Reviewerly/exHarmony huggingface dataset:
SSIN: Sentence Semantic Interaction Network for Multi-Choice Reading Comprehension

Multiple-choice reading comprehension is a challenging task in natural language processing, which aims to select the correct answer from a set of candidate options when given passage and question. Previous approaches usually focus only on word vector interactions and ignore the importance of sentence semantics for reading when modeling the relationship between passage and question. However, reading is a process that includes complex interactions of various knowledge such as vocabulary, syntax and semantics. Interactions based on word vectors alone do not effectively capture the relationship between passage and question. In this work, we propose the Sentence Semantic Interaction Network (SSIN), which models the relationship among passage, question, and answer options based on sentence semantics. The experimental results show that superior results are achieved on both the RACE and MCTest datasets, confirming that the interaction based on sentence semantic vectors can effectively improve the performance of the model reading comprehension.
Design of Cross Language Education Resource Sharing Platform Based on Hadoop Framework

The rapid development of information technology has promoted the modernization of cross language educational resource sharing. Educational resource sharing platform is the platform basis for carrying out distance education activities, which is of great significance to the development of modern distance education. Firstly, the hardware design of cross language education resource sharing platform is carried out, and the MCU controller is designed to play a powerful control function combined with FPGA system. The design is based on resource reconfiguration port and external input port to realize hardware connection. Then, the software design of cross language education resource sharing platform is carried out, the resource sharing algorithm based on Hadoop framework is optimized, and the multi line sharing ability of resource sharing algorithm is enhanced. The data model is designed to realize the design of shared platform. Finally, the platform test verifies that the response time of the platform is less than 4000 ms.
Robustness of Electrical Network Frequency Signals as a Fingerprint for Digital Media Authentication

Leveraging modern Artificial Intelligence (AI) technology, Deepfake attacks manipulate audio/video streams (AVS) to mimic any targeted person or scenario. Deepfake attacks are highly disturbing, and the misinformation can mislead the public, raising further challenges in policy, technical, social, and legal aspects. Electrical Network Frequency (ENF) signals embedded in AVS data are promising to be utilized as fingerprints to authenticate digital media and timely detect deepfaked audio or video. Meanwhile, the success of ENF-based deepfake detection approaches will be forfeited if attackers can create false ENF fingerprints to fool the detector. In this paper, a thorough experimental study validates the robustness of ENF signals as a fingerprint for digital media authentication. Taking statistical, supervised learning, and deep learning approaches, this work shows that it is infeasible to forecast the future ENF signals based on historical records. While strict theoretical proof is yet to be done, this work experimentally verifies ENF signals as a reliable fingerprint to authenticate digital media.
Lifelong Learning for Text Steganalysis Based on Chronological Task Sequence

The prevailing text steganalysis models deal with only one domain of steganographic text. When learning steganographic text from other domains, it will forget the previously learned features, resulting in degraded model performance on the previous domain. We create a chronological task sequence and draw on the ideas of lifelong learning to propose a novel steganalysis method. We use BERT to extract the common features shared among the current task and the previous tasks. Then, we extract explicit and latent features based on two learning scenarios: next sentence prediction and task ID classification, which are constrained by regularization to prevent the feature space from changing too much when switching tasks. And we design the replay mechanism and three loss functions to make a trade-off between the previous and the current tasks. Furthermore, we evaluate our model under hybrid steganography, which detects a mixture of texts generated by different steganography algorithms with different embedding capabilities. Extensive experiments show that our model mitigates the catastrophic forgetting issues and outperforms the state-of-the-art models in continual text steganalysis tasks.
Modern Value Based Reinforcement Learning: A Chronological Review

Investigation of value based Reinforcement Learning algorithms exhibited a resurgence into mainstream research in 2015 following demonstration of super-human performance when applied to Atari 2600 games.Since then, significant media attention and hype have accompanied this area, and the field of Artificial Intelligence generally, spread across distinct categories.This review paper is focused exclusively on the progression of value based Reinforcement Learning in the last five years.We aim to distill the incremental improvements to stability and performance in this period, highlighting the minimal changes to the base algorithm over this time.This holds true with all but the one exception of the Recurrent Experience Replay in Distributed Reinforcement Learning algorithm, representing a fundamental shift and increase in agent performance through an advanced memory representation.We suggest a new focus area for value based Reinforcement Learning research.
Multi-View Graph Autoencoder for Unsupervised Graph Representation Learning

Unsupervised graph representation learning based on graph autoencoder and graph variational autoencoder has achieved significant success in non-Euclidean data such as citation networks, social networks, and so on. However, the most existing graph autoencoders aggregate node features and graph structure from one view: local topology, and only reconstruct the node feature matrix or adjacency matrix, which neither learns a more useful and comprehensive embedding nor makes full use of the latent information of the embedding. In this paper, we propose a multi-view graph autoencoder which can aggregate latent information from local topology, global topology and feature similarity and reconstruct the graph structure and node features simultaneously. We validate the effectiveness of our framework on four datasets and the experimental results demonstrate the superior performance of our proposed framework compared with other advanced frameworks.
A geometric proof of the equivalence between AUC_ROC and Gini index area metrics for binary classifier performance assessment

Some area metrics are used in different domains for assessing inequalities / discriminating power. The Area Under the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$ROC$</tex> curve is used for assessing the quality of binary classifiers while the Gini index is used for measuring inequalities in populational studies related to dichotomic wealth distribution, in general. Do they have anything in common? This paper proves they are related and quantifies the equivalence between them. Despite the apparent difference between the curves, this paper presents a geometrically inspired proof which considers a compression of the area under the Lorenz curve which defines the Gini index. To overcome the area compression, the proof preserves the 1-to-1 correspondence between the ideal classifier on the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$ROC$</tex> and Lorenz curves domains. Quantitatively, this paper proves that the Gini index normalized by that of the ideal classifier is equivalent to the area under the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$ROC$</tex> curve above the chance diagonal (minus 0.5) also normalized by that of the ideal classifier. This also happens to be exactly the already known Somers' D metric and the Area Under the Kolmogorov-Smirnov curve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(AUC\_KS)$</tex> , recently proven. Therefore, this paper proposes these equivalent metrics to be a unified metric for binary classifiers' assessment Gini Index <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Ratio} =\mathbf{2}^{\ast}(AUC\_ROC-\mathbf{0.5})=2\ast AUC\_KS =\ \mathbf{Somers}$</tex> ‘ D metric. This integrates theoretical approaches used independently in different domains for measuring classes' separability in binary problems with the Somers' D metric already proved quantitatively equivalent to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\ast(AUC\_ROC-0.5)$</tex> .
Improving Covariance Conditioning of the SVD Meta-layer by Orthogonality

Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities. In this paper, we systematically study how to improve the covariance conditioning by enforcing orthogonality to the Pre-SVD layer. Existing orthogonal treatments on the weights are first investigated. However, these techniques can improve the conditioning but would hurt the performance. To avoid such a side effect, we propose the Nearest Orthogonal Gradient (NOG) and Optimal Learning Rate (OLR). The effectiveness of our methods is validated in two applications: decorrelated Batch Normalization (BN) and Global Covariance Pooling (GCP). Extensive experiments on visual recognition demonstrate that our methods can simultaneously improve the covariance conditioning and generalization. Moreover, the combinations with orthogonal weight can further boost the performances.
Well-being as a Composite Capability in the Smart Building Domain: A Formal and Technical Study

Smart building value-added capabilities are gaining significant attention from various stakeholders, including the general public, researchers, and industry. One such capability is well-being, a composition of multiple atomic capabilities that characterize a smart building. Atomic functions that compose a well-being capability include temperature, noise level, pollution level, and humidity, to name a few. Multiple efforts have addressed this specific capability and its composition requirements and techniques from standardization, technical, and quality of service aspects. One such effort is the IoT and CPS Composition Framework (ICCF), a novel framework for rapid modeling, specifying, verifying, and prototyping IoT and CPS capabilities. ICCF relies on the NIST CPS Framework guidelines to address different stakeholders' concerns; it also leverages composition semantics inspired by the mPlane platform to describe entities and interactions intuitively. In addition, it uses the Temporal Logic of Actions + (TLA+) formal verification techniques to verify the correctness of core functions. This work leverages the ICCF framework to provide the following contributions: i) description of a stakeholder-defined well-being composition capability based on the ICCF framework foundations, ii) an in-depth characterization of the well-being capability, iii) considerations regarding the formal aspects of the well-being capability, including verifying its correctness, deadlock, and state-space, iv) implementation of the composite capability using a lightweight microservices environment, v) discussion of results based on the different domains of interest including residential buildings and factories. Finally, a summary of this effort is provided, and challenges to capabilities composition as well as future plans for improvement are highlighted.
Coreference Resolution with Syntax and Semantics

Recent years have witnessed a widespread increase of interest in coreference resolution (CR) in the natural language processing (NLP) community. While most research efforts have focused on the span-based method, there has been relatively little research dedicated to CR with non-span method. Arguably, the non-span representation method like token representation can also be applied to reduce computing overhead by ignoring to many predicted spans. However, these methods are suboptimal in doing so, science it can hardly use the document syntax and semantics information well. This work addresses the research gap of CR with syntax and semantics. We present a new solution $$CRS_{2}$$ , short for Coreference Resolution with Syntax and Semantics to capture syntax and semantics information simultaneously. Technically speaking, we made two contributions: (1) We employ a word-level method to capture coreference resolution between tokens, which can reduce the cost of computation. (2) We enhance token representation by incorporating syntax and semantics information from neighborhood tokens to improve span prediction performance. We perform extensive experiments on real dataset OntoNotes 5.0 covering the Chinese and English parts, empirically verifying the effectiveness and efficiency of $$CRS_{2}$$ .
Efficient Sampling-based Multirotors Kinodynamic Planning with Fast Regional Optimization and Post Refining

For real-time multirotor kinodynamic planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages. In this paper, we address this issue by a hybrid scheme. We firstly propose a fast regional optimizer exploiting the information of local environments and then integrate it into a bidirectional global sampling process. The incorporation of the local optimization shows significantly improved success rates and less planning time in various types of challenging environments. We further present a refinement module utilizing the same framework as the regional optimizer. It comprehensively investigates the resulting trajectory of the global sampling and improves its smoothness with nearly negligible computation effort. Benchmark results illustrate that our proposed method can better exploit a previous trajectory compared to the state-of-the-art ones. The planning methods are applied to generate trajectories for a quadrotor system in simulation and real-world, and their capability is validated in real-time applications.
An Algebraic Theory for Shared-State Concurrency

We present a monadic denotational semantics for a higher-order programming language with shared-state concurrency, i.e. global-state in the presence of interleaving concurrency. Central to our approach is the use of Plotkin and Power's algebraic effect methodology: designing an equational theory that captures the intended semantics, and proving a monadic representation theorem for it. We use Hyland et al.'s equational theory of resumptions that extends non-deterministic global-state with an operator for yielding to the environment. The representation is based on Brookes-style traces. Based on this representation we define a denotational semantics that is directionally adequate with respect to a standard operational semantics. We use this semantics to justify compiler transformations of interest: redundant access eliminations, each following from a mundane algebraic calculation; while structural transformations follow from reasoning over the monad's interface.
GitRank

Open-source repositories provide wealth of information and are increasingly being used to build artificial intelligence (AI) based systems to solve problems in software engineering. Open-source repositories could be of varying quality levels, and bad-quality repositories could degrade performance of these systems. Evaluating quality of open-source repositories, which is not available directly on code hosting sites such as GitHub, is thus important. In this hackathon, we utilize known code quality measures and GrimoireLab toolkit to implement a framework, named GitRank, to rank open-source repositories on three different criteria. We discuss our findings and preliminary evaluation in this hackathon report.
Stroke Rehabilitation through Proxy Agency and Embodiment in Mixed Reality

The inclusion of VR, AR, MR and other technological approaches in the rehabilitation process of patients with chronic conditions has been significant and the role of gamification has proven to be instrumental in these cases. One of the leading causes of death and disability among adults across the globe is stroke. While only 10% recover completely following a stroke, 25% recover with minor impairments and 40% recover with moderate to severe impairments. Therefore, we aim to take advantage of the success of gamification and create a dance exergame application using an MR approach to aid in the rehabilitation of stroke patients. We will do this by animating the dance motions of an embodied virtual avatar and allowing the users to control this avatar using a variety of devices such as VR headsets and wearable sensors. Through this exergame application, our objective is to preserve and improve our target users' cognitive and motor functions as well as make them feel like they have control over their own body and their environment despite being confined in restricted spaces and having mobility issues. As such, we believe that this project will be beneficial for our intended users, as they will be able to see the results of their motions and improvements in their mobility functions over time.
On-line Teaching Appraisal System of Vocational Education Based on Convolutional Neural Network and Big Data Platform

Covid-19 has undeniably accelerated the process of world education informatization. How to evaluate result of On-line Teaching through scientific and effective means is an important issue in the development of on-line education. On the "JZ on-line" platform, this paper constructs an On-line Teaching Appraisal system of vocational education based on convolutional neural network algorithm, which mainly includes five parts: evaluation personnel management, evaluation index management, On-line Teaching evaluation, evaluation content and evaluation result analysis. Based on the content of an indicator Appraisal system of On-line Teaching, a convolution neural network On-line Teaching evaluation model is constructed. Through the analysis of the data results of the system test, the system can obtain scientific and objective On-line Teaching evaluation results, and can put forward teaching problems and suggestions. It can also further analyze the distribution of different disciplines, different majors and different courses in the overall On-line Teaching evaluation. The system error is small and the precision is high, which is helpful to improve the quality of on-line course construction and the professional quality of teachers' educational technology.
Fake Reviews Filtering System Using Supervised Machine Learning

As the surge in internet users is expanding prominently, the role of the online reviewing system is also rising. For companies, the legitimacy of internet evaluations is critical, as it can directly impact their reputation and profitability. It plays an indispensable role in influencing people’s perceptions of a product or service. This research projects light on the best technique to identify and filter out authentic reviews while proposing a flexible and user-friendly website. The website will have a tremendous sway on customers and will assist them in making a better judgment about a product/service. The website is deployed with the designed supervised learning model. Firstly, the user will have to enter the URL of the website where the product is located. After which, the dataset is extracted from the given URL using Python tools for Web Scraping. The data is then analyzed and dissected using Natural Language Processing techniques to extract sound features from it. Ultimately, different Machine Learning Models are further trained on the dataset. The experimental results of this research reveal that the model performs at an accuracy of 89.12% on the datasets. The major objective of this research is to provide a fake review filtering system that will provide users with more reliable review information and eliminate revenue loss of the companies at an exponential rate.
A Survey of Image Style Transfer Research

Image conversion is the process of combining content images and style images to build a new picture. To facilitate the research on image style transfer, the most important methods and results of image style transfer are summarized and discussed. First, the concept of image style transfer is reviewed, and introduced in detail the image style migration method based on deep learning. Secondly, the basic content and algorithms of image mode transformation based on Gan cycle and Transformer are introduced. Finally, the current problems and future research directions of image style transfer based on deep learning are summarized.
X-Space: A Tool for Extending Mixed Reality Space from Web2D Visualization Anywhere

Mixed reality provides a greater visualization space and more intuitive means of interaction for data exploration. Numbers of works have focused on combining 2D visualizations with mixed reality. However, for a 2D visualization, it needs to implement a mixed reality 3D space on its own, and it is a challenge to build a mixed reality space for 2D visualization conveniently. Furthermore, it is also a challenge to provide mixed reality 3D space for many existing Web-based 2D visualizations. This paper presents X-Space, a tool based on HoloLens and Web bookmarks. It can extend Web2D visualizations into mixed reality 3D space anywhere and anytime. X-Space captures data bound in 2D visualizations, deconstructs them into a data stream, and then transfers them to mixed reality 3D space by freehand interaction. In addition, we equipped X-Space with a data API for existing tools for constructing 3D visualization. We show a series of cases to demonstrate the capabilities of X-Space. In the end, we discussed the current limitations of X-Space and future work.
Fabricated Hadith Detection: A Novel Matn-Based Approach With Transformer Language Models

Muslims rely primarily on the Quran and the Hadiths in all their spiritual life and consider them as sacred sources.If the Quran is God's word, then the Hadiths are God's instructions in the words of the Prophet Muhammad.Since Hadiths are transmitted through multiple narrators, they have been extensively studied to ensure their authenticity.The purpose of this study is to detect fabricated Hadiths, or Mawdu, which is the type of Hadith most rejected by Muslim scholars.The study utilises the central text and content of Hadith, Matn, rather than solely focusing on Hadith chain of narrators, Sanad.In order to accomplish this, we create and release the first dataset dedicated to Mawdu Hadiths, called MAHADDAT.Furthermore, we set up a Mawdu Hadith (MH) detection system based on a transformer language model, BERT, achieving a 92.47%F1 MH score.