Reviewerly/exHarmony huggingface dataset:
The problem of uptake

To perform an illocutionary act – e.g. to apologise – is it necessary, sufficient, or irrelevant that a hearer understand you to be performing that act? This issue is sometimes called the ‘problem of uptake’. Famously, Austin’s inaugural account makes uptake necessary for illocutionary force. For decades, critics have objected to this view: Some have sought to show that uptake is irrelevant; others have argued that uptake is important though not essential; while still others have argued that uptake is necessary though must be radically reframed. Nevertheless, it is widely agreed that there is a problem of uptake in Austin. In this paper, I contend that this view is wrong. Austinian uptake, I argue, is essential; yet, contra the standard reading, it is not synonymous with understanding. Uptake is a narrow criterion pertaining to conventional-linguistic competencies, which rules out specific kinds of misunderstanding, not misunderstanding überhaupt. The importation of non-Austinian assumptions about uptake from subsequent speech act theorists – specifically, the mis-equivocation of uptake with understanding a speaker’s intention – I show, has muddied the water. This paper offers a novel reconsideration and salvaging of uptake and its significance, whose stakes I elucidate through intervention in the fraught issue of conceptualising sexual consent.
Towards an Awareness Taxonomy

[Context] Awareness is a well-known but still not fully reached concept in collaborative environments. At this point, two main potential issues remain open: First, the understanding of what awareness is and how to face it in collaborative applications; Second, the establishment of a solid basis for the assessment focusing on awareness aspects. [Goal] We elaborated an awareness taxonomy that contemplates a broad set of awareness elements and collaboration aspects necessary for cooperative work. We developed our awareness taxonomy through a systematic mapping study over the last-10-years CSCW publications regarding the design, development, and evaluation of groupware systems that addressed awareness and collaboration aspects. [Result] We developed a comprehensive multidimensional taxonomy, represented in three main awareness dimensions, namely, collaboration, workspace, and contextual, which encompass, in a simplified way, the awareness literature presented by other models.
Social Relationship Recognition Based on Relational Self-Attention Mechanism

Social relations are closely related to each of us and are a crucial part of society. Recognizing the social relationships of people in pictures can improve AI’s understanding of human behavior, thereby facilitating collaborative interactions between computers and humans. Previous work only focused on a single picture, so too little information can be obtained. In this paper, we proposed Picture Reasoning Model(PRM) to achieve relationship classification, which innovatively uses the self-attention method to learn the association between relationships. The association between relationships is at the social level, thus using it to assist relationship recognition can get rid of the problem of insufficient information in a single picture. In addition, the model also adopts a two-stream approach, extracting both characters and global features for getting multiple perspectives information. We conduct extensive experiments on two benchmark datasets PIPA and PISC. Experimental results show that our model has improved the accuracy metric of the datasets compared with SOTA. On the PIPA dataset, the accuracy increases from 64.4% to 65.6%, and on the PISC dataset, the mAP raises from 72.7% to 73.2%, which validates the effectiveness of our proposals.
A novel quantum-behaved binary firefly algorithm with gravitational search algorithm to optimize the features for human activity recognition

This paper proposes a novel optimization approach of the quantum-behaved binary firefly algorithm with a gravitational search algorithm (QBFA-GSA) for discrete feature optimization, which is utilized for the application of human activity recognition. The firefly algorithm (FA) and gravitational search algorithm (GSA) are recently introduced meta-heuristic algorithms that are efficient for optimizing the continuous solution set. The binarized version of the proposed approach enables it to optimize the discrete features and quantum behavior ensures the better diversity of the final optimized features. In the proposed QBFA-GSA approach, the features are optimized by following the combined advantageous attributes of FA and GSA in which the search space is initially explored by firefly agents until the current firefly finds the brighter firefly and further these agents adapt the attributes of GSA to complete the process. These optimized features are passed to deep neural networks (DNN) for the classification of human activities. Here, DNN models of deep convolutional neural networks (DCNN) and DCNN extended with residual blocks (DCNN-RB) are incorporated. The evaluation experiments for human activity recognition are conducted on a benchmark dataset of UCF-101, which is a composition of 101 different activities. The experimental results of the proposed QBFA-GSA approach are superlative to state-of-art techniques, which indicate that the proposed approach is efficient to optimize the features.
Awareness Support in Collaborative System: Reviewing Last 10 Years of CSCW Research

Context] Awareness has been a valuable concept in Computer-Supported Cooperative Work – CSCW – since its formation, and it is an essential part of collaborative systems. The awareness research followed side by side the evolution of the CSCW field over the last decades, and we can see the progress both in a mutual understanding of awareness and in developing concepts and technology of awareness support. [Goal] Using the awareness and collaboration concepts as a starting point, we performed a systematic mapping study to identify the state-of-the-art of approaches, models, methodologies, or processes adopted in the design, development, and evaluation of collaborative applications. We figured out our research through the six most relevant databases/search engines combined with snowballing techniques, and selected 42 papers from 4320 analyzed, considering publications in the last ten years. [Result] We established a conceptual awareness framework composed of five broad dimensions necessary in collaborative applications: contextual, collaboration, situational, workspace, and historical awareness. This framework constitutes a reasonable set of 17 design categories and 92 awareness elements that assists in providing awareness and collaboration aspects in collaborative systems.
A Comparative Study of Machine Learning Based Image Captioning Models

Automated image captioning is a crucial concept for numerous real-world applications as it is useful in robotics, image indexing, self-driving vehicles and greatly helpful for impaired eyesight people. An image provided in real-time can be converted into text using image captioning models developed by machine learning algorithms. Understanding an image mostly depends on the features of the image. Machine learning techniques are widely used for image captioning tasks. This research study has performed a comparative analysis on three Machine Learning (ML) algorithms, i.e. k-Nearest Neighbor (KNN), Convolution Neural Network (CNN) with Long Short Term Memory (LSTM) and Attention Based LSTM. In addition, an improved KNN algorithm with reduced time complexity and an improved CNN with LSTM and Attention Based LSTM model with an added beam search method is proposed to improve the underlying approaches further. The performance of the three selected models are empirically evaluated using BLEU, ROUGE and METEOR scores on the widely used flickr8k dataset, and the experimental results demonstrate the supremacy of the Attention Based LSTM over the other two approaches. Finally, the current study's findings help guide the researchers and practitioners in selecting the appropriate approach for Image Captioning with empirical evidence in terms of standard evaluation metrics.
Multiple Traffic Target Tracking with Spatial-Temporal Affinity Network

Traffic target tracking is a core task in intelligent transportation system because it is useful for scene understanding and vehicle autonomous driving. Most state-of-the-art (SOTA) multiple object tracking (MOT) methods adopt a two-step procedure: object detection followed by data association. The object detection has made great progress with the development of deep learning. However, the data association still heavily depends on hand crafted constraints, such as appearance, shape, and motion, which need to be elaborately trained for a special object. In this study, a spatial-temporal encoder-decoder affinity network is proposed for multiple traffic targets tracking, aiming to utilize the power of deep learning to learn a robust spatial-temporal affinity feature of the detections and tracklets for data association. The proposed spatial-temporal affinity network contains a two-stage transformer encoder module to encode the features of the detections and the tracked targets at the image level and the tracklet level, aiming to capture the spatial correlation and temporal history information. Then, a spatial transformer decoder module is designed to compute the association affinity, where the results from the two-stage transformer encoder module are fed back to fully capture and encode the spatial and temporal information from the detections and the tracklets of the tracked targets. Thus, efficient affinity computation can be applied to perform data association in online tracking. To validate the effectiveness of the proposed method, three popular multiple traffic target tracking datasets, KITTI, UA-DETRAC, and VisDrone, are used for evaluation. On the KITTI dataset, the proposed method is compared with 15 SOTA methods and achieves 86.9% multiple object tracking accuracy (MOTA) and 85.71% multiple object tracking precision (MOTP). On the UA-DETRAC dataset, 12 SOTA methods are used to compare with the proposed method, and the proposed method achieves 20.82% MOTA and 35.65% MOTP, respectively. On the VisDrone dataset, the proposed method is compared with 10 SOTA trackers and achieves 40.5% MOTA and 74.1% MOTP, respectively. All those experimental results show that the proposed method is competitive to the state-of-the-art methods by obtaining superior tracking performance.
Converting property graphs to RDF

Today's space of graph database solutions is characterized by two main technology stacks that have evolved separate from one another: on one hand, there are systems that focus on supporting the RDF family of standards; on the other hand, there is the Property Graph category of systems. As a basis for bringing these stacks together and, in particular, to facilitate data exchange between the different types of systems, different direct mappings between the underlying graph data models have been introduced in the literature. While fundamental properties are well-documented for most of these mappings, the same cannot be said about the practical implications of choosing one mapping over another. Our research aims to contribute towards closing this gap. In this paper we report on a preliminary study for which we have selected two direct mappings from (Labeled) Property Graphs to RDF, where one of them uses features of the RDF-star extension to RDF. We compare these mappings in terms of the query performance achieved by two popular commercial RDF stores, GraphDB and Stardog, in which the converted data is imported. While we find that, for both of these systems, none of the mappings is a clear winner in terms of guaranteeing better query performance, we also identify types of queries that are problematic for the systems when using one mapping but not the other.
Design of Multi-Robot Cooperative Transport System

In this paper, a multi-robot cooperative transport system based on NAO robot is designed. Based on the kinematics modeling, object recognition and localization and target grasping of humanoid robots in complex scenes, the binocular vision module is extended by using multiple humanoid robots NAO. Finally, based on multi-sensor fusion and driven by cooperative handling task, the dynamic identification of object position and pose, trajectory planning of robot motion and cooperative handling control are analyzed and designed. Experimental tests in real environment show that the system has the advantages of intelligence and good synergistic effect, which has reference value for the application and in-depth development of service robot.
Location Privacy Protection Method Based on Geohash Coding and Pseudo-Random Sequence

The popularity of mobile communication devices and the rapid progress of social networks have not only promoted the development of location-based services, but also brought many risks to users’ privacy. In order to better protect users’ location privacy, based on the centralized structure, this paper proposes a k-anonymous location privacy protection scheme based on geohash coding and pseudo-random sequence generation technology, which uses the properties of region coding and pseudorandom sequence to solve the dependence on the credibility of the central server. Theoretical analysis and experimental simulation results show that this scheme can not only resist the central server attack and area central attack, but also has higher efficiency in constructing anonymous area compared with similar schemes.
VERICONDOR

Condorcet voting, first proposed by Marquis de Condorcet in the 18th century, chooses a winner of an election as one that defeats every other candidate by a simple majority. According to Condorcet's criterion, a Condorcet winner is the socially optimal choice in a multi-candidate election. However, despite the crucial importance of this voting system in social-choice theory, it has not been widely used in practical applications. This is partly due to the complex tallying procedure, and also the fact that several candidates may form a tie. Existing systems that provide online Condorcet voting services in the real world try to speed up the tallying process by collecting and tallying Condorcet ballots in a digital form. However, they require voters to completely trust the server. In this paper, we propose VERICONDO, the first end-to-end verifiable Condorcet e-voting system without any tallying authorities. Our system allows a voter to fully verify the tallying integrity without involving any trustworthy tallying authorities and provides strong protection of the ballot secrecy. One main challenge in our work lies in proving the well-formedness of an encrypted ballot while being able to tally the ballots in a publicly verifiable yet privacy-preserving manner. We overcome this challenge by adopting a pairwise comparison matrix and applying a novel vector-sum technique to achieve exceptional efficiency. The overall computational cost per ballot is O (n2) where n is the number of candidates. This is probably the best that one may hope for given the use of a n x n matrix to record a Condorcet ballot. In case of a tie, we show how to apply known Condorcet methods to break the tie in a publicly verifiable manner. Finally, we present a prototype implementation and benchmark performance to show the feasibility of our system.
Natural Language Processing based Cross Lingual Summarization

Natural Language Processing (NLP) is a domain that programs machines to interpret/ comprehend human language like human beings do. Application of NLP to summarization and machine translation has been accelerating from past years. One such application, Cross-lingual summarization (CLS), produces a summary in a particular language (target language) for an input document in a different language (source language). CLS can help people understand an article's gist written in an unfamiliar language by translating the summary to their familiar language. Current methods divide CLS into two methods: Summarization (Abstractive or Extractive) and Translation. Although there has been a tremendous amount of progress in abstractive summarization in recent years, most research focuses on monolingual summarization because of lack of high-quality multilingual resources. While there have been a few studies to address the lack of resources for cross-lingual summarization, the datasets employed are very limited in size. This paper presents the Cross-Lingual Summarization from English to Kannada by summarizing English ne articles and translating the summaries to Kannada. Traditional CLS methods are pipeline-based i.e., it either involves summarization and then translation or vice versa. The summarization is followed by translation, hence it is termed as ‘Late Translation’.
Detection Algorithm of Tennis Serve Mistakes Based on Feature Point Trajectory

To address the issue of high recognition error in conventional action error detection methods, this article proposes a game of tennis serve error action detection algorithm based on feature point trajectory. To begin, a feature detection model for tennis serve images is established, followed by segmentation of the tennis serve images’ multiscale features. Second, the path of the tennis serving image is effectively corrected, thereby raising the bar for tennis training and competition. Additionally, a visual feature acquisition system for tennis serving action is being developed using remote video monitoring in order to correct the path of the serving image during play. The corner mark of the serving action error point is determined using this algorithm, and the optimal modeling of the tennis serving image’s path correction is realized using the developed edge segmentation algorithm. The results of simulations demonstrate that the aforementioned algorithm improves real-time performance and accuracy, and that it can accurately track players’ visual edge information feature points while they are serving, conduct real-time evaluation and guidance via an expert system, effectively correct the tennis serving image path, and enhance your capacity for service.
One-Shot Detection of Malicious TLS Traffic

Network security protocols (such as Transport Layer Security, TLS) are increasingly used by hackers to evade detection. The detection of encrypted malicious traffic is becoming a critical task for cyber security. To accomplish this task, researchers have proposed several machine learning or deep learning based methods. However, existing methods require a huge amount of malicious flows as annotated samples, which is difficult or even impossible to obtain for emerging attacks. In this paper, we propose a novel method to detect malicious TLS traffic with only one sample, i.e., one annotated malicious flow. Our observation is that there are a large number of benign HTTPS (HTTP+TLS) flows in the network, and these flows are similar but with subtle differences in behavior. If a model can efficiently distinguish different HTTPS flows by traffic behavior, it should know how to extract inherent characteristics of TLS traffic. Based on the observation, we visit Alexa top websites and train ResNet models in conjunction with statistical traffic data. The obtained conjunction models are used as the feature extractor for malicious TLS flows. To achieve one-shot classification, we further train a feed-forward neural network to transfer the extracted features to a classification decision boundary of the support vector machine (SVM). We test our approach with a thorough set of experiments, and the experimental results show that our approach significantly outperforms the baseline methods.
Untangling the Knot: Enabling Architecture Evolution with Search-Based Refactoring

Software-reliant systems need to evolve over time to meet new requirements and take advantage of new technology. However, all too often the structure of software becomes too complex to allow rapid and cost-effective improvements. This increasing complexity is often also a sign of degrading software architecture, making isolating a portion of software for use in a new context or for clean replacement by an improved version difficult. Isolating entangled software from the rest of the architecture typically relies on manual efforts to refactor code that can take thousands of days of effort, as tools provide only limited support for such activities. In this paper, we describe a search-based algorithm that recommends a series of refactorings that collectively isolate specified software from its tangle of architectural dependencies. This approach generates recommendations that reduce problematic dependencies by more than 87% on codebases as large as 1.2M LOC and has the potential to reduce the effort required for this kind of architecture improvement by two-thirds. In walkthroughs, developers found more than 84% of the recommended refactorings acceptable. Our approach provides a much needed foundation for tool support that addresses challenges commonly encountered when improving the architecture of existing software.
SDDSMOTE:Synthetic Minority Oversampling Technique based on Sample Density Distribution for Enhanced Classification on Imbalanced Microarray Data

Microarray gene expression data contain an unbalanced distribution of data samples among different classes, which poses a challenge to machine learning-based cancer diagnosis. In addition, microarray data consists of small samples and a huge number of genes, which cause the curse of dimensionality. In order to enhance the performance of learning models on imbalanced microarray data, we propose a novel preprocessing method based on the SMOTE, named SDDSMOTE (Synthetic Minority Oversampling Technique based on Sample Density Distribution). The whole preprocessing includes two steps. First, by using a feature selection technology, irrelevant genes are eliminated and obtaining reduced gene data. Second, SDDSMOTE is used to rebalance the reduced data. We performed comprehensive experiments to compare SDDSMOTE with other state-of-the-art Oversampling algorithms using two Support Vector Machine and Logistic Regression on 8 publicly available microarray expression data sets. The experimental results show that SDDSMOTE outperforms compared algorithms in terms of various evaluation criteria, such as Accuracy, F-score, G-mean, and AUC, which indicates its superiority.
A Blockchain-based Platform of Housing Provident Fund Asset-backed Securitization

This paper proposes a platform of housing provident fund asset-backed securitization based on blockchain to solve existing problems caused by opacity and barrier of data. Firstly, we introduce the system architecture consisting of four parts corresponding to different functions. Secondly, the specific program is illustrated in different modules during the whole life cycle of housing provident fund asset-backed securitization. Lastly, a real pilot project in Chongqing is introduced to prove the feasibility and benefit of the platform design. This platform realizes both the efficiency and data transparency under the premise of security with the help of blockchain technology.