Reviewerly/exHarmony huggingface dataset:
A Test Method for Instructional Software of Evaluation and Exercise Based on Mobile Platform

Abstract The research is based on the characteristics of instructional software of evaluation and exercise, taking a small examination system for driving license as an example, this study is devoted to exploring a feasible test method for instructional software of evaluation and exercise based on mobile platform. The test method consists of four testing phases of unit and integration testing, functionality testing, comprehensive testing and teaching evaluation which existed research of functionality testing for modern Android mobile Apps is fully used for reference. Furthermore, further work is put forward. Firstly, a special defect management database for bug definition and analysis needs to be established. Secondly, major test points in comprehensive testing phase should be converted to user stories in modern agile testing environment for software exploratory test methods positively.
Age-Related Features From Face Image For Hierarchical Age Estimation

Assigning studied face image to the related age is a challenging task due to the different attributes of aging patterns along different age stages. Different periods of human age are combined by different detailed effects on the human face, which belong to the specific period more than the others. Yet, there are other types of aging continue over all age periods. This paper utilizes the general-age changes and specific-age changes to build a hierarchical technique for age estimation. Face smoothness is adopted for classifying the age into three main age classes, young, adult and senior. For each of them, inner age progression signs are adopted for accurate estimation. In childhood, changes face shape caused by dramatic changes in the size and shape of the head. In adulthood, an adaptive from of Local Binary Pattern (LBP) is proposed as candidate features since this period witness changes in texture nature. For senior faces, gradient features are adopted to describe face lines, folds and wrinkles.  As such features are produced in huge number, Feature Selection Method (FSM) is used to choose efficient features and ignore weak ones. After selecting the best possible set of features, Support Vector Machine (SVM) classifier is used to estimate related ages. Regarding State of Art in age estimation, this paper yielded encouraging results which yielded better results than other published papers in age estimation.
An Integrated PCA-DAEGCN Model for Movie Recommendation in the Social Internet of Things

With the development of the Social Internet of Things (SIoT) and mobile technologies in recent years, movie recommendation systems have become popular in online movie recommendation that users may like to watch based on their historical movie viewing data monitored by the SIoT. This technology can bring considerable profits to online movie providers and has attracted the attention of a large number of related scholars. However, previous movie recommendation models based on autoencoders have insufficient model learning ability due to their defective features. Due to the errors in users’ operation, the user’s movie rating data will have some errors. The previous models cannot deal with this corrupted information, which leads to the degradation of their generalization performance. Presently, graph convolutional networks have made full progress in many fields, and they can outperform traditional methods. Therefore, in this work, we introduce a principal component analysis and denoising autoencoder integrated graph convolutional networks (PCA-DAEGCNs) for movie recommendation in the SIoT. The PCA-DAEGCN model uses the network structure of the graph autoencoder to obtain effective hidden features and, subsequently, uses denoising autoencoders to handle small changes in the feedback information. Finally, the captured hidden features of users and movies are used to derive the finally predicted scores. Comprehensive experiments show that the proposed PCA-DAEGCN is able to obtain far better efficiency than many comparative models.
Neural Paraphrase Generation with Multi-domain Corpus

Automatic paraphrase generation is an important task for natural language processing. However, progress in paraphrase generation has been hindered for a long time by the lack of large monolingual parallel corpora. We can alleviate the data shortage by effectively using multi-domain corpus. In this paper, we propose a novel model to exploit information from other source domains (out-of-domains) which benefits our target domain (in-domain). In our method, we maintain a private encoder and a private decoder for each domain which are used to model domain-specific information. In the meantime, we introduce a shared encoder and a shared decoder shared by all domains which only contain domain-independent information. Besides, we add a domain discriminator to the shared encoder to reinforce the ability to capture common features of shared encoder by adversarial training. Experimental results show that our method not only perform well in traditional domain adaptation tasks but also improve performance in all domains together. Moreover, we show that the shared layer learned by our proposed model can be regarded as an off-the-shelf layer and can be easily adapted to new domains.
InterAdam: Interpolating Dull Intervention to Adaptive Gradient Method

Most state-of-the-art first-order optimization methods, such as AMSGrad, SWATS, and AdaBound, adapt constant learning rates at the final training stage to achieve fast convergence. However, the generalizability of these methods can hardly outperform that of Adam. In this paper, we first demonstrate that these methods will encounter difficulty to escape from the neighborhood of the saddle points during the optimization process due to the SGD-like strategy, thereby resulting in generalizability problems. Our further analysis suggests that this issue can be addressed by a weaker constraint on the learning rates. Based on these findings, we proposed Inter Adam, which is a new variant of Adam with non-constant learning rates at the final training stage. Experimental results show that InterAdam outperforms SWATS-like methods, especially in escaping the saddle points and alleviating overfitting. Meanwhile, Inter Adam can maintain better convergence and robustness campared to Adam.
Incorporating Decision-level Reconstruction Quality in Adversarial Autoencoders for Anomaly Detection

Anomaly detection, also known as one-class classification, is a challenging problem due to the absence of abnormal data for training. One of the promising approaches for image anomaly detection is to employ adversarial autoencoders for anomaly measurement by computing the pixel-level reconstruction errors. However, such pixel-level anomaly measure is very sensitive to individual pixels with huge reconstruction difference, resulting in large errors even on normal images. In this paper, we propose a novel anomaly detection method by combining decision-level reconstruction quality with pixel-level reconstruction error. In the proposed method, an adversarial autoencoder is trained to not only measure pixel-level reconstruction error, but also select typical and atypical normal samples from the train set. These selected samples are used to generate positive samples with good reconstruction quality and pseudo negative samples with poor quality respectively for training a classifier which measures the decision-level reconstruction quality of input samples. Experiments on public benchmark datasets show that our method achieves better results by incorporating decision-level reconstruction quality with pixel-level reconstruction error for anomaly detection, and outperforms a wide range of existing methods.
Object Localization with Attribute Preference Based on Top-Down Attention

We propose a weakly-supervised approach for object localization based on top-down attention which is able to consider both attributes and object classes as attentional cues. This enables to not only search for objects but additionally for objects with specific attributes such as colors or shapes. Our approach consists of two streams: an attribute stream and an object stream. By tracing backward through these two streams and localizing activated neurons in hidden layers, we generate two top-down attention maps, one for attributes and one for objects. Fusing these maps generates a joint attention map, which highlights regions with a specific attribute and object. We show experimentally that our method can localize objects in cluttered images by only specifying their attributes, and that instances of the same class can be discriminated based on their attributes.
Skeleton-based Human Activity Analysis Using Deep Neural Networks with Adaptive Representation Transformation

Compared with RGB-D-based human action analysis, skeleton-based works reach higher robustness and better performance, which are widely applied in the real world. However, the diversity of action observation perspectives hinders the improvement of recognition accuracy. Most of the existing works solve this problem by increasing the amount of training data, which brings a huge computational cost and cannot improve the robustness of the models. This paper proposes an adaptive model to obtain high-performance representations to improve human action recognition accuracy. First, a skeleton representation transfer scheme is proposed to transform the input skeleton-based body model to the best perspective, in which all parameters can be adaptively learned. This is more robust and cost-effective than hand-crafted features. Next, a re-designed backbone is proposed to train the model with a small computational cost based on the 3D-CNN. In the training process, a data enhancement method is also introduced to enhance robustness. Finally, extensive experimental evaluations are conducted on two benchmarks. The results show that this deep model can effectively and adaptively obtain high-performance skeleton representation and its performance is better than other state-of-the-art methods.
Logic Theory of Text Contents

By the logic theory of the text's contents it is meant in the paper the search for a sufficient collection of predicates allowing to make only those inferences from the text that are anticipated by author. The collection of texts of a regulatory nature are taken as the bases for our research. The main our purpose was to discover the conditionality constructions that express the causality in the most general sense of the word. The most obvious means to format such constructions are the cause-effect conjunctions. However, it was found that a similar role may be played with some other parts of speech also providing the conditionality effect. In addition to the mentioned tools we proposed and studied another approach based on the syntactical structure analyses of the text in relation to its communicative structure. In result a number of algorithms had been built to define the main topic of a sentence and its predications.
THE CONSTRUCTION ACTIVITY AS A METHOD OF POLYTECHNIC AND SCIENCE LEARNING

The research orientation of the Department of technics, Faculty of Education, University of Hradec Kralove focuses on finding new ways for development of polytechnic and science education. The research is motivated by the fact that children’s interest in technical and science subjects is decreasing in recent years in the Czech Republic. The research aims are to prove the applicability and effectiveness of multidisciplinary methods in teaching of technical and science subjects. The research observed the effect of the use of construction activity in teaching of physics in lower secondary school. The results of research confirmed that working and construction activity can develop technical and science thinking of children. Keywords: polytechnic education, science education, lower secondary school, construction activity, construction kit.
Improving Security in SCADA Systems through Model-checking with TLA+

In today’s world, Supervisory Control and Data Acquisition (SCADA) networks have many critical tasks, including managing infrastructure such as power, water, and sewage systems, and controlling automated manufacturing and transportation systems. Securing these systems is crucial. Here we describe a project to design security into an example system using formal specifications. Our example system is a component in a cybersecurity testbed at the University of Cincinnati, which was described in previous work. We also show how a design flaw can be discovered and corrected early in the system development process.
Axiomatic Software Engineering

Abstract Axiomatic Design and Complexity Theory have primarily focused on refining the process of an abstracted need into a physical solution even under challenging conditions. The concepts have high applicability as guidelines for non-physical design, such as those found in programming and its applied field of software engineering. Software engineering came out of the software crisis of the 1960s: its goal is to deliver safe and secure software that meets its specifications on time and within its budget. The product of software engineering is a program. The program is written in a programming language that looked suitable for the problem at hand. For some time, object-oriented technology (OOT) looked popular, because it promised to address information hiding, composability, and extensibility of programs. In hindsight, OOT failed to deliver on its promises. We analyze how Axiomatic Design leads to solutions that are proposed by current programming technologies. The authors believe that different paradigms such as cohesion also need to be included.
Energy-saving CNN with Clustering Channel Pruning

Channel pruning has proven to be efficient compared to fine-grained pruning for the reason that it can achieve high compression rate and low computational complexity simultaneously without requiring additional hardware and software support for convolutional neural networks (CNNs). Most of the existing works rarely take correlation of filters into account which leads to the low pruning rates of channels and operations and large accuracy loss. To address this problem, we propose two novel channel pruning methods (CCP and SCRP) based on spectral clustering, which can efficiently find the correlation between filters and compress convolution neural network without obvious accuracy loss. In CCP, we first use spectral clustering algorithm to conduct unsupervised clustering analysis on input filters. Then the reserved channels are reconstructed by calculating the intra-category average value after clustering, which minimizes the loss between the pruned model and the pre-trained model. In order to reduce the loss of information due to pruning, we do sensitivity experiments with fine-tuning to determine the reasonable pruning rate of each layer. The sensitivity test with fine-tuning can better explain the effect of pruning, rather than directly observing the loss caused by pruning. Besides, we improve our CCP for higher sparse rate: SCRP. We add a spectral clustering loss which indeed helps to achieve higher accuracy and pruning rate in FLOPs to the classification loss. For example, our pruned VGGNet-16 achieves 93.66% accuracy with 84.6% reduction in FLOPs on CIFAR-10 and 73.34% accuracy with 76.8% reduction in FLOPs on CIFAR-100 (SCRP). Our code is avaliable at: https://github.com//tnn2018/Clustering-Channel-Pruning.
Target detection based on Parameter Derived Network

A new target detection network-Parameter Derivative Network is proposed for the first time in this paper, which is based on an anchor box-based detector. The parameter derivation module is the core of the network, and Its essence is to make use of image features to generate specific convolution kernels to extract the features of target objects. It solves the problem of reduced accuracy of small target objects in image detection. Compared with traditional convolution, this method can effectively obtain the context information of the target object and improve the detection accuracy. After the image features are extracted by the backbone network, they are sent to the parameter derivation module to perform a feature screening, which can effectively reduce the ratio between the foreground and the background in the feature map. It ensures the foreground context information and suppresses the background information at the same times. It is different from the traditional network feature extraction method. Firstly, the convolution kernel of the parameter derivation module is generated after the image is convoluted, and the convolution kernel is formed by processing the same channel, and then the feature map is convoluted. It can effectively suppress background information and reduce the imbalance of front background information. Secondly, in the parameter derivation module, the influence of average pooling and maximum pooling on image classification is verified in this paper. Compared with the maximum pooling, the average pooling has more local context information, which is more helpful for the detection and recognition accuracy of small target objects. Finally, the experimental results show that the parameter derivation module proposed in this paper is particularly effective in the detection and positioning of targets of different sizes without increasing any overhead, especially for small targets. At the same time, for targets of other sizes, this structure can also improve well and the maximum increase in detection performance is 0.36%.
Multi-Label Learning with Missing Features

Multi-label learning deals with the problem that each example is associated with multiple class labels simultaneously. Existing multi-label learning approaches all assume the feature space is completed and construct classification models using examples with sufficient feature information. However, in real-world applications, it is difficult to get a fully completed feature matrix, that is, only partial feature information of each example can be obtained. In this paper, we formalize this problem as multi-label learning with missing features. To tackle this problem, we learn a feature correlation matrix and apply it to obtain a new supplementary feature matrix, which has richer feature information than the original missing feature matrix. After that, to improve the performance of multi-label classification, we constrain feature correlation on coefficient matrix by assuming that if two features are strongly correlated, the similarity between their corresponding parameter vector will be large. Besides, we also constrain label correlation on output of labels to capture more sufficient relationships between different labels. Extensive experiments show a competitive performance of our method against other state-of the-art multi-label learning approaches.
Multi-scale Feature Relation Modeling for Facial Expression Restoration

Facial expression analysis in the wild is easily vulnerable to the quality of facial images, such as low resolution or occlusion. Existing facial image restoration studies have mostly failed to take full advantage of facial expression prior information, which leads to loss of information related to facial expression in the restoration results. In this paper, we propose a multi-scale feature relation modeling GAN (MFRM-GAN) for facial expression restoration by exploring the multi-scale property and relationship of facial action units. Based on the GAN model, the MFRM-GAN integrates the graph convolution network (GCN) for feature relation modeling and feature pyramid network (FPN) for multi-scale feature extraction. Extensive qualitative and quantitative experiments on BP4D and DISFA datasets demonstrate that our proposed MFRM-GAN (i) can conduct facial expression in-painting and facial image super-resolution jointly, (ii) can recover better facial expression details comparing with state-of-the-art method in both visual effect and AU detection task.
Generalized Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks

Big-data related applications frequently concern how to analyze large-scale undirected weighted network effectively. Such a network can be quantized into a Symmetric, High-Dimensional and Sparse (SHiDS) matrix, owing to its sparsity and symmetry. For considering these characteristics of an SHiDS matrix with care, a symmetric non-negative latent factor (SNLF) model is proposed. However, representation learning ability of an SNLF model is limited owing to its commonly-adopted learning objective, i.e., Euclidean distance. For addressing this issue, this study proposes a generalized symmetric nonnegative latent factor analysis (GSNL) model. Its main idea is two-fold: a) adopting <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha-\beta$</tex> -divergence to generalize SNLF's learning objective for achieving accurate representation ability of an SHiDS matrix; and b) utilizing a self-adaptive scheme on all involved hyperparameters brought by the resultant model for strong practicability. Empirical studies on four SHiDS matrices demonstrate that a GSNL model outperforms its peers regarding accuracy gain and computational efficiency.
Job Scheduling in Cloud Computing Based on DGPSO

In a Cloud Computing environment, dynamic and uncertain nature makes task scheduling problems more complex. It states the need for efficient task scheduling designed and implementation as a primary requirement for achieving QoS. A proper resource utilization enables maximum profit for the Cloud providers. The best scheduling algorithm does not consider the task set collected from the users, but it considers the resources provided by providers for operating the tasks. In this paper, we propose a Dynamic Group of Pair Scheduling and Optimization (DGPSO) algorithm. The proposed DGPSO is the performance-enhancing of AWSQP by using VM pair implementation and partition-based priority system into three levels. These three levels in the priority system such as low, medium, and high. According to the task size, the VM pairing is done. For this, the VM's parameters include communication time, system capacity, memory size, and processing speed. On the dataset, the task sizes are examined and separated according to the priority levels. On which the high priority comprises video files, the audio files under medium-level priority, and the remaining text documents, ppts, etc. included in under low priority levels. Based on the proposed task scheduling mechanism, an experiment is conducted on the aspects of computation cost, communication cost, execution time, CPU utilization, and bandwidth. The obtained results prove its achieved performance is far better than the existing approaches.
Efficient Resolution-preserving Network for Real-time Semantic Segmentation

Inference process of the semantic segmentation is a dense predictive task who requires heavy computational cost. However, the available computational resource is quite limited in many practical scenarios. Therefore, real-time semantic segmentation balancing both speed and accuracy is becoming particularly significant. In this paper, we propose a resolution-preserving framework for real-time semantic segmentation. A lightweight backbone network is employed to obtain high-level semantic features, which will be fully reused. A resolution-preserving module is designed to extract low-level spatial details while retaining high-resolution. We also introduce a feature refinement module to improve the performance. Experiments on Cityscapes dataset demonstrate that the proposed ERPNet achieves a balance between speed and accuracy. Especially, we obtain the results of 73.6% mIoU on the Cityscapes test set with the speed of 109.5 FPS on single GeForce GTX 2080 card.