Reviewerly/exHarmony huggingface dataset:
Audio Caption in a Car Setting with a Sentence-Level Loss

Captioning has attracted much attention in image and video understanding while a small amount of work examines audio captioning. This paper contributes a Mandarin-annotated dataset for audio captioning within a car scene. A sentence-level loss is proposed to be used in tandem with a GRU encoder-decoder model to generate captions with higher semantic similarity to human annotations. We evaluate the model on the newly-proposed Car dataset, a previously published Mandarin Hospital dataset and the Joint dataset, indicating its generalization capability across different scenes. An improvement in all metrics can be observed, including classical natural language generation (NLG) metrics, sentence richness and human evaluation ratings. However, though detailed audio captions can now be automatically generated, human annotations still outperform model captions on many aspects.
(F)unctional Sifting: A Privacy-Preserving Reputation System Through Multi-Input Functional Encryption

Functional Encryption (FE) allows users who hold a specific secret key (known as the functional key) to learn a specific function of encrypted data whilst learning nothing about the content of the underlying data. Considering this functionality and the fact that the field of FE is still in its infancy, we sought a route to apply this potent tool to solve the existing problem of designing decentralised additive reputation systems. To this end, we first built a symmetric FE scheme for the \(\ell _1\) norm of a vector space, which allows us to compute the sum of the components of an encrypted vector (i.e. the votes). Then, we utilized our construction, along with functionalities offered by Intel SGX, to design the first FE-based decentralized additive reputation system with Multi-Party Computation. While our reputation system faces certain limitations, this work is amongst the first attempts that seek to utilize FE in the solution of a real-life problem.
Efficient Rank-Based Diffusion Process with Assured Convergence

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art.
Research on Modeling of Power Grid Information System Based on Knowledge Graph

The knowledge graph is a relatively general formal representation framework for semantic knowledge, with rich semantic expression and open interconnection capabilities. Aiming at the data model in the power grid field, the paper proposes a model diagnosis method based on the knowledge graph by treating the data model as an entity in the knowledge graph, combining semantic analysis technology and entity similarity calculation technology, the compliance of the data model and consistency for diagnosis. Research shows that this method can make fuller use of the data model's relevant information and expand the thinking for traditional diagnosis methods.
Dual Generative Network with Discriminative Information for Generalized Zero-Shot Learning

Zero-shot learning is dedicated to solving the classification problem of unseen categories, while generalized zero-shot learning aims to classify the samples selected from both seen classes and unseen classes, in which “seen” and “unseen” classes indicate whether they can be used in the training process, and if so, they indicate seen classes, and vice versa. Nowadays, with the promotion of deep learning technology, the performance of zero-shot learning has been greatly improved. Generalized zero-shot learning is a challenging topic that has promising prospects in many realistic scenarios. Although the zero-shot learning task has made gratifying progress, there is still a strong deviation between seen classes and unseen classes in the existing methods. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the intrinsic characteristics of visual features which are discriminative enough to be classified by itself. To solve the above problems, we propose a novel model that uses the discriminative information of visual features to optimize the generative module, in which the generative module is a dual generation network framework composed of conditional VAE and improved WGAN. Specifically, the model uses the discrimination information of visual features, according to the relevant semantic embedding, synthesizes the visual features of unseen categories by using the learned generator, and then trains the final softmax classifier by using the generated visual features, thus realizing the recognition of unseen categories. In addition, this paper also analyzes the effect of the additional classifiers with different structures on the transmission of discriminative information. We have conducted a lot of experiments on six commonly used benchmark datasets (AWA1, AWA2, APY, FLO, SUN, and CUB). The experimental results show that our model outperforms several state-of-the-art methods for both traditional as well as generalized zero-shot learning.
Sharding Database for Fault Tolerance and Scalability of Data

In this paper, presenting the database system responsible for storing all this information scales have to be used to handle heavy loads. Database sharding is the process of segmenting the data into partitions that are spread on multiple database instances this is essentially to speed up query and scale the system. The sharding process that have database servers which takes the load of the request which are being sent into it, serve must have some kind of user id and each of the database is serve by one database server, with the advent of cloud computing, scaling database systems has become an affordable solution, making speed scaling, or horizontal distribution, a viable scalability option. If applications prefer standard relative database technology and have to Scale with mass data. Because sharding relevant databases in the public cloud is especially useful Used pay-per-view models, which already include licenses, and virtually unlimited high-speed delivery servers. Our goal is to create a catalog in the form of a database scalability pattern that involves accelerating data between database clusters nodes can be used using hash partitioning techniques to better balance loads between database servers. We intend to make the mapping between the scenario and its solution publicly available, to help developers identify when to adopt this model instead of other high-speed techniques.
Examining the Accuracy of a Conversation‐Based Assessment in Interpreting English Learners' Written Responses

Substantial progress has been made toward applying technology enhanced conversation‐based assessments (CBAs) to measure the English‐language proficiency of English learners (ELs). CBAs are conversation‐based systems that use conversations among computer‐animated agents and a test taker. We expanded the design and capability of prior conversation‐based instructional and assessment systems and developed a CBA designed to measure the English language skills and the mathematics knowledge of middle school ELs. The prototype CBA simulates an authentic and engaging mathematics classroom where the test taker interacts with two virtual agents to solve math problems. We embedded feedback and supports that are triggered by how the CBA interprets students' written responses. In this study, we administered the CBA to middle school ELs ( N = 82) residing in the United States. We examined the extent to which the CBA system was able to consistently interpret the students' responses (722 responses for the 82 students). The study findings helped us to understand the factors that affect the accuracy of the CBA system's interpretations and shed light on how to improve CBA systems that incorporate scaffolding.
Analyzing the effectiveness of rescheduling and Flexible Execution methods to address uncertainty in execution duration for a planetary rover

During execution, activity durations may vary from those predicted in the generated schedule. In this article we study (re) scheduling invocation, execution during rescheduling, and flexible execution to enable a high level of responsiveness to uncertainty in activity execution duration. We discuss these methods theoretically in the context of an embedded scheduler and practically in the context of a limited CPU embedded scheduler with a nonzero scheduler runtime intended for a planetary rover. We use the concept of a commit window to enable execution of the previously generated schedule while (re) scheduling. We define Fixed Cadence and Event Driven scheduling as methods to decide when to reinvoke the scheduler. We define and analyze Flexible Execution (FE) as an approach to execute the generated schedule while adapting it to variations in execution. Specifically, FE focuses on (1) how to take advantage of activities ending earlier than expected and (2) how to maintain a consistent schedule if activities take more time than expected. We present a theoretical model and empirical results documenting how these various methods interact and perform on both synthetic data and best available data for NASA’s next planetary rover, the Mars 2020 rover. We then describe how these analyses influenced the onboard software for the Mars 2020 rover. • Presents a model of temporally flexible execution and scheduler invocation to address execution variation (activity duration short or long). • Presents a theoretical model and empirical results on synthetic and expected planetary rover data. • Discusses how work influenced flight software for Mars 2020 rover.
Day-ahead forecasting of solar irradiance using hybrid improved Cuckoo Search-LSTM approach

Solar energy has emerged as one of the popular alternative sources of energy as the demand for clean fuels gathers pace around the globe. However, solar irradiance is intermittent and stochastic by nature, directly affecting the performance of solar-based energy systems. Accurate solar irradiance forecasting can help in significant improvement in their design and operation. This work presents a novel forecasting approach for day-ahead Global Horizontal Irradiance using LSTM networks. For this purpose, historical hourly time-series data of meteorological variables including temperature, pressure, wind direction, relative humidity, dew point and, wind speed have been considered as input factors.The proposed model is a combination of LSTM and an improved variant of Cuckoo search algorithm. The model performance is evaluated using two statistical parameters: RMSE and, MAE. Low values of these parameters as compared with other models, indicate that the proposed model provides a better day-ahead forecasting of global horizontal irradiance.
‘Making blockchain real’: regulatory discourses of blockchains as a smart, civic service

Policymakers across Canada are considering the blockchain as a way to enable smart(er) governance. Despite the technology’s infancy, jurisdictions perceive smart tools as one of many ways to govern efficiently. Critical geographers, however, remain circumspect of datafication as a value-making process, and have traced myriad ways data-driven technologies participate in the materialization of smart policy mobilities and governance. This article uses autobiography to highlight discourses positioning blockchain technology as a data-driving and -producing civic service. Empirical findings suggest that Canadian policymakers draw on entrepreneurial discourses of digital leadership, transparent data management and digital empowerment to justify their plans in ‘making blockchain real’. These regulatory discourses promote datafication through blockchains as a way to improve government services.
Decision support system using PROMETHEE Algorithm

Abstract The aim of this study is to analyze contemporary drinks marketing strategy using decision-making support techniques.. Drinks are a product that can quench thirst. Nowadays, many beverage businesses have sprung up (the term contemporary drink). Today’s drinks are one of the business opportunities for millennials because these drinks can be served practically, creatively, and innovatively. The number of brands and variants of contemporary drinks circulating, which makes it difficult for consumers to choose delicious contemporary drinks. In addition, this analysis can be used as information for those who want to find opportunities in the beverage business. Sources of data were obtained by conducting interviews and giving random questionnaires to 440 respondents of all ages in Pematangsiantar city. In this case, the decision support system technique used is the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) method. The assessment criteria used to assess the best alternative based on consumers are Attractiveness (G1), Product Information (G2), Taste (G3), Packaging (G4). Meanwhile, the alternative drinks that are currently used are Manja Cheese Tea (F1), Nam Nam Thai (F2), Ocha (F3), Es Gandeng (F4), and Es Gak Beres (F5). The results of the study indicate that the F1 alternative (Manja Cheese Tea) with Netflow = 0.08 is the first best alternative, and the F5 alternative (Es Gak Beres) with Netflow = 0.03 is the second-best alternative according to consumer interest. It is hoped that the results of the research can provide information and help consumers in determining current drinks and become information for those who want to find opportunities in the beverage business.
Credit Card Fraud Detection System based on Operational &amp; Transaction features using SVM and Random Forest Classifiers

This paper proposes a Credit Card Fraud Detection system based on Operational & Transaction features using Support Vector Machine (SVM) and Random Forest (RF) classifiers. In this system, in the first phase, the operational features of users are extracted, and then a random forest classifier is used to classify the features into benign and suspected. In the second phase, the transaction features of users are extracted from the user records, and then the M-class SVM classifier is applied to classify the features into benign and suspected. The performance of the system is evaluated in terms of standard measures precision, accuracy, recall, and F-1 score. By results, it was shown that both RF and SVM classifiers achieve a higher detection rate with good accuracy.
Robust Black-box Watermarking for Deep Neural Network using Inverse Document Frequency

Recently, Deep Neural Networks (DNNs), presented exceptional achievement in implementing human-level capabilities for various predicaments, such as Natural Language Processing (NLP), voice recognition, and image processing, etc. Training these models is expensive in terms of computational power and the existence of enough labelled data. Thus, ML-based models such as DNNs establish genuine business value and intellectual property (IP) for their owners. Therefore the trained models need to be protected from any adversary attacks such as illegal redistribution, reproducing, and derivation. Watermarking can be considered as an effective technique for securing a DNN model. However, so far, most of the watermarking algorithms focus on watermarking the DNN by adding noise to an image. To this end, we propose a framework for watermarking a DNN model designed for textual domain. The watermark generation scheme provides a secure watermarking method by combining Term Frequency (TF) and Inverse Document Frequency (IDF) of a particular word. The proposed embedding procedure takes place in the model's training stage, which makes the watermark verification stage straightforward by sending the watermarked document to the trained model. The experimental results show that watermarked models have the same accuracy as the original one, and the proposed framework accurately verifies the ownership of all surrogate models without impairing the performance. The proposed algorithm is robust against well-known attacks such as parameter pruning and brute force attack.
Image Retrieval using Bag-of-Features for Lung Cancer Classification

In this paper, Content Based Image Retrieval (CBIR) technique is used to retrieve the images from a large database that are identical to the image provided as a query. The CBIR based Bag-of-Features (BOF) technique is used for extracting features and their classification on the dataset of LIDC/IDRI. Therefore, a lung image retrieval using grid-based feature extraction method and Speeded-Up Robust Features (SURF) algorithm are implemented in this paper. K-Means clustering algorithm is extended to the features extracted by SURF algorithm. MATLAB simulation is performed on selective database, which provides 99% and 98.56% of training and testing accuracy respectively.
Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis

Background The prognosis, diagnosis, and treatment of many genetic disorders and familial diseases significantly improve if the family history (FH) of a patient is known. Such information is often written in the free text of clinical notes. Objective The aim of this study is to develop automated methods that enable access to FH data through natural language processing. Methods We performed information extraction by using transformers to extract disease mentions from notes. We also experimented with rule-based methods for extracting family member (FM) information from text and coreference resolution techniques. We evaluated different transfer learning strategies to improve the annotation of diseases. We provided a thorough error analysis of the contributing factors that affect such information extraction systems. Results Our experiments showed that the combination of domain-adaptive pretraining and intermediate-task pretraining achieved an F1 score of 81.63% for the extraction of diseases and FMs from notes when it was tested on a public shared task data set from the National Natural Language Processing Clinical Challenges (N2C2), providing a statistically significant improvement over the baseline (P&lt;.001). In comparison, in the 2019 N2C2/Open Health Natural Language Processing Shared Task, the median F1 score of all 17 participating teams was 76.59%. Conclusions Our approach, which leverages a state-of-the-art named entity recognition model for disease mention detection coupled with a hybrid method for FM mention detection, achieved an effectiveness that was close to that of the top 3 systems participating in the 2019 N2C2 FH extraction challenge, with only the top system convincingly outperforming our approach in terms of precision.
Dual-Mode deadbeat <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"><mml:msub><mml:mi>H</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> FIR filtering for discrete-Time systems

A new receding horizon (RH) finite impulse response (FIR) filter called the dual-mode deadbeat H2 FIR filter (DMDH2FF) is proposed for state estimation in discrete linear time-invariant (LTI) systems under external disturbances. The DMDH2FF is derived to minimize the H2 norm subject to the deadbeat condition in the disturbance-to-estimate channel, and it can manage the horizon size in two operation modes. The DMDH2FF has better robustness against temporary model uncertainty compared with a conventional H2 infinite impulse response filter. The performance of the DMDH2FF is demonstrated by an F-404 turbofan engine simulation.
Exploiting Natural Language for Efficient Risk-Aware Multi-Robot SaR Planning

The ability to develop a high-level understanding of a scene, such as perceiving danger levels, can prove valuable in planning multi-robot search and rescue (SaR) missions. In this work, we propose to uniquely leverage natural language descriptions from the mission commander in chief and image data captured by robots to estimate scene danger. Given a description and an image, a state-of-the-art deep neural network is used to assess a corresponding similarity score, which is then converted into a probabilistic distribution of danger levels. Because commonly used visio-linguistic datasets do not represent SaR missions well, we collect a large-scale image-description dataset from synthetic images taken from realistic disaster scenes and use it to train our machine learning model. A risk-aware variant of the Multi-robot Efficient Search Path Planning (MESPP) problem is then formulated to use the danger estimates in order to account for high-risk locations in the environment when planning the searchers' paths. The problem is solved via a distributed approach based on Mixed-Integer Linear Programming. Our experiments demonstrate that our framework allows to plan safer yet highly successful search missions, abiding to the two most important aspects of SaR missions: to ensure both searchers' and victim safety.
A Novel Smooth Variable Structure Filter for Target Tracking Under Model Uncertainty

Model uncertainty is a serious challenge for robustness of tracking algorithms in radar systems. The smooth variable structure filter (SVSF) achieves error-bounded estimations for target state by scaling the magnitude of kinematic modeling error and accordingly performing a flexible switching strategy for the correction gain. However, the SVSF, without any smoothing functions, suffers from undesired chattering phenomenon since the measurement noise causes random disturbance to the identification of actual level of uncertainties, leading to obvious deterioration of tracking accuracy. In this paper, we present a new switching function for SVSF, i.e. the hyperbolic tangent function, for effective chattering suppression. Then we propose a new algorithm named as the Tanh-SVSF, which reformulates the correction gain with the new switching function, to improve the estimation accuracy for target state. A mathematical definition of SVSF chattering is proposed to quantify the chattering amplitude. It is demonstrated that the new switching function exerts a nonlinear compressing effect on the likelihood of measurement innovation and substantially reduces the disturbance of measurement noise, leading to elimination of the chattering problem. The stability of the Tanh-SVSF is analyzed, based on a proposed stability theorem and the numerical exhaustion strategy. Finally, the proposed method is tested on a simulated vehicle tracking scenario and real-world radar data from the Oxford Radar RobotCar Dataset, and shows superior performance over existing SVSF formulations and the Kalman filter, in view of tracking accuracy, track continuity and the proposed chattering indicator.