Reviewerly/exHarmony huggingface dataset:
Study on CO concentration measurement of TDLAS based on baseline nonlinear improvement

CO is one of the main air pollution gases. The monitoring of CO concentration is very important for air pollution control. At present, TDLAS has been widely used in air environment monitoring. However, in the measurement process, the baseline fitting error will lead to a large error in CO concentration measurement. Therefore, this paper proposes to adjust the input signal of the signal generator iteratively and modify the baseline nonlinearity to improve the baseline fitting error, so as to effectively reduce the CO concentration measurement error. First of all, by comparing the real baseline with the polynomial fitting baseline of different orders, the polynomial fitting error is 33% ~ 56%, which proves that the non-linearity of baseline will lead to large concentration measurement error. Secondly, by modifying the baseline nonlinearity, polynomial fitting is used to measure the baseline concentration, the error is reduced to 6.2%, and the measurement accuracy is improved obviously. This method improves the polynomial fitting baseline error caused by baseline nonlinearity, improves the accuracy of CO concentration measurement, and provides technical support for CO monitoring in air pollution.
Research on the Design and Construction of Intelligent Learning Space in the Era of Big Data

Based on the rapid development of computer technology, mobile Internet technology and Internet of things technology, big data technology emerged at the right moment. We gradually entered the era of big data, and many industries began to use big data for experimental innovation. Education has also gradually got rid of the traditional way of education and started to develop towards smart education. Under this background, smart learning space emerges at the historic moment in the era of big data.
Application of Wireless Sensor Network in Automatic Detection of Spray Disinfection in Pig Epidemic Environment

To improve the health of pig breeding and reduce the incidence of pig epidemic, this paper studies and analyzes the environment of pig breeding. Pig breeding has the characteristics of closedness and intensiveness, for which a system based on the wireless sensor network for remote monitoring of pig houses has been developed. Aiming at the characteristics that the body temperature of pigs in pig breeding is difficult to be measured quickly and efficiently, thermal infrared imaging technology is used to automatically detect the body temperature of pigs. Finally, with regard to the monitoring and prevention of epidemic situations in pig farms, a method of automatic detection using non-contact ears is proposed as well as a vehicle for automatic inspection and disinfection is developed. The research results show that the wireless sensor network can monitor the environment of the pig house in realtime and regulate the environment to make it more suitable for the growth of pigs. The use of thermal infrared imaging technology realizes the temperature parameters of the characteristic regions of pig ears. The use of non-contact ear automatic detection technology and the vehicle for automatic inspection and disinfection can automatically disinfect the pig house and carry out automatic alarm work for epidemic situations. It is found that the use of automatic detection technology, as well as automatic inspection and disinfection vehicles can monitor the live environment of pigs, and ensure the health of the environment, thereby reducing the incidence of pig epidemics and the cost of pig breeding.
Combining Task Predictors via Enhancing Joint Predictability

Predictor combination aims to improve a (target) predictor of a learning task based on the (reference) predictors of potentially relevant tasks, without having access to the internals of individual predictors. We present a new predictor combination algorithm that improves the target by i) measuring the relevance of references based on their capabilities in predicting the target, and ii) strengthening such estimated relevance. Unlike existing predictor combination approaches that only exploit pairwise relationships between the target and each reference, and thereby ignore potentially useful dependence among references, our algorithm jointly assesses the relevance of all references by adopting a Bayesian framework. This also offers a rigorous way to automatically select only relevant references. Based on experiments on seven real-world datasets from visual attribute ranking and multi-class classification scenarios, we demonstrate that our algorithm offers a significant performance gain and broadens the application range of existing predictor combination approaches.
A Machine Learning Approach to Estimating Large Positive Definite Covariance Matrix of High Frequency Data

This paper proposes a novel large-dimensional positive definite covariance estimator for high-frequency data under a general factor model framework. We demonstrate an appealing connection between the proposed estimator and a weighted group-LASSO penalized least-squares estimator. The proposed estimator improves the traditional principal component analysis by allowing for weak factors, whose signal strengths are weak relative to idiosyncratic components. Despite microstructure noises and asynchronous trading, the proposed estimator achieves a guarded positive definiteness without sacrificing the convergence rate. To make our method fully operational, we provide an extended simultaneous alternating direction method of multipliers algorithm to solve the resultant constrained convex minimization problem efficiently. We offer a data-driven algorithm to select involved tuning parameters in practice. Empirically, we study the monthly high-frequency covariance structure of the stock constituents of the S&P 500 index from 2008 to 2016. We use all traded stocks from NYSE, AMEX, and NASDAQ stock markets to construct the high-frequency Fama-French four and extended eleven economic factors. We further examine the out-of-sample performance of the proposed method through vast portfolio allocations, which deliver significantly reduced out-of-sample portfolio risk and enhanced Sharpe ratios. The success of our method justifies the usefulness of machine learning in finance.
Shopping for Game levels: A Visual Analytics Approach to Exploring Procedurally Generated Content

Procedural content generation techniques can be used during game design to aid in exploration and expedite the content creation process. But this comes with the challenge of exploring a large quantity of generated content. This challenge is exacerbated by the lack of proper supporting tools. We contend that such tools should encourage exploration, and respect the nature of design judgment. We present a visual analytics tool, DesignSense, developed as a response to a similar challenge in the architectural design domain. In a case study, we applied DesignSense to a dataset of generated levels for a puzzle game. The initial observations suggest a match between the task described and the features present in DesignSense with room for improvement concerning its integration with the game development process.
Side-Channel Evaluation Methodology on Software

Cryptographic implementations need to be robust amidst the widespread use of crypto-libraries and attacks targeting their implementation, such as side-channel attacks (SCA). Many certification schemes, such as Common Criteria and FIPS 140, continue without addressing side-channel flaws. Research works mostly tackle sophisticated attacks with simple use-cases, which is not the reality where end-to-end evaluation is not trivial. In this study we used all due diligence to assess the invulnerability of a given implementation from the shoes of an evaluator. In this work we underline that there are two kinds of SCA: horizontal and vertical. In terms of quotation, measurement and exploitation, horizontal SCA is easier. If traces are constant-time, then vertical attacks become convenient, since there is no need for specific alignment (“value based analysis”). We introduce our new methodology: Vary the key to select sensitive samples, where the values depend upon the key, and subsequently vary the mask to uncover unmasked key-dependent leakage, i.e., the flaws. This can be done in the source code (pre-silicon) for the designer or on the actual traces (post-silicon) for the test-lab. We also propose a methodology for quotations regarding SCA unlike standards that focus on only one aspect (like number of traces) and forgets about other aspects (such as equipment; cf. ISO/IEC 20085-1.
Fast Depth Map Intra Mode Prediction Based on Self-organizing Map

3D-HEVC is developed by ISO/IEC MPEG and ITU-T Video Coding Experts Group (VCEG) as the highestprofile extension of HEVC for 3D video coding and Multi-View texture Videos plus Depth maps (MVD) compress. Concerning the current 3D-HEVC design, the uniform HEVC Intra prediction and Depth Modeling Modes (DMMs) are used in a sophisticated way to ameliorate the performance of the depth map video coding. Although, the enhancement of the coding efficiency is achieved at the cost of computational complexity load, which prevents 3D-HEVC from being used in real-world applications. Thus, we suggest a fast depth map Intra prediction mode decision based on the Self-organizing Map to resolve the aforementioned computational complexity increases. The experimental results demonstrate that the suggested algorithm can increase the encoding time savings up to 39.8%, with insignificant rate-distortion loss.
Interpretable Text-to-SQL Generation with Joint Optimization

The purpose of Text-to-SQL is to obtain the correct answer for a textual question from the database, which can take advantage of advanced database system to provide reliable and efficient response. Existing Text-to-SQL methods generally focus on accuracy by designing complex deep neural network models, and hardly consider interpretability, which is very important for serious applications. To address this, in this paper we propose a novel framework for Interpretable Text-to-SQL Generation (ITSG) with joint optimization, which achieves state-of-the-art accuracy and possesses two-level interpretability at the same time. The framework mainly consists of three layers: a sequence encoder which encodes questions, table headers and significant table contents, an attention-based LSTM layer which generates SQL queries and a reinforcement learning layer which boosts the execution accuracy. Comparing with state-of-the-art methods on benchmark datasets, the experimental results show the effectiveness and interpretability of our ITSG framework.
A Thermal Evaluation Method of Data-Driven Distribution Transformer

In view of the problem that it is difficult to evaluate the overload fault risk of distribution transformer, this paper proposes a data-driven thermal evaluation method of distribution transformer, points out that the difference equation of heat transfer is difficult to be applied to the thermal evaluation of the distribution transformer. Considering the three-phase load coefficient of distribution transformer, the risk evaluation model of overload fault of distribution transformer circulation neural network is established. The purpose of thermal evaluation of distribution transformer is achieved by predicting the highest oil temperature. The results show that the method is accurate, and the validity and practicability of the method are verified.
Event Detection on Literature by Utilizing Word Embedding

The events in literature refer to what the characters are going through, and the story revolves around them. In other words, figuring out events is understanding literature, which is an important concept in assessing the value of it. Event detection is the field of Information Retrieval and previous studies have been mainly based on Automatic Content Extraction (ACE) corpus. However, it costs a lot to make large datasets such as ACE and time consuming because all components like signals for events (entity, event mention, and event argument) are annotated. In addition, it is difficult to apply this large dataset to the special domain of literature. Therefore, we approach event detection in literature as using word embedding. Firstly, we make a set of keywords corresponding to each event, and then we apply Ranking by using word embedding. By utilizing this, this paper will provide a way of finding the sentences relating to the given queries, i.e. the sentences are supposed to be the event. Our method suggests a novel approach in event detection without creating large-scale datasets.
Security Posture Assessment Techniques for Marine Information

The structure of offshore platform is complex and the cost is too high. Once the platform has an accident, it will not only cause environmental pollution, but also bring about a series of problems such as economic losses and casualties, thus causing a bad impact on the society. Therefore, it is necessary to monitor the structure of offshore platform. Due to offshore platform structure has long been the bad service in the Marine environment, and suffered various effects, such as: the wind load, wave load, load current and ice etc., and even in some cases will be subjected to earthquake, typhoon, tsunami, etc and Marine platform itself also will be affected by the environment corrosion, Marine biological adhesion, water erosion, etc., these factors will be suffered damage to the Marine platform. Because the offshore platform has been in such a harsh environment or even the design and use of the platform is not appropriate, the platform structure will suffer damage, which will reduce the self-bearing capacity of the platform, and seriously will lead to platform failure. In this paper, the three-dimensional frame structure is applied to the offshore platform structure, and the damage situation of the offshore platform structure is analyzed, and a series of diagnostic methods are proposed. In this paper, the diagnosis method is mainly verified from three aspects: shear structure, three-dimensional frame structure and finite element model of offshore platform structure. In addition, the damage degree of the platform will be evaluated by modal parameter identification technology under the influence of certain noises.
MOOC Teachings of Chinese Culture for English Majors of Applied Universities Taking Anhui Sanlian University as an Example

Under the background of opening up, more and more attention has been paid to the course of Chinese Culture for English major students. Education is moving towards digitalization and the teaching of MOOC plus Chinese Culture is also an important content and inevitable trend of foreign language teaching reforms. Taking an example in Anhui Sanlian University, this paper explores the new teaching mode of Chinese culture course for English majors in Applied Universities based on MOOC.
News Classification to Notify About Traffic Incidents in a Mexican City

The primary objective of this chapter is to analyze the content retrieved from an RSS newspaper report written in the Spanish language and determine if it describes a traffic incident. The real-world case study occurs in a Mexican city with 1.5 million inhabitants, and its purpose is to offer an application to inform in real-time about traffic incidents that happen in the town, gathering its information from the reports published by a local newspaper. However, to do so, the authors first need to classify the articles and ignore those whose content does not imply any kind of road accident.
Real-Time Expression Recognition Improvement System Based on Deep Learning

Expression recognition is a relatively new direction in the field of face recognition, and the knowledge involved is not only unilateral, but its in-depth research has brought benefits to the development of artificial intelligence and even to human life. In response to this, this paper intends to design an improved real-time facial expression recognition system. The design first uses a deep learning method to design a facial expression recognition model, and then embeds the OpenCV framework and the cascading face detector of the Dlib open source library, and combines the ORB feature extraction algorithm to achieve real-time detection and recognition of facial expressions. To identify robustness, this study has achieved certain results in practice. The research has certain help in the fields of psychology and education, such as the feedback of classroom students’ teaching quality and the analysis of people’s real-time expressions. The dataset used in the study was the fer2013 facial expression dataset.
Application and Model of Virtual Reality in Physical Experiment

With the development of information technology and its application in education, education will undergo fundamental changes in terms of content, form, method and organization. Combining with various disciplines has brought a qualitative leap to education. The physics experiment in the physics course is an important part of students' physics learning. However, in reality teaching, many middle schools have poor teaching conditions due to limited experimental conditions, and cannot meet the learning needs of each student. As a technology with a wide imagination space, VR can construct and produce physical phenomena that are difficult to observe in reality. This article attempts to apply VR to physics experiments, and intends to use virtual experiment to improve the teaching effect, explore the value of VR in physics experiment teaching.
Science Development Fault of and Prediction on Quantum Computers and Quantum Communication

Science development suffers from science development fault, which is recognized from the research of quantum computers and quantum communication. Normally, the process of science development should be continuous; in another words, the latter is the extension of the former. Then science development fault is proposed to describe the case against the normal situation. It means that the former ends while the latter starts on a new base. This paper describes two science development faults and then predicts that the third science development fault probably comes from the field of quantum computers and quantum communication.
A Consolidated Dataset for Knowledge-based Question Generation using Predicate Mapping of Linked Data

In this paper, we present the ForwardQuestions data set, made of human-generated questions related to knowledge triples. This data set results from the conversion and merger of the existing SimpleDBPediaQA and SimpleQuestionsWikidata data sets, including the mapping of predicates from DBPedia to Wikidata, and the selection of ‘forward’ questions as opposed to ‘backward’ ones. The new data set can be used to generate novel questions given an unseen Wikidata triple, by replacing the subjects of existing questions with the new one and then selecting the best candidate questions using semantic and syntactic criteria. Evaluation results indicate that the question generation method using ForwardQuestions improves the quality of questions by about 20% with respect to a baseline not using ranking criteria.
Interactive Attention and Position-Aware Mechanism for Aspect-Level Sentiment Analysis

Aspect-level sentiment classification is a fine-grained work in sentiment analysis with the goal of predicting the sentiment categories of target words in a given text. In this paper, a framework of an interactive attention based on bidirectional LSTM networks and position-aware mechanism for aspect-level sentiment classification has been proposed, in which a fine-grained attention is introduced to capture the intersection between context and aspect words. Meanwhile, the position awareness mechanism is introduced to make the weight distribution of cross attention more reasonable. Experiments on the SemEval-2014 datasets show that our proposed model is always superior to the state-of-the-art methods of all datasets.
User-centered Implementation of Rehabilitation Exercising on an Assistive Robotic Platform

The paper focuses on the method and steps implementing a suite of rehabilitation exercises on an assistive robotic platform. The suite is based on extensive user needs identification procedures and consultation with medical and rehabilitation experts. For the design of the human-robot interaction (HRI) component of the platform, the user centered approach was adopted, which in this case employed a range of multimodal interaction facilities including a free user-robot dialogue, visual and speech signals.
Incorporating Concept Information into Term Weighting Schemes for Topic Models

Topic models demonstrate outstanding ability in discovering latent topics in text corpora. A coherent topic consists of words or entities related to similar concepts, i.e., abstract ideas of categories of things. To generate more coherent topics, term weighting schemes have been proposed for topic models by assigning weights to terms in text, such as promoting the informative entities. However, in current term weighting schemes, entities are not discriminated by their concepts, which may cause incoherent topics containing entities from unrelated concepts. To solve the problem, in this paper we propose two term weighting schemes for topic models, CEP scheme and DCEP scheme, to improve the topic coherence by incorporating the concept information of the entities. More specifically, the CEP term weighting scheme gives more weights to entities from the concepts that reveals the topics of the document. The DCEP scheme further reduces the co-occurrence of the entities from unrelated concepts and separates them into different duplicates of a document. We develop CEP-LDA and DCEP-LDA term weighting topic models by applying the two proposed term weighting schemes to LDA. Experimental results on two public datasets show that CEP-LDA and DCEP-LDA topic models can produce more coherent topics.
Information Security Operations

This chapter focuses on how to do successful planning that will build effective Information Security Operations. It provides specific actions or directions but will provide guidelines and principles for the planning that will allow an organization to be prepared and have effective information security operations. Risk management is a process for identifying, assessing and prioritizing risks to data and Information Technology (IT) systems. Tactical planning for security operations is the process of building a structure that can respond and the techniques to reduce the impact of incidents; both are highly important for security operational planning. The training plan should consider the information security skills that maximize the staff’s effectiveness for operations. Every organization that connects to the global Internet is at war, war that is extremely fast moving and with multiple opportunities for IT professionals to encounter the fog of war, an experience in which there is uncertainty.
DSP: Deep Sign Prediction in Signed Social Networks

In a signed social network, users can express emotional tendencies such as: like/dislike, friend/foe, support/oppose, trust/distrust to others. Sign prediction, which aims to predict the sign of an edge, is an important task of signed social networks. In this paper, we attempt to tackle the problem of sign prediction by proposing a Deep Sign Prediction (DSP) method, which uses deep learning technology to capture the structure information of the signed social networks. DSP considers the "triangle" structures each edge involves comprehensively, and takes both the "balance" theory and the "status" theory into account. We conduct experiments and evaluations on five real signed social networks and compare the proposed DSP method with multiple state-of-the-art methods. The experimental results show that the proposed DSP method is very effective and outperforms other methods in terms of four metrics (AUC, binary-F1, micro-F1, macro-F1).