Reviewerly/exHarmony huggingface dataset:
PointDet: An Object Detection Framework Based On Human local Features In The Task Of Identifying Violations

Object detection algorithms play an important role in the field of violation detection. However, small target detection is full of challenges in scenes related to human. The relationship between objects is usually not considered in object detection algorithms, which will make the model over relay on the high-order features and not make full use of local features. To address this issue, a novel framework named PointDet is proposed to learn local features which optimizes the detection effect of small targets in chemical plants. Since most of the targets to be detected in our dataset are highly correlated with human, human local features are used when designing the framework. First, we use a trained pose estimation model to extract local key point features. However, if local features are used directly, the relationship between them cannot be fully considered. Based on this situation, we have designed the one-vs-others module and the adaptive-graph-convolution module to reconstruct local features. In addition, for the output layer, the most challenging problem is how to better detect small targets. In our task, various small objects such as gloves, goggles, etc. have an obvious positional relationship with the local features of human body. In the output layer, we have designed a head attention module to make full use of this situation to optimize the small target detection problem. Specifically, our framework significantly outperforms state-of-the-art by 7.8 AP scores on field work dataset in chemical plants.
Design based exploration of medical system adoption: Case of wheelchair ramps

This study focuses on the exploration of wheelchair users' expectations from portable ramps and their adoption factors, then correspondingly offers a smart mass customization design tool. Briefly, portable ramps are generally used by wheelchair users, and provide a temporary solution to increase accessibility in their daily lives. In this research, a rollable ramp was examined as this prototype was developed in a prior funded research and thesis study. Our aim in this study is to explore the critical factors of satisfaction of users' and potential users' of portable ramps. To maximize the usability of portable ramp in various outdoor/indoor spaces and physical structures, an efficient permutation of flexible/adjustable components is offered. The research includes flexibility and customization and many external factors effective for adoption of portable ramp. In this context, three research methods were applied; semi structured-face-to-face interviews, observation, and experimental study. The constructs of the survey were extracted from literature and patent reviews then refined during observation and interviews. Wheelchair users answered a web-based survey with multiple constructs. The survey uncovered that personalization, flexibility, extension capability, and cost is critical. As well, users prefer the chance to try the model before purchasing. The effort needed to carry and learn how to use the ramp seems to be one of the key factors. It is also found that the user's life style and product match has an effect on adoption. At the end of the study, a smart mass customization design tool will be developed, which potential users or sales representatives are able to easily interact with in order to customize the portable ramp.
Person re-identification based on attention clustering and long short-term memory network

Local features are widely applied in person re-identification (ReID) because of the rich fine-grained information. However, there are two problems existing in the local feature methods: the local regions are not accurate and the contextual information between different regions is not considered. To solve these problems, we propose a person ReID method based on attention clustering and long short-term memory (LSTM) network. First, in the feature extraction stage, an attention mechanism is utilized to suppress the background noise and a clustering operation is performed to extract accurate local features of human body parts. Second, we propose to regard the human body parts from head to foot as a sequence and utilize LSTM to take into account the contextual information between human body parts. Through the above two strategies, accurate local features of human body parts with contextual information can be extracted. In addition, we introduce the vector approximation-file index for fast ReID. Experiments on three benchmark datasets demonstrate the effectiveness of our method.
Differentially Private Decision Tree Based on Pearson’s Correlation Coefficient

As one of popular machine learning algorithms, decision trees can be applied to both classification and regression tasks. Many decision tree models are trained and used by some companies and researchers on data obtained from users for business decision. However, the use of decision trees may pose a potential risk to privacy when training data contains sensitive personal information. In this paper, we proposed an effective differentially private decision tree algorithm based on Pearson's correlation coefficient, called Diff-PCCDT. The exponential mechanism is applied to select the best splitting attribute with the absolute value of Pearson's correlation coefficient as quality function at every intermediate node. The laplace noise is added into the true class count of every leaf node to ensure that the decision tree does not leak privacy during classification. Furthermore, a parallel algorithm of Diff-PCCDT over Map-Reduce, called DiffMR-PCCDT, is proposed for big data scenario. The experimental results demonstrate that the Diff-PCCDT algorithm can effectively train differentially private decision tree and its parallel implementation DiffMR-PCCDT can deal with the big data classification problems.
Point-Mass Filtering With Boundary Flow and Its Application to Terrain Referenced Navigation

Point-mass filter (PMF) is a numerical Bayesian filtering algorithm that estimates the probability density of state variables using a deterministically defined grid on state space. An important factor that determines the performance of the PMF is how accurately one guesses the probabilistically significant region (called a nonnegligible region) where a grid will be placed. In this article, we introduce the concept of boundary to express the nonnegligible region and propose a method to define a grid tailored to the nonnegligible region of posterior density by transporting boundary according to log-homotopy induced flow. The proposed method ensures accurate estimation performance and robustness especially for uncertain prior information. Terrain referenced navigation (TRN) is composed of a nonlinear observation model defined by terrains and an uncertain initial position. Therefore, it is a major application of the PMF, which can handle nonlinearity and has a more robust characteristic than other numerical filters. In order to compare the proposed PMF with the conventional PMFs, this article applies the proposed method to TRN. Monte Carlo simulation results show that the proposed algorithm is more accurate and robust than the conventional PMFs.
Multiscale Feature Learning Based on Enhanced Feature Pyramid for Vehicle Detection

Vehicle detection is a crucial task in autonomous driving systems. Due to large variance of scales and heavy occlusion of vehicle in an image, this task is still a challenging problem. Recent vehicle detection methods typically exploit feature pyramid to detect vehicles at different scales. However, the drawbacks in the design prevent the multiscale features from being completely exploited. This paper introduces a feature pyramid architecture to address this problem. In the proposed architecture, an improving region proposal network is designed to generate intermediate feature maps which are then used to add more discriminative representations to feature maps generated by the backbone network, as well as improving the computational cost of the network. To generate more discriminative feature representations, this paper introduces multilayer enhancement module to reweight feature representations of feature maps generated by the backbone network to increase the discrimination of foreground objects and background regions in each feature map. In addition, an adaptive RoI pooling module is proposed to pool features from all pyramid levels for each proposal and fuse them for the detection network. Experimental results on the KITTI vehicle detection benchmark and the PASCAL VOC 2007 car dataset show that the proposed approach obtains better detection performance compared with recent methods on vehicle detection.
A Comparative Analysis of Machine Learning Algorithms on Malicious URL Prediction

Phishing is a form of fraudulent behavior where an entity or a person mimics to be a valid user. Phishing has become popular in cyber space and it is used as a tool for deceiving the users. Most of the phishing messages are difficult to interpret. In the existing literature, there are many schemes have addressed the phishing issue. Yet, there is no concrete solution to thwart such attacks. Taking this into consideration, to detect phishing threats, a machine learning-based prediction scheme is proposed in this article. From the experimental analysis, it is identified that logistic regression outperforms the other schemes in terms of accuracy and error rate. The accuracy on URL prediction with logistic regression is 97%.
Scalable FPGA Median Filtering via a Directional Median Cascade

The 2-D median filter, one of the oldest and most well-established image-filtering techniques, still sees widespread use throughout computer vision. Despite its relative algorithmic simplicity, accelerating the 2-D median filter via a hardware implementation becomes increasingly challenging as the window size increases, since the resources required grow quartically with the window size. Previous works, in a non-FPGA context, have shown that separately applying several directional median filters to an image, and then taking the median of their results, yields performance that is competitive with, and in some cases even better than, that of a classic 2-D window median. Inspired by these approaches, we propose a novel way of substituting a 2-D median filter on an FPGA with a sequence of directional median filters, in our case arranged as a pipeline, in the pursuit of an FPGA implementation that achieves better scalability and hardware efficiency without sacrificing accuracy. We empirically show that the combination of three particular directional filters, in any order, achieves this, whilst requiring quadratically fewer resources on the FPGA and allowing for much higher throughput.
A Survey on Versioning Approaches and Tools

The evolution phenomenon has ever been spanning as various fields as politics, economy, laws, health, education and technology. Indeed, it an incremental process that consists in accommodating unavoidable changes to answer new real-life requirements within respective environments. This can be concretely performed by updating, adding or removing rules, services or simply any business knowledge. In computer science, many research axes have so far emerged to reflect such evolution needs. This can be obviously witnessed through the literature work related to the evolution issues within software, databases, data warehouses and ontology. To keep an evolution history, several versions should be naturally managed and saved. This survey aims at analyzing research issues related to versioning of software, XML documents, database and data warehouse. Ontology researchers may be inspired by advances made in these research fields and apply the techniques developed to manage ontology versions.
Adaptive-ID Secure Hierarchical ID-Based Authenticated Key Exchange Under Standard Assumptions Without Random Oracles

Hierarchical ID-based authenticated key exchange (HID-AKE) is a cryptographic protocol to establish a common session key between parties with authentication based on their IDs with the hierarchical delegation of key generation functionality. All existing HID-AKE schemes are selective ID secure, and the only known standard model scheme relies on a non-standard assumption such as the q-type assumption. In this paper, we propose a generic construction of HID-AKE that is adaptive ID secure in the HID-eCK model (maximal-exposure-resilient security model) without random oracles. One of the concrete instantiations of our generic construction achieves the first adaptive ID secure HID-AKE scheme under the (standard) k-lin assumption in the standard model. Furthermore, it has the advantage that the computational complexity of pairing and exponentiation operations and the communication complexity do not depend on the depth of the hierarchy. Also, the other concrete instantiation achieves the first HID-AKE scheme based on lattices (i.e., post-quantum).
Exploratory Analysis of Nose-gesture for Smartphone Aided Typing for Users with Clinical Conditions

Clinical disabilities might cause difficulty in typing using the traditional text entry systems that involve steady and precise finger movement within a restricted keyboard area in smartphones. Alternative approaches like voice-typing do not work well in a noisy environment, while gaze-typing needs large displays. In this paper, we develop an innovative approach called Nosype for hands-free typing over smartphones by utilizing nose gestures. With 10 users in a lab-scale, we observe that Nosype can help in hands-free typing at a typing-speed of 6.5 words/minute. Additionally, a usability study with 60 participants, including 10 participants having clinical disabilities, indicates an average usability score of 77.708.
An interactive query-based approach for summarizing scientific documents

Purpose Query-based summarization approaches might not be able to provide summaries compatible with the user’s information need, as they mostly rely on a limited source of information, usually represented as a single query by the user. This issue becomes even more challenging when dealing with scientific documents, as they contain more specific subject-related terms, while the user may not be able to express his/her specific information need in a query with limited terms. This study aims to propose an interactive multi-document text summarization approach that generates an eligible summary that is more compatible with the user’s information need. This approach allows the user to interactively specify the composition of a multi-document summary. Design/methodology/approach This approach exploits the user’s opinion in two stages. The initial query is refined by user-selected keywords/keyphrases and complete sentences extracted from the set of retrieved documents. It is followed by a novel method for sentence expansion using the genetic algorithm, and ranking the final set of sentences using the maximal marginal relevance method. Basically, for implementation, the Web of Science data set in the artificial intelligence (AI) category is considered. Findings The proposed approach receives feedback from the user in terms of favorable keywords and sentences. The feedback eventually improves the summary as the end. To assess the performance of the proposed system, this paper has asked 45 users who were graduate students in the field of AI to fill out a questionnaire. The quality of the final summary has been also evaluated from the user’s perspective and information redundancy. It has been investigated that the proposed approach leads to higher degrees of user satisfaction compared to the ones with no or only one step of the interaction. Originality/value The interactive summarization approach goes beyond the initial user’s query, while it includes the user’s preferred keywords/keyphrases and sentences through a systematic interaction. With respect to these interactions, the system gives the user a more clear idea of the information he/she is looking for and consequently adjusting the final result to the ultimate information need. Such interaction allows the summarization system to achieve a comprehensive understanding of the user’s information needs while expanding context-based knowledge and guiding the user toward his/her information journey.
ONT-X: An FPGA Approach to Real-time Portable Genomic Analysis

Oxford Nanopore Technologies (ONT) MinION is a pocket-sized portable DNA sequencer for on-the-field DNA sequencing. The ONT data analysis pipeline requires substantial amounts of computational resources for the basecalling and read alignment step in the pipeline. We study the compute and memory requirement of the basecalling step and present the detailed microarchitecture for accelerating it on FPGA. We also make the case that an embedded FPGA-based SoC with partial reconfiguration (PR) capability is an ideal substrate for accelerating ONT data analysis pipeline, due to the diversity in these application kernels and the evolving nature of the algorithms. Focusing on two widely used ONT data analysis pipelines, we present two modes of PR operation to cater to either latency or throughput requirements of these two pipelines and demonstrate how to amortize the partial reconfiguration cost, overlap the kernels, minimize the data transfer cost and achieve 111× higher throughput and 6× reduction in latency using these two PR modes.
Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting

Abstract We present a comprehensive comparative case study for the use of machine learning models for macroeconomics forecasting. We find that machine learning models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box critique of machine learning models, we apply and compare two variables attribution methods: permutation importance and Shapley values. While the aggregate information derived from both approaches is broadly in line, Shapley values offer several advantages, such as the discovery of unknown functional forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.
Multispectral Photometric Stereo for Spatially-Varying Spectral Reflectances: A well posed problem?

Multispectral photometric stereo (MPS) aims at recovering the surface normal of a scene from a single-shot multi-spectral image, which is known as an ill-posed problem. To make the problem well-posed, existing MPS methods rely on restrictive assumptions, such as shape prior, surfaces having a monochromatic with uniform albedo. This paper alleviates the restrictive assumptions in existing methods. We show that the problem becomes well-posed for a surface with a uniform chromaticity but spatially-varying albedos based on our new formulation. Specifically, if at least three (or two) scene points share the same chromaticity, the proposed method uniquely recovers their surface normals and spectral reflectance with the illumination of more than or equal to four (or five) spectral lights. Besides, our method can be made robust by having many (i.e., 4 or more) spectral bands using robust estimation techniques for conventional photometric stereo. Experiments on both synthetic and real-world scenes demonstrate the effectiveness of our method. Our data and result can be found at https://github.com/GH-HOME/MultispectralPS.git.
Classification of Kannada Hand Written Alphabets Using Multi-class Support Vector Machine with Convolution Neural Networks

Handwritten character recognition is onerous, due to enormous variations in handwriting among people. Recent past, many of the machine learning approaches developed to recognize hand written characters, but, most of this center around the English language, and some other languages like Arabic, Chinese and Bangla etc. This work intend to discern handwritten alphabets of Kannada language, spoken by people of Southern part of India. Many of the alphabets in Kannada differ marginally; so classification become arduous. Convolution Neural Networks (CNN) have shown great promise in this field, both as a classifier and as an image feature extractor. In this paper, a CNN model built on the ResNet50 pre-trained network used both as a classifier and as an image feature extractor. A Support Vector Machine (SVM) trained on the features extracted by the ReseNet-50 CNN found to outperform the single CNN model.. In this work, the single CNN model built and trained using Tensorflow with a Keras front-end, in Python 3. For the combined CNN and SVM model, MATLAB version 2019b was used.
Star-Net: Spatial-Temporal Attention Residual Network for Video Deraining

Learning-based video deraining has recently drawn increasing attention. They tend to directly package aligned frames to input a fully end-to-end network. However, the network is generally object-driven and cannot recognize how to utilize temporal information so that the results are unsatisfied. In this work, we design a novel Spatial-Temporal Attention Network (STAR-Net) to explicitly utilize the temporal information. Concretely, we define the self-spatial attention to characterizing the rain region of the target frame, and the temporal-spatial attention to learn the profitable information for remedying the rain region of the target frame from the adjacent frame. We also introduce a simple residual network to further strengthen the relationship between the target and the adjacent frame. These addressed frames are fused by a three-layers convolutional module to further improve the capability. Extensive evaluations indicate our superiority against state-of-the-art methods.
YOLOv4 for Multi-class Artefact Detection in Endoscopic Images

State of the art deep learning-based object detectors are deployed in various fields of computer vision such as face recognition, object tracking, self-driving cars and so on. Most of the object detectors are trained on common objects such as person, car, train, truck etc. but not on medical images. Considering specific applications like detecting multi-class artefacts in endoscopic images, a cutting-edge object detector like YOLOv4 which out-performed well in terms of speed and accuracy in general purpose dataset (like MS COCO dataset), is chosen. The aim of the paper is to carefully handpick techniques from Bag of Freebies of YOLOv4 and retrain the detector for detecting commonly occurring artefacts in endoscopic images using EAD2019 dataset. The experimental results proved a good mAP score of 49.82 % with the inference time of 76 milliseconds.
Improved RBF neural network algorithm in financial time series prediction

The financial market is often unpredictable and extremely susceptible to political, economic and other factors. How to achieve accurate predictions of financial time series is very important for scientific research and financial enterprise management. Based on this, this article takes the application of the improved RBF neural network(NN) algorithm in financial time series forecasting as the research object, and explores how to use the improved RBF NN algorithm to predict the stock market price, with a view to reducing investment risks and increasing returns for the majority of stock investors to provide help. This article uses the stock market prices of three listed companies in May 2019 as the data samples for this survey, including 72 training sample data and 21 test sample data. These three stocks were predicted by using the improved RBF NN algorithm Experiments, the experimental results show that the prediction errors of the improved RBF NN algorithm for the three stocks are 2.14%, 0.69% and 1.47%, while the traditional RBF NN algorithm’s prediction errors for the stocks are 5.74%, 2.38% and 11.37%. This shows that the improved algorithm is significantly more accurate and more effective than traditional algorithms. Therefore, the application of the improved RBF NN algorithm in financial time series prediction will be more extensive in the future.