Reviewerly/exHarmony huggingface dataset:
DL-PER: Deep Learning Model for Chinese Prehospital Emergency Record Classification

Prehospital emergency records contain much information about prehospital emergency patients.Extracting important patient information from many records has become the focus of all prehospital emergency personnel.The key to solving this problem is to achieve the automatic classification of prehospital emergency records.This study considers a deep learning-based prehospital emergency record classification model (DL-PER).The model uses a weighted text convolutional neural network to classify prehospital emergency records.First, we use prehospital emergency records to train a bi-directional encoder representation (BERT) model from the transformer.BERT obtains the word vectors.Then, we use a bidirectional long and short-term memory (BiLSTM) model to obtain text features from a global perspective.A weighted text convolutional neural network (WTextCNN) improves this model's local text feature extraction capability.We used activation functions instead of ReLu activation functions to improve the learning ability of the model.We conducted experiments using prehospital emergency records provided by the Handan Emergency Center.The results showed that the DL-PER model improved the F1 scores by up to 5.7%, 6.8%, 5.7%, and 4.9% on the four data sets, respectively, compared with the BiLSTM model.
On Verification of Smart Contracts via Model Checking

Combined with smart contracts, the application of blockchain techniques has grown faster and broader. However, it is very difficult to write secure and functionally correct smart contracts because of the openness of blockchain platforms. Formal verification, such as model checking, has been proven to be an effective way of guaranteeing security and correctness of systems. In this paper, we propose a novel model checking based framework, called mcVer, to support the verification of smart contracts written in Solidity. Built on model checking tool VERDS, the mcVer framework is able to verify not only safety properties but also liveness properties of smart contracts. For the properties that are not satisfied, mcVer produces a counter example by showing a sequence of statements in the original Solidity program as a hint for fault localization. We implemented the automatic transformation from a subset of the Solidity language to the modeling language of VERDS, that therefore provides automatic verification for smart contracts. Experiments are carried out on various cases, including checking contracts for finding typical security vulnerabilities and verifying properties of an access control smart contract. The experimental results demonstrate the flexibility and efficiency of mcVer.
Multi-instance Embedding Learning Through High-level Instance Selection

Multi-instance learning (MIL) handles complex structured data represented by bags and their instances. MIL embedded algorithms based on representative instance selection transform bags into a single-instance space. However, they may select weak representative instances due to the ignorance of the internal bag structure. In this paper, we propose the multi-instance embedding learning through high-level instance selection (MIHI) algorithm with two techniques. The fast bag-inside instance selection technique obtains instance prototypes of each bag. It fully utilizes the bag information using our new density and affinity metrics. Based on the instance prototypes, the high-level instance selection technique chooses instances using the peak density metric. It obtains high-level instances with higher representative power than the instance prototypes. Experiments were conducted on six learning tasks and nine comparison algorithms. The results confirmed that MIHI achieved better performance in terms of efficiency and classification accuracy. This method, in particular, has a substantial advantage in image retrieval and web data sets.
Spectral Mappings for Graph Wavelets

Graph wavelet filters can be used to construct dictionaries of atoms, and signals defined over graphs can be represented as linear combinations of these atoms. These representations can facilitate the development of various signal processing tasks, e.g. denoising or feature extraction. We present here a method to construct these filters, using mappings on simple prototype filters, that readily allows for spectral domain shaping. Two types of spectral maps will be developed in this work. The first type of maps sharpens (increase the frequency selectivity) of the prototype filter. The second type of maps is equivalent to upsampling in the spectral domain, and can be used to construct a&#x2019;trous like transforms. The a&#x2019;trous like transform allows for a tree-structured implementation, and is reminiscent of the classical a&#x2019;trous (with-holes) wavelet transform. The resulting filters, constructed using these maps, are polynomial functions, and can therefore be implemented without the need for any approximation or eigendecomposition. Both non-redundant and redundant frames can be constructed with these filters. A variety of design examples will be presented to demonstrate the versatility of the proposed method. Applications of the filters to non-linear approximation and denoising will also be considered.
Low-Light Images In-the-Wild: A Novel Visibility Perception-Guided Blind Quality Indicator

Owing to the increasing deployment of CMOS camera modules, it is inevitable to take photographs under weak illumination. Therefore, low-light imaging quality is one of the most important factors affecting user experience as well as the product values of consumer electronics, automobile, surveillance, factory automation, and other industrial applications. Inspired by human vision, this article jointly considers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visibility perception</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">luminosity cognition</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">color sensation</i> and presents a new visibility perception-guided blind quality indicator for low-light images in-the-wild. To excavate effective descriptors for authentic distortions under weak illumination, we utilize maximum ignorable visible difference to characterize the reduced visibility, and employ the luminance statistical properties and color sensation characteristics to represent brightness and colorfulness distortions. Extensive experimental results on the benchmark dataset verify that the proposed blind quality indicator outperforms nine representative methods including general-purpose and distortion-specific methods.
Pose and Motion Estimation of Free-Flying Objects: Aerodynamics, Constrained Filtering, and Graph-Based Feature Tracking

In this article, we investigate the problem of dynamically estimating the instantaneous position, orientation, velocity, and angular velocity of an arbitrarily shaped object during its free flight based on image frames taken simultaneously by two high-speed cameras. Aerodynamic effects, including drag, lift, and Magnus forces, are modeled to describe the object’s flight. Observables are derived from combining dynamics with a camera projection model assuming two-view geometry, via the use of multiple quaternions. The state of the composed system can then be estimated via constrained Kalman filtering, to which a solution is presented for the case of multiple quadratic constraints. To keep track of appearing and disappearing visual features during flight, the estimation algorithm employs a graph matching-based technique to maintain a set of evolving hypotheses through evaluation, pruning, and addition. Experiments conducted over various objects have either provided validation against motions independently estimated using multiple accelerometers, or formed verification by matching flight images against projections based on the state estimates.
Making an English Speech Resemble the User’s Voice Using UTAU and Interactive Evolutionary Computation

In general, learning English is difficult for non-native speakers because of the differences in vowels and consonants. There are some ways to practice English pronunciation such as shadowing, however, if the audio’s voice features greatly differ from the learner’s voice, it might impede learning and sound reproduction. In order to solve this problem, we propose a method to make the pronunciation data of the model pronunciation resemble the learner’s own voice by using UTAU and Interactive Evolutionary Computation. As a result of the experiments, we found that this method was capable of searching for highly evaluated solutions. The Wilcoxon signed-rank test was used to examine the statistical difference between the evaluations of the initial and final generations, and a significant difference was observed at P<0.01. Regarding to the pitch parameters, we could find different tendencies between males and females. This means the parameters were actually making the voice similar to examinee’s voice. However, there were some problems, such as the parameters that did not work well, the UTAU voice quality, the lack of female examinees, and so on. We plan to eliminate or at least reduce the effects from those problems in future experiments and make a better system for English learners so that they can learn more efficiently.
Explanation Plug-In for Stream-Based Collaborative Filtering

Collaborative filtering is a widely used recommendation technique, which often relies on rating information shared by users, i.e., crowdsourced data. These filters rely on predictive algorithms, such as, memory or model based predictors, to build direct or latent user and item profiles from crowdsourced data. To predict unknown ratings, memory-based approaches rely on the similarity between users or items, whereas model-based mechanisms explore user and item latent profiles. However, many of these filters are opaque by design, leaving users with unexplained recommendations. To overcome this drawback, this paper introduces Explug, a local model-agnostic plug-in that works alongside stream-based collaborative filters to reorder and explain recommendations. The explanations are based on incremental user Trust & Reputation profiling and co-rater relationships. Experiments performed with crowdsourced data from TripAdvisor show that Explug explains and improves the quality of stream-based collaborative filter recommendations.
Intrinsic Low-Dimensional Nonlinear Manifold Structure of Radio Frequency Signals

Nonlinear dynamic features are crucial in specific emitter identification (SEI), which works based on the nonlinearity of the emitter. Nevertheless, its underlying principles and stability have not been systematically studied, so these methods are close to a black box, making the recognition performance of the system unstable in applications. In this study, we provide an in-depth analysis of its mechanism and theoretical model, extracting, for the first time, the intrinsic low-dimensional nonlinear manifold structure (IMS) of radio frequency signals based on manifold learning and analyzing its robustness and distinctiveness for SEI. Hardware type and intentional modulation are found to determine the overall shape of IMS, and the device-specific information for SEI is subtle and distributed locally on IMS. Notably, sampling-related random disturbances cause the distortion, zooming, or rotation of IMS, which lowers the identification accuracy by a range of 1.74%–52%, depending on the feature, device type, signal type, and other factors.
Geo-Distributed IoT Data Analytics With Deadline Constraints Across Network Edge

Owing to the advancement of the Internet of Things (IoT) and 5G mobile technologies, various IoT devices produce massive data, which is usually transferred to nearby sites, such as edge nodes or datacenters. Many large-scale IoT applications need to analyze the data distributed across multiple sites to obtain final results. A dominant challenge of this type of data analytics is the heterogeneities of resource capacities across geo-distributed sites. In this article, we find that the resource capacity as well as the resource price differ among sites, and the price heterogeneity has a significant impact on geo-distributed IoT data analytics. Thus, each geo-distributed IoT data analytics job prefers to minimize the job execution cost while guaranteeing its deadline requirement under the resource constraints of involved sites. Specifically, we propose to jointly consider the resource heterogeneities of both capacity and price, and minimize the cost of each job before its deadline. We characterize this optimization problem as a quadratically constrained quadratic programming problem. To tackle such an NP-hard problem, we propose the minimize the job completion cost before a given deadline (MCGL) method, which calculates a task placement solution by the gradient adjustment strategy according to the remarkable negative correlation relationship between job completion time and job completion cost of geo-distributed IoT data analytics job. The task placement strategy can optimize resource cost with respect to the deadline requirement of any geo-distributed data analytics job. The trace-driven evaluations indicate that MCGL significantly reduces the total cost compared with existing methods; moreover, they satisfy the deadline constraints simultaneously.
Exploiting Feature Selection in Human Activity Recognition: Methodological Insights and Empirical Results Using Mobile Sensor Data

Human Activity Recognition (HAR) using mobile sensor data has gained increasing attention over the last few years, with a fast-growing number of reported applications.The central role of machine learning in this field has been discussed by a vast amount of research works, with several strategies proposed for processing raw data, extracting suitable features, and inducing predictive models capable of recognizing multiple types of daily activities.Since many HAR systems are implemented in resourceconstrained mobile devices, the efficiency of the induced models is a crucial aspect to consider.This paper highlights the importance of exploiting dimensionality reduction techniques that can simplify the model and increase efficiency by identifying and retaining only the most informative and predictive features for activity recognition.More in detail, a large experimental study is presented that encompasses different feature selection algorithms as well as multiple HAR benchmarks containing mobile sensor data.Such a comparative evaluation relies on a methodological framework that is meant to assess not only the extent to which each selection method is effective in identifying the most predictive features but also the overall stability of the selection process, i.e., its robustness to changes in the input data.Although often neglected, in fact, the stability of the selected feature sets is important for a wider exploitability of the induced models.Our experimental results give an interesting insight into which selection algorithms may be most suited in the HAR domain, complementing and significantly extending the studies currently available in this field.
Synthesis of Optimal Defenses for System Architecture Design Model in MaxSMT

Attack-Defense Trees (ADTrees) are widely used in the security analysis of software systems. In this paper, we introduce a novel approach to analyze system architecture models via ADTrees and to synthesize an optimal cost defense solution using MaxSMT. We generate an ADTree from the Architecture Analysis and Design Language (AADL) model with its possible attacks, and implemented defenses. We analyze these ADTrees to see if they satisfy their cyber-requirements. We then translate the ADTree into a set of logical formulas, that encapsulate both the logical structure of the tree, and the constraints on the cost of implementing the corresponding defenses, such that a minimization query to the MaxSMT solver returns a set of defenses that mitigate all possible attacks with minimal cost. We provide an initial evaluation of our tool on a delivery drone system model which shows promising results.
A Fault Diagnosis Method for Unbalanced Data Based on a Deep Cost Sensitive Convolutional Neural Network

For the research of industrial machinery intelligent fault diagnosis, traditional methods generally require that the samples share the same distribution or are balanced. In fact, the machines switch working conditions frequently during operation, accordingly resulting in changes in data distributions and the data can be unbalanced. To solve the above, combining transfer learning method, an intelligent diagnosis method for imbalanced data based on Deep Cost Sensitive Convolutional Neural Network is proposed. According to this model, the cost sensitive classification loss function adaptively assigns different error classification costs for all class, and it can mitigate data unbalancing. The domain adaptation is constructed by domain adversary and distance measure to automatically learn domain invariant features. The proposed method is verified by transfer experiments under various working conditions with three published bearing vibration data sets, and compared with other method under different imbalanced ratio. The results show that the proposed method is effectiveness and able to classify the imbalanced data.
FindSampo: A Linked Data Based Portal and Data Service for Analyzing and Disseminating Archaeological Object Finds

This paper presents the FindSampo system for analyzing and disseminating archaeological object finds made by the public. The system is based on Linked Open Data (LOD), and consists of a web portal and an open data service. The underlying knowledge graph contains data of some 3000 archaeological object finds catalogued in the archaeological collection of the Finnish Heritage Agency (FHA) from 2015 to 2020. The portal and LOD service have been open to public use since May 2021.
Ensemble learning-based deep neural network model for face recognition

Due to the poor regularization capability of single Convolutional Neural Network (CNN), the performance of face recognition system is severely affected. Also, the input facial image data for recognition is influenced by surrounding information like light, expression, brightness, contrast, pose variations and other factors. This issue is overcome by using ensemble-based feature learning ofCNN and local binary patterns (LBP). It also aids in improving the occlusion-related low pedestrian detection rate. To begin, the LBP operator is used to extract texture features from the face and by using different 10 CNN architectures, different part of facial information is learnt. This is mainly used to improve the performance of underlying networking attributes and thereby it leads to enhanced classification results using the Softmaxactivation function. With the introduced new form of face recognition focused on parallel ensemble learning of convolutional neural network and local binary pattern for feature extraction, the obtained results outperforms the existing state-of-art techniques. Finally, utilizing majority voting, the approach of parallel ensemble learning is employed to obtain the final result of face recognition. The ORL and Yale-B face datasets identification rates climb to 98.1% and 99.2 percent, respectively, for our propose system. The proposed approach is demonstrated in the experiments to improve not only the model’s immunity to withstand with different illumination, expression, lighting, brightness and posture conditions, but also the accuracy of face recognition with the poor regularization metrics, which denies in reaching the global solution in the solution space.
Study on Class Imbalance Problem with Modified KNN for Classification

Identification of data imbalance is a very challenging one in the modern era. When we go for a data warehouse, there would be a vast data available in it but managing data and sustaining the balanced state of data is very difficult to handle in any type of sector. Occurrence of data imbalance comes when specimens are classified based on their behaviour. In this paper, the imbalance state of data is analysed and the machine learning techniques are studied carefully to choose the best technique to handle data imbalance problems. Wide analysis of the k-nearest neighbour (KNN) algorithm can be carried out to keep the classification of specimens grouped equally.
Vietnamese Text Summarization Based on Neural Network Models

Text summarization produces a shortened or condensed version of input text highlighting its central ideas. Generating text summarization manually takes time and effort. This paper investigates several text summarization models based on neural networks, including extractive summarization, abstractive summarization, and abstractive summarization based on the re-writer approach and bottom-up approach. We perform experiments on the CTUNLPSum dataset in Vietnamese comprising 95,579 documents collected from commonly read Vietnamese online newspapers. The summarization model based on the bottom-up approach creates the best summaries. The F1-scores of ROUGE-1, ROUGE2, and ROUGE-L of the bottom-up approach are 0.598, 0.260, and 0.455, respectively.
Problem Solution Strategy Assessment of a Hybrid Knowledge-Based System in Teaching and Learning Practice

This paper presents the main features of an assessment method for a knowledge-based software system, which uses Socratic style teaching and learning practice in the higher education environment. Software system assessment happens in a hybrid legal intelligent tutoring system, Guidance for Business Merger and Acquisition (GBMA). The legal automated application for GBMA is presented in two different types, as normative guidance (e.g., rules) and past court cases. In addition, distinguishing the two distinct types of information representation, the paper presents the proper use of these types in a software system architecture deployed to produce a possible outcome for a new business scenario with the help of normative reasoning (RBR) and past experience-based reasoning (CBR) in a hybrid software system architecture. The system presented outcome's suitability assessment is evaluated using fuzzy linguistic terms. This way, the assessment used discussions and questionnaires with different user groups in a scenario-based teaching and learning practice. Also, the answers to questionnaires use fuzzy linguistic concepts for evaluation purpose. Finally, the experimental results suggest that fuzzy linguistic concept-based assessment helps evaluate knowledge-based systems.
Preliminary Findings: Application of Maintenance Instructions Displayed in Augmented Reality

This paper explores the use of Augmented Reality (AR) in aviation maintenance and describes a new research study conducted in partnership with a manufacturer and a Part 121 operator. The study aims to understand the applicability of displaying maintenance instructions on a head-worn AR device. In the current study, four aviation maintenance technicians interacted with novel AR technology while performing a landing gear lubrication task. Participants reported generally positive perceptions of usability and user experience. However, some ergonomic/comfort and safety concerns were identified, along with considerations and practical applications for the AR technology. These preliminary findings illustrate the possible utility of AR in aviation maintenance, but further work is needed.