Reviewerly/exHarmony huggingface dataset:
InFi

Mobile-centric AI applications put forward high requirements for resource-efficiency of model inference. Input filtering is a promising approach to eliminate the redundancy in the input so as to reduce the cost of inference. Previous efforts have tailored effective solutions for many applications, but left two essential questions unanswered: (1) theoretical filterability of an inference workload to guide the application of input filtering techniques, thereby avoiding the trial-and-error cost for resource-constrained mobile applications; (2) robust discriminability of feature embedding to allow input filtering to be widely effective for diverse inference tasks and input content. To answer these questions, we first provide a generic formalization of the input filtering problem and theoretically compare the hypothesis complexity of inference models and their input filters to understand the optimization potential of applying input filtering. Then we propose the first end-to-end learnable input filtering framework that covers most state-of-the-art methods and surpasses them in feature embedding with robust discriminability. Based on our framework, we design and implement an input filtering system InFi supporting six input modalities. InFi is the first to support text and sensor signal inputs and model partitioning deployments widely adopted by under-resourced mobile systems. Comprehensive evaluations confirm our theoretical results and show that InFi outperforms strong baselines in applicability, accuracy, and efficiency, owing to its generality and end-to-end learnability. InFi can achieve 8.5X throughput and save 95% bandwidth, while keeping over 90% accuracy, for a video analytics app on mobile platforms.
An Efficient Lightweight Image Encryption Scheme Using Multichaos

With an immense increase in Internet multimedia applications over the past few years, digital content such as digital images are stored and shared over global networks, the probability for information leakage and illegal modifications to the digital content is at high risk. These digital images are transferred using the network bandwidth; therefore, secure encryption schemes facilitate both information security and bandwidth issues. Hence, a state-of-the-art lightweight information security methodology is required to address this challenge. The main objective of this work is to develop a lightweight nonlinear mechanism for digital image security using chaos theory. The proposed scheme starts by changing a plain image into an encrypted image to improve its security. A block cipher, using lightweight chaos, has been added to achieve this objective for digital image security. We utilized multiple chaotic maps to generate random keys for each channel. Also, Arnold cat map and chaotic gingerbread map are used to add confusion and diffusion. During the permutation stage, image pixels are permuted, while in diffusion stage, pixels are distorted utilizing gingerbread map to add more security. The proposed scheme has been validated using different security parameter tests such as correlation coefficient tests (CC), whose results have been observed closer to zero and information entropy (IE) value is 7.99, respectively, which is almost equal to the ideal value of 8. Moreover, number of pixels changing rate (NPCR) obtained value is higher than 99.50%, while the unified average changing intensity (UACI) is 33.33. Other parameters such as mean absolute error (MAE), mean square error (MSE), lower value of peak to signal noise ratio (PSNR), structural content (SC), maximum difference (MD), average difference (AD), normalized cross-correlation (NCC), and histogram analysis (HA) is tested. The computed values of the proposed scheme are better. The achieved results after comparison with existing schemes highlight that the proposed scheme is highly secure, lightweight, and feasible for real-time communications.
Variational Autoencoder for Classification and Regression for Out-of-Distribution Detection in Learning-Enabled Cyber-Physical Systems

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mmGesture: Semi-supervised gesture recognition system using mmWave radar

Gesture recognition has found versatile applications in natural human–computer interaction (HCI). Compared with traditional camera-based or wearable sensors-based solutions, gesture recognition using the millimeter wave (mmWave) radar has attracted growing attention for its characteristics of contact-free, privacy-preserving and less environment-dependence. Recently, most of studies adopted one of the Range Doppler Image (RDI), Range Angle Image (RAI), Doppler Angle Image (DAI) or Micro-Doppler Spectrogram extracted from the raw radar signal as the input of a deep neural network to realize gesture recognition. However, the effectiveness of these four inputs in gesture recognition has attracted little attention so far. Moreover, the lack of large amounts of labeled data restricts the performance of traditional supervised learning network. In this paper, we first conducted extensive experiments to compare the effectiveness of these four inputs in the gesture recognition, respectively. Then we proposed a semi-supervised leaning framework by utilizing few labeled data in the source domain and large amounts of unlabeled data in the target domain. Specially, we combine the ∏-model and some specific data augmentation tricks on the mmWave signal to realize the domain-independent gesture recognition. Extensive experiments on a public mmWave gesture dataset demonstrate the superior effectiveness of the proposed system.
A novel metaheuristic optimisation approach for text sentiment analysis

Abstract Automated sentiment analysis is considered an area in natural language processing research that seeks to understand a text author's mood, thoughts, and feelings. New opportunities and challenges have arisen in this field due to the popularity and accessibility of a variety of resources of ideas, such as online review websites, personal blogs, and social media. Feature selection, which can be conducted using metaheuristic algorithms, is one of the steps of sentiment analysis. It is crucial to use high-performing algorithms for feature selection. This paper applies the Horse herd Optimisation Algorithm (HOA) for feature selection in text sentiment analysis. HOA is a metaheuristic algorithm and uses six key behaviours to simulate the social performance of horses of various ages, to solve high-dimensional optimisation problems. In order to improve HOA, this paper adds another behaviour of horses to the basic algorithm; thus, the new algorithm uses seven key behaviours of horses of different ages to imitate their social performance. It is then discretised and converted to a multi-objective algorithm. The improved algorithm's performance is evaluated using 15 CEC benchmark functions, and the results are compared to the Binary Social Spider Algorithm, the Binary Grey Wolf Optimizer, and the Binary Butterfly Optimization Algorithm. The new algorithm, the Multi-objective Binary Horse herd Optimisation Algorithm (MBHOA), excels at solving high-dimensional complex problems. To evaluate the algorithm's performance in feature selection, as a practical example, it is employed in text sentiment analysis and examined on various data sets. The simulation results indicate that MBHOA has a better performance in analysing sentiment compared to similar approaches.
Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis

Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO).
SPOT

Knowledge-enhanced pre-trained models for language representation have been shown to be more effective in knowledge base construction tasks (i.e.,~relation extraction) than language models such as BERT. These knowledge-enhanced language models incorporate knowledge into pre-training to generate representations of entities or relationships. However, existing methods typically represent each entity with a separate embedding. As a result, these methods struggle to represent out-of-vocabulary entities and a large amount of parameters, on top of their underlying token models (i.e., the transformer), must be used and the number of entities that can be handled is limited in practice due to memory constraints. Moreover, existing models still struggle to represent entities and relationships simultaneously. To address these problems, we propose a new pre-trained model that learns representations of both entities and relationships from token spans and span pairs in the text respectively. By encoding spans efficiently with span modules, our model can represent both entities and their relationships but requires fewer parameters than existing models. We pre-trained our model with the knowledge graph extracted from Wikipedia and test it on a broad range of supervised and unsupervised information extraction tasks. Results show that our model learns better representations for both entities and relationships than baselines, while in supervised settings, fine-tuning our model outperforms RoBERTa consistently and achieves competitive results on information extraction tasks.
Self-Supervised Learning for Recommendation

Recommender systems are playing an increasingly critical role to alleviate information overload and satisfy users' information seeking requirements in a wide spectrum of online platforms. However, the ubiquity of data sparsity and noise notably limits the representation capacity of existing recommender systems to learn high-quality user (item) embeddings. Inspired by recent advances of self-supervised learning (SSL) techniques, SSL-based representation learning models benefit a variety of recommendation domains. Such methods have achieved new levels of performance while reducing the dependence on observed supervision labels in diverse recommendation tasks. In this tutorial, we aim to provide a systemic review of state-of-the-art SSL-based recommender systems. To be specific, we summarize and categorize existing work of SSL-based recommender systems in terms of recommendation scenarios. For each type of recommendation task, the corresponding challenges and methods will be presented in a comprehensive way. Finally, some future directions and open questions will be raised to inspire more investigation on this important research line.
MAE4Rec

Sequential recommender systems (SRS) aim to infer the users' preferences from their interaction history and predict items that will be of interest to the users. The majority of SRS models typically incorporate all historical interactions for next-item recommendations. Despite their success, feeding all interactions into the model without filtering may lead to severe practical issues: (i) redundant interactions hinder the SRS model from capturing the users' intentions; (ii) the computational cost is huge, as the computational complexity is proportional to the length of the interaction sequence; (iii) more memory space is necessitated to store all interaction records from all users. To this end, we propose a novel storage-saving SRS framework, MAE4Rec, based on a unidirectional self-attentive mechanism and masked autoencoder. Specifically, in order to lower the storage consumption, MAE4Rec first masks and discards a large percentage of historical interactions, and then infers the next interacted item solely based on the latent representation of unmarked ones. Experiments on two real-world datasets demonstrate that the proposed model achieves competitive performance against state-of-the-art SRS models with more than 40% compression of storage.
Multi-task Learning with Adaptive Global Temporal Structure for Predicting Alzheimer's Disease Progression

In this paper, we propose a multi-task learning approach for predicting the progression of Alzheimer's disease (AD), known as the most common form of dementia. The vital challenge is to identify how the tasks are related and build learning models to capture such task relatedness. Unlike previous methods that assume low-rank structure, chase the predefined local temporal relatedness or utilize local approximation, we propose a novel penalty termed <u>L</u> ongitudinal <u>S</u> tability <u>A</u> djustment (LSA) to adaptively capture the intrinsic global temporal correlation among multiple time points and thus utilize the accumulated disease progression information. We combine LSA with sparse group Lasso to present a novel multi-task learning formulation to identify biomarkers closely related to cognitive measurement and predict AD progression. Two efficient algorithms are designed for large-scale dataset. Experimental results conducted on two AD data sets demonstrate our framework outperforms competing methods in terms of overall and each task performances. We also perform stability selection to identify stable biomarkers from the MRI feature set and analyze their temporal patterns in disease progression.
Dr. Can See: Towards a Multi-modal Disease Diagnosis Virtual Assistant

Artificial Intelligence-based clinical decision support is gaining ever-growing popularity and demand in both the research and industry communities. One such manifestation is automatic disease diagnosis, which aims to assist clinicians in conducting symptom investigations and disease diagnoses. When we consult with doctors, we often report and describe our health conditions with visual aids. Moreover, many people are unacquainted with several symptoms and medical terms, such as mouth ulcer and skin growth. Therefore, visual form of symptom reporting is a necessity. Motivated by the efficacy of visual form of symptom reporting, we propose and build a novel end-to-end Multi-modal Disease Diagnosis Virtual Assistant (MDD-VA) using reinforcement learning technique. In conversation, users' responses are heavily influenced by the ongoing dialogue context, and multi-modal responses appear to be of no difference. We also propose and incorporate a Context-aware Symptom Image Identification module that leverages discourse context in addition to the symptom image for identifying symptoms effectively. Furthermore, we first curate a multi-modal conversational medical dialogue corpus in English that is annotated with intent, symptoms, and visual information. The proposed MDD-VA outperforms multiple uni-modal baselines in both automatic and human evaluation, which firmly establishes the critical role of symptom information provided by visuals . The dataset and code are available at https://github.com/NLP-RL/DrCanSee
ranx.fuse: A Python Library for Metasearch

This paper presents ranx.fuse, a Python library for Metasearch. Built following a user-centered design, it provides easy-to-use tools for combining the results of multiple search engines. ranx.fuse comprises 25 Metasearch algorithms implemented with Numba, a just-in-time compiler for Python code, for efficient vector operations and automatic parallelization. Moreover, in conjunction with the Metasearch algorithms, our library implements six normalization strategies that transform the search engines' result lists to make them comparable, a mandatory step for Metasearch. Finally, as many Metasearch algorithms require a training or optimization step, ranx.fuse offers a convenient functionality for their optimization that evaluates pre-defined hyper-parameters configurations via grid search. By relying on the provided functions, the user can optimally combine the results of multiple search engines in very few lines of code. ranx.fuse can also serve as a user-friendly tool for fusing the rankings computed by a first-stage retriever and a re-ranker, as a library providing several baselines for Metasearch, and as a playground to test novel normalization strategies.
Early Stage Sparse Retrieval with Entity Linking

Despite the advantages of their low-resource settings, traditional sparse retrievers depend on exact matching approaches between high-dimensional bag-of-words (BoW) representations of both the queries and the collection. As a result, retrieval performance is restricted by semantic discrepancies and vocabulary gaps. On the other hand, transformer-based dense retrievers introduce significant improvements in information retrieval tasks by exploiting low-dimensional contextualized representations of the corpus. While dense retrievers are known for their relative effectiveness, they suffer from lower efficiency and lack of generalization issues, when compared to sparse retrievers. For a lightweight retrieval task, high computational resources and time consumption are major barriers encouraging the renunciation of dense models despite potential gains. In this work, we propose boosting the performance of sparse retrievers by expanding both the queries and the documents with linked entities in two formats for the entity names: 1) explicit and 2) hashed. We employ a zero-shot end-to-end dense entity linking system for entity recognition and disambiguation to augment the corpus. By leveraging the advanced entity linking methods, we believe that the effectiveness gap between sparse and dense retrievers can be narrowed. We conduct our experiments on the MS MARCO passage dataset. Since we are concerned with the early stage retrieval in cascaded ranking architectures of large information retrieval systems, we evaluate our results using [email protected] Our approach is also capable of retrieving documents for query subsets judged to be particularly difficult in prior work. We further demonstrate that the non-expanded and the expanded runs with both explicit and hashed entities retrieve complementary results. Consequently, we adopt a run fusion approach to maximize the benefits of entity linking.
The SimIIR 2.0 Framework

Simulated user retrieval system interactions enable studies with controlled user behavior. To this end, the SimIIR framework offers static, rule-based methods. We present an extended SimIIR 2.0 version with new components for dynamic user type-specific Markov model-based interactions and more realistic query generation. A flexible modularization ensures that the SimIIR 2.0 framework can serve as a platform to implement, combine, and run the growing number of proposed search behavior and query simulation ideas.
Distributed-Ledger-Based Blockchain Technology for Reliable Electronic Voting System with Statistical Analysis

In today’s society, voting is crucial to choosing the representatives of the people. The current voting process is filled with a vast array of disputes and manipulations. The leader must be selected in a precise manner without any malpractices. In addition, the people and authorities are not happy with the election results and label them unpredictable. We offer a better solution to the current problems, such as tampering, non-residents voting outside of the polling place, quick results analysis, quick counting, and reduced use of staff and funds during the electoral franchise process. In this offer, blockchain technology is used to create the distributed application (dApp) framework that will be used for the proposed e-voting system. Additionally, it offers unique characteristics such as immutability, transparency, privacy, and reception freedom that reduce crimes involving the processing of sensitive data in the electoral process. Ganache, MetaMask, and specified dagger hashing algorithm are used to develop the dApp. A key strength of this paper is the statistical analysis of transactions on the blockchain. Moreover, it also provides security to voters’ identity and leads to immediate acceptable counting results with more accuracy.
CSPP-IQA: a multi-scale spatial pyramid pooling-based approach for blind image quality assessment

The traditional image quality assessment (IQA) methods are usually based on convolutional neural networks (CNNs). For these IQA methods using CNNs, limited by the feature size of the fully connected layer, the input image needs be tailored to a pre-defined size, which usually results in destroying the original structure and content of the input image and thus reduces the accuracy of the quality assessment. In this paper, a blind image quality assessment method (named CSPP-IQA), which is based on multi-scale spatial pyramid pooling, is proposed. CSPP-IQA allows inputting the original image when assessing the image quality without any image adjustment. Moreover, by facilitating the convolutional block attention module and image understanding module, CSPP-IQA achieved better accuracy, generalization and efficiency than traditional IQA methods. The result of experiments running on real-scene IQA datasets in this study verified the effectiveness and efficiency of CSPP-IQA.
Detection of Glass Insulators Using Deep Neural Networks Based on Optical Imaging

As the pre-part of tasks such as fault detection and line inspection, insulator detection is a crucial task. However, considering the complex environment of high-voltage transmission lines, the traditional insulator detection accuracy is unsatisfactory, and manual inspection is dangerous and inefficient. To improve this situation, this paper proposes an insulator detection model Siamese ID-YOLO based on a deep neural network. The model achieves the best balance between speed and accuracy compared with traditional detection methods. In order to achieve the purpose of image enhancement, this paper adopts the canny-based edge detection operator to highlight the edges of insulators to obtain more semantic information. In this paper, based on the Darknet53 network and Siamese network, the insulator original image and the edge image are jointly input into the model. Siamese IN-YOLO model achieves more fine-grained extraction of insulators through weight sharing between Siamese networks, thereby improving the detection accuracy of insulators. This paper uses statistical clustering analysis on the area and aspect ratio of the insulator data set, then pre-set and adjusts the hyperparameters of the model anchor box to make it more suitable for the insulator detection task. In addition, this paper makes an insulator dataset named InsuDaSet based on UAV(Unmanned Aerial Vehicle) shoot insulator images for model training. The experiments show that the insulator detection can reach 92.72% detection accuracy and 84FPS detection speed, which can fully meet the online insulator detection requirements.