Reviewerly/exHarmony huggingface dataset:
Performance of CatBoost and XGBoost in Medicare Fraud Detection

Due to the size of the data involved, performance is an important consideration in the task of detecting fraudulent Medicare insurance claims. We evaluate CatBoost and XGBoost on the task of Medicare fraud detection, and report performance in terms of running time and Area Under the Receiver Operating Characteristic Curve (AUC). We show that adding a categorical feature for XGBoost and CatBoost improves performance in terms of AUC, and that CatBoost's performance is higher in a statistically significant sense. Moreover, we conduct experiments to find the optimal number of decision trees to use for XGBoost and CatBoost in the task of Medicare fraud detection. This is an important contribution because the number of trees in the ensemble governs overall resource consumption of a Gradient Boosted Decision Tree implementation. We find that with a purely numerical dataset, CatBoost and XGBoost yield nearly equivalent performance in terms of AUC, and XGBoost has a shorter training time. With respect to Medicare fraud detection, to the best of our knowledge, this is the first study to evaluate the performance of CatBoost and XGBoost in terms of running time and AUC on highly imbalanced, Big Data. Our contribution of evaluating running time performance on a large imbalanced dataset benefits researchers looking for more efficient utilization of valuable resources.
Meeting Changing Customer Requirements in Food and Agriculture Through the Application of Blockchain Technology

This research summarizes the implementation of blockchain technology in the food and agriculture industry in Canada. Our research indicates that blockchain solutions are an existing and proven set of technologies. We also describe how blockchain based supply chain traceability information has many more benefits than its current use for food safety and product recalls. We recommend that costs for development of blockchain based solutions should also be distributed across stakeholders, and apportioned by the relevant industry associations. Our research indicates that adoption of blockchain technology in agriculture will achieve critical mass earlier when the industry applies a consortium approach, in a regulatory environment that is supported by government. This report also makes recommendations relevant to the integration of blockchain for end consumers of food.
Diversification-Aware Learning to Rank using Distributed Representation

Existing work on search result diversification typically falls into the “next document” paradigm, that is, selecting the next document based on the ones already chosen. A sequential process of selecting documents one-by-one is naturally modeled in learning-based approaches. However, such a process makes the learning difficult because there are an exponential number of ranking lists to consider. Sampling is usually used to reduce the computational complexity but this makes the learning less effective. In this paper, we propose a soft version of the “next document” paradigm in which we associate each document with an approximate rank, and thus the subtopics covered prior to a document can also be estimated. We show that we can derive differentiable diversification-aware losses, which are smooth approximation of diversity metrics like α-NDCG, based on these estimates. We further propose to optimize the losses in the learning-to-rank setting using neural distributed representations of queries and documents. Experiments are conducted on the public benchmark TREC datasets. By comparing with an extensive list of baseline methods, we show that our Diversification-Aware LEarning-TO-Rank (DALETOR) approaches outperform them by a large margin, while being much simpler during learning and inference.
Autonomous Obstacle Avoidance for UAV based on Fusion of Radar and Monocular Camera

UAVs face many challenges in autonomous obstacle avoidance in large outdoor scenarios, specifically the long communication distance from ground stations. The computing power of onboard computers is limited, and the unknown obstacles cannot be accurately detected. In this paper, an autonomous obstacle avoidance scheme based on the fusion of millimeter wave radar and monocular camera is proposed. The visual detection is designed to detect unknown obstacles which is more robust than traditional algorithms. Then extended Kalman filter (EKF) data fusion is used to build exact real 3D coordinates of the obstacles. Finally, an efficient path planning algorithm is used to obtain the path to avoid obstacles. Based on the theoretical design, an experimental platform is built to verify the UAV autonomous obstacle avoidance scheme proposed in this paper. The experiment results show the proposed scheme cannot only detect different kinds of unknown obstacles, but can also take up very little computing resources to run on an onboard computer. The outdoor flight experiment shows the feasibility of the proposed scheme.
Learning Constellation Map with Deep CNN for Accurate Modulation Recognition

Modulation classification, recognized as the intermediate step between signal detection and demodulation, is widely deployed in several modern wireless communication systems. Although many approaches have been studied in the last decades for identifying the modulation format of an incoming signal, they often reveal the obstacle of learning radio characteristics for most traditional machine learning algorithms. To overcome this drawback, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. Particularly, a convolutional neural network is developed for proficiently learning the most relevant radio characteristics of gray-scale constellation image. The deep network is specified by multiple processing blocks, where several grouped and asymmetric convolutional layers in each block are organized by a flow-in-flow structure for feature enrichment. These blocks are connected via skip-connection to prevent the vanishing gradient problem while effectively preserving the information identity throughout the network. Regarding several intensive simulations on the constellation image dataset of eight digital modulations, the proposed deep network achieves the remarkable classification accuracy of approximately 87% at 0 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further outperforms some state-of-the-art deep models of constellation-based modulation classification.
On deep neural network for trust aware cross domain recommendations in E-commerce

Over the years, cross-domain and trust-based recommendation systems are proven to be very helpful in solving issues pertaining to data sparsity and cold start. Many e-commerce sites used recommender systems as business tools for increasing their sale productivity and help their customers in finding suitable products. However, due to sparse rating and lack of historical information, such systems cannot generate effective recommendations. Matrix factorization and deep learning techniques have been the focus of research community for the last few years to solve the problem of data sparsity and cold start. In this paper, we have proposed a model called Trust Aware Cross-Domain Deep Neural Matrix Factorization (TCrossDNMF) that predicts rating of an item for an active user and solves user cold start problem in the cross-domain scenario of 'User Overlap' in e-commerce system. TCrossDNMF model is divided into four main steps: i) Features learning that learns the users' features using a latent factor model and then finds the similarity between users of source and target domains. As the users are shared between two domains, the proposed model learns the common information and transfers the knowledge from a source to target domain. ii) Ranking that finds set of similar users (neighbors), and then filters out the dissimilar users based on similarity threshold θ, and then generates a bipartite trust graph from these reduced set of users and executes Ant Colony Optimization, to find trustworthy neighbors for an active user. iii) Weighting computes the trust degree between an active user and his or her top-k neighbors. iv) Prediction that trains the TCrossDNMF model using multilayer perceptron (MLP) and generalized matrix factorization (GMF) by representing user-item interactions in higher dimensions and ensembles the GMF and MLP with trust information for rating prediction. We evaluated proposed model on a real dataset collected from a popular e-commerce retail service 'AliExpress'. We used categories available in 'AliExpress' as source domain and a target domain. For observing the performance of proposed model, we took six domains that have a higher ratio of sparsity. The proposed model is evaluated by using MAE, RMSE and F-measure metrics and compared it with baselines. The experiments show that the proposed model is a viable solution for the mentioned problem with significant improvements in results.
Robust and efficient post-processing for video object detection

Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks. Object recognition using video data is more challenging than using still images due to blur, occlusions or rare object poses. Specific video detectors with high computational cost or standard image detectors together with a fast post-processing algorithm achieve the current state-of-the-art. This work introduces a novel post-processing pipeline that overcomes some of the limitations of previous post-processing methods by introducing a learning-based similarity evaluation between detections across frames. Our method improves the results of stat-of-the-art specific video detectors, specially regarding fast moving objects, and presents low resource requirements. And applied to efficient still image detectors, such as YOLO, provides comparable results to much more computationally intensive detectors.
World of code: enabling a research workflow for mining and analyzing the universe of open source VCS data

Open source software (OSS) is essential for modern society and, while substantial research has been done on individual (typically central) projects, only a limited understanding of the periphery of the entire OSS ecosystem exists. For example, how are the tens of millions of projects in the periphery interconnected through technical dependencies, code sharing, or knowledge flow? To answer such questions we: a) create a very large and frequently updated collection of version control data in the entire FLOSS ecosystems named World of Code (WoC), that can completely cross-reference authors, projects, commits, blobs, dependencies, and history of the FLOSS ecosystems and b) provide capabilities to efficiently correct, augment, query, and analyze that data. Our current WoC implementation is capable of being updated on a monthly basis and contains over 18B Git objects. To evaluate its research potential and to create vignettes for its usage, we employ WoC in conducting several research tasks. In particular, we find that it is capable of supporting trend evaluation, ecosystem measurement, and the determination of package usage. We expect WoC to spur investigation into global properties of OSS development leading to increased resiliency of the entire OSS ecosystem. Our infrastructure facilitates the discovery of key technical dependencies, code flow, and social networks that provide the basis to determine the structure and evolution of the relationships that drive FLOSS activities and innovation.
Software smell detection techniques: A systematic literature review

Abstract Software smells indicate design or code issues that might degrade the evolution and maintenance of software systems. Detecting and identifying these issues are challenging tasks. This paper explores, identifies, and analyzes the existing software smell detection techniques at design and code levels. We carried out a systematic literature review (SLR) to identify and collect 145 primary studies related to smell detection in software design and code. Based on these studies, we address several questions related to the analysis of the existing smell detection techniques in terms of abstraction level (design or code), targeted smells, used metrics, implementation, and validation. Our analysis identified several detection techniques categories. We observed that 57% of the studies did not use any performance measures, 41% of them omitted details on the targeted programing language, and the detection techniques were not validated in 14% of these studies. With respect to the abstraction level, only 18% of the studies addressed bad smell detection at the design level. This low coverage urges for more focus on bad smell detection at the design level to handle them at early stages. Finally, our SLR brings to the attention of the research community several opportunities for future research.
WITHDRAWN: Usage and adoption of artificial intelligence in SMEs

The usage of artificial intelligence has been increased in the last 15–20 years. The corporates and governments are also concerned about their business administration, production, and logistics due to covid-19 crises. The usage of artificial intelligence can help in business development from small-medium enterprises to big shot organisations. The main purpose of this paper is to explore the usage of artificial intelligence to develop business activities in SMEs and the factors responsible for the adoption of artificial intelligence. Systematic literature review technique has been adopted. The main findings are that artificial intelligence can be used for maintaining social distance, performing business activities from a safe place, enhance customer deliverance, generate business for organizations, and give a competitive edge at the same time during the coronavirus pandemic. It was noticed that artificial was already in use by the big organizations like Google, Facebook, bumble, Netflix, Amazon, etc., but now small organizations could also harness this resource to boost their production and operation activities to compete and grow in the current scenario. The researchers have identified ten factors responsible for the adoption of artificial intelligence in SMEs, which can be further tested at the primary level by future researchers.
Comparative Analysis of Software Life Cycle Models

This research paper provides software life cycle models and compares their performance. In a software industry every day a new SDLC model introduced but each model have some merits and demerits. So none of them fulfill all customer requirements. In software industry more than 50% software never use and more than 70% software not satisfies user requirements. In this paper we compare software life cycle models and try to identify which model is useful for which customer. So as per requirement developer selects one model and try to satisfy customer requirements.
SWM: A High-Performance Sparse-Winograd Matrix Multiplication CNN Accelerator

Many convolutional neural network (CNN) accelerators are proposed to exploit the sparsity of the networks recently to enjoy the benefits of both computation and memory reduction. However, most accelerators cannot exploit the sparsity of both activations and weights. For those works that exploit both sparsity opportunities, they cannot achieve the stable load balance through a static scheduling (SS) strategy, which is vulnerable to the sparsity distribution. In this work, a balanced compressed sparse row format and a dynamic scheduling strategy are proposed to improve the load balance. A set-associate structure is also presented to tradeoff the load balance and hardware resource overhead. We propose SWM to accelerate the CNN inference, which supports both sparse convolution and sparse fully connected (FC) layers. SWM provides Winograd adaptability for large convolution kernels and supports both 16-bit and 8-bit quantized CNNs. Due to the activation sharing, 8-bit processing can achieve theoretically twice the performance of the 16-bit processing with the same sparsity. The architecture is evaluated with VGG16 and ResNet50, which achieves: at most 7.6 TOP/s for sparse-Winograd convolution and three TOP/s for sparse matrix multiplication with 16-bit quantization on Xilinx VCU1525 platform. SWM can process 310/725 images per second for VGG16/ResNet50 with 16-bit quantization. Compared with the state-of-the-art works, our design can achieve at least 1.53 × speedup and 1.8 × energy efficiency improvement.
Multi-Perspective Trust Management Framework for Crowdsourced IoT Services

We propose a novel generic trust management framework for crowdsourced IoT services. The framework exploits a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-perspective trust model</i> that captures the inherent characteristics of crowdsourced IoT services. Each perspective is defined by a set of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attributes</i> that contribute to the perspective's influence on trust. The attributes are fed into a machine-learning-based algorithm to generate a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trust model</i> for crowdsourced services in IoT environments. We demonstrate the effectiveness of our approach by conducting experiments on real-world datasets.
MEMF: Multi-level-attention embedding and multi-layer-feature fusion model for person re-identification

Person re-identification (re-ID) methods need to extract representative, rich and discriminative features in order to deal with the effect of imperfect pedestrian detectors, illumination changes, occlusions, and background confusion. In this paper, a multi-level-attention embedding and multi-layer-feature fusion (MEMF) model is proposed for person re-ID. Specifically, a novel backbone network is designed, in which multi-level-attention blocks are embedded into a multi-layer-feature fusion architecture. Multi-level-attention blocks can highlight representative features and assist global feature expression, and multi-layer-feature fusion can increase the fine granularity of feature expression and obtain richer features. Besides, a new eigenvalue difference orthogonality (EDO) loss is designed to reduce the correlation between features. The final loss is defined as the combination of the cross-entropy loss and the EDO loss, which improves re-ID results. The proposed method is evaluated on four popular and challenging datasets. Detailed experiments demonstrate that the application of various elements of the MEMF model can help improve person re-ID performance. Compared with start-of-the-art methods, the MEMF model gets a promising result.
A fuzzy ontology framework in information retrieval using semantic query expansion

World Wide Web (WWW) constitutes fuzzy information and requires soft computing techniques to deal context of the query. It works on the principle of keyword matching yielding low precision and recall. Semantic web, an extension WWW improves the information retrieval process. Query expansion is utmost importance in information retrieval to retrieve relevant results. To overcome the weaknesses of current web system and to utilize the strengths query expansion a novel framework based on fuzzy ontology is proposed for information retrieval. In the proposed framework, domain specific knowledge is utilized for ontology construction. In framework pre-defined domain ontologies and Global ontology, ConceptNet is used to construct a fuzzy ontology. Based on constructed fuzzy ontology most semantically related words for a query are identified and query is expanded. A fuzzy membership function is defined for different semantic relationships present among the Global ontology ConceptNet. Based on the proposed framework queries are expanded (Semantic query expansion) and evaluated on four popular search engines namely Google, Yahoo, Bing and Exalead. The performance metrics used are Precision, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), R-precision and Number of documents retrieved. The Web search engines are precision oriented. Based on the proposed framework all the metrics are improved approx. by 10%. Precision before the query expansion lies between 0.75-0.81 whereas after the query expansion lies between 0.85-0.89 on various search engines. The number of documents retrieved is almost improved 1/1000 after the query expansion.
Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)

While most of the research in Human-Robot Interaction (HRI) focuses on short-term interactions, long-term interactions require bolder developments and a substantial amount of resources, especially if the robots are deployed in the wild. Robots need to incrementally learn new concepts or abilities in a lifelong fashion to adapt their behaviors within new situations and personalize their interactions with users to maintain their interest and engagement. The "Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)" Workshop aims to take a leap from the traditional HRI approaches towards addressing the developments and challenges in these areas and create a medium for researchers to share their work in progress, present preliminary results, learn from the experience of invited researchers and discuss relevant topics. The workshop extends the topics covered in the "Personalization in Long-Term Human-Robot Interaction (PLOT-HRI)" Workshop at the 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) and "Lifelong Learning for Long-term Human-Robot Interaction (LL4LHRI)" Workshop at the 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), and focuses on studies on lifelong learning and adaptivity to users, context, environment, and tasks in long-term interactions in a variety of fields (e.g., education, rehabilitation, elderly care, collaborative tasks, customer-oriented service and companion robots).
GFTE: Graph-Based Financial Table Extraction

Tabular data is a crucial form of information expression, which can organize data in a standard structure for easy information retrieval and comparison. However, in financial industry and many other fields, tables are often disclosed in unstructured digital files, e.g. Portable Document Format (PDF) and images, which are difficult to be extracted directly. In this paper, to facilitate deep learning based table extraction from unstructured digital files, we publish a standard Chinese dataset named FinTab, which contains more than 1,600 financial tables of diverse kinds and their corresponding structure representation in JSON. In addition, we propose a novel graph-based convolutional neural network model named GFTE as a baseline for future comparison. GFTE integrates image feature, position feature and textual feature together for precise edge prediction and reaches overall good results https://github.com/Irene323/GFTE.