Reviewerly/exHarmony huggingface dataset:
College Enquiry Chatbot using Rasa Framework

The growth of technologies like Artificial Intelligence (AI), Big Data & Internet of Things (IoT), etc. has marked many advancements in the technological world since the last decade. These technologies have a wide range of applications. One such application is “Chatterbot or “Chatbot”. Chatbots are conversational AIs, which mimics the human while conversing & eliminates the need of human by automating mundane tasks. In the study undertaken, we have created a chatbot in education domain & it is named as “College Enquiry Chatbot”, This chatbot is a web-based application that analyses and understands user's queries and provides an instant and accurate response. Rasa technology is used to construct this chatbot. It's an open-source technology, which uses its two main packages i.e., Rasa Core & Rasa Natural Language Understanding (NLU) in order to build a Contextual AI Chatbot. NLU is used to infer the intent and to extract the necessary entities from user input & the Rasa Core provides the output by building a probabilistic model with the help of Recurrent Neural Network (RNN). Evaluation of the model is done by getting a confusion matrix and performance measures like Precision, Accuracy & F1 Score which come out to be 0.628, 0.725 and 0.669 respectively on average basis. This chatbot's accuracy, lack of dependability on human resources, 24 x 7 accessibility and low maintenance creates various opportunities for its implementation. This conversational agent can not only be used in educational institutions but also in places where enquiry becomes a tedious task.
Using binary classifiers for one-class classification

In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets.
T-Net: Effective Permutation-Equivariant Network for Two-View Correspondence Learning

We develop a conceptually simple, flexible, and effective framework (named T-Net) for two-view correspondence learning. Given a set of putative correspondences, we reject outliers and regress the relative pose encoded by the essential matrix, by an end-to-end framework, which is consisted of two novel structures: "−" structure and "|" structure. " − " structure adopts an iterative strategy to learn correspondence features. "|" structure integrates all the features of the iterations and outputs the correspondence weight. In addition, we introduce Permutation-Equivariant Context Squeeze-and-Excitation module, an adapted version of SE module, to process sparse correspondences in a permutation-equivariant way and capture both global and channel-wise contextual information. Extensive experiments on outdoor and indoor scenes show that the proposed T-Net achieves state-of-the-art performance. On outdoor scenes (YFCC100M dataset), T-Net achieves an mAP of 52.28%, a 34.22% precision increase from the best-published result (38.95%). On indoor scenes (SUN3D dataset), T-Net (19.71%) obtains a 21.82% precision increase from the best-published result (16.18%). Source code: https://github.com/x-gb/T-Net.
BE-BLC: BERT-ELMO-Based Deep Neural Network Architecture for English Named Entity Recognition Task

One of the most important factors which considerably affects the quality of the neural sequence labeling model is the selection and encoding of input features to generate rich semantic and grammatical word representation vectors. Recently, pre-training text encoders have significantly ameliorated the performance on various Natural Language Processing (NLP) tasks. In this paper, we introduce a deep neural network model to address a challenging task of the sequence labeling problem, the task of named entity recognition. The model consists of three sub-networks to fully exploit current Google’s representations of the most powerful NLP transfer learning BERT and ELMO model, as well as the traditional state-of-the-art BiLSTM-CRF (BLC) model. The model outperforms the state-of-the-art systems on CoNLL-2003 by achieving a 95.56% F1-score and getting results comparable to the previous best-performing system models on OntoNotes 5.0 by achieving an 89.21% F1-score without using any external knowledge.
Blockchain-enabled Federated Learning: A Survey

In recent years, human society has been witnessed to evolve fast to the era of big data, rendering the data sharing and privacy protection a key issue for the development of digital economies. Federated learning, as a novel pattern for distributed machine learning, is aimed to train a centralized model from decentralized datasets while protecting user privacy, and is now intensively studied in literature. However, a variety of technical challenges, e.g., security issues, incentive mechanism design, are still awaiting further research efforts. In this respect, blockchain proves to be an elegant solution for federated learning to overcome these issues, and thus has been applied in federated learning in many scenarios with success. In this paper, we presented a comprehensive survey for blockchain-enabled federated learning, proposed its technical framework and discussed the key research issues. This work represents the first attempt for reviewing the literature aiming at integrating blockchain and federated learning, and can be expected to offer useful guidance for establishing a new infrastructure for decentralized and secured data circulation, as well as promoting the development of related industries.
RGB-Fusion: Monocular 3D reconstruction with learned depth prediction

Generating large-scale and high-quality 3D scene reconstruction from monocular images is an essential technical foundation in augmented reality and robotics. However, the apparent shortcomings (e.g., scale ambiguity, dense depth estimation in texture-less areas) make applying monocular 3D reconstruction to real-world practice challenging. In this work, we combine the advantage of deep learning and multi-view geometry to propose RGB-Fusion, which effectively solves the inherent limitations of traditional monocular reconstruction. To eliminate the confinements of tracking accuracy imposed by the prediction deficiency of neural networks, we propose integrating the PnP (Perspective-n-Point) algorithm into the tracking module. We employ 3D ICP (Iterative Closest Point) matching and 2D feature matching to construct separate error terms and jointly optimize them, reducing the dependence on the accuracy of depth prediction and improving pose estimation accuracy. The approximate pose predicted by the neural network is employed as the initial optimization value to avoid the trapping of local minimums. We formulate a depth map refinement strategy based on the uncertainty of the depth value, which can naturally lead to a refined depth map. Through our method, low-uncertainty elements can significantly update the current depth value while avoiding high-uncertainty elements from adversely affecting depth estimation accuracy. Numerical qualitative and quantitative evaluation results of tracking, depth prediction, and 3D reconstruction show that RGB-Fusion exceeds most monocular 3D reconstruction systems.
Cross-modality synergy network for referring expression comprehension and segmentation

Referring expression comprehension and segmentation aim to locate and segment a referred instance in an image according to a natural language expression. However, existing methods tend to ignore the interaction between visual and language modalities for visual feature learning, and establishing a synergy between the visual and language modalities remains a considerable challenge. To tackle the above problems, we propose a novel end-to-end framework, Cross-Modality Synergy Network (CMS-Net), to address the two tasks jointly. In this work, we propose an attention-aware representation learning module to learn modal representations for both images and expressions. A language self-attention submodule is proposed in this module to learn expression representations by leveraging the intra-modality relations, and a language-guided channel-spatial attention submodule is introduced to obtain the language-aware visual representations under language guidance, which helps the model pay more attention to the referent-relevant regions in the images and relieve background interference. Then, we design a cross-modality synergy module to establish the inter-modality relations for modality fusion. Specifically, a language-visual similarity is obtained at each position of the visual feature map, and the synergy is achieved between the two modalities in both semantic and spatial dimensions. Furthermore, we propose a multi-scale feature fusion module with a selective strategy to aggregate the important information from multi-scale features, yielding target results. We conduct extensive experiments on four challenging benchmarks, and our framework achieves significant performance gains over state-of-the-art methods.
Cross-domain person re-identification with pose-invariant feature decomposition and hypergraph structure alignment

Person Re-identification (Re-ID) has attracted more and more attention thanks to its great practical value in the field of video surveillance. Most works have focused on solving the problem of supervised Re-ID on a single domain and made significant progress. However, the cross-domain Re-ID is still challenging due to the domain bias between the source and target domains. To this end, we propose a dictionary learning algorithm based on matrix factorization to eliminate the influence of style and pedestrian pose information on the cross-domain Re-ID. Specifically, the proposed approach includes two novel parts: (1) the original visual feature is decomposed into pose-invariant feature space, camera-style feature space and residual feature space to extract discriminant pose-invariant feature that is not affected by style and pedestrian pose information, such that the influence of interference information between pedestrians on recognition can be eliminated; (2) considering the domain-invariance of attribute, a hypergraph structure alignment is introduced to integrate pose-invariant feature, attribute and pedestrian identity into a dictionary learning framework. The relationship between pose-invariant feature and attribute is built so that the pedestrian attribute of the target dataset can be accurately predicted during testing. Finally, the pedestrian similarity measurement can be carried out by combining the pose-invariant feature and attribute of pedestrians. The effectiveness of the proposed algorithm is verified with the experiments on several benchmark Re-ID datasets.
Redox-controlled antimony isotope fractionation in the epithermal system: New insights from a multiple metal stable isotopic combination study of the Zhaxikang Sb–Pb–Zn–Ag deposit in Southern Tibet

Redox reactions, biological processes, evaporation and precipitation processes, adsorption, and Rayleigh distillation mechanisms have been proposed to account for the fractionation of antimony (Sb) isotopes in natural materials. However, only one paper was published to support and characterize the Rayleigh distillation induced Sb isotopic fractionation in a hydrothermal system. Therefore, the Sb isotopic fractionation mechanism research in the epithermal system is still negligible, which severely restricts future application. The Zhaxikang Sb–Pb–Zn–Ag deposit is the only super-large deposit within the North Himalayan Metallogenic Belt (NHMB), which comprises two episodes of mineralization (First episode: Pb–Zn mineralization, Stages 1 and 2; Second episode: Sb mineralization, Stages 3 to 6). Herein, we measured the δ 123 Sb NIST 3102A values of stage 4 sulfosalt minerals and stage 5 stibnite from the Zhaxikang Sb–Pb–Zn–Ag deposit, to resolve the fractionation direction and relative magnitude of Sb isotopes caused by these aforesaid geologically prominent mechanisms in the epithermal systems with an auxiliary investigation of Fe–Cu isotopes. The second episode of Sb-containing hydrothermal fluid has leached metals (e.g., Pb, Ag, Fe, Cu) from pre-existing Pb–Zn orebodies, to initiate the formation of stage 4 sulfosalt minerals. According to XPS (X-ray Photoelectron Spectroscopy) analyses, the Cu isotopic variation of stage 4 bournonite (0.08‰ – 0.89‰) must be controlled primarily by the reduction reaction (Cu 2+ + e − → Cu + ) during leaching and precipitation. Keeping in mind a good linear correlation between δ 123 Sb and δ 65 Cu values (R 2 = 0.99) for bournonite, the same mechanism was used to decipher the variation in δ 123 Sb values (0.10‰ – 0.48‰) of bournonite (Sb 5+ + 2e − → Sb 3+ ). Abundant native sulfur within the orefield allowed for the overall reaction equation of CuFeS 2 + FeS 2 + PbS + Sb 5+ (aq) = CuPbSbS 3 + Fe 2+ (aq) + 2S to describe the bournonite formation. Accordingly, the Sb isotopic variations (−0.27‰ to 0.48‰) of Sb-bearing minerals (boulangerite, bournonite and stibnite) were empirically attributed to redox reactions. The Rayleigh distillation model indicated that the observed fractionation was most likely induced by redox reactions, while the related theoretical calculations revealed a great antimony exploration potential in deeper parts of the orefield. Overall, the Sb isotopes could be used to identify redox changes in the epithermal system, and exhibit great potential in tracing metal sources, monitoring fluid evolution and ore formation, and providing insights into mineral exploration.
The impact of multimodal cohesion on attention and interpretation in film

This article presents results of an exploratory investigation combining multimodal cohesion analysis and eye-tracking studies. Multimodal cohesion, as a tool of multimodal discourse analysis, goes beyond linguistic cohesive mechanisms to enable the construction of cross-modal discourse structures that systematically relate technical details of audio, visual and verbal modalities. Patterns of multimodal cohesion from these discourse structures were used to design eye-tracking experiments and questionnaires in order to empirically investigate how auditory and visual cohesive cues affect attention and comprehension. We argue that the cross-modal structures of cohesion revealed by our method offer a strong methodology for addressing empirical questions concerning viewers' comprehension of narrative settings and the comparative salience of visual, verbal and audio cues. Analyses are presented of the beginning of Hitchcock's The Birds (1963) and a sketch from Monty Python filmed in 1971. Our approach balances the narrative-based issue of how narrative elements in film guide meaning interpretation and the recipient-based question of where a film viewer's attention is directed during viewing and how this affects comprehension.
Human gaze-aware attentive object detection for ambient intelligence

Understanding human behavior and the surrounding environment is essential for realizing ambient intelligence (AmI), for which eye gaze and object information are reliable cues. In this study, the authors propose a novel human gaze-aware attentive object detection framework as an elemental technology for AmI. The proposed framework detects users’ attentive objects and shows more precise and robust performance against object-scale variations. A novel Adaptive-3D-Region-of-Interest (Ada-3D-RoI) scheme is designed as a front-end module, and scalable detection network structures are proposed to maximize cost-efficiency. The experiments show that the detection rate is improved up to 97.6% on small objects (14.1% on average), and it is selectively tunable with a tradeoff between accuracy and computational complexity. In addition, the qualitative results demonstrate that the proposed framework detects a user’s single object-of-interest only, even when the target object is occluded or extremely small. Complementary matters for follow-up study are presented as suggestions to extend the results of the proposed framework to further practical AmI applications. This study will help develop advanced AmI applications that demand a higher-level understanding of scene context and human behavior such as human–robot symbiosis, remote-/autonomous control, and augmented/mixed reality.
MiniLedger: Compact-Sized Anonymous and Auditable Distributed Payments

In this work we present MiniLedger, a distributed payment system which not only guarantees the privacy of transactions, but also offers built-in functionalities for various types of audits by any external authority. MiniLedger is the first private and auditable payment system with storage costs independent of the number of transactions. To achieve such a storage improvement, we introduce pruning functionalities for the transaction history while maintaining integrity and auditing. We provide formal security definitions and a number of extensions for various auditing levels. Our evaluation results show that MiniLedger is practical in terms of storage requiring as low as 70 KB per participant for 128 bits of security, and depending on the implementation choices, can prune 1 million transactions in less than a second.
Clinically Correct Report Generation from Chest X-Rays Using Templates

We address the task of automatically generating a medical report from chest X-rays. Many authors have proposed deep learning models to solve this task, but they focus mainly on improving NLP metrics, such as BLEU and CIDEr, which are not suitable to measure clinical correctness in clinical reports. In this work, we propose CNN-TRG, a Template-based Report Generation model that detects a set of abnormalities and verbalizes them via fixed sentences, which is much simpler than other state-of-the-art NLG methods and achieves better results in medical correctness metrics.We benchmark our model in the IU X-ray and MIMIC-CXR datasets against naive baselines as well as deep learning-based models, by employing the Chexpert labeler and MIRQI as clinical correctness evaluations, and NLP metrics as secondary evaluation. We also provide further evidence indicating that traditional NLP metrics are not suitable for this task by presenting their lack of robustness in multiple cases. We show that slightly altering a template-based model can increase NLP metrics considerably while maintaining high clinical performance. Our work contributes by a simple but effective approach for chest X-ray report generation, as well as by supporting a model evaluation focused primarily on clinical correctness metrics and secondarily on NLP metrics.
A variable selection method for a hierarchical interval type-2 TSK fuzzy inference system

In this paper, we propose a method to judge the degree of the relationship closeness between system input variables and theoretical output through independence test, and apply this method to construct a hierarchical interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy inference system. Interval type-2 TSK fuzzy inference systems have been applied in many fields. An interval type-2 fuzzy inference system, which is a special case of generalized type-2 fuzzy inference systems, can reduce the computational complexity of type-reduction and save computational cost. An hierarchical fuzzy system can further reduce the complexity of systems. However, it is found that, with the increase in data dimensions, the computational complexity and error of the system will increase. The system accuracy of a hierarchical fuzzy system is also found related to the order of its input variables. Variable selection can select meaningful input variables to decrease input space dimensions, and sort the input variables in some way. This paper proposes a variable selection and sorting method based on independence test. Firstly, input variables data and theoretical output data are classified and contingency tables are established. Chi-square statistics of the contingency tables are calculated for independence test, and input variables that are not independent of theoretical output are selected. Then, the total inertia of the contingency tables is calculated to represent the degree of the relationship closeness between input variables and theoretical output. According to the total inertia, the input variables are sorted and entered the system in order. The proposed method and the model built by the method are tested by dealing with regression problems. By comparing the proposed method on several types of systems, the hierarchical interval type-2 TSK inference system shows a better performance. Then the model is compared with other advanced fuzzy models and non-fuzzy models. Experiments show that its performance is better than the rest.
Graph representations in genetic programming

Abstract Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and $$(1+\lambda )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:mi>λ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the ( $$1 + \lambda$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:mi>λ</mml:mi> </mml:mrow> </mml:math> ) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP’s number of registers and CGP’s levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse.