tracking-by-attention paradigm, this paper introduces an object-centric, transformer-based framework for tracking in 3D. Traditional model-based tracking approaches incorporate the geometric effect of object- and ego motion between frames with a geometric motion model. Inspired by this, we propose STAR-TRACK which uses a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">latent motion model</i> (LMM) to additionally adjust object queries to account for changes in viewing direction and lighting conditions directly in the latent space, while still modeling the geometric motion explicitly. Combined with a novel learnable track embedding that aids in modeling the existence probability of tracks, this results in a generic tracking framework that can be integrated with any query-based detector. Extensive experiments on the nuScenes benchmark demonstrate the benefits of our approach, showing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">state-of-the-art</i> (SOTA) performance for DETR3D-based trackers while drastically reducing the number of identity switches of tracks at the same time.
Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks

Mobile manipulators are envisioned to serve more complex roles in people's everyday lives. With recent breakthroughs in large language models, task planners have become better at translating human verbal instructions into a sequence of tasks. However, there is still a need for a decision-making algorithm that can seamlessly interface with the high-level task planner to carry out the sequence of tasks efficiently. In this work, building on the idea of nonlinear lexicographic optimization, we propose a novel Hierarchical-Task Model Predictive Control framework that is able to complete sequential tasks with improved performance and reactivity by effectively leveraging the robot's redundancy. Compared to the state-of-the-art task-prioritized inverse kinematic control method, our approach has improved hierarchical trajectory tracking performance by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {42\%}$</tex-math></inline-formula> on average when facing task changes, robot singularity and reference variations. Compared to a typical single-task architecture, our proposed hierarchical task control architecture enables the robot to traverse a shorter path in task space and achieves an execution time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {2.3}$</tex-math></inline-formula> times faster when executing a sequence of delivery tasks. We demonstrated the results with real-world experiments on a 9 degrees of freedom mobile manipulator.
Teachers’ digital literacy overview in secondary school

&lt;span lang="EN-US"&gt;This study explored the teachers’ digital literacy in secondary school and deciphered the most suggested way of enhancing secondary teachers’ digital literacy, overview levels, and the trends factors in the last five years of research. The methodologies in this study employed the traditional systematic literature review (SLR), which compiled some previous studies to be examined. The initial studies were only selected from an article published in a reputable journal indexed by Scopus. Of the 118 articles reviewed, 46 were selected to be included in the analysis. Significant findings summarize the fact that most of the last five-year research about teachers in secondary schools was impacted by the rapid development of technology and the demand for 21st-century skills. To summarize, as it is at the intermediate level, most secondary school teachers are digitally literate. However, future research may evaluate digital literacy training programs for teachers to assess what improvements should be made to delivery as well as existing frameworks used to test teachers’ digital literacy.&lt;/span&gt;