Reviewerly/exHarmony huggingface dataset:
Quantum Correlation Revealed by Bell State for Classification Tasks

In machine learning, classification algorithms often use statistical methods to build the correspondence between features (or attributes) and categories (or labels), that is, the statistical correlation between features and categories. In quantum theory, a large number of experimental results show that quantum correlation is far stronger than what can be explained by local hidden theory (i.e., classical or non-quantum theory), that is, quantum mechanics theory reveals a statistical correlation stronger than that described by classical theory. Based on this, this paper will use the strong statistical correlation revealed by Bell state to build a classification algorithm to verify the validity and superiority of the formal framework of quantum mechanics in specific classification tasks. Specifically, we use quantum joint probabilities derived from the measurement process of Bell state to model the quantum statistical correlation between features and categories. The paper first theoretically proves that the formal framework used has the ability to violate Bell inequality; moreover, a classification algorithm is implemented and verified on classic machine learning datasets. Experimental results show that the algorithm is significantly better than most mainstream machine learning algorithms.
A Time Series Decomposition Algorithm Based on Gaussian Processes

In this paper, we present an algorithm for decomposing time series based on Gaussian processes. Gaussian processes can be viewed as infinite-dimensional probability distributions over smooth functions and also provide a natural basis for additive decomposition of time series, since we can sum mutually independent Gaussian processes with a simple and elegant algebra involving covariance kernels. The component estimation algorithm we propose in this paper is general and does not depend on the number of components, nor on the correlation structure and interpretation of each component. Specifically, the proposed algorithm is based on nonparametric Bayesian Gaussian process regression, where the log-likelihood covariance is suitably structured to account for the presence of additive subcomponents. The numerical parameter estimation procedure finds MAP estimates by maximizing the unnormalized log-posterior density, with a great advantage in terms of computational cost and efficiency.
Blockchain Technology for Vehicle Maintenance Registration

Vehicle maintenance is done regularly by vehicle owners to maintain the best performance to avoid harmful effects, such as accidents. However, according to the news, counterfeit spare parts can easily found in vehicle maintenance workshops. Therefore, this study tries to use blockchain technology to record the vehicles for sparepart maintenance. Several previous studies have mentioned the benefits of blockchain technology that can carry out tracking and tracing in the automotive industry, but no discussion on the laboratory-based experiments to see how the implementation of blockchain technology for vehicle identification registration which in the future can be used for tracking components in vehicles. Therefore, this study simulates the recording of vehicle identities using a multichain application. This qualitative research was carried out by doing field observations and discussions with experts in related fields. The simulation results in the laboratory are shown to the expert as a discussion material for validation. From the validation results, it can be concluded that blockchain technology can be used to record vehicle identities, which can be developed for tracking spare parts in vehicles. This result is beneficial for business actors to improve the quality of vehicles.
Web Document Categorization Using Knowledge Graph and Semantic Textual Topic Detection

In several contexts, the amount of available digital documents increases every day. One of these challenging contexts is the Web. The management of this large amount of information needs more efficient and effective methods and techniques for analyzing data and generate information. Specific application as information retrieval systems have more and more high performances in the document seeking process, but often they lack of semantic understanding about documents topics. In this context, another issue arising from a massive amount of data is the problem of information overload, which affects the quality and performances of information retrieval systems. This work aims to show an approach for document classification based on semantic, which allows a topic detection of analyzed documents using an ontology-based model implemented as a semantic knowledge base using a No SQL graph DB. Finally, we present and discuss experimental results in order to show the effectiveness of our approach.
The Bites Eclectic: Critique-Based Conversational Recommendation for Diversity-Focused Meal Planning

Diet diversification has been shown both to improve nutritional health outcomes and to promote greater enjoyment in food consumption. CBR has a rich history in direct recommendation of recipes and meal planning, as well as conversational exploration of the possibilities for new food items. But more limited attention has been given to incorporating diversity outcomes as a primary factor in conversational critique for exploration. Critiquing as a method of feedback has proven effective for conversational interactions, and diversifying recommended items during exploration can help users broaden their food options, which critiquing alone may not achieve. And all of these aspects together are important elements for recommender applications in the food domain. In this paper, we introduce DiversityBite, a novel CBR approach that brings together critique and diversity to support conversational recommendation in the recipe domain. Our initial user study evaluation shows that DiversityBite is effective in promoting meal plan diversity.
A Framework for the Composition of IoT and CPS Capabilities

By 2030, over a half trillion devices will be connected to the internet. With so many devices providing a wide range of features, there is a need for a framework for innovation and reuse of Internet of Things (IoT) and Cyber-Physical Systems (CPS) capabilities. Such framework should facilitate the composition of capabilities and provide stakeholders means to reliably model and verify compositions. An IoT and CPS Composition Framework (ICCF) is proposed to achieve this goal. ICCF is based on the NIST CPS framework composition guidelines, intuitive composition semantics inspired from the mPlane protocol, and strong formal verification capabilities of the Temporal Logic of Actions (TLA) formal descriptors and tools. This paper demonstrates why such framework, semantics, and formal specification and verification components form a powerful and intuitive composition framework that satisfies different stakeholders concerns. To achieve this purpose, semantics and formal specification of the composition algebra were provided, a well-being composite capability within a smart building was specified, its prototype model in a formal verification tool was run, an analysis of the results of symbolic execution quantitatively and qualitatively was performed, and assessment of the trustworthiness of the composition was done. Lastly, implementation details were provided and proposed extensions to other domains such as smart transportation and smart health were discussed.
Full cardiac cycle asynchronous temporal compounding of 3D echocardiography images

It is important to improve echocardiography image quality, because the accuracy of echocardiographic assessment and diagnosis relies on image quality. Previous work on 2D temporal image compounding for image frames with matching cardiac phases (synchronous), and for temporally neighbouring image frames (asynchronous) over small ranges of time frames showed good improvement to image quality. Here, we extend this by performing asynchronous temporal compounding to echocardiographic images in 3D, involving all frames within a cardiac cycle, via a robust 3D cardiac motion estimation algorithm to describe the large image deformations. After compounding, the images can be reanimated via the motion model. Various methods of fusing image frames together are tested, including mean, max, and wavelet methods, and outlier rejection algorithms. The compounding algorithm is applied on 3D human adult, porcine adolescent, and human fetal echocardiography images. Results show significant improvements to contrast-to-noise ratio (CNR) and boundary clarity, and significantly decreased variability in manual quantification of cardiac chamber volumes after compounding. Interestingly, compounding can extend the field of view of the echo images, by reconstructing cardiac structures that momentarily exceeded the field of view, using the motion estimation algorithm to calculate their locations outside the field of view during these time periods. Although all compounding methods provide general improvements, the mean method led to blurred boundaries, while the max methods led to high variability of CNR. Outlier rejection algorithms were found to be useful in addressing these weaknesses.
The relation between visual search and visual conspicuity for moving targets

In order to assess camouflage and the role of movement under widely ranging (lighting, weather, background) conditions simulation techniques are highly useful. However, sufficient level of fidelity of the simulated scenes is required to draw conclusions. Here, live recordings were obtained of moving soldiers and simulations of similar scenes were created. To assess the fidelity of the simulation a search experiment was carried out in which performance of recorded and simulated scenes was compared. Several movies of bushland environments were shown (recorded as well as simulated scenes) and participants were instructed to find the moving target as rapidly as possible within a time limit. In another experiment, visual conspicuity of the targets was measured. For static targets it is well known that the conspicuity (i.e., the maximum distance to detect a target in visual periphery) is a valid measure for camouflage efficiency as it predicts visual search performance. In the present study, we investigate whether conspicuity also predicts search performance for moving targets. In the conspicuity task, participants saw a short (560 ms) part of the movies used for the search experiments. This movie was presented in a loop such that the target moved forward, backward, forward, etcetera. Conspicuity was determined as follows: a participant starts by fixating a location in the scene far away from the target so that he/she is not able to detect it. Next, the participant fixates progressively closer to the target location until the target can just be detected in peripheral vision; at this point the distance to the target is recorded. As with static stimuli, we show that visual conspicuity predicts search performance. This suggests that conspicuity may be used as a means to establish whether simulated scenes show sufficiently fidelity to be used for camouflage assessment (and the effect of motion).
CNN-based AMC for Internet of Underwater Things

Automatic modulation classification (AMC) is a technique to identify the modulation of signals. Traditionally, this has been done through feature extraction; however, recently machine learning has been used. Convolutional neural networks (CNNs) are a type of deep learning that have the ability to learn feature extraction. They are excellent for classification, and analyzing large amounts of data. In this research, CNNs were used to automatically classify the modulation type of underwater acoustic signals. The architecture of the CNN was iteratively improved, until a final accuracy of approximately 93% was achieved. The training and testing was done using a dataset collected by Applied Research Laboratories at the University of Texas Austin.
Native-Nonnative Voice Conversion by Residual Warping in a Sparse, Anchor-Based Representation

Voice conversion (VC) techniques can be used to synthesize utterances from second language learners to appear as if they have a native accent, providing learners with an ideal target to imitate in pronunciation training. In prior work, we presented a low-resource technique called SABR (Sparse, Anchor-Based Representation of Speech), which uses acoustic anchorsone per English phonemeto represent an utterance as a sparse, linear combination of nonnegative weights. SABR produces intelligible speech, but its compact size limits the acoustic quality of the synthesis, in large part due to the significant residual left out by the compact model. In this article, we propose SABR+Res, which uses a linear combination of frequency warp transforms to convert the source residual spectrum to be closer to that of the target speaker and use it in synthesis. We evaluate the proposed method on speakers from the ARCTIC and L2-ARCTIC databases and compare them to state-of-the-art exemplar and frequency-warping VC methods. We find that SABR+Res had the lowest objective VC error for native-to-nonnative conversion and was preferred in subjective tests. Additionally, when compared to the baseline systems, SABR+Res had a much higher synthesis quality on native-to-nonnative speakers, performing similarly to native-to-native speaker pairs. We discuss the implications for the residual warping system and applying the residual transform to other exemplar-based systems.
Towards versatile conversations with data-driven dialog management and its integration in commercial platforms

Conversational interfaces have recently become a ubiquitous element in both the personal sphere by easing access to services, and industrial environments by the automation of services, improved customer support and its corresponding cost savings. However, designing the dialog model used by these interfaces to decide system responses is still a hard-to-accomplish task for complex conversational interactions. This paper describes a data-driven dialog management technique, which provides flexibility to develop, deploy and maintain this module. Various configurations for classification algorithms are assessed with two dialog corpora of different application domains, size, dimensionalities and set of possible system responses. The results of the evaluation show satisfactory accuracy and coherence rates in both tasks. As a proof of concept, our proposal has also been integrated with DialogFlow, a platform provided by Google to design conversational user interfaces. Our proposal has been assessed with a real use case, proving that it can be deployed in conjunction with commercial platforms, obtaining satisfactory results for the objective and subjective assessments completed.
A Novel clustering tendency assessment algorithm for WSN generated Spatio-Temporal data

Wireless sensor networks are an important component of the internet of things implementation in several domains. These sensor networks generate large amount of spatiotemporal data, which need to be analyzed for knowledge creation. Exploratory analysis of this data using clustering (and assessing clustering tendency prior to actual clustering) can help understand underlying characteristics of the data. Most algorithms for clustering spatio-temporal data does not consider the spatial and temporal contiguity of datapoints before assigning them to clusters, leading to false results. In this paper, we propose nccVAT, a novel algorithm which assess clustering tendency of the spatio-temporal data and find contiguous data clusters for better understanding of the phenomenon being observed. The proposed algorithm is compared with state of the art algorithms such as ST-DBSCAN, ST-OPTICS, and clusiVAT, and Dunn's index is used to validate the results. The experiments performed on a real-life Intel Berkeley Research Laboratory (IBRL) dataset confirm superiority of nccVAT clusters as they have highest Dunn's index.
Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping

Despite its relatively recent history, deep learning (DL)-based source code analysis is already a cornerstone in machine learning for compiler optimization. When applied to the classification of pieces of code to identify the best computational unit in a heterogeneous Systems-on-Chip, it can be effective in supporting decisions that a programmer has otherwise to take manually. Several techniques have been proposed exploiting different networks and input information, prominently sequence-based and graph-based representations, complemented by auxiliary information typically related to payload and device configuration. While the accuracy of DL methods strongly depends on the training and test datasets, so far no exhaustive and statistically meaningful analysis has been done on its impact on the results and on how to effectively extract the available information. This is relevant also considering the scarce availability of source code datasets that can be labeled by profiling on heterogeneous compute units. In this article, we first present such a study, which leads us to devise the contribution of code sequences and auxiliary inputs separately. Starting from this analysis, we then demonstrate that by using the normalization of auxiliary information, it is possible to improve state-of-the-art results in terms of accuracy. Finally, we propose a novel approach exploiting Siamese networks that further improve mapping accuracy by increasing the cardinality of the dataset, thus compensating for its relatively small size.
Learning Hierarchical Reasoning for Text-Based Visual Question Answering

Text-based visual question answering (TextVQA) task needs to answer questions based on the objects and text information in image, which involves the joint reasoning over three modalities - question, visual objects, and text in image. Recent approaches on textVQA regard three modalities as joint input of transformers. However, these implicit reasoning methods do not make full use of multi-modal information, especially visual modality. To this end, we propose a novel model for textVQA based on reasoning explicitly in human-like mode. Firstly, the relevance between different objects and question is obtained. Then, the object modality is fused into the text modality weighted by obtained relevance. Finally, the amended text modality is used to predict the answer. In contrast to previous multi-modal free fusion strategy, our method can make the reasoning process more explicit and robust. Moreover, a prior-based loss is proposed to constrain object-question relevance. Extensive experimental results on several benchmark datasets well demonstrate the superior performance of our hierarchical reasoning framework over current state-of-the-art methods.
Semantic-Specific Hierarchical Alignment Network for Heterogeneous Graph Adaptation

Node classification has been substantially improved with the advent of Heterogeneous Graph Neural Networks (HGNNs). However, collecting numerous labeled data is expensive and time-consuming in many applications. Domain Adaptation (DA) tackles this problem by transferring knowledge from a label-rich domain to a label-scarce one. However the heterogeneity and rich semantic information bring great challenges for adapting HGNN for DA. In this paper, we propose a novel semantic-specific hierarchical alignment network for heterogeneous graph adaptation, called HGA. HGA designs a sharing-parameters HGNN aggregating path-based neighbors and hierarchical domain alignment strategies with the MMD and \(L_1\) normalization term. Extensive experiments on four datasets demonstrate that the proposed model can achieve remarkable results on node classification.
Detecting Event-synced Navigation Attacks across User-generated Content Platforms

With the spread of service platforms that enable users to generate content, people use user-generated content (UGC) to search for and access information on the web instead of search engines. Attackers can also use UGC on a service platform (UGC platform) to spread web-based social engineering (SE) attacks to a large number of people. In this paper, we focus on a type of web-based SE attack, called an event-synced navigation attack, which generates UGC with links navigating users to malicious websites and distribute it synced with a real-life event at a specific time. To understand the attacks in the wild, we propose a new system for detecting event-synced navigation attacks in real time by capturing the inevitable footprints left by attacks that affect a large number of users. We evaluate each of the three steps of the proposed system and finally find that the system can classify malicious and non-malicious UGC with 97% accuracy. Furthermore, we perform a comprehensive measurement study on event-synced navigation attacks spread from popular UGC platforms (Twitter, Facebook, YouTube, and Reddit) and confirm that many event-synced navigation attacks are deployed in the wild.
Predicting blur visual discomfort for natural scenes by the loss of positional information

The perception of blur due to accommodation failures, insufficient optical correction or imperfect image reproduction is a common source of visual discomfort, usually attributed to an anomalous and annoying distribution of the image spectrum in the spatial frequency domain. In the present paper, this discomfort is related to a loss of the localization accuracy of the observed patterns. It is assumed, as a starting perceptual principle, that the visual system is optimally adapted to pattern localization in a natural environment. Thus, since the best possible accuracy of the image patterns localization is indicated by the positional Fisher Information, it is argued that blur discomfort is strictly related to a loss of this information. Following this concept, a receptive field functional model is adopted to predict the visual discomfort. It is a complex-valued operator, orientation-selective both in the space domain and in the spatial frequency domain. Starting from the case of Gaussian blur, the analysis is extended to a generic type of blur by applying a positional Fisher Information equivalence criterion. Out-of-focus blur and astigmatic blur are presented as significant examples. The validity of the proposed model is verified by comparing its predictions with subjective ratings. The model fits linearly with the experiments reported in independent databases, based on different protocols and settings.
Meta-graph Embedding in Heterogeneous Information Network for Top-N Recommendation

Heterogeneous Information Network (HIN) is a graph that contains variety of nodes and their relationships. It can provide abundant auxiliary information for the feature engineering of the recommendation model and thus help to improve its recommendation performance. Most work applying the auxiliary information is to calculate node similarities over meta-paths or meta-graphs of HIN and then recommend based on those similarities through matrix factorization or other analogous recommendation algorithms. In this paper, we propose a novel meta-graph embedding based deep learning recommendation model, MGRec. Types of meta-graphs of HIN are embedded as input features through multiple same structured Attention-enhanced CNNs, which help to learn the weight of each node and get a more accurate vector representation of the meta-graph. Besides, a Wide&Multi-Deep structured recommendation framework is designed to learn both the shallow and deep interactions among features, in which multiple independent deep modules are used to learn the distinguishable correlation degree of each type of meta-graph to the target user and item to highlight the distinguishable contribution of each meta-graph to the recommendation. Experiments on two real-world datasets show that, compared with other popular recommendation models, our MGRec model achieves the best performance in multiple evaluation metrics.