Reviewerly/exHarmony huggingface dataset:
Unsupervised Feature Selection with self-Weighted and ℓ<sub>2,0</sub>-Norm Constraint

At data mining field, it is a fundamental problem to dispose of high-dimensional data. Many existing unsupervised methods select features by manifold learning or exploring spectral analysis, thus preserving the intrinsic structure of raw data. But most of them follow an assumption that all features are equally importance. To settle this problem, we draw a novel feature selection module that simultaneously performs learning of feature weights matrix, similarity graph structure and projection matrix, so that the local structure after feature weighting and subspace sparse projection is received. Finally, we solve the model based on ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,0</inf> -norm directly by an iterative optimization algorithm and demonstrate the feasibility and effectiveness of our approach via extensive experiments.
A Mutual Implicit Sentiment Analysis Model with Bundle-Aware Contrastive Learning

As a crucial task in natural language processing, sentiment analysis is widely applied in different fields. However, various implicit expressions interspersed in explicit expressions have become a stumbling block for the further improvement of sentiment analysis. Existing studies usually introduce external knowledge, such as knowledge graph, the context of implicit expressions, or external corpus, to enhance implicit sentiment detection. Different from them, the core idea of this paper is to form explicit-implicit bundles to ensure each batch has the two expressions, which does not rely on external resources. The proposed MB-CL is a Mutual learning model with Bundle-aware Contrastive Learning for the implicit sentiment analysis. Our MB-CL leverages mutual learning between contextualized and local-perceived feature extractors and adopts bundle-aware contrastive learning to align explicit and implicit expressions with the same sentiment in the embedding space while pushing apart expressions with different sentiments. The experiment results on the SMP2019 dataset show that MB-CL outperforms comparative methods in terms of F1 measure.
On Tracking a Stochastically Time-Varying Subspace

Subspace tracking has a long and still growing list of applications with recent interest stimulated by the advent of ’streaming’ data. Despite the considerable literature, all of it motivated by the desire to track time varying subspaces, there seems to be almost no analysis of that time varying problem. Rather existing analyses assume the subspace is fixed. Here, for the first time we develop a true subspace tracking analysis in continuous time. It turns out to be a very hard problem and so we tackle the problem of a rank 1 subspace.
HyperSteg: Hyperbolic Learning for Deep Steganography

Steganography is the art of hiding a secret message signal inside a publicly visible carrier with minimum perceptual loss in the carrier. In order to better hide information, it is critical to optimally represent the message-carrier wave interference while blending the message with the carrier. We propose HyperSteg: a novel steganography method in the hyperbolic space grounded in the hyperbolic properties of wave interference. Through hyperbolic learning, HyperSteg learns to better represent the hyperbolic properties of message-carrier interference with minimum additional computational cost. Through extensive experiments over image and audio datasets, we introduce HyperSteg as a practical, model and modality agnostic approach for information hiding.
A Benchmark for Evaluating Robustness of Spoken Language Understanding Models in Slot Filling

Slot filling is a major problem in spoken language understanding (SLU) task. However, the current SLU models may experience performance degradation when encountering unfamiliar data in different datasets. Meanwhile, as recent models are becoming more complex, retraining the model in a new application scenario would be too expensive. So we think it is important to study the robustness and generalization capability of SLU models. Then we propose the Natural Adversarial Slot Evaluator (NASE), a benchmark with adversarial SLU data to evaluate the robustness and generalization capability of SLU models on the task of slot filling. Our experiments and analysis reveal that all of the six SLU models have a significant performance degradation on NASE. The further analysis points out that the models rely more on the context of the slots than the slot values themselves to make predictions. In addition, the widespread use of joint learning strategy makes unfamiliar intents also affect the slot filling. Based on our findings, we also propose a simple data augmentation method to improve the robustness of SLU models in slot filling. The F1 Scores improve up to about 30% compared to the original model.
Multi-Layer Feature Division Transferable Adversarial Attack

Improving the transferability of adversarial examples for the purpose of attacking unknown black-box models has been intensively studied. In particular, feature-level transfer-based attacks, which destroy the intermediate feature outputs of source models, are proven to generate more transferable adversarial examples. However, existing state-of-the-art feature-level attacks only destroy a single intermediate layer, this severely limits the transferability of adversarial examples. And all of these attacks have a vague distinction between positive and negative features. By contrast, we propose the Multi-layer Feature Division Attack (MFDA), which aggregates multi-layer feature information on the basis of feature division to attack. Extensive experimental evaluation demonstrates that MFDA can significantly boost the adversarial transferability and quantitatively distinguish the effects of positive and negative features on transferability. Compared to the state-of-the-art feature-level attacks, our improvement methods with MFDA increase the average success rate by 2.8% against normally trained models and 3.0% against adversarially trained models.
Robust Video Object Segmentation with Restricted Attention

This paper focuses on the two problems of the similar objects distraction and the lack of robustness for unseen object categories in semi-supervised video object segmentation task. Existing methods have achieved great results on the benchmark dataset, but these two problems still have not been completely solved. We propose the Robust Video Object Segmentation With Restricted Attention (RVOSR), which can suppress the effects caused by similar objects and filter out noise confusion from other irrelevant regions. Meanwhile augmenting the semantic information of features, which makes the features more suitable for video object segmentation task. Extensive experiments demonstrate the effectiveness of our approach and achieve the state-of-the-art performance on the widely-used VOS benchmarks including DAVIS-2016 (92.1% $\mathcal{J}{{\& }}\mathcal{F}$), DAVIS-2017 (86.8% $\mathcal{J}{{\& }}\mathcal{F}$) and YouTubeVOS-2019 (84.8%).
Multi-Level Fusion for Burst Super-Resolution with Deep Permutation-Invariant Conditioning

Developing deep learning techniques for super-resolving bursts of images acquired by mobile cameras is a topic that has recently gained significant interest. This topic fits the general problem of learning-based multi-image super-resolution (SR), which, contrary to its sibling single-image SR, has so far received little attention despite its potential. In this work, we introduce a neural network architecture for burst SR, called MLB-FuseNet (Multi-Level Burst Fusion Network), that is capable of extracting features in a manner that is invariant to permutations in the burst and to progressively condition features extracted from a reference image. Permutation invariance is desirable as it is known that the order of images in a burst does not matter in this problem, but its study has so far been neglected. Moreover, we also introduce a module exploiting a polyphase decomposition to improve feature extraction from mosaiced raw images. Results show an improvement over the state of the art on the BurstSR dataset – a recent and popular benchmark for this problem.
SPECTRANET-SO(3): Learning Satellite Orientation from Optical Spectra by Implicitly Modeling Mutually Exclusive Probability Distributions on The Rotation Manifold

In the space domain, remotely measuring the rotational state of a spacecraft provides critical information for assessing its operational health. For the large family of space assets that lie in regions of space too distant to resolve spatial features with ground sensors, measurement of the energy spectrum of reflected sunlight has shown recent promise in probing their material and spatial properties. In this work, we explore the utility of using the recently proposed Implicit-PDF network to implicitly learn challenging probability distributions associated with satellite orientations using only raw optical spectra as input. Originally designed for computer vision applications, we detail how the Implicit-PDF architecture can be applied directly to spectra and further extended to a broad class of signal inputs. We discuss key aspects to improving performance and demonstrate the generalizability of the implicit framework by showing how a single network can efficiently learn mutually exclusive probability distributions over multiple satellite classes without meaningful performance loss. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Bit Error and Block Error Rate Training for ML-Assisted Communication

Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention.In this paper, we show that the commonly used binary cross-entropy (BCE) loss is a sensible choice in uncoded systems, e.g., for training ML-assisted data detectors, but may not be optimal in coded systems.We propose new loss functions targeted at minimizing the block error rate and SNR deweighting, a novel method that trains communication systems for optimal performance over a range of signal-to-noise ratios.The utility of the proposed loss functions as well as of SNR deweighting is shown through simulations in NVIDIA Sionna.An extended version of this work that includes an appendix with all the proofs, some information-theoretic comments, and experiments for another communication scenario is available on arXiv [1].
Towards Accurate and Real-Time End-of-Speech Estimation

We introduce a variant of the endpoint (EP) detection problem in automatic speech recognition (ASR), which we call the end-of-speech (EOS) estimation. Given an utterance, EOS estimation aims to identify the timestamp when the utterance waveform has fully decayed and is then used to measure the EP latency. Accurate EOS estimation is difficult in large-scale streaming audio scenarios due to the hefty traffic and hardware limitations. To this end, we develop an efficient and accurate framework by performing force alignment on the 1-best ASR hypothesis. In particular, we propose to use binarized states sequences for alignment, which yields an EOS estimation robust to ASR hypothesis, and the estimation error is reduced by 28% compared to aligning on phoneme states. In addition, we further observe a 30% error reduction by applying the intermediate-stage embeddings of the encoder as additional features to compute the binary probabilities.
Query-Utterance Attention With Joint Modeuing For Query-Focused Meeting Summarization

Query-focused meeting summarization (QFMS) aims to generate suimnaries from meeting transcripts in response to a given query. Previous works typically concatenate the query with meeting transcripts and implicitly model the query relevance only at the token level with attention mechanism. However, due to the dilution of key query-relevant infonnation caused by long meeting transcripts, the original transformer-based model is insufficient to highlight the key parts related to the query. In this paper, we propose a query-aware framework with joint modeling token and utterance based on Query-Utterance Attention. It calculates the utterance-level relevance to the query with a dense retrieval module. Then both token-level query relevance and utterance-level query relevance are combined and incorporated into the generation process with attention mechanism explicitly. We show that the query relevance of different granularities contributes to generating a suimnary more related to the query. Experimental results on the QMSum dataset show that the proposed model achieves new state-of-the-art perfonnance.
Self-Adaptive Reasoning on Sub-Questions for Multi-Hop Question Answering

In this paper, we present the Self-Adapting Reasoning Model (SAR) for solving multi-hop question answering (MHQA) tasks, where the QA system is supposed to find the correct answer within the given multiple documents and a multi-hop question. One feasible track on MHQA is question decomposition, based on the idea that a multi-hop question is usually made from several single-hop questions, which are much easier to answer. However, ignoring the inner connection between sub-questions, existing works usually train additional single-hop question-answering models and answer sub-questions separately. To tackle this problem, we design an end-to-end self-adaptive multi-hop reasoning model. Specifically, given a multi-hop question, a question decomposer first decomposes it into two simple questions and identifies the question type. Then, based on the question type, different reasoning strategies are applied for reasoning. This enables our model to be self-adapting and more explainable regarding different types of questions. Experiments are carried out to demonstrate the effectiveness of our model, and SAR achieves remarkable results on the HotpotQA dataset.
Nasty-SFDA: Source Free Domain Adaptation from a Nasty Model

A challenging problem called Nasty Source Free Domain Adaptation (Nasty-SFDA) is proposed in this work, where only a nasty source model and unlabeled target samples are available for DA. Further, after DA, the target model is expected to be a nasty model. In order to deal with Nasty-SFDA, Nasty HypOthesis Transfer (NHOT) with an improved version of Information Maximization (IM) loss called Multi-Peak Constraints (MPC) and several Label Generation (LG) techniques is proposed. Experiments on four popular datasets show the superiority of NHOT for both Nasty-SFDA and SFDA. In addition, the target model obtained via NHOT is proven to be a nasty model.
Role of Bias Terms in Dot-Product Attention

Dot-product attention is a core module in the present generation of neural network models, particularly transformers, and is being leveraged across numerous areas such as natural language processing and computer vision. This attention module is comprised of three linear transformations, namely query, key, and value linear transformations, each of which has a bias term. In this work, we study the role of these bias terms, and mathematically show that the bias term of the key linear transformation is redundant and could be omitted without any impact on the attention module. Moreover, we argue that the bias term of the value linear transformation has a more prominent role than that of the bias term of the query linear transformation. We empirically verify these findings through multiple experiments on language modeling, natural language understanding, and natural language generation tasks.
Asymmetric Polynomial Loss for Multi-Label Classification

Various tasks are reformulated as multi-label classification problems, in which the binary cross-entropy (BCE) loss is frequently utilized for optimizing well-designed models. However, the vanilla BCE loss cannot be tailored for diverse tasks, resulting in a suboptimal performance for different models. Besides, the imbalance between redundant negative samples and rare positive samples could degrade the model performance. In this paper, we propose an effective Asymmetric Polynomial Loss (APL) to mitigate the above issues. Specifically, we first perform Taylor expansion on BCE loss. Then we ameliorate the coefficients of polynomial functions. We further employ the asymmetric focusing mechanism to decouple the gradient contribution from the negative and positive samples. Moreover, we validate that the polynomial coefficients can recalibrate the asymmetric focusing hyperparameters. Experiments on relation extraction, text classification, and image classification show that our APL loss can consistently improve performance without extra training burden. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Design and Implementation of Smart Multipurpose Robot using Vex and Node MCU

The main objective of this research is to design and implement a Node Micro controller Unit (MCU) based multipurpose innovative robot which is capable of washing and painting the exterior and interior walls of high-rise buildings, watering plants and other tasks. Robots have grown to be crucial in modern society, due to their ability to complete tasks faster than humans. In this project work, a Vex cortex microcontroller is utilized to operate the robot. The most significant risks associated with cleaning and painting interior and external walls include breathing in paint fumes, causes issues with their eyes and respiratory systems. These reasons have led to the development of multipurpose robot that can wash and paint tall building walls, and irrigate farms professionally and safely in a short amount of time. There is also a servo motor that controls the lever that does the painting, and an ultrasonic sensor that detects the robot’s distance from the wall to be painted or the plant to be watered. The designed robot has the ability to paint an area of 0.302 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in 28.6 minutes which has an accuracy of 95.4%. It also has a water level and battery monitoring sensor that measures water or color and battery status & sends message to the user via Thing Speak. The designed multipurpose robot exhibits excellent precision performance, effective, simple and easy to implement.
Significance of Deep Learning in Artificial Intelligence Systems

This paper presents an idea about artificial intelligence, its applications along with deep learning, and how it has been used. The objective and outcomes of this paper are the objectives and an outcome of this paper is to gain knowledge of deep learning concepts. Learn how to train the model and improve the accuracy of the network model. And enable to development of sample projects using deep learning techniques.
Streaming Joint Speech Recognition and Disfluency Detection

Disfluency detection has mainly been solved in a pipeline approach, as post-processing of speech recognition. In this study, we propose Transformer-based encoder-decoder models that jointly solve speech recognition and disfluency detection, which work in a streaming manner. Compared to pipeline approaches, the joint models can leverage acoustic information that makes disfluency detection robust to recognition errors and provide non-verbal clues. Moreover, joint modeling results in low-latency and lightweight inference. We investigate two joint model variants for streaming disfluency detection: a transcript-enriched model and a multi-task model. The transcript- enriched model is trained on text with special tags indicating the starting and ending points of the disfluent part. However, it has problems with latency and standard language model adaptation, which arise from the additional disfluency tags. We propose a multi-task model to solve such problems, which has two output layers at the Transformer decoder; one for speech recognition and the other for disfluency detection. It is modeled to be conditioned on the currently recognized token with an additional token-dependency mechanism. We show that the proposed joint models outperformed a BERT-based pipeline approach in both accuracy and latency, on both the Switch- board and the corpus of spontaneous Japanese.
Gesture Based Computer Control System

The whole world has been witnessing the gigantic enemy in the form of COVID-19 since March 2020. With its super-fast spread, it has devastated a major part of the world and found to be the most dangerous virus of the 21st Century. All countries went into a lockdown to control the spread of the virus, and the economy dropped down to an all- time low index. The major guideline to avoid the spread of diseases like COVID- 19 at work is avoiding contact with people and their belongings. It is not safe to use computing devices because it may result in the spread of the virus by touching them. This paper presents an Artificial Intelligence- based virtual mouse that detects or recognizes hand gestures to control the various functions of a personal computer. The virtual mouse Algorithm uses a webcam or a built-in camera of the system to capture hand gestures, then uses an algorithm to detect the palm boundaries similar to that of the face detection model of the media pipe face mesh algorithm. After tracing the palm boundaries, it uses a regression model and locates the 21 3D hand-knuckle coordinate points inside the recognized hand/palm boundaries. Once the Hand Landmarks are detected, they are used to call windows Application Programming Interface (API) functions to control the functionalities of the system. The proposed algorithm is tested for volume control and cursor control in a laptop with the Windows operating system and a webcam. The proposedsystem took only 1ms to identify the gestures and control the volume and cursor in real-time.
Creating An Artificial Intelligence-Powered Image Classification Model For Specially Abled Persons

This article describes how to use Python’s Keras deep learning package to build an Artificial Intelligence-powered image classification model. Speech, text, and other types of input are transformed into machine-readable form by the system using natural language processing (NLP) and speech recognition technology. Other assistive technologies can be adapted to and used with the system. The purpose of this study is to show how straightforward and simple it is to use Keras to create an accurate picture categorization model. Convolutional neural network (CNN) architecture, a kind of deep learning model best suited for image classification tasks, is built first in the procedure. Following that, the model is assembled and trained on a sizable dataset, where it discovers patterns and features in the photos. The model is tested and evaluated on fresh data after training to ensure accuracy. The outcomes demonstrate that Keras can efficiently and accurately construct an AI-powered picture classification model, making it a useful tool for resolving practical computer vision issues. This demonstrates the capabilities of AI in this area and offers a thorough manual for people wishing to create their own picture classification models using Keras.