Reviewerly/exHarmony huggingface dataset:
An Effective System for User Queries Assistance

The Big Data paradigm has recently come on scene in a quite pervasive manner. Sifting through massive amounts of this kind of data, parsing them, transferring them from a source to a target database, and analyzing them to improve business decision-making processes is too complex for traditional approaches. In this respect, there have been recent proposals that enrich data while exchanging them, such as the Data Posting framework. This framework requires the ability of using domain relations and count constraints, which may be difficult to manage for non-expert users. In this paper, we propose Smart Data Posting, a framework using intuitive constructs that are automatically translated in the standard Data Posting framework. In particular, we allow the use of smart mapping rules extended with additional selection criteria and the direct use of tuple generating dependencies and equality generating dependences. We present a complexity analysis of the framework and describe the architecture of a system for advanced search, tailored for Big Data, that implements the Smart Data Posting framework.
The Study on Opening Book Based on Computer Game of Draughts

Computer game is one of the important research directions in the field of artificial intelligence. The opening book is an important way to improve the level of computer game program. This paper first discusses the understanding of computer game and gives a brief introduction to the rules of draughts. The moves in the opening are of great significance to the development of the whole game. This paper takes the draughts with 10*10 board computer game program as the research object and introduces the method of applying the opening book. The application of opening book avoids the time-consuming search of the program in the opening and the strategic mistakes that affect the overall development. This paper also studies the generation method and structural design of the opening book. It demonstrates the necessity and superiority of the application of the opening book in the draughts computer game program.
Designing a “software quality model” based on RCCA of defects and validating based on “quality algorithm”

Abstract Software systems are very complex, and its complexity has grown exponentially as users' requirements and their usage have grown significantly. Users' requirements and the environment in which they use the software system keep changing, so maintaining its quality is very challenging. If the requirements of users are not met as per their expectations, they encounter defects that cause degradation of the quality of the software system. Such defects are analyzed to understand their impact, and they are fixed to help improve quality. In this research, a defect analysis technique called root cause and corrective actions (RCCA) is followed to analyzes different defect attributes, their interdependencies, and how they help in improving software system quality. The methodologies followed here are defining a hypothesis, finding a causal relationship between quality attributes, and testing the hypothesis. This research is based on quantitative or experimental research to understand the impact of theindependent variables on the dependent variable, which is software quality. This research identifies the “legacy quality score”, designs a new software system quality model, and quality algorithm to calculate the “new quality score”. The results indicate that the newly designed quality model helps improve the quality of the software system.
Command Behavior Evaluation Based on Statistical Learning Modeling

According to the characteristics of command behavior, a command behavior evaluation model based on statistical learning modeling is established, and Gaussian process prediction method is used to evaluate the command behavior. Compared with machine learning methods such as neural network and Support Vector Machine, the output results of Gaussian process have probability meanings and can evaluate the validity and confidence of the predicted results. The algorithm is proved to be effective by comparing it with Support Vector Machine (SVM).
ConfiConv: A Confidence Measure for Disparity Estimation Based on Deep Learning

In this paper, a confidence measure for disparity estimation is proposed to encode the degree of uncertainty of each point in disparity map. Based on Convolutional Neural Network (CNN), a network is set up which is named as Confidence Convolutional neural network (ConfiConv). Compared with four different Confidence Measures (CMs) in Middlebury 2014 dataset using two stereo vision algorithms, it is shown that the average Area Under Curve (AUC) values of ConfiConv are better than other measures. And ConfiConv is also tested in another dataset which contains multiple movie clips.
The Transfer of Film Style Based on Meta-Learning

The film style refers to the combination and configuration of colors in the film, which is often dominated by one color, making the picture show a certain tendency. However, the creation of special effects not only requires special professional skills, but also takes a lot of manual labor. If artificial intelligence technology can be transferred to the picture style of the film industry, production costs will be greatly reduced. In this paper, we propose a technique which combines the style transfer and meta-learning to create a new way of thinking. Compared with traditional image style transfer, the transfer of film style based on meta-learning could save the cost of film production significantly and take much less time to perform the transfer process. Toward the end, extensive experimental results were presented to validate our proposed method, which clearly outperforms the traditional image style transfer.
A Steganographic Method Based on Remainder Function with Weight Matching Embedding Rate

The basic requirements of the information hiding system are good visual quality, high hiding performance, high embedding rate and high steganography security. This paper proposes a new hiding data method in grayscale image. In the case of a high embedding rate, the distortion of the stego grayscale image can be reduced. In this paper, we combine 2 image pixels into a group. First, we determine the number of secret message bit embedded in each group based on the embedding rate. Then we calculate the matching weight and steganographic function by the length of the message bit. Finally, we calculate the remainder of the steganographic function. Because the secret message is recorded as the remainder of the function, the original image pixel conversion to the stego image pixel must appear in four schemes. Finally, the least distortion in the four schemes is embedded. In order to verify the method of this paper. The algorithm in this paper compares the PSNR values of PVD algorithm, DE algorithm, APPM algorithm and so on. Experimental results have demonstrated that the PSNR value of this paper is higher than the traditional algorithm. The algorithm in this paper does not have any falling-off-boundary problem.
Generative Adversarial Networks -based Reconstruction of Low Dimensional Autoencoder Representations

Autoencoders comprise both an encoder and decoder, hence they hold the inherent capability of data compression and compressed sensing. However, the state-of-the-art for adversarial autoencoders (AAE) still cannot attain lossless recovery of original data, especially in the situation of low dimensional AAE representations. This paper proposes a general adversarial networks (GAN) based method to improve the reestablishment performance of AAEs. Adversarial learning is employed to generate high quality rebuilding sample which maximally approaches the original sample. The relationship between the dimensionality of AAE latent space and the reconstruction property is also explored. This paper focuses on the reconstruction of low dimensional AAE representation which is beneficial to signal, image and video coding and compressive sensing. It is demonstrated by the experimental results that the proposed method is superior to the AAE decoders in the recovery capability of original data.
Deformation measurements using a stereo microscope

A fringe projection technique embedded into a stereo microscope to perform 3D deformation measurements is presented. Fringes projected on the inspected object are blurred by motion, providing additional information to describe the surface deformation. In conjunction to a stereo microscope, to inspect the deformation of micro-objects is available. Only oneshot measurement is required. The full-field property makes it possible to inspect several objects at the same time.
Workshop presentation of a social wearable that affords vulnerability

We present 'True Colors,' a social wearable prototype designed to augment co-located social interaction among players in a LARP (live action role play). We designed it to enable the emergence of rich social dynamics between wearers and non-wearers. True Colors is Y-shaped, worn around the upper body, and has distinct front and back interfaces to afford actions taken by the wearer (front), and actions taken by others (back). To design True Colors [3], we followed a Research-through-Design approach, used experiential qualities and social affordances to guide our process, and co-designed with LARP designers. 13 True Colors wearables were deployed in a 3-day LARP event, attended by 109 people. Out of all the functionalities and interactivity the device afforded, players gravitated most towards those that emphasized the social value of experiencing vulnerability as a prompt to get together.
Mime Artist: Bypassing Whitelisting for the Web with JavaScript Mimicry Attacks

Despite numerous efforts to mitigate Cross-Site Scripting (xss) attacks, xss remains one of the most prevalent threats to modern web applications. Recently, a number of novel xss patterns, based on code-reuse and obfuscated payloads, were introduced to bypass different protection mechanisms such as sanitization frameworks, web application firewalls, and the Content Security Policy (csp). Nevertheless, a class of script-whitelisting defenses that perform their checks inside the JavaScript engine of the browser, remains effective against these new patterns. We have evaluated the effectiveness of whitelisting mechanisms for the web by introducing “JavaScript mimicry attacks”. The concept behind such attacks is to use slight transformations (i.e. changing the leaf values of the abstract syntax tree) of an application’s benign scripts as attack vectors, for malicious purposes. Our proof-of-concept exploitations indicate that JavaScript mimicry can bypass script-whitelisting mechanisms affecting either users (e.g. cookie stealing) or applications (e.g. cryptocurrency miner hijacking). Furthermore, we have examined the applicability of such attacks at scale by performing two studies: one based on popular application frameworks (e.g. WordPress) and the other focusing on scripts coming from Alexa’s top 20 websites. Finally, we have developed an automated method to help researchers and practitioners discover mimicry scripts in the wild. To do so, our method employs symbolic analysis based on a lightweight weakest precondition calculation.
Enculturation of Cyber Safety Awareness for Communities in South Africa

The internet has become so integrated with users’ daily activities, that cyber users interweave their daily activities automatically between the physical and cyberspace without noticing. Cyberspace can be referred to as a virtual computer world, which includes the connectivity of multiple networks. These connections between multiple networks within cyberspace form a global computer network to enable online communication between cyber users. Cyber users connect to cyberspace for socialising, work and educational purposes. The advantages of cyberspace are enormous and to a great benefit to all cyber users and cyber business. However, cyberspace opens the door to a number of possible cyber risks and cybercrimes that can affect cyber users. Cybercrimes and risks relate to either financial loss, disruption or damage to the reputation of a cyber user or organisation. These cybercrimes can include hacking, phishing or identity theft. Cyber users may not be aware of or knowledgeable regarding cyber risks and cybercrimes. The cyber user needs to be cyber safety conscious in order to be protected against cyber risks and cybercrimes. In many instances, cyber users within the industrial sector are being made aware of cyber risks through education and training programmes within their working environment. However, many cyber users within communities in South Africa are not working in industry and therefore do not have access to opportunities regarding cyber safety awareness. This research aims to investigate the level of cyber safety awareness within communities and propose a number of approaches that can be used to create and implement cyber-safety awareness programmes and material within different communities. Differently communities within South Africa have different needs that can range from different languages, learning approaches and community-defined processes and procedures. A quantitative research method and random sampling were used to obtain data about cyber safety awareness within communities. In the research, a survey with full ethical clearance was used.
An Efficient 3D-NAS Method for Video-Based Gesture Recognition

3D convolutional neural network (3DCNN) is a powerful and effective model utilizing spatial-temporal features, especially for gesture recognition. Unfortunately, so many parameters are modified in 3DCNN that lots of researchers choose 2DCNN or hybrid models, but these models are designed manually. In this paper, we propose a framework to automatically construct a model based on 3DCNN by network architecture search (NAS) [1]. In our method called 3DNAS, a 3D teacher network is trained from scratch as a pre-trained model to accelerate the convergence of the child networks. Then series of child networks with various architectures are generated randomly and each is trained under the direction of converted teacher model. Finally, the controller predicts a network architecture according to the rewards of all the child networks. We evaluate our method on a video-based gesture recognition dataset 20BN-Jester dataset v1 [2] and the result shows our approach is superiority against prior methods both in efficiency and accuracy.
Applying sparse based spatial super-resolution for Himawari-8 satellite image

Super-resolution for meteorological satellite images is expected to improve the accuracy performance in the systems such as elucidations of meteorological, solar irradiance estimation, utilization of photovoltaic power generation, storage batteries and electric vehicles. While various super-resolution studies have been conducted for planimetric features, there seldom exists for meteorological features. In this study, we apply sparse based super-resolution for meteorological satellite images and verify the characteristics of error between original high-resolution image and super-resolution results. We confirmed super-resolution by sparse-based method which keeps the boundary feature of clouds. A sparse constraint gains an advantage for cloud image which contains boundary features from the meteorological satellite. The peak signal-to-noise ratio by the sparse based method was improved 1.43dB at the maximum compared with bicubic interpolation. On the other hand, we show that the sparse-based method still needs further studies to handle the blurry cloud and absent cloud situations.
Tree Memory Networks for Sequence Processing

Long-term dependencies are difficult to learn using Recurrent Neural Networks due to the vanishing and exploding gradient problems, since their hidden transform operation is applied linearly in sequence length. We introduce a new layer type (the Tree Memory Unit), whose weight application scales logarithmically in the sequence length. We evaluate this on two pathologically hard memory benchmarks and two datasets. On those three tasks which require long-term dependencies, it strongly outperforms Long Short-Term Memory baselines. However, it does show weaker performance on sequences with few long-term dependencies. We believe that our approach can lead to more efficient sequence learning if used on sequences with long-term dependencies.
The Hitchhiker's guide to the eyewear applications

We have become accustomed to flat screens over a long time because flat screens are both easy to manufacture and easy to carry around. However, there is a huge gap between the 3D experiences that we see directly with our eyes and those viewed through a flat screen. This paper compares 3D experiences through eyewear and other media. Based on the strengths of eyewear, we suggest several design considerations for eyewear applications vividly conveying the actual 3D experience.
A LBP Texture Analysis Based Liveness Detection for Face Authentication

Face authentication systems are becoming more and more prevalent, but it has an intrinsic vulnerability against the media-based face forgery (MFF) where adversaries display photos or videos containing victims’ faces to deceive face authentication systems. Liveness detection is an important defense technique to prevent such attacks. In this paper, we propose a practical and effective liveness detection mechanism to protect the face authentication system against the MFF-based attacks. Our approach send the challenge to the user in random and the camera capture the response as a video. The Local Binary Pattern (LBP) is a widely used descriptor in texture analysis due to its efficiency. We utilize $$\delta $$-LBP, a LBP variant, to detect the expression frame from the video. Additionally, We improve the original LBP by using proper sampling radius in different subareas of a facial image and apply the approach in extracting the facial texture feature from the expression frame as a histogram. Our method detects the MFF-based attacks by measuring the consistency between the LBP histogram and the real facial texture feature. To demonstrate its effectiveness, We collect real-world photo data and video data from both legitimate authentication requests and the MFF-based attacks. The experiment results show that it can detect the MFF-based attacks with an accuracy of 96.45%.
Reagent dosage intelligent optimal setting for iron ore flotation process based on CBR

Reagent dosage setting is closely related to the product quality and economic benefit of an iron ore flotation process. The strong nonlinearities, uncertainties and coupling of the flotation process make it difficult to set proper reagent dosage. In this paper, a reagent dosage intelligent optimal setting method based on case-based reasoning (CBR) is put forward. It can calculate the reagent dosage optimal setting value according to ore properties. Result of the industrial experiments conducted in a Chinese iron ore dressing mill shows that the proposed method has a higher computational accuracy and can realize a more stable product quality when compared with other setting methods.
Performance Evaluation of Recurrent Neural Network on Large-Scale Translated Dataset for Question Generation in NLP for Educational Purposes

In recent years, neural networks have been used widely to solve many NLP tasks that involve large-scale datasets. Recently, Question Generation (QG) has called great attention since it is a subtask of Question Answering (QA) that has many applications in the real world, mainly for educational purposes. The importance of it could be seen on many recently released large-scale datasets prepared exclusively for this task, most the data used in NLP are available in the English language, but it is not the case for the rest of the languages, like Spanish, which is the third most used language in the world. This research is focused on analyzing the performance of current state-of-the-art neural network models used in QG using translated Spanish large-scale dataset from English. To know the accuracy of the translated Spanish data from English, it has been used state-of-the-art OpenNMT machine translator and Google Translation API, then the results have been analyzed with the corresponding automatic metrics -BLEU, METEOR, ROUGE -and human evaluations such as fluency and adequacy, later, it has been trained a state-of-the-art question generation (QG) neural network model using Spanish translated data to generate automatic questions in Spanish language. Surprisingly, the results outperform the original English results in average 37% on all automatic evaluation metrics. To the best of our knowledge, this work is the first one using large-scaleSpanish translated data for QG task using recurrent neural networks for educational purposes.
Discussion on the innovative mixed model of Automatic control experiment MOOC teaching

The online course is a new education platform, and its credits have been gradually accepted as college credit. China University Love Course Platform can provide course certificates. At present, universities encourage teachers to teach MOOC courses, which promotes both the internationalization of higher education and the innovation of traditional teaching. However, there are several issues with the current MOOC teaching. First, there is an unbalanced low proportion of laboratory courses; Second, the offered laboratory courses mainly focus on theory, leading to the lack of experimental design and other teaching links; Third, the lack of some practical trainings, such as practical experiments and remote experiments, reduces teaching effects; Fourth, MOOC belongs to the network teaching mode. It is difficult for students to complete the MOOC course if it is not combined with offline course management,. To solve these issues, this paper designed an experiment on automatic control theory, which is a mixed teaching mode, namely the MOOC laboratory teaching + auxiliary Web online teaching platform + off-line laboratory teaching and management. It enables the students to not only take online MOOC content, also manage and download the study materials remotely. We combine online education and offline experiment teaching, thereby providing students with a variety of learning modes.
Adaptive Graph Fusion for Unsupervised Feature Selection

The massive high-dimensional data brings about great time complexity, high storage burden and poor generalization ability of learning models. Feature selection can alleviate curse of dimensionality by selecting a subset of features. Unsupervised feature selection is much challenging due to lack of label information. Most methods rely on spectral clustering to generate pseudo labels to guide feature selection in unsupervised setting. Graphs for spectral clustering can be constructed in different ways, e.g., kernel similarity, or self-representation. The construction of adjacency graphs could be affected by the parameters of kernel functions, the number of nearest neighbors or the size of the neighborhood. However, it is difficult to evaluate the effectiveness of different graphs in unsupervised feature selection. Most existing algorithms only select one graph by experience. In this paper, we propose a novel adaptive multi-graph fusion based unsupervised feature selection model (GFFS). The proposed model is free of graph selection and can combine the complementary information of different graphs. Experiments on benchmark datasets show that GFFS outperforms the state-of-the-art unsupervised feature selection algorithms.