Reviewerly/exHarmony huggingface dataset:
Towards Learning Affine-Invariant Representations via Data-Efficient CNNs

In this paper we propose integrating a priori knowledge into both design and training of convolutional neural networks (CNNs) to learn object representations that are invariant to affine transformations (i.e. translation, scale, rotation). Accordingly we propose a novel multi-scale maxout CNN and train it end-to-end with a novel rotation-invariant regularizer. This regularizer aims to enforce the weights in each 2D spatial filter to approximate circular patterns. In this way, we manage to handle affine transformations in training using convolution, multi-scale maxout, and circular filters. Empirically we demonstrate that such knowledge can significantly improve the data-efficiency as well as generalization and robustness of learned models. For instance, on the Traffic Sign data set and trained with only 10 images per class, our method can achieve 84.15% that outperforms the state-of-the-art by 29.80% in terms of test accuracy.
A survey: Comparison between Convolutional Neural Network and YOLO in image identification

Abstract The main purpose of this paper is discussing Convolutional Neural Network (CNN) family and You Only Look Once (YOLO) family, comparing with the structure of the frame, speed of calculating and efficiency of identifying objects. There are two main summaries. The first summary is that training Faster Region-based Convolutional Neural Networks (Faster R-CNN) can achieve excellent detection effect, which not only reduces the time cost but also improves the quality of the proposal. Therefore, the method of alternating training Region Proposal Network (RPN) + Faster R-CNN in the Faster R-CNN is more advanced than the original SlectiveSeach + Faster R-CNN. Another summary is that YOLO is a convolutional neural network that supports end-to-end training and testing and can detect and recognize multiple targets in images with certain accuracy.
An Adversarial Domain Adaptation Network For Cross-Domain Fine-Grained Recognition

In this paper, we tackle a valuable yet very challenging visual recognition task, where the instances are within a subordinate category, and the target domain undergoes a shift with the source domain. This task, termed as cross-domain fine-grained recognition, relates closely to many real-life scenarios, e.g., recognizing retail products in storage racks by models trained with images collected in controlled environments. To deal with this problem, we design a new algorithm and propose a corresponding fine-grained domain adaptation dataset. Firstly, we propose a novel end-to-end CNN architecture that integrates two specialized modules: an adversarial module for domain alignment and a self-attention module for fine-grained recognition. The adversarial module is used to handle domain shift by gradually aligning the different domains with domain-level and class-level alignments, and strive to help the classifier learn with domain-invariant features generated by nets. The self-attention module is designed to capture discriminative image regions which are crucial for fine-grained visual recognition. Secondly, we collect a large-scale fine-grained domain adaptation dataset of retail products, which contains 52,011 images of 263 classes from 3 domains. Thirdly, we validate the effectiveness of our method on three datasets, showing that the proposed method can yield significant improvements over baseline methods on fine-grained datasets. Besides, we also evaluate the effectiveness of the self-attention module by performing visualization, which can capture the discriminative image regions in both source and target domains.
A new hybrid image segmentation approach using clustering and black hole algorithm

Abstract Clustering technique is used in image segmentation because of its simple and easy approach. However, the existing clustering techniques required prior information as input and the performance are entirely dependent on this prior information, which is the main drawback of the clustering approaches. Therefore, many researchers are trying to introduce a novel method with user free parameter. We proposed a clustering method, that is, independent of user parameters and later we used a region merging technique to improve the performance of the clustering output. In this article, we proposed a hybrid image segmentation method which is based on a clustering algorithm and black hole algorithm. In the clustering technique, we have used recursive density estimation technique of surrounding pixels. After clustering technique, presence of small segments may be present and it would give lower a performance of segmentation output. Therefore, a segment is merged with another segment by finding best matched segment. Black hole algorithm concept has been used to define the fitness of each segment and to find the best matching segment. We have compared the proposed method with the other clustering‐based segmentation methods and different evaluation indices are used to calculate the performance, and the result proved the effectiveness of the proposed algorithm.
Review of Research on Text Sentiment Analysis Based on Deep Learning

Sentiment analysis is part of the field of natural language processing (NLP), and its purpose is to dig out the process of emotional tendencies by analyzing some subjective texts.With the development of word vector, deep learning develops rapidly in natural language processing.Therefore, the text emotion analysis based on deep learning has also been widely studied.This article is mainly divided into two parts.The first part briefly introduces the traditional methods of sentiment analysis.The second part introduces several typical methods of sentiment analysis based on deep learning.The advantages and disadvantages of sentiment analysis are summarized and analyzed, which lays a foundation for the in-depth research of scholars.
Path Planning of a Sweeping Robot Based on Path Estimation of a Curling Stone Using Sensor Fusion

In this paper, we proposed an enhanced path planning strategy for sweeper robots, which were created for the curling Olympic games. The main task for the multi-robot system is to clean the ice surface making a smooth path for a curling stone. The sweeping robots should have a motion planning on how to follow the curling stone slide and to prevent any collisions. In order to find the next position of the sweeping robot, it needs to establish the current position and to compute the next position of the curling stone. The initial and goal points of the sweeping robots are found and set up based on the simulation results from the main server. While the curling stone moves, the sweeping robots measure its position and adjust their motions according to the stone position trajectory. If the distance between the current and the next positions of a curling stone exceeds the threshold value, the sweeping robots should activate the sweeping mechanism preventing collisions with the stone. Since the estimation of the sweeping robot motion solely depends on the stone’s trajectory, the accumulation of errors is undesirable. Thus, the stone trajectory should be recalculated in a certain time step using the trend-adjusted exponential smoothing method. Then, the formation of the sweeping robot system can be calibrated according to the stone path computation. The obtained experimental results proved the efficiency of the proposed path planning method.
Action-based Recommendation in Pull-request Development

Pull requests (PRs) selection is a challenging task faced by integrators in pull-based development (PbD), with hundreds of PRs submitted on a daily basis to large open-source projects. Managing these PRs manually consumes integrators' time and resources and may lead to delays in the acceptance, response, or rejection of PRs that can propose bug fixes or feature enhancements. On the one hand, well-known platforms for performing PbD, like GitHub, do not provide built-in recommendation mechanisms for facilitating the management of PRs. On the other hand, prior research on PRs recommendation has focused on the likelihood of either a PR being accepted or receive a response by the integrator. In this paper, we consider both those likelihoods, this to help integrators in the PRs selection process by suggesting to them the appropriate actions to undertake on each specific PR. To this aim, we propose an approach, called CARTESIAN (aCceptance And Response classificaTion-based requESt IdentificAtioN) modeling the PRs recommendation according to PR actions. In particular, CARTESIAN is able to recommend three types of PR actions: accept, respond, and reject. We evaluated CARTESIAN on the PRs of 19 popular GitHub projects. The results of our study demonstrate that our approach can identify PR actions with an average precision and recall of about 86%. Moreover, our findings also highlight that CARTESIAN outperforms the results of two baseline approaches in the task of PRs selection.
Unsupervised Keyword Extraction Methods Based on a Word Graph Network

Supervised keyword extraction methods usually require a large human-annotated corpus to train the model. Expensive manual labeling has made unsupervised technology using word graph networks attractive. Traditional word graph networks simply consider the co-occurrence relationship of words or the topological structure of the network, ignoring the influence of semantic relations between words on keyword extraction. To solve these problems, an unsupervised keyword extraction method based on word graph networks for both Chinese and English is proposed. This method uses word embedding to applying a “word attraction score” to semantic relevance between words in a document. Combination of the bias weight of the node and a weighted PageRank algorithm is used to compute the final scores of words. The experimental results demonstrate that the method is more effective than the traditional methods.
Intelligent Chatbot for Requirements Elicitation and Classification

Software Requirements (SR) are considered as the foundation for a supreme quality software development process and each step of the software development process is dependent and is related to the SR. Software requirements elicitation may be the most important area of requirements engineering and possibly of the entire software development process. There is a lot of human work required in the process of software requirements elicitation and software requirements classification and in cases where the requirements are huge in number, this requirements elicitation and classification process becomes tedious and is prone to errors. We propose a novel approach to automate Requirements Elicitation and Classification using an intelligent conversational chatbot. Utilizing Machine Learning and Artificial Intelligence, the chatbot converses with stakeholders in Natural Language and elicits formal system requirements from the interaction, and subsequently classifies the elicited requirements into Functional and Non-functional system requirements.
A Permissioned Blockchain Approach to the Authorization Process in Electronic Health Records

In recent years, electronic health records (EHRs) have been subject to data breaches. The result of these data breaches indicates that the current EHRs infrastructure is no longer suitable for safeguarding health records. Blockchain technology has been evolving and could possibly improve the current EHRs infrastructure. Literature suggests that EHRs commonly utilize a role-based access control (RBAC) model. Permissioned blockchain technology could improve the authorization model by leveraging smart contracts and attribute-based access control. Hyperledger Fabric has been identified as a permissioned blockchain technology suited towards use-cases that require privacy. An experiment has been conducted with Hyperledger Fabric to illustrate the benefits permissioned blockchain technology could provide to EHRs. The result of the experiment revealed that permissioned blockchain technology could improve the authorization model used by traditional EHRs.
MoveAE

We propose a method for modifying affective robot movements using neural networks. Social robots use gestures and other movements to express their internal states. However, a robot's interactive capabilities are hindered by the predominant use of a limited set of preprogrammed or hand-animated behaviors, which can be repetitive and predictable, making sustained human-robot interactions difficult to maintain. To address this, we developed a method for modifying existing emotive robot movements by using neural networks. We use hand-crafted movement samples and a classifying variational autoencoder trained on these samples. Our method then allows for adjustment of affective movement features by using simple arithmetic in the network's latent embedding space. We present the implementation and evaluation of this approach and show that editing in the latent space can modify the emotive quality of the movements while preserving recognizability and legibility in many cases. This supports neural networks as viable tools for creating and modifying expressive robot behaviors.
Style Transfer for Light Field Photography

As light field images continue to increase in use and application, it becomes necessary to adapt existing image processing methods to this unique form of photography. In this paper we explore methods for applying neural style transfer to light field images. Feed-forward style transfer networks provide fast, high-quality results for monocular images, but no such networks exist for full light field images. Because of the size of these images, current light field data sets are small and are insufficient for training purely feed-forward style-transfer networks from scratch. Thus, it is necessary to adapt existing monocular style transfer networks in a way that allows for the stylization of each view of the light field while maintaining visual consistencies between views. To do this, we first generate disparity maps for each view given a single depth image for the light field. Then in a fashion similar to neural stylization of stereo images, we use disparity maps to enforce a consistency loss between views and to warp feature maps during the feed forward stylization. Unlike previous work, however, light fields have too many views to train a purely feed-forward network that can stylize the entire light field with angular consistency. Instead, the proposed method uses an iterative optimization for each view of a single light field image that backpropagates the consistency loss through the network. Thus, the network architecture allows for the incorporation of pre-trained fast monocular stylization network while avoiding the need for a large light field training set.
U4D: Unsupervised 4D Dynamic Scene Understanding

We introduce the first approach to solve the challenging problem of unsupervised 4D visual scene understanding for complex dynamic scenes with multiple interacting people from multi-view video. Our approach simultaneously estimates a detailed model that includes a per-pixel semantically and temporally coherent reconstruction, together with instance-level segmentation exploiting photo-consistency, semantic and motion information. We further leverage recent advances in 3D pose estimation to constrain the joint semantic instance segmentation and 4D temporally coherent reconstruction. This enables per person semantic instance segmentation of multiple interacting people in complex dynamic scenes. Extensive evaluation of the joint visual scene understanding framework against state-of-the-art methods on challenging indoor and outdoor sequences demonstrates a significant (approx 40%) improvement in semantic segmentation, reconstruction and scene flow accuracy.
Crime and Fraud Detection Using Clustering Techniques

Criminal attacks have drastically increased over the years which make its detection increasingly vital. Fraud detection is a technique of identifying fraudulent activities. We intend to apply clustering techniques in order to analyze and detect fraud or crime patterns from a large set of data. By using various clustering techniques, distinct areas or clusters can be generated by mapping crime instances (i.e., by identifying the factors that lead to fraud). These are areas which have high probability of criminal occurrences which are derived based on historical crime records. Thus, with the help of results obtained based on clustering analysis of the crime data, crime trends today can be identified. Crime can be divided into different types such as location-based crimes, theft, murder, kidnap, fraud, etc., and slums, residential areas, commercial areas, etc., are different types of areas where criminal activities may occur. Primary database is collected based on the types of crimes, the location, and the physical description of the suspects and also the time period in which felony has taken place including the other available data relevant to the analysis. The available data is then processed and clustered, thereby revealing a general crime pattern which in turn helps detect frauds.
Propose-and-Attend Single Shot Detector

We present a simple yet effective prediction module for a one-stage detector. The main process is conducted in a coarse-to-fine manner. First, the module roughly adjusts the default boxes to well capture the extent of target objects in an image. Second, given the adjusted boxes, the module aligns the receptive field of the convolution filters accordingly, not requiring any embedding layers. Both steps build a propose-and-attend mechanism, mimicking two-stage detectors in a highly efficient manner. To verify its effectiveness, we apply the proposed module to a basic one-stage detector SSD. We empirically show that our module significantly lifts the detection accuracy with marginal parameter overhead. Our final model achieves an accuracy comparable to that of state-of-the-art detectors while using a fraction of their model parameter and computational overheads. Moreover, we found that the proposed module has two strong applications. 1) The module can be successfully integrated into a lightweight backbone, further pushing the efficiency of the one-stage detector. 2) The module also allows train-from-scratch without relying on any sophisticated base networks as previous methods do.
LAST: Location-Appearance-Semantic-Temporal Clustering Based POI Summarization

When planning a trip, users tend to browse Place-of-Interest (POI) information on the Internet and then depart. Many works aimed at summarizing POIs by visual and textual analysis, while many of them ignored the inter-relationship between different views offered by the community-contributed information. In this paper, we propose a City-POI-LOI (CPL) summarization method to automatically mine POIs from the city-level landmark images. And a Location-Appearance-Semantic-Temporal (LAST) clustering method is proposed to mine the popular viewpoints termed Location-Of-Interest (LOI) in each POI by taking location, appearance, semantic, and temporal feature into consideration. We perform text and image summarization for each LOI, and we further summarize the POIs based on season. We conduct a series of experiments based on DIV400 and ATCF Dataset. Experimental results show the effectiveness of the proposed POI summarization approach.
A Fast, Secure and Distributed Consensus Mechanism for Energy Trading Among Vehicles using Hashgraph

Energy trading among inter vehicles (V2V) offers an efficient response to most of the problems presented by future electricity supply. V2V is often envisioned as a peer-to-peer (P2P) model of electric mercantilism for electric vehicle (EV) merchandising. With security vulnerability increasing, confidence in secured third parties is declining. Blockchain is becoming increasingly common as it provides a system for privacy conservation and efficient agreement without the need for trusted third parties. However, all operations in such a scheme are restricted by memory, time, computing capital, energy etc. and it is quite obvious that the mechanism for blockchain agreement is not sufficient to address all of them. In this paper, we present an alternative to the blockchain using Hashgraph which is scalable, fast, fault-tolerant and fair. It is efficient, inexpensive, and DoS resistant fulfilling the requirement of V2V energy trading.
MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding

Visual-semantic embedding enables various tasks such as image-text retrieval, image captioning, and visual question answering. The key to successful visual-semantic embedding is to express visual and textual data properly by accounting for their intricate relationship. While previous studies have achieved much advance by encoding the visual and textual data into a joint space where similar concepts are closely located, they often represent data by a single vector ignoring the presence of multiple important components in an image or text. Thus, in addition to the joint embedding space, we propose a novel multi-head self-attention network to capture various components of visual and textual data by attending to important parts in data. Our approach achieves the new state-of-the-art results in image-text retrieval tasks on MS-COCO and Flicker30K datasets. Through the visualization of the attention maps that capture distinct semantic components at multiple positions in the image and the text, we demonstrate that our method achieves an effective and interpretable visual-semantic joint space.
Spectral characterization of non-Gaussian quantum noise: Keldysh approach and application to photon shot noise

Having accurate tools to describe non-classical, non-Gaussian environmental fluctuations is crucial for designing effective quantum control protocols and understanding the physics of underlying quantum dissipative environments. We show how the Keldysh approach to quantum noise characterization can be usefully employed to characterize frequency-dependent noise, focusing on the quantum bispectrum (i.e., frequency-resolved third cumulant). Using the paradigmatic example of photon shot noise fluctuations in a driven bosonic mode, we show that the quantum bispectrum can be a powerful tool for revealing distinctive non-classical noise properties, including an effective breaking of detailed balance by quantum fluctuations. The Keldysh-ordered quantum bispectrum can be directly accessed using existing noise spectroscopy protocols.
Structural optimization of fuzzy rule-based models: Towards efficient complexity management

The primary aim of this study is concerned with the structural optimization of data-driven fuzzy rule-based systems (FRBS), with the intent of their complexity management. This is accomplished in two ways: the first one involves a structuralization of the antecedents and the second one deals with a structuralization of the consequents of the fuzzy rules. More specifically, this study contributes to the complexity management of fuzzy models by focusing on (i) the efficient arrangement (reduction) of the input spaces over which the antecedents of rules are formed and (ii) allocating the orders of local polynomial functions across the consequents of the rules. The originality of the study comes with the flexibility of FRBS that is endowed by admitting variability of input spaces standing in the antecedents of different rules as well as the variability of orders of polynomials (local functions) forming the consequents of the rules. Particle swarm optimization (PSO) is guided by the root mean squared error (RMSE) accuracy criterion to realize the efficient arrangement of input spaces and an allocation of the orders of the individual polynomials. In this optimization process, the Fuzzy C-Means (FCM) algorithm is employed to create fuzzy sets in the antecedents of the rules, while the standard Least Square Error (LSE) criterion is minimized to determine the coefficients of the polynomials in the consequents. The performance of the proposed model is quantified using some numeric data, including both synthetic and machine learning datasets.