Reviewerly/exHarmony huggingface dataset:
A Robust Pedestrian and Cyclist Detection Method Using Thermal Images

Computer vision techniques have been frequently applied to pedestrian and cyclist detection for the purpose of providing sensing capabilities to autonomous vehicles, and delivery robots among other use cases. Most current computer vision approaches for pedestrian and cyclist detection utilize RGB data alone. However, RGB-only systems struggle in poor lighting and weather conditions, such as at night, or during fog or precipitation, often present in pedestrian detection contexts. Thermal imaging presents a solution to these challenges as its quality is independent of time of day and lighting conditions. The use of thermal imaging input, such as those in the Long Wave Infrared (LWIR) range, is thus beneficial in computer vision models as it allows the detection of pedestrians and cyclists in variable illumination conditions that would pose challenges for RGB-only detection systems. In this paper, we present a pedestrian and cyclist detection method via thermal imaging using a deep neural network architecture. We have evaluated our proposed method by applying it to the KAIST Pedestrian Benchmark dataset, a multispectral dataset with paired RGB and thermal images of pedestrians and cyclists. The results suggest that our method achieved an F1-score of 81.34%, indicating that our proposed approach can successfully detect pedestrians and cyclists from thermal images alone.
A fuzzy-weighted approach for malicious web domain identification

Malicious web domains represent a serious threat to online users’ privacy and security, causing monetary loss, theft of private information, and malware attacks, among others. In recent years, machine learning methods have been widely used as prediction models to identify malicious web domains. In this study, we propose a Fuzzy-Weighted Least Squares Support Vector Machine (FW-LS-SVM) model for malicious web domain identification. In our proposed model, a fuzzy-weighted operation is applied to each data sample considering the fact that different samples may have different importance. This fuzzy-weighted operation is also able to alleviate the influence of noise data and improve the model’s robustness by assigning weights to error constraints. For comparison purposes, three commonly used single machine learning classifiers and three widely used ensemble models are included in our experiments, in order to assess the performance of our proposed FW-LS-SVM and its ensemble version. Hyperlink indicators and uniform resource locator-based features are used to train the prediction models. Experimental results show that our proposed approach is highly effective in identifying malicious web domains, outperforming the well-established single and ensemble models being compared.
Bayesian Additive Matrix Approximation for Social Recommendation

Social relations between users have been proven to be a good type of auxiliary information to improve the recommendation performance. However, it is a challenging issue to sufficiently exploit the social relations and correctly determine the user preference from both social and rating information. In this article, we propose a unified Bayesian Additive Matrix Approximation model (BAMA), which takes advantage of rating preference and social network to provide high-quality recommendation. The basic idea of BAMA is to extract social influence from social networks, integrate them to Bayesian additive co-clustering for effectively determining the user clusters and item clusters, and provide an accurate rating prediction. In addition, an efficient algorithm with collapsed Gibbs Sampling is designed to inference the proposed model. A series of experiments were conducted on six real-world social datasets. The results demonstrate the superiority of the proposed BAMA by comparing with the state-of-the-art methods from three views, all users, cold-start users, and users with few social relations. With the aid of social information, furthermore, BAMA has ability to provide the explainable recommendation.
Library Carpentry: Towards a New Professional Dimension (Part II – Automatic Authority Control to Enhance Retrieval)

Authority control for bibliographic data management in Indian libraries is generally a neglected area and thereby library OPACs of the country (including OPAC of the National Library) supports only the finding function of a catalogue and not the collocating function. In this context, the part II of the three part series on library carpentry (part I has been published in April issue) is an attempt to apply library carpentry methods in building authority datasets from scratch. It deals with the methodologies for developing authority datasets by applying data wrangling techniques and subsequent transformations of these datasets into ready-to-import MARC 21 format (for authority data). Like the previous part of the series, this research study is also represented through a case study. The case study narrates development of geographic name authority datasets for - states and union territories (level I), districts of India (level II), sub-districts (level III) and community development blocks of India (level IV). It also demonstrates that how the merged geographic name authority file for India can be implemented in an open source ILS and can become instrumental in enhancing retrieval efficiency through geodetic search feature in an open source library discovery system. It concludes that the proposed mechanisms and methodology (supported with proofs of the concept) may lead to a new era of authority-controlled cataloguing in Indian libraries.
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning

With the ubiquitous graph-structured data in various applications, models that can learn compact but expressive vector representations of nodes have become highly desirable.Recently, bearing the message passing paradigm, graph neural networks (GNNs) have greatly advanced the performance of node representation learning on graphs.However, a majority class of GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more informative heterogeneous graphs with various types of nodes and edges.Also, despite the necessity of inductively producing representations for completely new nodes (e.g., in streaming scenarios), few heterogeneous GNNs can bypass the transductive learning scheme where all nodes must be known during training.Furthermore, the training efficiency of most heterogeneous GNNs has been hindered by their sophisticated designs for extracting the semantics associated with each meta path or relation.In this paper, we propose wide and deep message passing network (WIDEN) to cope with the aforementioned problems about heterogeneity, inductiveness, and efficiency that are rarely investigated together in graph representation learning.In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low-and high-order neighbor nodes.To further improve the training efficiency, we innovatively present an active downsampling strategy that drops unimportant neighbor nodes to facilitate faster information propagation.Experiments on three real-world heterogeneous graphs have further validated the efficacy of WIDEN on both transductive and inductive node representation learning, as well as the superior training efficiency against state-of-the-art baselines.
FuSeConv: Fully Separable Convolutions for Fast Inference on Systolic Arrays

Both efficient neural networks and hardware accelerators are being explored to speed up DNN inference on edge devices. For example, MobileNet uses depthwise separable convolution to achieve much lower latency, while systolic arrays provide much higher performance per watt. Interestingly however, the combination of these two ideas is inefficient: The computational patterns of depth-wise separable convolution are not systolic and lack data reuse to saturate the systolic array's constrained dataflow. In this paper, we propose FuSeConv (Fully-Separable Convolution) as a drop-in replacement for depth-wise separable convolution. FuSeConv generalizes the decomposition of convolutions fully to separable 1D convolutions along spatial and depth dimensions. The resultant computation is systolic and efficiently utilizes the systolic array with a slightly modified dataflow. With FuSeConv, we achieve a significant speed-up of 3x-7x with the MobileNet family of networks on a systolic array of size 64x64, with comparable accuracy on the ImageNet dataset. The high speed-up motivates exploration of hardware-aware Neural Operator Search (NOS) in complement to ongoing efforts on Neural Architecture Search (NAS).
A New Approach to Social Engineering with Natural Language Processing: RAKE

Nowadays, with the advancement of technology, the way of thinking of communities has become easily manipulated. Scientific results put forward during the Arab Spring period, which is one of the biggest examples, show that social media is easily used to guide people in different ways. This paper introduces our project, Rake, which has emerged as a method against the aforementioned manipulations. The Rake used Nat-ural Language Processing and Machine Learning algorithms, to generate its own dataset and it consist of three major procedures: Emotion, Reaction, and Word Mapping, Rake is a high performance application that can adapt to areas such as community analysis, business area analysis and advertising. As shown in the experiments, it has achieved a machine learning performance of 94%. In addition, the stages that convey how to adopt the Rake application in large-scale projects were included in the study.
Patient-Provider Communication Training Models for Interactive Speech Devices

Patient-provider communication plays a major role in healthcare with its main goal being to improve the patient’s health and build a trustworthy relationship between the patient and the doctor. Provider’s efficiency and effectiveness in communication can be improved through training in order to meet the essential elements of communication that are relevant during medical encounters. We surmised that speech-enabled conversational agents could be used as a training tool. In this study, we propose designing an ontology-based interaction model that can direct software agents to train dental and medical students. We transformed sample scenario scripts into a formalized ontology training model that links utterances of the user and the machine that expresses patient-provider communication. We created two instance-based models from the ontology to test the operational execution of the model using a prototype software engine. The assessment revealed that the dialogue engine was able to handle about 62% of the dialogue links. Future direction of this work will focus on further enhancing and capturing the features of patient-provider communication, and eventual deployment for pilot testing.
A Polylobar Nucleus Identifying and Extracting Method for Leukocyte Counting

Accurate counting of leukocytes is an important method for diagnosing human blood diseases. Because most nuclei of neutrophils and eosinophils are polylobar, it is easily confused with the unilobar nuclei in nucleus segmentation. Therefore, it is very essential to accurately identify and determine the polylobar leukocytes. In this paper, a polylobar nucleus identification and extracting method is proposed. Firstly, by using the Otsu threshold and area threshold method, the nuclei of leukocytes are accurately segmented. According to the morphological characteristics of polylobar leukocytes, the edges of the mitotic polylobar leukocytes are detected, and the numbers of polylobar leukocytes are determined according to the minimal distance rule. Therefore, the accurate counting of leukocytes can be realized. From the experimental results, we can see that using the Otsu method and the area threshold to segment the polylobar nuclear leukocytes, the segmentation ratio of the leukocyte nucleus reached 98.3%. After using the morphological features, the polylobar nuclear leukocytes can be accurately counted. The experimental results have verified the feasibility and practicability of the proposed method.
Chinese Remainder Encoding for Hamiltonian Cycles

The Hamiltonian Cycle Problem (HCP) consists of two constraints: i) each vertex contributes exactly two edges to the cycle; and ii) there is exactly one cycle. The former can be encoded naturally and compactly, while the encodings of the latter either lack arc consistency or require an exponential number of clauses. We present a new, small encoding for HCP based on the Chinese remainder theorem. We demonstrate the effectiveness of the encoding on challenging HCP instances.
Machine learning for holistic visualization of STEMI registry data

Widespread adoption of evidence-based guidelines and treatment pathways in ST-Elevation Myocardial Infarction (STEMI) patients has considerably improved cardiac survival and decreased the risk of recurrent myocardial infarction. However, survival outcomes appear to have plateaued over the last decade. The hope underpinning the current study is to engage data visualization to develop a more holistic understanding of the patient space, supported by principles and techniques borrowed from traditionally disparate disciplines, like cartography and machine learning.The Minnesota Heart Institute Foundation (MHIF) STEMI database is a large prospective regional STEMI registry consisting of 180 variables of heterogeneous data types on more than 5000 patients spanning 15 years. Initial assessment and preprocessing of the registry database was undertaken, followed by a first proof-of-concept implementation of an analytical workflow that involved machine learning, dimensionality reduction, and data visualization. 38 pre-admission variables were analyzed in an all-encompassing representation of pre-index STEMI event data. We aim to generate a holistic visual representation - a map of the multivariate patient space - by training a high-resolution self-organizing neural network consisting of several thousand neurons. The resulting 2-D lattice arrangement of n-dimensional neuron vectors allowed patients to be represented as point locations in a 2-D display space. Patient attributes were then visually examined and contextualized in the same display space, from demographics to pre-existing conditions, event-specific procedures, and STEMI outcomes. Data visualizations implemented in this study include a small-multiple display of neural component planes, composite visualization of the multivariate patient space, and overlay visualization of non-training attributes.Our study represents the first known marriage of cartography and machine learning techniques to obtain visualizations of the multivariate space of a regional STEMI registry. Combining cartographic mapping techniques and artificial neural networks permitted the transformation of the STEMI database into novel, two-dimensional visualizations of patient characteristics and outcomes. Notably, these visualizations also drive the discovery of anomalies in the data set, informing corrections applied to detected outliers, thereby further refining the registry for integrity and accuracy. Building on these advances, future efforts will focus on supporting further understanding of risk factors and predictors of outcomes in STEMI patients. More broadly, the thorough visual exploration of display spaces generated through a conjunction of dimensionality reduction with the mature technology base of geographic information systems appears a promising direction for biomedical research.
New Multi-View Classification Method with Uncertain Data

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.
Discovery of Hybrid Ensemble Models Resilient to Input Resolution Deterioration

Recent advances in deep learning (DL) and ensemble learning frameworks significantly improved performance of machine learning (ML) models in different scopes. However, severe data limitations and absence of relevant problems for transfer and meta learning can drastically reduce capabilities of these techniques. Previously we have shown that hybrid approaches leveraging domain-expert knowledge, boosting and DL could discover robust solutions in cases with very limited data availability. Similar challenges are encountered in applications with reduced data sampling rate and resolution due to technological and operational restrictions such as longterm data collection by wearable biomedical devices and fully autonomous robotic systems. Multi-scale features discovered by DL or other empirical ML techniques from high-resolution data could become much less effective or unstable when used with lower resolution inputs. Direct training on low-resolution data may result in the selection of oversimplified and biased model with suboptimal performance. Here we argue that models resilient to input resolution deterioration can be discovered using our previously proposed hybrid framework where boosting-like algorithms are used for effective utilization and enhancement of existing domain-expert models with further non-linear combination of boosted ensemble components via DL or other ML algorithms. Our approach is illustrated in the biomedical context using cardio data from www.physionet.org.
A Progressive Architecture for Learned Fractional Downsampling

In many image and video processing applications, the ability to resize by a fractional factor, such as from 1080p to 720p, is essential. However, conventional CNN layers can only be used to alter the resolution of their inputs with integer scale factors. In this paper, we propose a downsampling network architecture that progressively reconstructs residuals at different scales. In particular, the aforementioned problem is solved by combining an upsampling sub-network and a downsampling subnetwork, both with integer scale factor. As an application, we apply the proposed downsampling network to an adaptive bitrate video streaming scenario. We extensively evaluate with different video codecs and upsampling algorithms to show the generality of our model. Our experimental results show that improvements in coding efficiency over the conventional Lanczos downsampling and state-of-the-art methods are attained, measured in different perceptual video quality models on large-resolution test videos.
Learning Spatio-Temporal Representations With a Dual-Stream 3-D Residual Network for Nondriving Activity Recognition

Accurate recognition of nondriving activity (NDA) is important for the design of intelligent human machine interface to achieve a smooth and safe control transition in the conditionally automated driving vehicle. However, some characteristics of such activities like limited-extent movement and similar background pose a challenge to the existing 3-D convolutional neural network based action recognition methods. In this article, we propose a dual-stream 3-D residual network, named DS3D residual network (ResNet), to enhance the learning of spatio-temporal representation and improve the activity recognition performance. Specifically, a parallel two-stream structure is introduced to focus on the learning of short-time spatial representation and small-region temporal representation. A two-feed driver behavior monitoring framework is further build to classify four types of NDAs and two types of driving behavior based on the driver's head and hand movement. A novel NDA dataset has been constructed for the evaluation, where the proposed DS3D ResNet achieves 83.35% average accuracy, at least 5% above three selected state-of-the-art methods. Furthermore, this study investigates the spatio-temporal features learned in the hidden layer through the saliency map, which explains the superiority of the proposed model on the selected NDAs.
Yet another combination of IR- and neural-based comment generation

Code comment generation techniques aim to generate natural language descriptions for source code. There are two orthogonal approaches for this task, i.e., information retrieval (IR) based and neural-based methods. Recent studies have focused on combining their strengths by feeding the input code and its similar code snippets retrieved by the IR-based approach to the neural-based approach, which can enhance the neural-based approach’s ability to output low-frequency words and further improve the performance. However, despite the tremendous progress, our pilot study reveals that the current combination is not generalizable and can lead to performance degradation. In this paper, we propose a straightforward but effective approach to tackle the issue of existing combinations of these two comment generation approaches. Instead of binding IR- and neural-based approaches statically, we combine them in a dynamic manner. Specifically, given an input code snippet, we first use an IR-based technique to retrieve a similar code snippet from the corpus. Then we use a Cross-Encoder based classifier to decide the comment generation method to be used dynamically, i.e., if the retrieved similar code snippet is a true positive (i.e., is semantically similar to the input), we directly use the IR-based technique. Otherwise, we pass the input to the neural-based model to generate the comment. We evaluate our approach on a large-scale dataset of Java projects. Experiment results show that our approach can achieve 25.45 BLEU score, which improves the state-of-the-art IR-based approach, neural-based approach, and their combination by 41%, 26%, and 7%, respectively. We propose a straightforward but effective dynamic combination of IR-based and neural-based comment generation, which outperforms state-of-the-art approaches by a substantial margin.
Quantum guessing games with posterior information

Quantum guessing games form a versatile framework for studying different tasks of information processing. A quantum guessing game with posterior information uses quantum systems to encode messages and classical communication to give partial information after a quantum measurement has been performed. We present a general framework for quantum guessing games with posterior information and derive structure and reduction theorems that enable to analyze any such game. We formalize symmetry of guessing games and characterize the optimal measurements in cases where the symmetry is related to an irreducible representation. The application of guessing games to incompatibility detection is reviewed and clarified. All the presented main concepts and results are demonstrated with examples.