Reviewerly/exHarmony huggingface dataset:
Knowledge distillation in deep learning and its applications

Deep learning based models are relatively large, and it is hard to deploy such models on resource-limited devices such as mobile phones and embedded devices. One possible solution is knowledge distillation whereby a smaller model (student model) is trained by utilizing the information from a larger model (teacher model). In this paper, we present an outlook of knowledge distillation techniques applied to deep learning models. To compare the performances of different techniques, we propose a new metric called distillation metric which compares different knowledge distillation solutions based on models' sizes and accuracy scores. Based on the survey, some interesting conclusions are drawn and presented in this paper including the current challenges and possible research directions.
OpenAttack: An Open-source Textual Adversarial Attack Toolkit

Textual adversarial attacking has received wide and increasing attention in recent years.Various attack models have been proposed, which are enormously distinct and implemented with different programming frameworks and settings.These facts hinder quick utilization and fair comparison of attack models.In this paper, we present an opensource textual adversarial attack toolkit named OpenAttack to solve these issues.Compared with existing other textual adversarial attack toolkits, OpenAttack has its unique strengths in support for all attack types, multilinguality, and parallel processing.Currently, OpenAttack includes 15 typical attack models that cover all attack types.Its highly inclusive modular design not only supports quick utilization of existing attack models, but also enables great flexibility and extensibility.OpenAttack has broad uses including comparing and evaluating attack models, measuring robustness of a model, assisting in developing new attack models, and adversarial training.
Fusion of Labeled RFS Densities With Minimum Information Loss

This paper addresses fusion of labeled random finite set (LRFS) densities according to the criterion of minimum information loss (MIL). The MIL criterion amounts to minimizing the (weighted) sum of Kullback-Leibler divergences (KLDs) with the fused density appearing as righth- and argument of the KLDs. The optimal fused density following the MIL rule is the linear opinion pool of local ones. However such results cannot be directly applied to the marginalized δ-generalized labeled multi-Bernoulli (Mδ-GLMB) and labeled multi-Bernoulli (LMB) densities since the resulting fused densities are not belonging to the same family of local ones, thus cannot be adopted in the context of Bayesian filtering which requires the conjugacy of prior. In order to overcome such difficulties, when LRFS densities are Mδ-GLMB or LMB, the MIL rule is further elaborated by imposing the constraint that the fused density be in the same family of local ones. The resulting fusion rules are derived in closed form and the error of replacing the optimal fused density with the best Mδ-GLMB/LMB one is theoretically analyzed. The derivation of MIL fusion of Mδ-GLMB/LMB densities is based on the assumption that their label spaces have been matched, which, however, cannot be satisfied in the majority of practical applications. It is shown theoretically that label matching can be solved as a separate problem with respect to LRFS density fusion by resorting to the MIL criterion. Finally, the performance of the proposed fusion approach is assessed via simulation experiments concerning distributed multitarget tracking (DMT) on a sensor network.
Multi‐model deep learning approach for collaborative filtering recommendation system

As a result of a huge volume of implicit feedback such as browsing and clicks, many researchers are involving in designing recommender systems (RSs) based on implicit feedback. Though implicit feedback is too challenging, it is highly applicable to use in building recommendation systems. Conventional collaborative filtering techniques such as matrix decomposition, which consider user preferences as a linear combination of user and item latent features, have limited learning capacities, hence suffer from a cold start and data sparsity problems. To tackle these problems, the research direction towards considering the integration of conventional collaborative filtering with deep neural networks to maps user and item features. Conversely, the scalability and the sparsity of the data affect the performance of the methods and limit the worthiness of the results of the recommendations. Therefore, the authors proposed a multi-model deep learning (MMDL) approach by integrating user and item functions to construct a hybrid RS and significant improvement. The MMDL approach combines deep autoencoder with a one-dimensional convolution neural network model that learns user and item features to predict user preferences. From detail experimentation on two real-world datasets, the proposed work exhibits substantial performance when compared to the existing methods.
SpeechBERT: An Audio-and-Text Jointly Learned Language Model for End-to-End Spoken Question Answering

While various end-to-end models for spoken language understanding tasks have been explored recently, this paper is probably the first known attempt to challenge the very difficult task of end-to-end spoken question answering (SQA). Learning from the very successful BERT model for various text processing tasks, here we proposed an audio-and-text jointly learned SpeechBERT model. This model outperformed the conventional approach of cascading ASR with the following text question answering (TQA) model on datasets including ASR errors in answer spans, because the end-to-end model was shown to be able to extract information out of audio data before ASR produced errors. When ensembling the proposed end-to-end model with the cascade architecture, even better performance was achieved. In addition to the potential of end-to-end SQA, the SpeechBERT can also be considered for many other spoken language understanding tasks just as BERT for many text processing tasks.
An Improved Blockchain-Based Authentication Protocol for IoT Network Management

Communication security between IoT devices is a major concern in this area, and the blockchain has raised hopes that this concern will be addressed. In the blockchain concept, the majority or even all network nodes check the validity and accuracy of exchanged data before accepting and recording them, whether this data is related to financial transactions or measurements of a sensor or an authentication message. In evaluating the validity of an exchanged data, nodes must reach a consensus in order to perform a special action, in which case the opportunity to enter and record transactions and unreliable interactions with the system is significantly reduced. Recently, in order to share and access management of IoT devices information with distributed attitude a new authentication protocol based on blockchain is proposed and it is claimed that this protocol satisfies user privacy preserving and security. However, in this paper, we show that this protocol has security vulnerabilities against secret disclosure, replay, traceability, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>Token</mtext> </math> reuse attacks with the success probability of 1 and constant complexity of also 1. We also proposed an improved blockchain-based authentication protocol (IBCbAP) that has security properties such as secure access management and anonymity. We implemented IBCbAP using JavaScript programming language and Ethereum local blockchain. We also proved IBCbAP’s security both informally and formally through the Scyther tool. Our comparisons showed that IBCbAP could provide suitable security along with reasonable cost.
RETRACTED: Ecological evolution path of smart education platform based on deep learning and image detection

Smart environments are becoming a reality in our society, and the number of smart devices integrated into these spaces is overgrowing. End users are being provided a simplified way to handle complex smart features, as the combination of smart elements opens up a wide range of new opportunities to facilitate. This article explores the significant challenges to be overcome in designing an intelligent educational environment for the main characteristics and the personalized support of ecology. To integrate intelligent learning environments into learning ecology and educational environments, innovative applications, and new teaching methods should be implemented to coordinate formal and informal learning. However, despite the increased use of smart learning environments in higher education, at the same time, there is an excellent network that does not define a set of demand models for the development and evaluation of smart learning environment education that considers teaching, evaluation, and design. Deep learning is one of the modern methods that can be used to automate the process of effective intellectual education based on image detection. The deep learning process is based on image discovery. It provides an overview of ecological evaluation based smart education level analysis used image detection. The system that has been proposed here is an intelligent education system that has been customized to provide the resources of the evolution of the ecosystem to the learner to suit their perceptions and education center to start the platform.
The Online and Campus (OaC) model as a sustainable blended approach to teaching and learning in higher education: A response to COVID-19

The COVID-19 pandemic represents an unprecedented challenge for wider society and has impacted all facets of life, including Higher Education Institution (HEIs) provision for teaching and learning – demanding an immediate digital response. The core challenge lies with the inherent choice made by students upon embarking on an undergraduate degree; that face-to-face learning was their preference. Now, HEIs must address this by utilising a range of digital solutions – that crucially, must also be embraced by those that no longer have the luxury to be risk averse or believe that digital solutions align with their existing pedagogical approaches. Higher Education Institutions should be – to an extent – well placed to deliver online provision. This paper aims to explore pertinent literature surrounding blended approaches with regards to key pedagogical and learning theories, with an overall aim of suggesting the Online and Campus (OaC) model as a potential ‘blueprint’ that incorporates campus, synchronous and asynchronous learning experiences. We refer to asynchronous as flexible, self-paced learning, and synchronous as an environment in which learners are in the same place at a given time (either online or campus) and accessing the same materials. For the purposes of this paper – and the OaC model – both asynchronous and synchronous learning refers to online provision, and we make the distinction between face-to-face teaching by reference to ‘Campus’.
Measuring Sentences Similarity: A Survey

Objective/Methods: This study is to review the approaches used for measuring sentences similarity.Measuring similarity between natural language sentences is a crucial task for many Natural Language Processing applications such as text classification, information retrieval, question answering, and plagiarism detection.This survey classifies approaches of calculating sentences similarity based on the adopted methodology into three categories.Word-to-word based, structurebased, and vector-based are the most widely used approaches to find sentences similarity.Findings/Application: Each approach measures relatedness between short texts based on a specific perspective.In addition, datasets that are mostly used as benchmarks for evaluating techniques in this field are introduced to provide a complete view on this issue.The approaches that combine more than one perspective give better results.Moreover, structure based similarity that measures similarity between sentences' structures needs more investigation.
Real-time camera pose estimation for sports fields

Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera. To this end, we propose a novel framework that combines accurate localization and robust identification of specific keypoints in the image by using a fully convolutional deep architecture. Our algorithm exploits both the field lines and the players' image locations, assuming their ground plane positions to be given, to achieve accuracy and robustness that is beyond the current state of the art. We will demonstrate its effectiveness on challenging soccer, basketball, and volleyball benchmark datasets.
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation

Data augmentation is one of the most effective ways to make end-to-end automatic speech recognition (ASR) perform close to the conventional hybrid approach, especially when dealing with low-resource tasks. Using recent advances in speech synthesis (text-to-speech, or TTS), we build our TTS system on an ASR training database and then extend the data with synthesized speech to train a recognition model. We argue that, when the training data amount is relatively low, this approach can allow an end-to-end model to reach hybrid systems' quality. For an artificial low-to-medium-resource setup, we compare the proposed augmentation with the semi-supervised learning technique. We also investigate the influence of vocoder usage on final ASR performance by comparing Griffin-Lim algorithm with our modified LPCNet. When applied with an external language model, our approach outperforms a semi-supervised setup for LibriSpeech test-clean and only 33% worse than a comparable supervised setup. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for test-other.
Explanation Ontology: A Model of Explanations for User-Centered AI

Explainability has been a goal for Artificial Intelligence (AI) systems since their conception, with the need for explainability growing as more complex AI models are increasingly used in critical, high-stakes settings such as healthcare. Explanations have often added to an AI system in a non-principled, post-hoc manner. With greater adoption of these systems and emphasis on user-centric explainability, there is a need for a structured representation that treats explainability as a primary consideration, mapping end user needs to specific explanation types and the system’s AI capabilities. We design an explanation ontology to model both the role of explanations, accounting for the system and user attributes in the process, and the range of different literature-derived explanation types. We indicate how the ontology can support user requirements for explanations in the domain of healthcare. We evaluate our ontology with a set of competency questions geared towards a system designer who might use our ontology to decide which explanation types to include, given a combination of users’ needs and a system’s capabilities, both in system design settings and in real-time operations. Through the use of this ontology, system designers will be able to make informed choices on which explanations AI systems can and should provide.
A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow

Cloud gaming is a class of services that promises to revolutionize the videogame market. It allows the user to play a videogame with essential equipment while using a remote server for the actual execution. The multimedia content is streamed through the network from the server to the user. Hence, this service requires low latency and a large bandwidth to work properly with low response time and high-definition video. Three of the leading tech companies (Google, Sony, and NVIDIA) entered this market with their products, and others, like Microsoft and Amazon, are also launching their platforms. However, these companies have released little information about their cloud gaming operation and how they utilize the network. In this work, we study cloud gaming services from the network point of view. We collect more than 200 packet traces under different application settings and network conditions from a broadband network to poor mobile network conditions, for 3 cloud gaming services, namely Stadia from Google, GeForce Now from NVIDIA and PS Now from Sony. We analyze the employed protocols and the workload that they impose on the network. We find that GeForce Now and Stadia use the RTP protocol to stream the multimedia content, with the latter relying on the standard WebRTC APIs. Depending on the network and video quality, they result in bandwidth-hungry services consuming up to 45 Mbit/s. PS Now instead uses only undocumented protocols and never exceeds 13 Mbit/s. 4G mobile networks can often sustain these loads, while traditional 3G connections struggle. The systems quickly react to deteriorated network conditions, and packet losses up to 5% do not cause a reduction in resolution.
An Adaptive Improved Ant Colony System Based on Population Information Entropy for Path Planning of Mobile Robot

In this paper, an adaptive improved ant colony algorithm based on population information entropy(AIACSE) is proposed to improve the optimization ability of the algorithm. The diversity of the population in the iterative process is described by the information entropy. The non-uniform distribution initial pheromone is constructed to reduce the blindness of the search at the starting phase. The pheromone diffusion model is used to enhance the exploration and collaboration capacity between ants. The adaptive parameter adjusting strategy and the novel pheromone updating mechanism based on the evolutionary characteristics of the population are designed to achieve a better balance between exploration of the search space and exploitation of the knowledge during the optimization progress. The performance of AIACSE is evaluated on the path planning of mobile robots. Friedman's test is further conducted to check the significant difference in performance between AIACSE and the other selected algorithms. The experimental results and statistical tests demonstrate that the presented approach significantly improves the performance of the ant colony system (ACS) and outperforms the other algorithms used in the experiments.
Refining a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>k</mml:mi></mml:math>-nearest neighbor graph for a computationally efficient spectral clustering

Spectral clustering became a popular choice for data clustering for its ability of uncovering clusters of different shapes. However, it is not always preferable over other clustering methods due to its computational demands. One of the effective ways to bypass these computational demands is to perform spectral clustering on a subset of points (data representatives) then generalize the clustering outcome, this is known as approximate spectral clustering (ASC). ASC uses sampling or quantization to select data representatives. This makes it vulnerable to 1) performance inconsistency (since these methods have a random step either in initialization or training), 2) local statistics loss (because the pairwise similarities are extracted from data representatives instead of data points). We proposed a refined version of k-nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency. Local statistics were exploited to keep the edges that do not violate the intra-cluster distances and nullify all other edges in the k-nearest neighbor graph. We also introduced an optional step to automatically select the number of clusters C. The proposed method was tested on synthetic and real datasets. Compared to ASC methods, the proposed method delivered a consistent performance despite significant reduction of edges.
3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.
Self-supervised Pretraining of Visual Features in the Wild

Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment, that is the highly curated ImageNet dataset. However, the premise of self-supervised learning is that it can learn from any random image and from any unbounded dataset. In this work, we explore if self-supervision lives to its expectation by training large models on random, uncurated images with no supervision. Our final SElf-supERvised (SEER) model, a RegNetY with 1.3B parameters trained on 1B random images with 512 GPUs achieves 84.2% top-1 accuracy, surpassing the best self-supervised pretrained model by 1% and confirming that self-supervised learning works in a real world setting. Interestingly, we also observe that self-supervised models are good few-shot learners achieving 77.9% top-1 with access to only 10% of ImageNet. Code: https://github.com/facebookresearch/vissl