Reviewerly/exHarmony huggingface dataset:
Learning Graph Matching Substitution Weights based on a Linear Regression

Attributed graphs are structures that are useful to represent objects through the information of their local parts and their relations. Each characteristic in the local parts is represented by different attributes on the nodes. In this context, the comparison between structured objects is performed through a distance between attributed graphs. If we want to correctly tune the distance and the node correspondence between graphs, we have to add some weights on the node attributes to gauge the importance of each local characteristic. In this paper, we present a method to learn the weights on each node attribute. It is based on building an embedded space and imposing the weights we want to learn to be the constants of the hyperplane deduced by a linear regression applied on a cloud of points. These points represent the node-to-node mappings.
Semantic feature augmentation for fine-grained visual categorization with few-sample training

Small data challenges have emerged in many learning problems, since the success of deep neural networks often relies on the availability of a huge number of labeled data that is expensive to collect. We explore a highly challenging task, few-sample training, which uses a small number of labeled images of each category and corresponding textual descriptions to train a model for fine-grained visual categorization. In order to tackle overfitting caused by small data, in this paper, we propose two novel feature augmentation approaches, Semantic Gate Feature Augmentation (SGFA) and Semantic Boundary Feature Augmentation (SBFA). Instead of generating a new image instance, we propose to directly synthesize instance features by leveraging semantic information, and its main novelties are: (1) The SGFA method is proposed to reduce the overfitting of small data by adding random noise to different regions of the image's feature maps through a gating mechanism. (2) The SBFA approach is proposed to optimize the decision boundary of the classifier. Technically, the decision boundary of the image feature is estimated through the assistance of semantic information, and then feature augmentation is performed by sampling in this region. Experiments in fine-grained visual categorization benchmark demonstrate that our proposed approach can significantly improve the categorization performance.
Multi-Annotator Probabilistic Active Learning

Classifiers require annotations of instances, i.e., class labels, for training. An annotation process is often costly due to its manual execution through human annotators. Active learning (AL) aims at reducing the annotation costs by selecting instances from which the classifier is expected to learn the most. Many AL strategies assume the availability of a single omniscient annotator. In this article, we overcome this limitation by considering multiple error-prone annotators. We propose the novel AL strategy multi-annotator probabilistic active learning (MaPAL). Due to the nature of learning with error-prone annotators, it must not only select instances but annotators, too. MaPAL builds on a decision-theoretic framework and selects instance-annotator pairs maximizing the classifier's expected performance. Experiments on a variety of data sets demonstrate MaPAL's superior performance compared to five related AL strategies.
Unbiased FIR Filtering of Uncertain Systems with Randomly Binary-Delayed and Missing Data

In this work, the problem of unbiased finite impulse response (UFIR) state estimation is solved to systems with randomly binary-delayed and missing data. The phenomenon of one-step delayed data is characterized by the Bernoulli distribution with known delay probability. To avoid congestion, only first received data are processed in the transmission channel. A binary distribution is used to describe an intermittent packet loss. When the data lost occurs, a prediction is involved to bridge a gap caused by a lost point. The system observation is transformed into a linear function by combining both errors in the sensor measurement and the transmission channels. Under such conditions, the UFIR filter is derived. Numerical simulation is provided for a tracking network system employing GPS navigation information. A high efficiency of the UFIR filter derived is demonstrated experimentally.
Grid Architecture and Metadata Model of Computer Education Resources Based on Network Teaching Mode

Abstract With the continuous progress of our modern science and information technology, network computer distance teaching has gradually become an indispensable part of our school education. In view of the major shortcomings of the current network physical education teaching system, such as the isolation of information resources and the difficulty of updating, the comprehensive research and application of network education information grid management technology and education metadata processing model are established. In the research and analysis, this paper selects 235 network teaching model involved in the sample survey, and through the survey of network teaching mode and traditional teaching mode multimedia, non-linear, independent comparative analysis, from the comparative results show that the network teaching mode is superior to the traditional teaching mode, not only can promote the development of computer teaching mode, but also can improve computer teaching effect. Through the investigation, it is found that both teachers and students have more than 85% recognition of the network teaching mode, which provides a guarantee for the network teaching mode.
Exploring Internal Correlations in Timing Features of Keystroke Dynamics at Word Boundaries and Their Usage for Authentication and Identification

The internal correlation of timing features inside word boundaries of free text sessions are studied, together with the classification performance in both authentication and identification tasks, using three publicly available datasets. Increasing distance between keystrokes was not found to be predictive of decreased correlation, even when individual users are considered and not the dataset as a whole. Considering five or more letter words, classification performance ranged 75%–90%+ for authentication and 60%–90%+ for identification tasks, with Random Forest classifier performing the best, and the simple k-nearest neighbours (k-NN) the worst. All the conclusions and observations generalised to the three datasets and its subsets, and particularly to both Spanish and English languages.
Cell-Cluster Network-Assisted Adaptive Streaming Media optimization over Wireless Network

Mobile streaming media on subways or trains has become a typical and popular scenario in daily life. However, because of the unstable wireless network channel and fluctuations caused by movement, the constant bit rate streaming media brings low-quality service. The adaptive streaming media seems to solve the problems, but the premise is that clients can accurately estimate the network bandwidth. When the client moves at high speed, the irregular fluctuations of network channel make accurate estimation difficult, leading to the low efficiency of the adaptive streaming media. When clients move on subways or trains, the location, speed and the cell load can be known by the wireless network side. With this information, the available network bandwidth can be accurately calculated in advance. Thus, we propose an architecture called Cell-Cluster Network-Assisted Adaptive Media Streaming (CCNA) to optimize the vehicular streaming media over wireless network. We build a cell cluster to introduce the assistance of the wireless network side and estimate the network bandwidth more accurately. The architecture can help clients select the video segment with the highest QoE under the constraint of limited network bandwidth and make the best use of the network bandwidth resources. The experimental results show that compared with traditional adaptive streaming media transmission modes, our architecture improves the average playback bit rate by 32% and the utilization of network bandwidth by 34% at the same time with the least video stalls.
Multi-view Weighted Kernel Fuzzy Clustering Algorithm Based on the Collaboration of Visible and Hidden Views

With the development of media technology, data types that cluster analysis needs to face become more and more complicated. One of the more typical problems is the clustering of multi-view data sets. Existing clustering methods are difficult to handle such data well. To remedy this deficiency, a multi-view weighted kernel fuzzy clustering method with collaborative evident and concealed views (MV-Co-KFCM) is put forward. To begin with, the hidden shared information is extracted from several different views of the data set by means of non-negative matrix factorization, then applied to this iterative process of clustering. This not only takes advantage of the difference information in distinct views, but also utilizes the consistency knowledge in distinct views. This pre-processing algorithm of extracting hidden information from multiple views (EHI-MV) is obtained. Furthermore, in order to coordinate different views during the iteration, a weight is distributed. In addition, so as to regulate the weight adaptively, shannon entropy regularization term is also introduced. Entropy can be maximized as far as possible by minimizing the objective function, thus MV-Co-KFCM algorithm is proposed. Facing 5 multi-view databases and comparing with 6 current leading algorithms, it is found that the algorithm which we put forward is more excellent as for 5 clustering validity indexes.
Generation of Typical Meteorological Year Data for a City in South-Western Nigeria

Abstract Weather data is essential in determination of the performance of energy systems such as HVAC and solar energy systems. Weather data therefore, can be employed in designing more energy-efficient systems. This work focuses on the generation of a Typical Meteorological Year (TMY) Data for Abeokuta (7°7′1″ N, 3°22′13″E), a state in South-Western Nigeria by the use of a modified Sandia method. It makes use of seven weather parameters obtained over 29 years (1984 - 2012) in the construction of the TMY. Sandia method is highly affected by solar radiation even if its weight is reduced by half. The weights of other parameters such as temperature, wind, and relative humidity have less impact on the selection of TMY. The results of this study show that the weather pattern of Abeokuta follows the general diurnal variation of dry/harmattan season and wet/rainy season, typical to Nigeria. In this work also, the effect of general rise in temperature and solar radiation, indicative of global warming, is highlighted. Higher solar radiation values are majorly typical of a low cloud cover, and high clearness index. Suggestions were also made as to the preferable type of sustainable energy system to make use of in this location, as well as in the development of energy systems and data collection in Nigeria. The relevance of this work is that it would help to strengthen building energy standards, understand the local climate of Abeokuta and hence facilitate building energy system performance.
MABEL: An AI-Powered Mammographic Breast Lesion Diagnostic System

Mammography plays an essential role in early detection of breast cancer. Interpreting mammography is a professional task that requires well-trained radiologists with longtime clinical experience. In this paper, we present MABEL, an artificial intelligence-powered system to assist doctors for breast cancer screening and diagnosis in mammograms, in order to reduce their workloads and accelerate the diagnostic process. Our system smoothly integrates our upgraded lesion identification models, provides a doctor-oriented annotation tool and web interface, and can communicate with Picture Archiving and Communication System (PACS) in our collaborative hospital. Our lesion identification performance is evaluated on both public and in-house datasets, in which mass detection has achieved state-of-the-art accuracy in the single-view manner. The overall high satisfaction from doctors of our system is also demonstrated.
Multi-label Image Recognition with Asymmetric Co- Dependency Graphs

Multi-label image recognition is a practical and challenging task. Modeling co-occurrence dependencies between categories is the key to improve performance. Existing methods use conditional probability to measure co-occurrence dependencies, and represent co-occurrence dependencies among all categories in the form of directed graph. Then the Graph Convolution Network (GCN) is applied on the directed graph to transfer dependent category features along the edge direction. However, the occurrence frequencies of different categories are different. Accordingly, the conditional probabilities between a pair of common and rare categories are highly asymmetric so that most rare categories have no in-edges to receive transferred knowledges from other categories. Therefore, this paper investigates the effects of edge direction between two co-occurred categories on the recognition performance, then proposes a model to work on a pair of directed graphs to learn a comprehensive representation of co-occurrence dependency. Extensive experiments on public benchmarks show that our method can achieve better performance than baseline models. On some multi-label image recognition datasets with strong co-occurrence dependencies, our method can improve the mAP by 4%.
Progressive Mobile Web Application Subresource Tampering During Penetration Testing

Since the boost of mobile devices popularity, both operating systems and mobile applications have become more complex which in turn transfers into greater vulnerability to hackers' attacks. Penetration testing is aimed at detection of security gaps in mobile systems. On the other hand, Progressive Web uses Web browser API to enhance the range of functionalities to cross-platform. Thus, this paper focuses on mobile Web application penetration tests of Progressive Web. First, some new functionalities were evaluated for vulnerabilities, then an in-depth analysis of the Web push functionalities was carried out. External resources, which deliver Web push services, were explored for the libraries security. Then, Man-in-the-Middle attack on Subresource Integrity Mechanism (SIM) was analyzed to exploit the vulnerabilities detected.
FlatPack

Efficient use of automated planning and scheduling has been proposed in the Industry 4.0 context involving multiple robotic agents. Temporal planners are typically employed with durative actions included for concurrent plan scheduling and execution. However, the planned durations are either mean values or within closed intervals, which can be easily violated during execution due to uncontrollable events or agent deviations. These variations trigger violation of safety properties in multi-agent scenarios, mandating further verification and control during execution. In this paper, we propose Flat-Pack, a framework to generate flexible temporal plans that can ensure satisfaction of specified properties during execution. Temporal plans are translated to timed-game automata models for verification in Uppaal-Tiga prior to dispatch. Further, using the strategy synthesized by Uppaal-Tiga, controlled plan executions may be performed that are resilient to temporal deviations. The optimal number of controllable transitions are further derived with tradeoffs against control complexity and end-to-end plan execution timelines. These aspects are demonstrated over an Industry 4.0 warehouse use case involving coordination of multiple robots.
Correlation Filter and Deep Siamese Network Hybrid Algorithm for Visual Object Tracking

In practical applications, the tracking performance and the computational efficiency play an important role in object tracking. According to the significant computational efficiency of correlation filter tracking algorithms and the excellent tracking performance of deep learning-based tracking algorithms, in this paper we proposed Correlation Filter and deep Siamese Network hybrid algorithm for object tracking (CF\_SIAM). A video grouping object tracking strategy is designed, which uses the correlation filtering tracking algorithm within the group, and applies the tracking algorithm with deep Siamese network among groups to revise the tracking target. The experimental results show that our algorithm can significantly improve tracking performance. On the OTB100 dataset, the AUC success rate of our CF\_SIAM algorithm outperforms KCF and SiameseFC by 11.5% and 3.2% respectively.
Distances to compositional equilibrium

Geochemical samples can be restricted to be on a locus in the sample space. When this occurs there is a chemical equilibrium of some kind. Typical examples are chemical equilibrium regulated by the mass action law or stoichiometry given by the structure of crystals. These equilibria can correspond to linear restrictions in the simplex, for which the restriction is on a linear manifold (hyperplane) or, more generally, restriction is on a general manifold (warped hypersurface). Exact equilibrium is seldom observed in geochemistry and so the focus is shifted to measuring distances or deviations from equilibrium. A linear equilibrium locus is defined by a constant logcontrast. When this logcontrast is evaluated on a sample, the difference between the equilibrium constant and the value obtained for each sample is a deviation from equilibrium. It is—in absolute values—the Aitchison distance to equilibrium. The non-linear case is more involved. Here, a linearization technique is proposed. It consists of adding terms to the composition so that the deviation or distance to the equilibrium locus reduces to the linear case. These techniques are illustrated with some examples: chemical reactions producing bicarbonate ions in water (linear), the weathering of microcline and the formation of kaolinite (non-linear), both concerning stream water composition at a European scale. Stoichiometric behaviour of olivines (non-linear) is also analyzed. Finally, a discussion on methods for discovering unknown equilibria in a sample is presented.
Deep texture cartoonization via unsupervised appearance regularization

Texture plays an important role in cartoon images to represent materials of objects and enrich visual attractiveness. However, manually crafting a cartoon texture is not easy, so amateurs usually directly use cartoon textures downloaded from the Internet. Unfortunately, Internet resources are quite limited and often patented, which restrict the users from generating visually pleasant and personalized cartoon textures. In this paper, we propose a deep learning based method to generate cartoon textures from natural textures. Different from the existing photo cartoonization methods that only aim to generate cartoonic images, the key to our method is to generate cartoon textures that are both cartoonic and regular. To achieve this goal, we propose a regularization module to generate a regular natural texture with similar appearance as the input, and a cartoonization module to cartoffonize the regularized natural texture into a regular cartoon texture. Our method successfully produces cartoonic and regular textures from various natural textures.
Research on Movie Recommendation Algorithm Based on Stack De-noising Auto-encoder

Abstract Focused on the problems that the randomness of the noise-adding operation in the de-noising auto-encoder (DAE), and the data matrix does not consider the impact of trusted users on the deep preferences of target users, this paper proposes a recommendation algorithm based on stack de-noising auto-encoder (SDAE) which integrates the preferences of trusted users. Firstly, the score vector is used as the input of the auto-encoder, and the mask vector is designed to train the potential preference of the target user. Secondly, the deep preference of the target user and the trusted user is obtained by the weighted fusion of the features of the two hidden layers of the auto-encoder. Thirdly, in order to reduce the impact of noise on the prediction accuracy, the cascaded auto-encoder model is constructed and trained according to the greedy training method layer by layer training. Finally, SDAE model is compared with other models on different data sets. The experimental results show that SDAE model has better recommendation performance.
Fixations based personal target objects segmentation

With the development of the eye-tracking technique, the fixation becomes an emergent interactive mode in many human-computer interaction study field. For a personal target objects segmentation task, although the fixation can be taken as a novel and more convenient interactive input, it induces a heavy ambiguity problem of the input's indication so that the segmentation quality is severely degraded. In this paper, to address this challenge, we develop an "extraction-to-fusion" strategy based iterative lightweight neural network, whose input is composed by an original image, a fixation map and a position map. Our neural network consists of two main parts: The first extraction part is a concise interlaced structure of standard convolution layers and progressively higher dilated convolution layers to better extract and integrate local and global features of target objects. The second fusion part is a convolutional long short-term memory component to refine the extracted features and store them. Depending on the iteration framework, current extracted features are refined by fusing them with stored features extracted in the previous iterations, which is a feature transmission mechanism in our neural network. Then, current improved segmentation result is generated to further adjust the fixation map and the position map in the next iteration. Thus, the ambiguity problem induced by the fixations can be alleviated. Experiments demonstrate better segmentation performance of our method and effectiveness of each part in our model.
Resistance of a novel denture identification system to various assault: An in-vitro study

Aim: Denture marking has been advocated and recommended by many forensic organizations. The prosthodontists can play a significant role in the identification of geriatric population by adopting denture marking as a routine procedure. These stickers are easily readable and can be connected to smartphone devices without the need of specific equipment, store information in variety of ways, and cost-effective.The purpose of this study is to evaluate NFC stickers against physical insult; acid, base, and heat.Settings and Design: In-vitro evaluation study.Materials and Methods: Denture bases were fabricated, using chemical and heat-cured acrylic resin. NFC stickers were incorporated using postfabrication inclusion method for chemically cured resin base and prefabrication inclusion method for heat-cured acrylic resin base. These bases were subjected to acid, alkali, and thermal insults.Statistical Analysis Used: Descriptive statistics.Results: Both pre and postfabrication inclusion NFC stickers were capable of withstanding various chemical and thermal assaults.Conclusion: NFC stickers could be used as an adjunct to radio frequency identification (RFID) tags for denture identification. NFC stickers appear to be easy to use and more cost-effective than RFID tags.