Reviewerly/exHarmony huggingface dataset:
Hardware and Algorithm Co-Optimization for pointwise convolution and channel shuffle in ShuffleNet V2

Since the convolutional neural network (CNN) was proposed, it has achieved remarkable results in image recognition, and its accuracy rate surpasses other methods. In recent years, with the continuous improvement of computational complexity and the development of research on the deployment of neural networks on terminal devices, new neural network models suitable for terminal devices such as MobileNet, SqueezeNet and ShuffleNet have also appeared in CNN model. These network models have special structures or units to reduce the amount of calculation.the lightweight features of the model cannot take advantage of due to the lack of customized design for them when GPU is used for acceleration. The characteristics of FPGA such as customization and parallelism just meet the needs of research. However, there is a little research on implementing and optimizing new lightweight networks on FPGAs. In this paper, we optimize each part of the model based on FPGA, achieve 8bit quantification, and redesign depthwise separable convolution operation and channel shuffle module, which make the module can be operated in a hardware-friendly way. Under the premise of maintaining the original model structure and performance, we make full use of the abundant LUT, FF and other resources on the FPGA. we use HLS (high-level synthesis) to analyze the structure of the ShuffleNet V2 model on xilinxzynqxc-7Z045. Compared with the performance of the original model in CPU, GPU and FPGA, our design achieves the acceleration performance of 8.7x, 1.3x and 3.3x respectively, and the accuracy is only reduced by 0.8%. Compared with other designs, it also achieved a result of 3.09x 60x in terms of latency.
Shotgun assembly of Erdős-Rényi random graphs

Graph shotgun assembly refers to the problem of reconstructing a graph from a collection of local neighborhoods. In this paper, we consider shotgun assembly of Erdős–Rényi random graphs G(n,pn), where pn=n−α for 0<α<1. We consider both reconstruction up to isomorphism as well as exact reconstruction (recovering the vertex labels as well as the structure). We show that given the collection of distance-1 neighborhoods, G is exactly reconstructable for 0<α<1 3, but not reconstructable for 1 2<α<1. Given the collection of distance-2 neighborhoods, G is exactly reconstructable for α∈0,1 2∪1 2,3 5, but not reconstructable for 3 4<α<1.
Individual Feral Cat Identification through Deep Learning

Computer vision opportunities based on deep learning have seen an increasing trend in recent years through the proliferation of convolutional neural networks (CNN) and related deep learning models. However, limited training data often constrains the performance and accuracy of such models - this is a common situation for many detection and classification tasks especially in the ecological field. In this paper, we present a case study of how one such model: You Only Look Once (YOLO) version 5 can be applied to individual feral cat identification using a small unbalanced data set. We describe the procedures for preparing the training and validation data set, training the model using data augmentation and transfer learning techniques, and testing the model on both in-distribution and out-of-distribution samples. We explore the effectiveness of data augmentation methods including basic image manipulations and more advanced augmentation techniques that are now available.
Automated Pipeline for Sentiment Analysis of Political Tweets

Social media such as Twitter have proven to be great resources of information regarding many events that proliferate the entire world. It also has the power to change the opinions of millions which is especially useful to sway the masses during political campaigns like presidential elections. Sentiment analysis can be performed on tweets to determine how people feel about certain political events which can be used to predict the behavior of people and propagate real-time change as the events play out. Therefore we decided to create a sentiment analysis and polarity detection pipeline generalized for all political data. The pipeline attempts to automate all the NLP tasks from data scraping to cleaning and pre-processing the dataset to make it ready for the classification tasks. The predictions are visualized via word clouds and a map color coded to reveal the sentiments of key nations around the world regarding the political event. This pipeline is tested from end-to-end with our own personal use case being to determine which candidate do countries around the world preferred during the 2020 US Presidential Elections: Trump or Biden. The pipeline provides fruitful results with a test accuracy of 73.73 percent.
Feature toggles as code: Heuristics and metrics for structuring feature toggles

Context: Using feature toggles is a technique to turn a feature either on or off in program code by checking the value of a variable in a conditional statement.This technique is increasingly used by software practitioners to support continuous integration and continuous delivery (CI/CD).However, using feature toggles may increase code complexity, create dead code, and decrease the quality of a codebase.Objective: The goal of this research is to aid software practitioners in structuring feature toggles in the codebase by proposing and evaluating a set of heuristics and corresponding complexity, comprehensibility, and maintainability metrics based upon an empirical study of open source repositories.Method: We identified 80 GitHub repositories that use feature toggles in their development cycle.We conducted a qualitative analysis using 60 of the 80 repositories to identify heuristics and metrics.Then, we conducted a survey of practitioners of 80 repositories to obtain their feedback that the proposed heuristics can be used to guide the structure of feature toggles and to reduce technical debt.We also conducted a case study of the all 80 repositories to analyze relations between heuristics and metrics.Results: From the qualitative analysis, we proposed 7 heuristics to guide structuring feature toggles and identified 12 metrics to support the principles
State of the art

Genetic algorithms (GAs) are one of the most popular types of nature-inspired algorithms. GAs have been successfully implemented in a wide range of applications across domains such as optimization, bioinformatics, pattern recognition, image processing, and economics. The fitness function is used to measure the quality of the solution in GAs. Typically, the particular value for each gene begins to dominate as the search evolves. During the evolutionary search, fitness decreases as the population converges. This leads to the problems of premature convergence and slow finishing. In this chapter, a detailed and comprehensive review of different approaches implemented to prevent premature convergence, as well as their strengths and weaknesses, are presented. This chapter also discusses factors affecting performance during the search for global optima and brief details about the theoretical framework of the GA. A detailed summary and analysis of reviewed literature are provided for quick review. Existing premature convergence handling algorithms are compared by considering various factors. The main motivation for presenting this comparison is to identify new strategies to develop more powerful algorithms to prevent premature convergence.
Deceptive Level Generation for Angry Birds

The Angry Birds AI competition has been held over many years to encourage the development of AI agents that can play Angry Birds game levels better than human players. Many different agents with various approaches have been employed over the competition's lifetime to solve this task. Even though the performance of these agents has increased significantly over the past few years, they still show major drawbacks in playing deceptive levels. This is because most of the current agents try to identify the best next shot rather than planning an effective sequence of shots. In order to encourage advancements in such agents, we present an automated methodology to generate deceptive game levels for Angry Birds. Even though there are many existing content generators for Angry Birds, they do not focus on generating deceptive levels. In this paper, we propose a procedure to generate deceptive levels for six deception categories that can fool the state-of-the-art Angry Birds playing AI agents. Our results show that generated deceptive levels exhibit similar characteristics of human-created deceptive levels. Additionally, we define metrics to measure the stability, solvability, and degree of deception of the generated levels.
An Overview of Topic Representation and Topic Modelling Methods for Short Texts and Long Corpus

Topic Modelling is a popular method to extract hidden semantic knowledge present in the collection of documents. Due to increase in textual data, topic models play significant role to infer meaningful topics in texts. In this paper, we present a survey of various topic representations and topic models used to discover topics in long and short texts. Topic modelling methods are grouped into standard topic modelling methods, clustering based methods, self-aggregating methods, and Deep Learning based methods. The conventional topic models such as Vector Space Model (VSM), Latent Semantic Analysis (LSA), Probabilistic latent semantic analysis (PLSA), Latent Dirichlet Allocation (LDA), Multinomial Mixture (MM) are discussed with their merits and limitations. Clustering based topic models and self-aggregating topic models used for short text corpus are discussed. Deep learning-based methods with traditional topic modelling to enhance the extraction of high quality topics are discussed.
Open-Domain Trending Hashtag Recommendation for Videos

We describe a novel algorithm for an open-domain trending hashtag recommendation task using zero-shot hashtag prediction in an online learning paradigm. Our method utilizes joint representation learning of latent embeddings for features extracted from long-form videos and semantic embeddings of hashtags trending on social platforms. In particular, we apply graph convolutional networks to a link prediction task using videos and hashtags as nodes in a heterogeneous graph. Comparing it to the existing models for closely related tasks, we demonstrate state-of-the-art results in trending hashtag recommendations for videos. The architecture is designed to be modular in a plug-and-play fashion to enable quick and easy incorporation of the latest advances in natural language understanding and image and video processing, with a practical view to its implementation in a real-time, online setting.
Rest Web Services: An Elementary Learning

The Current Start of Global Business after settling in their flied and advancing growing their business spreads their services over the internet, with web Application forms, SOAP services, SMS and more recently, REST full Services. The REST full framework provides many Features to the company in terms of JSON and HTTPS secure data so REST has now begun to gather more popularity in the industry so here in this paper key features considered are compliance standards and best commitment practices. A number of drifts are over served (e.g., wide-ranging JSON support), but, in unison, high variability in services, with only 0.8% of jobs fully compliant with all REST policies. The result can help professionals understand how REST services have been used to create a more effective website and continue to use the API to connect the mobile application and backend services. Investigators can also benefit from the documentation of main research expanses, underwriting to the advance of very useful applications from these services.
How (not) to Achieve both Coercion Resistance and Cast as Intended Verifiability in Remote eVoting

We consider the problem of achieving, at the same time, cast-as-intended verifiability and coercion resistance, in remote electronic voting systems where there are no secure channels through which voters can receive secret information/credentials before the voting phase.
Exploring data using patterns: A survey

We present a survey of data exploration methods that extract multidimensional patterns from datasets consisting of dimension and measure attributes. These patterns are designed to summarize common properties of tuples sharing the same values of the measure attributes. We review motivating applications, we provide a categorization of the characteristics of patterns produced by various solutions to this problem, we categorize and experimentally evaluate commonly used performance optimizations, and we suggest directions for future research.
CoMe-KE: A New Transformers Based Approach for Knowledge Extraction in Conflict and Mediation Domain

Knowledge discovery and extraction approaches attract special attention across industries and areas moving toward the 5V Era. In the political and social sciences, scholars and governments dedicate considerable resources to develop intelligent systems for monitoring, analyzing and predicting conflicts and affairs involving political entities across the globe. Such systems rely on background knowledge from external knowledge bases, that conflict experts commonly maintain manually. The high costs and extensive human efforts associated with updating and extending these repositories often compromise their correctness of. Here we introduce CoMe-KE (Conflict and Mediation Knowledge Extractor) to extend automatically knowledge bases about conflict and mediation events. We explore state-of-the-art natural language models to discover new political entities, their roles and status from news. We propose a distant supervised method and propose an innovative zero-shot approach based on a dynamic hypothesis procedure. Our methods leverage pre-trained models through transfer learning techniques to obtain excellent results with no need for a labeled data. Finally, we demonstrate the superiority of our method through a comprehensive set of experiments involving two study cases in the social sciences domain. CoMe-KE significantly outperforms the existing baseline, with (on average) double of the performance retrieving new political entities.
Aspect-based Sentiment Classification with Dependency Relation and Structured Attention

Aspect-based sentiment classification (ASC) is a task to determine the sentiment polarities of specific aspects in a review. Syntactic information like dependency relation has been proven effective when extracting sentiment features. On the other hand, multiple semantic segments in a review may influence the sentiment polarity. Thus, we propose a neural network based on dependency relation and structured attention (DRSAN) to fuse both dependency relation features and multiple semantic segments with different attention mechanisms. To our knowledge, we are the first to explicitly integrate dependency relation and structured attention for the ASC task. The experimental results on SemEval 2014 Task4 and Twitter datasets show that the proposed model outperforms all other benchmark models.
Dual Attention Feature Fusion Network for Monocular Depth Estimation

Monocular depth estimation is a traditional computer vision task, which plays a crucial role in 3D reconstruction and scene understanding. Recent works based on deep convolutional neural networks (DCNNs) have achieved great success. However, these works did not make full use of structural information, resulting in discontinuity of depth and ambiguity of boundaries. This paper proposes Dual Attention Feature Fusion Network (DAFFNet), which utilizes the structural relationship between the RGB image and the predicted depth to solve the above problems. It contains two critical modules, Dual Attention Fusion Module (DAFM) and Iterative Dual Attention Fusion Module (IDAFM). Specifically, DAFM includes two blocks (spatial attention block and channel attention block), which fuse global context and local information respectively. To aggregate information of different encoder and decoder levels, we design IDAFM, an iterative version of DAFM. Extensive experimental results on the KITTI dataset show that our model achieves the competitive performance with recent state-of-the-art models .
Mixture Gaussian Prototypes for Few-Shot Learning

In this paper, we provide a new model named Mixture Gaussian Prototypes in few-shot classification problems. In order to describe their features more accurately, our mixture Gaussian prototypes use both the variance and the mean of the data in the support set, while the Gaussian distribution is concise so that it is suitable for the small sample task. Moreover, we verify the validity of our mixture Gaussian prototype through experiments. Also, we perform experiments on Omniglot, mini-ImageNet, and CUB for few-shot Classification and obtain high classification accuracy.
Constrained-SIoU: A Metric for Horizontal Candidates in Multi-Oriented Object Detection

Intersection over union (IoU) has been widely adopted to evaluate and select candidate regions in multi-oriented object detection.Intuitively, overlaps between candidates and multioriented ground-truth boxes make more sense when assessing the quality of horizontal candidates.However, the horizontal minimum bounding box (HMBB) of the ground-truth box is generally used for the IoU calculation in practice, bringing about biased results.In this article, we propose a novel Splicing Intersection over Union (SIoU) to provide a more preferable metric for horizontal candidate selection when detecting multi-oriented objects.By computing the intersection between the candidate region and the ground-truth box rather than its HMBB, SIoU provides a better description of how much object information a candidate contains.Furthermore, we introduce two variants of constraints for the center of each candidate to ensure its location focusing on the objects.Candidates whose centers deviate too far from the objects will be penalized.We integrate the constraint with SIoU, denoted as constrained-SIoU, to select horizontal candidates more efficiently.Comparative experiments on two datasets of aerial images, DOTA and HRSC2016, demonstrate the effectiveness of the proposed method.
Tri-Directional Tasks Complementary Learning for Unsupervised Domain Adaptation of Cross-modality Medical Image Semantic Segmentation

Cross-modality adaptation is challenging due to the internal domain discrepancy in appearance and representation. When the trained model of source domain is transferred to the target domain, the domain shift will reduce accuracy. Meanwhile, unsupervised domain adaptation has the potential to recover this degradation among medical images of different modalities, so it is of clinical significance and meaningful in bioinformatics. However, previous related works usually try to align domains in a single direction or two directions, failing to take advantage of the complementary relationship between different directions and alignment tasks. In this paper, we propose the Tri-directional learning framework to solve domain shift in the task of medical image semantic segmentation. The proposed framework is able to synergize image style transformation, mask segmentation and edge segmentation. The above three tasks are mutually boosted through complementary training in each iteration. In this way, our method performs cross-modality medical images semantic segmentation from labeled source domain (MRI) to unlabeled target domain (CT). The experimental results demonstrate the effectiveness of the proposed method. For the task of cardiac structure segmentation from cross-modality medical images, our proposed framework achieves state-of-the-art performance. The code is available at https://github.com/lichen14/TriDL.
Diverse Image Captioning with Grounded Style

Stylized image captioning as presented in prior work aims to generate captions that reflect characteristics beyond a factual description of the scene composition, such as sentiments. Such prior work relies on given sentiment identifiers, which are used to express a certain global style in the caption, e.g. positive or negative, however without taking into account the stylistic content of the visual scene. To address this shortcoming, we first analyze the limitations of current stylized captioning datasets and propose COCO attribute-based augmentations to obtain varied stylized captions from COCO annotations. Furthermore, we encode the stylized information in the latent space of a Variational Autoencoder; specifically, we leverage extracted image attributes to explicitly structure its sequential latent space according to different localized style characteristics. Our experiments on the Senticap and COCO datasets show the ability of our approach to generate accurate captions with diversity in styles that are grounded in the image.
An Analytical Survey of User Privacy on Social Media Platform

Social media is so popular internet enabled digital platform to share the ideas, views and connect with the people around the globe. These days, everyone using social media platform the share, meet, virtually through this digital platform. User identity and privacy are directly related to the users on social media. In this paper, we have surveyed and collect the different data from social media to derive the user privacy and exposure of user identity on the social media. The results which are derived are in the form of graphs and tabular metrics. The analytical result of findings by the derived survey and data of responder at last section of this research article.
A model for mobile curation services in academic libraries

Purpose To better respond to user needs for personalized information services in the context of academic libraries, this study aims at developing a prototype to provide mobile curation services by using the concept of content curation. Design/methodology/approach This study used a mobile application software development process, which consisted of five phases: user and organizational requirements, architecture design, navigation design, page design and implementation and usability testing. Usability testing was conducted with a total of 20 college students by online surveys at a university. Findings Meta-analysis identified key user needs: diversification of services, more personalized services, active communication with librarians, quality improvement of information and interface improvement. User and organizational requirements derived four main service modules – namely, Curation Services, Live Chat, My Page and My Log. Usability testing regarding ease of use, perceived usefulness and satisfaction indicated that participants were satisfied with the prototype. Research limitations/implications The study extends the discussion of quality academic library services by introducing the concept of content curation that uses the advantages of mobile technologies to overcome existing limitations in library services insufficient for meeting individual user needs. Originality/value Although many studies have discussed mobile library services, few studies have focused on developing a system for such services. The model developed in the study fills the research gap. Above all, the key modules specified by the prototype – Curation Services, Live Chat, My Page and My Log services – are expected to improve existing research and learning support services currently offered by academic libraries.