Reviewerly/exHarmony huggingface dataset:
Practical overview of image classification with tensor-network quantum circuits

Circuit design for quantum machine learning remains a formidable challenge. Inspired by the applications of tensor networks across different fields and their novel presence in the classical machine learning context, one proposed method to design variational circuits is to base the circuit architecture on tensor networks. Here, we comprehensively describe tensor-network quantum circuits and how to implement them in simulations. This includes leveraging circuit cutting, a technique used to evaluate circuits with more qubits than those available on current quantum devices. We then illustrate the computational requirements and possible applications by simulating various tensor-network quantum circuits with PennyLane, an open-source python library for differential programming of quantum computers. Finally, we demonstrate how to apply these circuits to increasingly complex image processing tasks, completing this overview of a flexible method to design circuits that can be applied to industrially-relevant machine learning tasks.
Secure Federated Learning: An Evaluation of Homomorphic Encrypted Network Traffic Prediction

With the increasing level of connectivity to the internet, especially wireless system, network traffic monitoring has become an active field of research. Network traffic analysis has many applications, including in resource allocation or management. However, the growing concern regarding privacy makes it difficult for different entities to share network traffic information. Federated learning and homomorphic encryption have been proposed in previous research as a solution to a secure collaborative analysis, but the practicality as well as a thorough evaluation of the approach have to be explored. This article aims to provide a practical study that could be implemented in real life. Aspects like secure multi-party computation are investigated, which allows organization to use different private keys. In addition, data used for the evaluation are generated in totally different environments. These new features are considered since in practice, companies will not use the same private keys and also network traffic data often come from different type of companies. A detailed evaluation of the approach is also presented.
LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices

Data integrity becomes paramount as the number of Internet of Things (ioT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanisms that detect drifts and irregularities can prevent disruptions and data bias in the state of an IoT application. This paper presents LE3D, an ensemble framework of data drift estimators capable of detecting abnormal sensor behaviours. Working collaboratively with surrounding ioT devices, the type of drift (natural/abnormal) can also be identified and reported to the end-user. The proposed framework is a lightweight and unsupervised implementation able to run on resource-constrained IoT devices. Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration. We compare our method against state-of-the-art ensemble data drift detection frameworks, evaluating both the real-world detection accuracy as well as the resource utilisation of the implementation. Experimenting with real-world data and emulated drifts, we show the effectiveness of our method, which achieves up to 97% of detection accuracy while requiring minimal resources to run.
Compression of ECG Signals Using an Adaptive Algorithm Based on DWT and SPIHT Decoder for Health Care Applications

A novel concept of mobile health applications has been proposed as a combination of telecommunication and multimedia technologies in providing telemedicine facility. In medical practises, the storage capacity of ECG records is a major issue and data could take large space for storage to save the records. To deal this issue, compression of ECG signal is widely used along with adaptive algorithm for noise removal of the signal. In this paper, an efficient method of ECG signal compression using discrete wavelet transform and SPIHT Decoder is proposed. The Adaptive Filtering techniques are mostly used to resolve the problems in digital communications environment and in the area of biomedical engineering. Various adaptive filtering methods have been proposed for cancellation of spurious noise in Electrocardiogram (ECG) signals. In order to make compression of ECG signals in which mainly need to remove the noise interference, a new adaptive filtering technique using FNLMS algorithm was proposed to reduce mean square error to denoise the ECG signal. A novel and powerful ECG signal compression using Discrete Wavelet Transform based image compression technique called Set Partitioning in Hierarchical Trees (SPIHT) is becoming a state-of-art algorithm for image compression due to high image quality and simple quantization algorithm. The proposed design was implemented in Xilinx platform and MATLAB tool. The performance of SPIHT Decoder design using FNLMS algorithm can be estimated by the metric parameters like Compression Ratio, Signal to Noise Ratio, Percent Root Mean Square Difference and Root Mean Square Error.
Research Guide for ML based Smart Tourist System

Sri Lanka needs to adapt to new trends in order to give tourists better service if it hopes to dominate the world of tourism. One of the sectors producing the most money throughout the world is the tourist business. The greatest objective of the project is to assist the development of the tourism sector by recommending the suitable spots for users based on factors such as age group, sex, religious doctrine, region, travelled month, type(s) of travel group, and food choices, as ll as by suggesting the most appropriate transport systems and modes, customizing and managing travel times. Additionally, the proposed approach gives hotels and other relevant service providers the chance to tailor their operations to the demands of tourists. Also got a previous visitor data set from the Sri Lankan Tourism Board as ll as data from travelers utilizing a Google poll conducted by a travel operator. In this work, provided a categorization of AI models to foretell the best sites and the most effective transport methods. Random Forest was chosen as the classifier for this investigation because it has a 90% accurateness for certainly intended and an 86% accuracy level for transit type prediction. The other is a simple exponential time series model, which has demonstrated accuracy at a level of 84.74%. An algorithm that produces journey plans according to user choices is used to create an optimistic travel plan.
Public-attention-based Adversarial Attack on Traffic Sign Recognition

Autonomous driving systems (ADS) can instantaneously and accurately recognize traffic signs by using deep neural networks (DNNs). Although adversarial attacks are well-known to easily fool DNNs by adding tiny but malicious perturbations, most attack methods require sufficient information about the victim models (white-box) to perform. In this paper, we propose a black-box attack in the recognition system of ADS, Public Attention Attacks (PAA), that can attack a black-box model by collecting the generic attention patterns of other white-box DNNs to transfer the attack. Particularly, we select multiple dual or triple attention patterns of white-box model combinations to generate the transferable adversarial perturbations for PAA attacks. We perform the experimentation on four well-trained models in different adversarial settings separately. The results indicate that when more white-box models the adversary collects to perform PAA, the higher the attack success rate (ASR) he can achieve to attack the target black-box model.
Early Fusion of Phone Embeddings for Recognition of Low-Resourced Accented Speech

We describe a supervised technique in this paper for accented speech recognition using constrained resources. One problem with existing research is that less work has been accomplished on using phonology for understanding spoken text. We suggest using early fusion of phone embeddings to recognize accented speech from a small sample dataset. Using PocketSphinx, the phonemes are recovered from the .WAV recordings. FastText's character n-gram based subword embeddings transforms the phonemes into vectors. To keep the vectors uniform, we concatenated and padded the vectors. The accuracy of the early fusion of phone embeddings for accented speech recognition is 49.01% for 10 sentence categories of the L2- ARCTIC accented speech corpus, which is higher than the accuracy of existing techniques. We seek to prove through our work that audio phonemes can play a significant role in accented speech recognition even when the number of training samples are few.
Triple-attention interaction network for breast tumor classification based on multi-modality images

Breast cancer can be diagnosed using medical imaging. Classification performance of medical imaging can be improved by multi-modality image fusion. However, existing fusion algorithm fail to consider the importance of modality interactions and cannot fully utilize multi-modality information. Attention mechanisms can effectively explore and combine multi-modality information. Thus, we propose a novel triple-attention interaction network for breast tumor classification based on diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images. A triple inter-modality interaction mechanism is proposed to fully fuse the multi-modality information. Three modal interactions were performed through the developed inter-modality relation module, channel interaction module, and multi-level attention fusion module to explore the correlation, complementary, and discriminative information, respectively. Additionally, we introduce a novel dual parallel-attention module for the incorporation of spatial and channel attention to improve the discriminative ability of single-modality features. Using these mechanisms, the proposed algorithm can mine and explore useful multi-modality information fully, to improve classification performance. Experimental results demonstrate that our algorithm outperforms other multi-modality fusion algorithm, and extensive ablation studies were conducted to verify the advantages of our algorithm. The area under the receiver operating characteristic curve, accuracy, specificity, and sensitivity were 90.5%, 89.0%, 85.6%, and 92.4%, respectively.
LiteF2DNet: a lightweight learning framework for 3D reconstruction using fringe projection profilometry

Fringe projection profilometry (FPP) is the most commonly used structured light approach for 3D object profiling. Traditional FPP algorithms have multistage procedures that can lead to error propagation. Deep-learning-based end-to-end models currently have been developed to mitigate this error propagation and provide faithful reconstruction. In this paper, we propose LiteF2DNet, a lightweight deep-learning framework to estimate the depth profile of objects, given reference and deformed fringes. The proposed framework has dense connections in the feature extraction module to aid better information flow. The parameters in the framework are 40% less than those in the base model, which also means less inference time and limited memory requirements, making it suitable for real-time 3D reconstruction. To circumvent the tedious process of collecting real samples, synthetic sample training was adopted in this work using Gaussian mixture models and computer-aided design objects. The qualitative and quantitative results presented in this work demonstrate that the proposed network performs well compared to other standard methods in the literature. Various analysis plots also illustrate the model's superior performance at high dynamic ranges, even with low-frequency fringes and high noise. Moreover, the reconstruction results on real samples show that the proposed model can predict 3D profiles of real objects with synthetic sample training.
Rethinking Serendipity in Recommender Systems

Recommender systems suggest items, such as movies or books, to users based on their interests. These systems often suggest items that users are either already familiar with or could easily have found on their own without additional assistance. To overcome these problems, recommender systems aim to suggest serendipitous items. While there is a lack of consensus in the recommender systems research community on the definition of serendipity, it is often conceptualized as a complex combination of relevance, novelty and unexpectedness. However, the common understanding and original meaning of serendipity is conceptually broader, requiring serendipitous encounters to be neither novel nor unexpected. Recent work in the social sciences has highlighted the various ways that serendipity can manifest, leading to a more generalized definition of serendipity. We argue that the study of serendipity in recommender systems would benefit from considering items that are serendipitous under this more general definition, giving us a deeper understanding of the item characteristics and behavioral impact of serendipitous recommendations. These findings will help us to better optimize recommender systems for serendipity. In this paper, we explore various definitions of serendipity and propose a novel formalization of what it means for recommendations to be serendipitous. Lastly, we present an experimental design for how serendipity can be measured in a deployed recommender system.
Uncertainties and coherence in DQC1

Abstract Correlations have been identified as a necessary ingredient for the speedup of certain mixed-state quantum computation. In this work, we analyze the model of deterministic quantum computation with one bit (DQC1) and study its information-theoretic aspects involving state-channel interaction. This state-channel interaction provides a new perspective for investigating DQC1. More specifically, by calculating some important information-theoretic quantities related to the state-channel interaction, such as uncertainties, coherence of quantum states as measured with respect to channels, etc., we reveal some basic features of quantum resources for the quantum speedup in this model. Moreover, since the state-channel framework is general enough to encompass all computational models, the studies performed here can be similarly applied to analyze other computational models from the perspective of quantum resources.
Multi-domain adaptation for regression under conditional distribution shift

Domain adaptation (DA) methods facilitate cross-domain learning by minimizing the marginal or conditional distribution shift between domains. However, the conditional distribution shift is not well addressed by existing DA techniques for the cross-domain regression learning task. In this paper, we propose Multi-Domain Adaptation for Regression under Conditional shift (DARC) method. DARC constructs a shared feature space such that linear regression on top of that space generalizes to all domains. In other words, DARC aligns different domains according to the task-related information encoded in the values of the dependent variable. It is achieved using a novel Pairwise Similarity Preserver (PSP) loss function. PSP incentivizes the differences between the outcomes of any two samples, regardless of their domain(s), to match the distance between these samples in the constructed space. We perform experiments in both two-domain and multi-domain settings. The two-domain setting is helpful, especially when one domain contains few available labeled samples and can benefit from adaptation to a domain with many labeled samples. The multi-domain setting allows several domains, each with limited data, to be adapted collectively; thus, multiple domains compensate for each other's lack of data. The results from all the experiments conducted both on synthetic and real-world datasets confirm the effectiveness of DARC.
Bidirectional relation-guided attention network with semantics and knowledge for relational triple extraction

Relational triple extraction is aimed at detecting entity pairs with relations from sentences, which is a key technology for large-scale knowledge graph construction. Recent studies focus on the overlapping triple problem, where multiple relational triples may have overlaps in a sentence. However, these methods disregard the bidirectionality of triple extraction, which may lead to extracting invalid triples. In addition, many relational triples are labeled in datasets of the triple extraction task, implying domain knowledge information of these datasets, but current methods rarely consider it. In this paper, we present a bidirectional relation-guided attention network with semantics and knowledge (BRASK) for relational triple extraction. BRASK is a bidirectional extraction framework that is based on multitask learning and contains forward and backward triple extraction tasks. Forward triple extraction and backward triple extraction are parallel and complementary, which can obtain predicted results with high confidence. We utilize semantic relations and knowledge relations as guidance in forward triple extraction and backward triple extraction, respectively, thus integrating general and domain knowledge into our model. In addition, we adopt an attention mechanism to learn fine-grained sentence representations for different relations. BRASK can solve the triple overlap problem and capture bidirectional dependencies between subjects and objects. Experimental results show that BRASK achieves new state-of-the-art results in two public datasets, which demonstrates its effectiveness.
Job Applications Selection and Identification: Study of Resumes with Natural Language Processing and Machine Learning

For each job ad, the organization received a high number of applicants. Finding the right person's application from various applications is a time-consuming task for any company these days. The categorization of a representative's resume is a time-consuming, labor-intensive, and resource-intensive procedure. Natural language processing and advanced analytics can understand and analyze unstructured written material to extract the required information. The idea is to teach the machine to study text in the same manner that people do. By reviewing applications with natural language processing and machine-learning processing, we may be able to locate people with the abilities and attributes we need more quickly. This study provides a comprehensive assessment of resume selection and identifies the existing work gaps. Different algorithms for machine learning and methodologies for analyzing and interpreting unstructured data have been researched. This study also addresses the research problems and future potential of resume analysis regarding the writing style, word choice, and grammar of unstructured written communication.
Attention-based Model for Multi-modal sentiment recognition using Text-Image Pairs

Multi-modal sentiment recognition (MSR) is an emerging classification task that aims to categorize sentiment polarities for a given multi-modal dataset. The majority of work done in the past relied heavily on text-based information. However, in many scenarios, text alone is frequently insufficient to predict sentiment accurately; as a result, academics are more motivated to engage in the subject of MSR. In light of this, we proposed an attention-based model for MSR using image-text pairs of tweets. To effectively capture the vital information from both modalities, our approach combines BERT and ConvNet with CBAM (convolution block attention module) attention. The outcomes of our experimentations on the Twitter-17 dataset demonstrate that our method is capable of sentiment classification accuracy that is superior to that of competing approaches.
XR4DRAMA Knowledge Graph: A Knowledge Graph for Disaster Management

The evolution of Knowledge Graphs (KGs), during the last two decades, has encouraged developers to create more and more context related KGs. This advance is extremely important because Artificial Intelligence (AI) applications can access open domain specific information in a semantically rich, machine understandable format. In this paper, we present the XR4DRAMA KG which can represent information for disaster management. More specifically, the XR4DRAMA KG can represent information about: (a) Observations and Events (e.g., data collection of biometric sensors, information in photos and text messages), (b) Spatio-temporal (e.g., highlighted locations and timestamps), (c) Mitigation and response plans in crisis (e.g., first responder teams). Moreover, we offer a mechanism that can create or update Points-Of-Interest (POIs), based on a visual or textual messages received from users.
Coherence based Document Clustering

Unsupervised document clustering and subsequent topic extraction has gained increasing importance with the ever growing availability of large text corpora. While there are a variety of algorithms that achieve meaningful results in either document clustering and subsequent topic extraction or classical topic modeling, most of them have in common that hyperparameter optimization is difficult and is mainly achieved by maximizing the extracted topic coherence values via grid search. Models using word-document embeddings can automatically detect the number of latent topics, but tend to have problems with smaller datasets and often require pre-trained embedding layers for successful topic extraction. We leverage widely used coherence scores by integrating them into a novel document-level clustering approach that uses keyword extraction methods for small to medium sized datasets. The metric by which most topic extraction methods optimize their hyperparameters is thus optimized during clustering, resulting in ultra-coherent clusters. Moreover, unlike traditional methods, the number of extracted topics or clusters does not need to be determined in advance, saving an additional optimization step and a time- and computationally-intensive grid search. Additionally, we show that the number of topics is accurately detected automatically.
Homunculus: Auto-Generating Efficient Data-Plane ML Pipelines for Datacenter Networks

Support for Machine Learning (ML) applications in networking has significantly improved over the last decade. The availability of public datasets and programmable switching fabrics (including low-level languages to program them) presents a full-stack to the programmer for deploying in-network ML. However, the diversity of tools involved, coupled with complex optimization tasks of ML model design and hyperparameter tuning while complying with the network constraints (like throughput and latency), puts the onus on the network operator to be an expert in ML, network design, and programmable hardware.
Tokenization in the Theory of Knowledge

Tokenization is a procedure for recovering the elements of interest in a sequence of data. This term is commonly used to describe an initial step in the processing of programming languages, and also for the preparation of input data in the case of artificial neural networks; however, it is a generalizable concept that applies to reducing a complex form to its basic elements, whether in the context of computer science or in natural processes. In this entry, the general concept of a token and its attributes are defined, along with its role in different contexts, such as deep learning methods. Included here are suggestions for further theoretical and empirical analysis of tokenization, particularly regarding its use in deep learning, as it is a rate-limiting step and a possible bottleneck when the results do not meet expectations.