Reviewerly/exHarmony huggingface dataset:
The impact of automatic text translation on classification of online discussions for social and cognitive presences

This paper reports the findings of a study that measured the effectiveness of employing automatic text translation methods in automated classification of online discussion messages according to the categories of social and cognitive presences. Specifically, we examined the classification of 1,500 Portuguese and 1,747 English discussion messages using classifiers trained on the datasets before and after the application of text translation. While the English model generated, with the original and translated texts, achieved results (accuracy and Cohen's κ) similar to those of the previously reported studies, the translation to Portuguese led to a decrease in the performance. The indicates the general viability of the proposed approach when converting the text to English. Moreover, this study highlighted the importance of different features and resources, and the limitations of the resources for Portuguese as reasons of the results obtained.
Multi-scale single-stage pose detection with adaptive sample training in the classroom scene

Student pose information plays an important role in teaching management and evaluation. Thus, it is meaningful to acquire student pose information fast and precisely. Although the pose estimation techniques are well-studied recently, it is still challenging to apply existing methods to handle this task due to heavy occlusion in the educational environment. Different from the pose estimation, we consider pose detection as a specific problem of object detection. However, compared with generic object detection problems, pose detection usually suffers from poor detection performance due to the challenges of similar categories, object with small sizes, quantity imbalance of categories, etc. To address these issues, we propose a new pose detection method based on a single-stage object detector. We present a multi-scale feature enrichment branch to obtain balanced and robust features. Then we adopt an adaptive fusion mechanism to learn complementary spatial features, making our feature extractor more discriminative. Besides, an adaptive positive sample training strategy is adopted to select robust positive samples and make full use of high-quality predicted positive samples when training by the adaptive Smooth L1 loss. Experimental results show that the proposed method obviously outperforms other single-stage object detection methods on the real classroom pose datasets.
End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection

In this paper, we present a conditional multitask learning method for end-to-end neural speaker diarization (EEND). The EEND system has shown promising performance compared with traditional clustering-based methods, especially in the case of overlapping speech. In this paper, to further improve the performance of the EEND system, we propose a novel multitask learning framework that solves speaker diarization and a desired subtask while explicitly considering the task dependency. We optimize speaker diarization conditioned on speech activity and overlap detection that are subtasks of speaker diarization, based on the probabilistic chain rule. Experimental results show that our proposed method can leverage a subtask to effectively model speaker diarization, and outperforms conventional EEND systems in terms of diarization error rate.
The Use of Blockchain Technology in Public Sector Entities Management: An Example of Security and Energy Efficiency in Cloud Computing Data Processing

Blockchain technology is currently one of the trends considered to have a tremendous future ahead. It ensures data security, data sharing protection and automation development—elements that are of colossal importance in the era of cloud solutions, big data and Internet of Things (IoT) reality. Additionally, blockchain technology allows one to create new programmable ecosystems on an unprecedented scale. The implementation of blockchain technology leads not only to improving the flow of documents and data storage, as is the case with the creation of shared service centers (SSCs), but—as this paper shows—allows one to reduce the carbon footprint when servicing SSCs at a considerably higher organizational level at the same time. The example of an SSC in Elbląg, Poland, proves that cloud solutions enabling electronic documents flow and data storage combined with blockchain technology are tools essential for further SSCs development. Furthermore, such tools allow us not only to obtain economic effects (i.e., cost reduction), but also to achieve positive ecological effects (i.e., carbon footprint reduction).
Improved Glasius bio-inspired neural network for target search by multi-agents

This paper focuses on solving the multi-agent cooperative target search problem with the demand for obtaining the maximal cumulative detection reward, given the prior target probability map and the sensor detection ability under various constraints. First, a topologically organized model of Glasius bio-inspired neural network (GBNN) is constructed individually for each agent in order to represent the searching environment. The neural activities are determined not only by the activity propagation among neurons, but also by the external input containing the single detection reward and various constraints synthetically. Then, the agent's searching motion can be selected greedily based on the dynamic activity landscape of GBNN. With the disadvantages of propagation time delay and activity attenuation, however, the relatively global mechanism in GBNN may lead to unsatisfactory performance or even fail to avoid the local optimal problem. Hence the Gaussian mixture model (GMM) is utilized to extract the high-value subregions and compute the future detection reward quantitatively, which can be introduced into the neuron's external excitatory input of GBNN directly. The simulation results verify the high efficiency and strong robustness of GBNN-GMM in the searching scenarios.
ASER: Towards large-scale commonsense knowledge acquisition via higher-order selectional preference over eventualities

Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect commonsense knowledge. In this paper, we propose to develop principles for collecting commonsense knowledge based on selectional preference, which is a common phenomenon in human languages that has been shown to be related to semantics. We generalize the definition of selectional preference from one-hop linguistic syntactic relations to higher-order relations over linguistic graphs. Unlike previous commonsense knowledge definitions (e.g., ConceptNet), the selectional preference (SP) knowledge only relies on statistical distributions over linguistic graphs, which can be efficiently and accurately acquired from the unlabeled corpora with modern tools, rather than human-defined relations. As a result, acquiring SP knowledge is a much more scalable way of acquiring commonsense knowledge. Following this principle, we develop a large-scale eventuality (a linguistic term covering activity, state, and event)-based knowledge graph ASER, where each eventuality is represented as a dependency graph, and the relation between them is a discourse relation defined in shallow discourse parsing. The higher-order selectional preference over collected linguistic graphs reflects various kinds of commonsense knowledge. For example, dogs are more likely to bark than cats as the eventuality "dog barks" appears 14,998 times in ASER while "cat barks" only appears 6 times. "Be hungry" is more likely to be the reason rather than result of "eat food" as the edge 〈"be hungry," Cause, "eat food"〉 appears in ASER while 〈"eat food," Cause, "be hungry"〉 does not. Moreover, motivated by the observation that humans understand events by abstracting the observed events to a higher level and can thus transfer their knowledge to new events, we propose a conceptualization module on top of the collected knowledge to significantly boost the coverage of ASER. In total, ASER contains 648 million edges between 438 million eventualities. After conceptualization with Probase, a selectional preference based concept-instance relational knowledge base, our concept graph contains 15 million conceptualized eventualities and 224 million edges between them. Detailed analysis is provided to demonstrate its quality. All the collected data, APIs, and tools that can help convert collected SP knowledge into the format of ConceptNet are available at https://github.com/HKUST-KnowComp/ASER.
Discussoo: Towards an intelligent tool for multi-scale participatory modeling

Abstract In participatory modeling (PM), a conceptual model emerges from an exchange of information and opinions among stakeholders. This usually happens in a series of in-person workshops restricted to a certain number of attendees during designated time intervals. Our goal is to open up the PM workshop process to engage an unlimited number of participants at various locations, while supporting them with the functionality that the modeling context can offer. We develop a real-time, moderated steering environment, named Discussoo, to facilitate online PM. Users express their opinions about a topic by providing their comments in online discussions. As the discussion evolves, an ensemble of artificial intelligence algorithms in the background automatically produces a dynamic conceptual model to visualize the on-going exchange of opinions. Moderators can use this model to provide feedback to users and guide the discussion. Policymakers and managers can use Discussoo to support more transparent and meaningful engagement of stakeholders.
A Hybrid Probabilistic Ensemble based Extreme Gradient Boosting Approach For Breast Cancer Diagnosis

Breast cancer has been identified as one of the most common invasive cancers and the second leading cause of cancer death among women. The survival rates have, however, improved dramatically in recent years, thanks to the advances in the screening and treatment process, hugely depending on how early the disease was detected. Along with the physicians, this had also initiated researchers all over the globe to dedicate themselves to extensive research to produce automated diagnosis strategies for breast cancer. Realizing the extraordinary potential of machine learning-based models in the biomedical domain, a large number of diagnosis methods have been proposed in this direction. In our study, we propose a hybrid unique machine learning framework that integrates individual prediction probabilities from 3 machine learning (Logistic Regression, Support Vector Machine, and K Nearest Neighbors) classifiers, then enhances the performance of these 3 classifiers through hybridization, stacking a gradient boosting algorithm over the combination of these classifiers which ultimately results in a 10 Fold Cross Validation Score of 98.4%, Recall of 100% and Precision of 97.3%. Besides, to handle the class imbalance problem we have incorporated SMOTE(Synthetic Minority Oversampling Technique) for minority classes and also Robust Scaling for normalization to deal with outliers in the dataset. In our proposed hybrid solution, we successfully adopted the breast cancer domain in every stage of our framework, starting from data pre-processing, feature extraction and finally classification. Our framework outperformed some recent state of the art studies in the breast cancer domain.
Image encryption based on adversarial neural cryptography and SHA controlled chaos

To overcome the vulnerability to known-plaintext attack or chosen-plaintext attack of a linear image encryption system, a new approach for image encryption is proposed based on adversarial neural cryptography (ANC) combined with SHA-256 controlled chaotic systems. In this image encryption approach, the optimal network model is first obtained by training a generative adversarial network (GAN), and then the GAN model is used to achieve a noise-like intermediate image. Subsequently, the XOR operation based on a logistic map is performed on the intermediate image to obtain the final ciphertext. The intrinsic non-linearity of the neural network (NN) guarantees the ability of the proposed system to resist common attacks like known-plaintext attack or chosen-plaintext attack. The plaintext dependent SHA-256 controlled logistic map greatly improves the diffusion performance so that the encryption system can resist differential attacks. Numerical simulation results prove the reliability, effectiveness, and security of the proposed scheme.
Representation-Optimal Multi-Robot Motion Planning Using Conflict-Based Search

Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of agents each with individual start and goal states within a continuous state space. Existing approaches can be split into coupled methods which provide optimal solutions but struggle with scalability or decoupled methods which provide scalable solutions but offer no optimality guarantees. Recent work has explored hybrid approaches that leverage the advantages of both coupled and decoupled approaches in an easier discrete subproblem, Multi-Agent Pathfinding (MAPF). In this work, we adapt recent developments in hybrid MAPF to the continuous domain of MAMP. We demonstrate the scalability of our method to manage groups of up to 32 agents, demonstrate the ability to handle up to 8 high-DOF manipulators, and plan for heterogeneous teams. In all scenarios, our approach plans significantly faster while providing higher quality solutions.
Improved multi-scale dynamic feature encoding network for image demoiréing

The popularity of smartphones with digital cameras makes photographing using smartphones an important daily activity. Moiré patterns can easily appear when shooting objects with rich textures, such as computer screens, and will severely degrade the image quality. Image demoiréing is an important image restoration task that aims to remove moiré patterns and reveal the underlying clean image. Two key properties of moiré patterns—the widely distributed frequency spectrum and the dynamic nature of moiré textures—challenge the image demoiréing task. In this paper, we propose an improved Multi-scale convolutional network with Dynamic feature encoding for image DeMoiréing (MDDM+). We design two schemes in our network to respectively attack the broad frequency spectrum and the dynamic texture of moiré: a multi-scale structure to process images at different spatial resolutions and a dynamic feature encoding module to encode the texture dynamically. To capture more moiré and texture information from different frequencies, we further propose a novel L1 wavelet loss used to train our model. Extensive experiments on two benchmarks show that our proposed image demoiréing network can outperform the state of the arts in terms of fidelity as well as perception.
Multi-Class Spectral Clustering with Overlaps for Speaker Diarization

This paper describes a method for overlap-aware speaker diarization. Given an overlap detector and a speaker embedding extractor, our method performs spectral clustering of segments informed by the output of the overlap detector. This is achieved by transforming the discrete clustering problem into a convex optimization problem which is solved by eigen-decomposition. Thereafter, we discretize the solution by alternatively using singular value decomposition and a modified version of non-maximal suppression which is constrained by the output of the overlap detector. Furthermore, we detail an HMM-DNN based overlap detector which performs frame-level classification and enforces duration constraints through HMM state transitions. Our method achieves a test diarization error rate (DER) of 24.0% on the mixed-headset setting of the AMI meeting corpus, which is a relative improvement of 15.2% over a strong agglomerative hierarchical clustering baseline, and compares favorably with other overlap-aware diarization methods. Further analysis on the LibriCSS data demonstrates the effectiveness of the proposed method in high overlap conditions.
MeetGo: A Trusted Execution Environment for Remote Applications on FPGA

Remote computing has emerged as a trendy computing model that enables users to process an immense number of computations efficiently on the remote server where the necessary data and high-performance computing power are provisioned. Unfortunately, despite such an advantage, this computing model suffers from insider threats that are committed by adversarial administrators of remote servers who attempt to steal or corrupt users' private data. These security threats are somewhat innate to remote computing in that there is no means to control administrators' unlimited data access. In this paper, we present our novel hardware-centric solution, called MeetGo, to address the intrinsic threats to remote computing. MeetGo is a field-programmable gate array (FPGA)-based trusted execution environment (TEE) that aims to operate independently of the host system architecture. To exhibit the ability and effectiveness of MeetGo as a TEE ensuring secure remote computing, we have built two concrete applications: cryptocurrency wallet and GPGPU. MeetGo provides a trust anchor for these applications that enable their users to trade cryptocurrency or to run a GPGPU program server on a remote server while staying safe from threats by insiders. Our experimental results clearly demonstrate that MeetGo incurs only a negligible performance overhead to the applications.
Rapid Reviews in Software Engineering

Integrating research evidence into practice is one of the main goals of evidence-based software engineering (EBSE). Secondary studies, one of the main EBSE products, are intended to summarize the "best" research evidence and make them easily consumable by practitioners. However, recent studies show that some secondary studies lack connections with software engineering practice. In this chapter, we present the concept of Rapid Reviews, which are lightweight secondary studies focused on delivering evidence to practitioners in a timely manner. Rapid reviews support practitioners in their decision-making, and should be conducted bounded to a practical problem, inserted into a practical context. Thus, Rapid Reviews can be easily integrated in a knowledge/technology transfer initiative. After describing the basic concepts, we present the results and experiences of conducting two Rapid Reviews. We also provide guidelines to help researchers and practitioners who want to conduct Rapid Reviews, and we finally discuss topics that may concern the research community about the feasibility of Rapid Reviews as an evidence-based method. In conclusion, we believe Rapid Reviews might be of interest to researchers and practitioners working on the intersection of software engineering research and practice.
Hate Speech Detection in Twitter using Natural Language Processing

Twitter’s central goal is to enable everybody to make and share thoughts and data, and to communicate their suppositions and convictions without boundaries. Twitter’s job is to serve the public discussion, which requires portrayal of a different scope of points of view. Yet, it does not advance viciousness against or straightforwardly assault or undermine others based on race, nationality, public cause, rank, sexual direction, age, inability, or genuine illness. Hate Speech can hurt a person or a community. So, it is not appropriate to use hate speech. Now, due to increase in social media usage, hate speech is very commonly used on these platforms. So, it is not possible to identify hate speeches manually. So, it is essnetial to develop an automated hate speech detection model and this resaech work shows different approaches of Natural Language Processing for classification of Hate Speech through Machine Learning Algorithms.
Fruit category classification by fractional Fourier entropy with rotation angle vector grid and stacked sparse autoencoder

Abstract Aim Fruit category classification is important in factory packing and transportation, price prediction, dietary intake, and so forth. Methods This study proposed a novel artificial intelligence system to classify fruit categories. First, 2D fractional Fourier entropy with rotation angle vector grid was used to extract features from fruit images. Afterwards, a five‐layer stacked sparse autoencoder was used as the classifier. Results Ten runs on the test set showed our method achieved a micro‐averaged F1 score of 95.08% for an 18‐category fruit dataset. Conclusion Our method gives better micro‐averaged F1 score than 10 state‐of‐the‐art approaches.
Distilling a Powerful Student Model via Online Knowledge Distillation

Existing online knowledge distillation approaches either adopt the student with the best performance or construct an ensemble model for better holistic performance. However, the former strategy ignores other students' information, while the latter increases the computational complexity during deployment. In this article, we propose a novel method for online knowledge distillation, termed feature fusion and self-distillation (FFSD), which comprises two key components: FFSD, toward solving the above problems in a unified framework. Different from previous works, where all students are treated equally, the proposed FFSD splits them into a leader student set and a common student set. Then, the feature fusion module converts the concatenation of feature maps from all common students into a fused feature map. The fused representation is used to assist the learning of the leader student. To enable the leader student to absorb more diverse information, we design an enhancement strategy to increase the diversity among students. Besides, a self-distillation module is adopted to convert the feature map of deeper layers into a shallower one. Then, the shallower layers are encouraged to mimic the transformed feature maps of the deeper layers, which helps the students to generalize better. After training, we simply adopt the leader student, which achieves superior performance, over the common students, without increasing the storage or inference cost. Extensive experiments on CIFAR-100 and ImageNet demonstrate the superiority of our FFSD over existing works. The code is available at https://github.com/SJLeo/FFSD.