Reviewerly/exHarmony huggingface dataset:
MCOM-Live: A Multi-Codec Optimization Model at the Edge for Live Streaming

HTTP Adaptive Streaming (HAS) is the predominant technique to deliver video contents across the Internet with the increasing demand of its applications. With the evolution of videos to deliver more immersive experiences, such as their evolution in resolution and framerate, highly efficient video compression schemes are required to ease the burden on the delivery process. While AVC/H.264 still represents the most adopted codec, we are experiencing an increase in the usage of new generation codecs (HEVC/H.265, VP9, AV1, VVC/H.266, etc.). Compared to AVC/H.264, these codecs can either achieve the same quality besides a bitrate reduction or improve the quality while targeting the same bitrate. In this paper, we propose a Mixed-Binary Linear Programming (MBLP) model called Multi-Codec Optimization Model at the edge for Live streaming (MCOM-Live) to jointly optimize (i) the overall streaming costs, and (ii) the visual quality of the content played out by the end-users by efficiently enabling multi-codec content delivery. Given a video content encoded with multiple codecs according to a fixed bitrate ladder, the model will choose among three available policies, i.e., fetch, transcode, or skip, the best option to handle the representations. We compare the proposed model with traditional approaches used in the industry. The experimental results show that our proposed method can reduce the additional latency by up to 23% and the streaming costs by up to 78%, besides improving the visual quality of the delivered segments by up to 0.5 dB, in terms of PSNR.
Multilevel receptive field expansion network for small object detection

Abstract Small object detection remains a bottleneck because there is little visual information about them, especially in the deep layers. To improve the detection performance of small objects, here, Swin Transformer is introduced as the model backbone network to extract rich features of small objects. Then, a multilevel receptive field expansion network (MRFENet) is proposed based on the characteristics of different stages in the Swin Transformer. Specifically, a receptive field expansion block (RFEB) is designed to acquire contextual cues and extract detailed information. The RFEB is carefully designed to target the required receptive fields of different layers and further refine the features. MRFENet combined with RFEBs implements the retention of small object context cues and the acquisition of receptive fields for the adaptive detection tasks. Finally, a union loss function is designed to enhance the localization ability. Experiments on the MS COCO dataset demonstrate that the proposed MRFENet has a significant improvement against other state‐of‐the‐art methods, which further validates that MRFENet can effectively utilize small object information.
An extensive overview on Human-Computer Interaction (HCI) application

Human-Computer Interaction (HCI) is the area of intersection between psychology and the social sciences. HCI assists companies in creating technological products accessible to individuals with disabilities. In addition, HCI helps user experience (UX) designers and other professionals understand each user's requirements relating to technology. This paper aims to give an overview of the concept of Human-Computer Interaction. The overview comprises basic definitions, frameworks, goals, and types of HCI. It also includes a voice user interface and sociophonetics in HCI. Finally, Norman's model of interaction and future scope are discussed.
Pseudo-label Diversity Exploitation for Few-Shot Object Detection

Few-Shot Object Detection (FSOD) task is widely used in various data-scarce scenarios, aiming to expand the object detector with a few novel class samples. The current mainstream FSOD models improve the accuracy by mining novel class instances in the training set and fine-tuning the detector with mined pseudo set. Substantial progress has been made using pseudo-label approaches, but the impact of pseudo-labels diversity on FSOD tasks has not been explored. In our work, for the purpose of fully utilizing the pseudo-label set and exploring their diversity, we propose a new framework mainly including Novel Instance Bank (NIB) and Correlation-Guided Loss Correction (CGLC). Dynamically updated NIB stores the novel class instances to increase the diversity of novel instances in each batch. Moreover, to better exploit the pseudo-label diversity, CGLC adaptively employs k-shot samples to guide correct and incorrect pseudo-labels to pull away from each other. Experimental results on the MS-COCO dataset demonstrate the effectiveness of our method, which does not require any additional training samples or parameters. Our code is available at: https://github.com/lotuser1/PDE .
Tangless: Optimizing Cost and Transaction Rate in IOTA by Using Lyapunov Optimization Theory

IOTA has emerged as a promising and feeless decentralized computing paradigm for developing blockchain-based Internet of Things (IoT) applications with high-performance transaction rates and incremental scalability. To support micro-payments of IoT devices, IOTA has abandoned the original blockchain reward mechanism while IOTA nodes voluntarily contribute resources to maintain network stability. However, removing the mining rewards results in the resource cost of generating IOTA ledgers (known as the Tangle) being borne only by IOTA nodes. With the continuous expansion of the IOTA network, cost consumption is increasing. Thus, the inability to effectively reduce the cost of Tangle generation would lead to people being reluctant to dedicate resources to IOTA for maintaining the network robustness. In this paper, for the first time, we present a full-fledged transaction cost optimization scheme for IOTA, called Tangless, which can assist IOTA nodes in effectively reducing the Tangle generation cost while maintaining the strong robustness of the IOTA network. By using our proposed scheme, each IOTA node can effectively formulate the threshold of transaction approval rate in real time, maintaining the stability of the IOTA network with the optimal computational cost. We harness Lyapunov optimization theory to design a computational optimization algorithm for minimizing the total cost of nodes in IOTA. Then, we resort to large deviations theory to devise an optimized transaction rate control algorithm to further eliminate orphan Tangles that waste computational costs. Comprehensive theoretical analysis and simulation experiments confirm the effectiveness and practicability of our proposed scheme.
LEMON: Alternative Sampling for More Faithful Explanation Through Local Surrogate Models

Abstract Local surrogate learning is a popular and successful method for machine learning explanation. It uses synthetic transfer data to approximate a complex reference model. The sampling technique used for this transfer data has a significant impact on the provided explanation, but remains relatively unexplored in literature. In this work, we explore alternative sampling techniques in pursuit of more faithful and robust explanations, and present LEMON: a sampling technique that samples directly from the desired distribution instead of reweighting samples as done in other explanation techniques (e.g., LIME). Next, we evaluate our technique in a synthetic and UCI dataset-based experiment, and show that our sampling technique yields more faithful explanations compared to current state-of-the-art explainers.
Communication-Efficient Personalized Federated Meta-Learning in Edge Networks

Due to the privacy breach risks and data aggregation of traditional centralized machine learning (ML) approaches, applications, data and computing power are being pushed from centralized data centers to network edge nodes. Federated Learning (FL) is an emerging privacy-preserving distributed ML paradigm suitable for edge network applications, which is able to address the above two issues of traditional ML. However, the current FL methods cannot flexibly deal with the challenges of model personalization and communication overhead in the network applications. Inspired by the mixture of global and local models, we proposed a Communication-Efficient Personalized Federated Meta-Learning algorithm to obtain a novel personalized model by introducing the personalization parameter. We can improve model accuracy and accelerate its convergence by adjusting the size of the personalized parameter. Further, the local model to be uploaded is transformed into the latent space through autoencoder, thereby reducing the amount of communication data, and further reducing communication overhead. And local and task-global differential privacy are applied to provide privacy protection for model generation. Simulation experiments demonstrate that our method can obtain better personalized models at a lower communication overhead for edge network applications, while compared with several other algorithms.
Conceptual approach to the development of technology for virtual mobility modeling of distance learning students

The article deals with the issues of distance learning for students. The technology of managing the formation of virtual mobility of students of distance learning is presented. Value-volitional, normative, resource, procedural-functional, managerial-diagnostic and resulting blocks are identified in the technology. Mobile learning technology includes a purposeful and ordered set and sequence of actions of a teacher and a student through joint or individual study of structured learning resources, work and educational communities. The article presents a knowledge map (structured interpretation of the concept under study) of virtual mobility of distance learning students. The mind map is considered, on the one hand, as a logical and semantic verbal-like model of educational material, reflecting its content and structure, on the other hand, as a model of students’ individual knowledge.
Feature analysis of sentence vectors by an image-generation model using Sentence-BERT

In this study, using k-means and UMAP, we verified that the sentence vectors generated by Sentence-BERT as distributed representations of sentences capture the meaning of sentences well. To this end, we visualized the sentence vectors by generating images matching the meaning of the sentence from the sentence vectors generated by Sentence-BERT. The results confirm that although there were differences in the information represented by each dimension as distributional features of the sentence vector, this information overlapped substantially.
Features of teaching students of technical universities in the field of ecology and environmental health

This article discusses the possibilities of training in the field of ecology and environmental safety at a technical university. When learning a foreign language, environmental health students may encounter difficulties. Some of the psychological difficulties they may face are listed in detail. To overcome the difficulties that have arisen, methods of increasing motivation for learning are considered. When teaching, it is necessary to take into account the importance of environmental education of students. The article describes what difficulties a student and a teacher may encounter when studying the topic of ecology. The method of associations, which have recently begun to be seriously considered by teachers, is described in detail. Examples of vocabulary are given that make it possible to master this method of work when studying vocabulary in classes devoted to the conservation and management of natural resources.
Practical Homomorphic Evaluation of Block-Cipher-Based Hash Functions with Applications

Fully homomorphic encryption (FHE) is a powerful cryptographic technique allowing to perform computation directly over encrypted data. Motivated by the overhead induced by the homomorphic ciphertexts during encryption and transmission, the transciphering technique, consisting in switching from a symmetric encryption to FHE encrypted data was investigated in several papers. Different stream and block ciphers were evaluated in terms of their "FHE-friendliness", meaning practical implementations costs while maintaining sufficient security levels. In this work, we present a first evaluation of hash functions in the homomorphic domain, based on well-chosen block ciphers. More precisely, we investigate the cost of transforming PRINCE, SIMON, SPECK, and LowMC, a set of lightweight block-ciphers into secure hash primitives using well-established hash functions constructions based on block-ciphers, and provide evaluation under bootstrappable FHE schemes. We also motivate the necessity of practical homomorphic evaluation of hash functions by providing several use cases in which the integrity of private data is also required. In particular, our hash constructions can be of significant use in a threshold-homomorphic based protocol for the single secret leader election problem occurring in blockchains with Proof-of-stake consensus. Our experiments showed that using a TFHE implementation of a hash function, we are able to achieve practical runtime, and appropriate security levels (e.g., for PRINCE it takes 1.28 minutes to obtain a 128 bits of hash).
Ether-IoT: A Realtime Lightweight and Scalable Blockchain-Enabled Cache Algorithm for IoT Access Control

Several unique characteristics of Internet of Things (IoT) devices, such as distributed deployment and limited storage, make it challenging for standard centralized access control systems to enable access control in today’s large-scale IoT ecosystem. To solve these challenges, this study presents an IoT access control system called Ether-IoT based on the Ethereum Blockchain (BC) infrastructure with Attribute-Based Access Control (ABAC). Access Contract (AC), Cache Contract (CC), Device Contract (DC), and Policy Contract (PC) are the four central smart contracts (SCs) that are included in the proposed system. CC offers a way to save user characteristics in a local cache system to avoid delays during transactions between BC and IoT devices. AC is the fundamental program users typically need to run to build an access control technique. DC offers a means for storing the resource data created by devices and a method for querying that data. PC offers administrative settings to handle ABAC policies on users’ behalf. Ether-IoT, combined with ABAC and the BC, enables IoT access control management that is decentralized, fine-grained and dynamically scalable. This research gives a real-world case study to illustrate the suggested framework’s implementation. In the end, a simulation experiment is performed to evaluate the system’s performance. To ensure data integrity in dispersed systems, the results show that Ether-IoT can sustain high throughput in contexts with a large number of requests.
Convolutional Transformer Fusion Blocks for Multi-Modal Gesture Recognition

Gesture recognition defines an important information channel in human-computer interaction.Intuitively, combining inputs from multiple modalities improves the recognition rate.In this work, we explore multi-modal video-based gesture recognition tasks by fusing spatio-temporal representation of relevant distinguishing features from different modalities.We present a self-attention based transformer fusion architecture to distill the knowledge from different modalities in two-stream convolutional neural networks (CNNs).For this, we introduce convolutions into the self-attention function and design the Convolutional Transformer Fusion Blocks (CTFB) for multi-modal data fusion.These fusion blocks can be easily added at different abstraction levels of the feature hierarchy in existing two-stream CNNs.In addition, the information exchange between two-stream CNNs along the feature hierarchy has so far been barely explored.We propose and evaluate different architectures for multi-level fusion pathways using CTFB to gain insights into the information flow between both streams.Our method achieves state-of-the-art or competitive performance on three benchmark gesture recognition datasets: a) IsoGD, b) NVGesture, and c) IPN hand.Extensive evaluation demonstrates the effectiveness of the proposed CTFB both in terms of recognition rate as well as resource efficiency.
Non-iterative Wagner-Hagras General Type-2 Mamdani Singleton Fuzzy Logic System Optimized by Central Composite Design in Quality Assurance by Image Processing

This paper presents the implementation of a non-iterative General Type-2 (GT2) Fuzzy Logic System (FLS) in quality assurance by image processing using Mamdani singleton model based on Wagner-Hagras (WH) algorithm. The antecedents and consequents are modelled and remain fixed. The modelling of the rule base uses the Central Composite Design (CCD) model to create a classifier in an industrial quality area. Results show that the implementation of the WH GT2FLS model provides very close or better results with a few alpha-cut versus an Interval Type-2 model (IT-2) FLS system depending on the type of membership function selected for the system.
Resource-Aware Knowledge Distillation for Federated Learning

The rise of deep learning and the Internet of Things (IoT) has driven a number of smart-world applications, which are mostly deployed in distributed environments. Federated learning, a privacy-preserving collaborative learning paradigm, has shown considerable potential to leverage the rich distributed data at network edges. Nonetheless, the heterogeneity of IoT devices and their connected network environment impedes federated learning applications in IoT systems. Particularly, the stale gradients updated by slower local learners impact the effectiveness of federated learning. Transmitting weight updates with a large number of users leads to network congestion at the edge of the network and incurs unaffordable communication costs. To overcome these challenges, we propose a transfer knowledge based federated learning framework under a resource-limited distributed system. We formulate a knowledge distillation based federated learning optimization problem with the consideration of dynamic local resource. The proposed approach carries out federated learning with the help of knowledge distillation to avoid occupying the expensive network bandwidth or bringing a heavy burden to the network. Theoretical analysis demonstrates convergence of the learning process. The experimental results on three public datasets illustrate that the proposed framework is capable of substantially improving the efficiency of federated learning and outperforming state-of-the-art schemes.
Locality-Aware Transformer for Video-Based Sign Language Translation

Recently, the application of transformer makes significant progress in sign language translation. However, several characteristics of sign videos are neglected in existing transformer-based methods that hinder translation performance. Firstly, in sign videos, multiple consecutive frames represent a single sign gloss thus the local temporal relations are crucial. Secondly, the inconsistency between video and text demands the non-local and global context modeling ability of the model. To address these issues, a locality-aware transformer is proposed for sign language translation. Concretely, the multi-stride position encoding scheme assigns the same position index to adjacent frames with various strides to enhance the local dependency. Afterward, the adaptive temporal interaction module is utilized to capture non-local and flexible local frame correlation simultaneously. Moreover, a gloss counting task is designed to facilitate the holistic understanding of sign videos. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed framework.
Unitarity Estimation for Quantum Channels

Estimating the unitarity of an unknown quantum channel $\mathcal{E}$ provides information on how much it is unitary, which is a basic and important problem in quantum device certification and benchmarking. Unitarity estimation can be performed with either coherent or incoherent access, where the former in general leads to better query complexity while the latter allows more practical implementations. In this paper, we provide a unified framework for unitarity estimation, which induces ancilla-efficient algorithms that use $O(\epsilon^{-2})$ and $O(\sqrt{d}\cdot\epsilon^{-2})$ calls to $\mathcal{E}$ with coherent and incoherent accesses, respectively, where $d$ is the dimension of the system that $\mathcal{E}$ acts on and $\epsilon$ is the required precision. We further show that both the $d$-dependence and $\epsilon$-dependence of our algorithms are optimal. As part of our results, we settle the query complexity of the distinguishing problem for depolarizing and unitary channels with incoherent access by giving a matching lower bound $\Omega(\sqrt{d})$, improving the prior best lower bound $\Omega(\sqrt[3]{d})$ by Aharonov et al. (Nat. Commun. 2022) and Chen et al. (FOCS 2021).
Depth-Guided Optimization of Neural Radiance Fields for Indoor Multi-View Stereo

In this work, we present a new multi-view depth estimation method NerfingMVS that utilizes both conventional reconstruction and learning-based priors over the recently proposed neural radiance fields (NeRF). Unlike existing neural network based optimization method that relies on estimated correspondences, our method directly optimizes over implicit volumes, eliminating the challenging step of matching pixels in indoor scenes. The key to our approach is to utilize the learning-based priors to guide the optimization process of NeRF. Our system first adapts a monocular depth network over the target scene by finetuning on its MVS reconstruction from COLMAP. Then, we show that the shape-radiance ambiguity of NeRF still exists in indoor environments and propose to address the issue by employing the adapted depth priors to monitor the sampling process of volume rendering. Finally, a per-pixel confidence map acquired by error computation on the rendered image can be used to further improve the depth quality. We further present NerfingMVS++, where a coarse-to-fine depth priors training strategy is proposed to directly utilize sparse SfM points and the uniform sampling is replaced by Gaussian sampling to boost the performance. Experiments show that our NerfingMVS and its extension NerfingMVS++ achieve state-of-the-art performances on indoor datasets ScanNet and NYU Depth V2. In addition, we show that the guided optimization scheme does not sacrifice the original synthesis capability of neural radiance fields, improving the rendering quality on both seen and novel views. Code is available at https://github.com/weiyithu/NerfingMVS.