Reviewerly/exHarmony huggingface dataset:
Hierarchical deep transfer learning for fine-grained categorization on micro datasets

Fine-grained categorization is challenging due to its small inter-class and large intra-class variance. Moreover, requiring domain expertise makes fine-grained labelled data much more expensive to acquire. Existing models predominantly require extra information such as bounding box and part annotation in addition to the image category labels, which involves heavy manual labor. In this paper, we propose a novel hierarchical deep transfer learning model, based on a compression convolutional neural network. Our model transfers the learned image representation from large-scale labelled fine-grained datasets to micro fine-grained datasets, which avoids using expensive annotations and realizes visual categorization task effectively. Firstly, we introduce a cohesion domain to measure correlation degree between source domain and target domain. Secondly, the source-domain convolutional neural network is adjusted according to its metrical feedback, in order to select task-specific features that are suitable for transferring to the target domain. Finally, we make most of perspective-class labels, which are inherent attributes of fine-grained data for multi-task learning and learn all the attributes through joint learning to extract more discriminative representations. The proposed model not only economizes training time effectively and achieves high categorization accuracy, but also verifies that the inter-domain feature transition can accelerate learning and optimization.
Multi-person Head Segmentation in Low Resolution Crowd Scenes Using Convolutional Encoder-Decoder Framework

Person head detection in crowded scenes becomes a challenging task if facial features are absent, resolution is low and viewing angles are unfavorable. Motion and out-of-focus blur along with headwear of varying shapes exacerbate this problem. Therefore, existing head/face detection algorithms exhibit high failure rates. We propose a multi-person head segmentation algorithm in crowded environments using a convolutional encoder-decoder network which is trained using head probability heatmaps. The network learns to assign high probability to head pixels and low probability to non-head pixels in an input image. The image is first down sampled in encoder blocks and then up sampled in decoder blocks to capture multiresolution information. The information loss due to down sampling is compensated by using copy links which directly copy data from encoder blocks to the decoder blocks. All heads and faces in an image patch are simultaneously detected contrasting to the traditional sliding window based detectors. Compared to the existing state-of-the-art methods, the proposed algorithm has demonstrated excellent performance on a challenging spectator crowd dataset.
Edge Exclusion Tests for Improper Complex Gaussian Graphical Model Selection

We consider the problem of inferring the conditional independence graph (CIG) of improper complex-valued Gaussian vectors. A p-variate improper complex Gaussian graphical model associated with an undirected graph with p vertices is defined as the family of improper complex Gaussian distributions that obey the conditional independence restrictions implied by the edge set of the graph. For real random vectors, considerable body of paper exists where one first tests for exclusion of each edge from the saturated model, and then infers the CIG. Prior work on proper complex Gaussian graphical models is sparse, while that on improper complex Gaussian graphical models is non-existent. In this paper, we propose and analyze a generalized likelihood ratio test (GLRT) based edge exclusion test statistic for improper complex Gaussian graphical models. The null distribution of the test statistic is specified explicitly to allow analytical calculation of the test threshold. An alternative computationally fast version of the GLRT statistic is also derived. Simulation examples are presented to illustrate the proposed statistic.
SDVisor: Secure Debug Enclave with Hypervisor

Cloud computing is developing rapidly nowadays. The cloud computing infrastructure requires cloud users' complete confidence in cloud administrators. The importance of trust issue increases when come into joint cloud computing situation because substantial trust is necessary not only between customers and providers but also between different providers. Although cloud users may expect to keep their code and data to be secret, it is indeed difficult to promise. Various trusted executing environments are widely used to provide security guarantees to cloud computing. Among these Intel's Security Guard Extension (SGX) is now a common choice to protect sensitive code and data. SGX provide hardware supports to protect memory inside enclaves. However, the production mode SGX enclaves with full protection available can not be launched without a production license from Intel, while the free non-production enclaves are under the risk of leaking sensitive data to malicious privileged parties. To address this problem, we introduce SDVisor, a system aiming to achieve the security guarantees of Intel's SGX on enclaves without availability of production license. SDVisor provides a lightweight hypervisor which extends the SGX's safeguard policy to ensure the confidentiality and integrity of non-production enclaves. The enclaves are then named secure debug enclaves. SDVisor is also compatible with normal production enclaves so that production mode SGX-enabled applications can be executed originally as well. SDVisor leverages hardware virtualization to protect these secure debug enclaves so it maintains good performance.
REK-SVM: a robust and efficient SVM algorithm based on K-medians clustering

Support vector machines (SVMs) have been widely used for binary classification. But large-scale training set will bring huge computation to the SVM. Researcher have proposed many techniques to improve the training efficiency of SVMs, and a typical class of improved SVMs is based on sparsely reducing training samples. To achieve this, clustering-based methods are most commonly used. However, clustering-based methods are ready to be disturbed by noise points. In order to solve this problem, this paper proposes a robust and efficient SVM algorithm based on K-Medians clustering (REK-SVM). Here, for each cluster, the cluster center takes the median value of each dimension attribute in the cluster, which can reduce the noise points. Especially, when the number of noise points distributed discretely is less than half of the total number of samples in the cluster, noise interference can be completely removed. The noise-free or noise-reduced subset data is used to train the SVM model. Experimental results show that our algorithm is fast and effective. For the processing of noise-containing classification data, its performance far exceeds SVM in terms of classification accuracy and efficiency. Compared to the K-SVM, they have the same computational complexity, but our algorithm is much higher than K-SVM in classification accuracy.
Bayesian evolutionary hypernetworks for interpretable learning from high-dimensional data

Higher-order representation is suitable for the complicated relationships among many factors. However, existing higher-order classification models have difficulties in learning from high-dimensional data due to their large combinatorial hypothesis spaces. The interpretability of models is also significant for causality analysis. Here we propose a Bayesian evolutionary method to learn a higher-order graphical model for high-dimensional data, called Bayesian evolutionary hypernetwork (BEHN). Our method represents the combinatorial feature space using a generalized graph, hypernetwork. A hypernetwork contains a large population of hyperedges encoding higher-order relationships among feature variables, and is optimized by an evolutionary algorithm formulated as sequential Bayesian sampling. This Bayesian evolutionary approach allows for probabilistic search through the higher-order feature space while satisfying soft constraints defined by the priors. We show that two information-theoretic and complexity-related priors are effective to balance model accuracy and parsimony. Also, BEHN provides interpretable representations to investigate feature interactions. Using two benchmarking and three real-world datasets we demonstrate that BEHN outperforms baseline classification models while tackling large-scale data of dimensionality up to O(104). We also analyze the stability and the scalability of the proposed method with respect to accuracy, computational cost, and the interpretability of the model structures.
Distributed Mining of Significant Frequent Colossal Closed Itemsets from Long Biological Dataset

Mining colossal itemsets have gained more attention in recent times. An extensive set of short and average sized itemsets do not confine complete and valuable information for decision making. But, the traditional itemset mining algorithms expend a gigantic measure of time in mining these little and average sized itemsets. Colossal itemsets are very significant for numerous applications including the field of bioinformatics and are influential during the decision making. The new mode of dataset known as long biological dataset was contributed by Bioinformatics. These datasets are high dimensional datasets, which are depicted by an expansive number of features (attributes) and a less number of rows (samples). Extracting huge amount of information and knowledge from high dimensional long biological dataset is a nontrivial task. The existing algorithms are computationally expensive and sequential in mining significant Frequent Colossal Closed itemsets (FCCI) from long biological dataset. Distributed computing is a good strategy to overcome the inefficiency of the existing sequential algorithm. The paper proposes a distributed computing approach for mining FCCI. The row enumerated mining search space is efficiently cut down by pruning strategy enclosed in Distributed Row Enumerated Frequent Colossal Closed Itemset Mining (DREFCCIM) algorithm. The proposed DREFCCIM algorithm is the first distributed algorithm to mine FCCI from long biological dataset. The experimental results demonstrate the efficient performance of the DREFCCIM algorithm in comparison to the current algorithms.
How the Multimodal Media in Augmented Reality Affects Museum Learning Experience

As AR is envisioned to become the leading yet paradigm-shifting user interface metaphor for situated computing, it is becoming essential to understand how users process and perceive AR and its enabling technologies since the effect of AR relied heavily on users' perception to integrate digital information with the real world. From the perspective of situated cognition and learning, this evaluation study investigated how the integrative multimodal representations in AR affects visitors' museum experiences. A genuine exhibit of citrus fruits in an experimental gallery was constructed using marker-based AR and video see-through displays. A between-subject experiment with 48 college students was conducted to evaluate the effects of integrative multimodal representations on users' flow experiences and interaction behaviors. Preliminary findings suggested that users perceived the integrative multimodal representations in AR as more engaging, compared to the static exhibit. Participants regarded themselves to have clearer goals of visit toward the AR exhibit, and they appreciated the immediate feedback provided by AR for them to confirm their actions.
Engaging Students in STEM Based Learning Through Media and Technology

Engaging students in physics learning is a challenge for teachers, since few students were interested in it. Therefore, STEM based learning that embedded scientific and engineering practice, was chosen to solve the problem. It has been developing in Indonesia since 2013 at secondary school level. The implementation evaluation showed that most of STEM based learning was conducted through media and technology. Therefore, these studies were analysed students response toward STEM based learning through a media and technology. The media and technology was invited into electricity learning. 92 students were involved as samples of this study that cane from two different classes. The analysis result showed that most of student enjoyed the lesson and eager to engage in other STEM based learning. Moreover, they agree that media and technology can help them in understanding the concept. It convinced by the students understanding improvement after the learning processes.
A Visual Web Service Composition System Based on Process Tree

From a service provider's perspective, the functionality provided by a single web service is relatively simple. It is necessary to combine the isolated single web services to form a combined web service which contains more complex features. This paper designs a visual drag-and-drop system and introduces an asynchronous mechanism based on message subscription, we can easily combine web services asynchronously or synchronously and publish them quickly.
Aspects of grammaticalization and reanalysis in the voice domain in the transition from Latin to early Italo-Romance

This article discusses some aspects of the grammaticalization and reanalysis of lexical verbs as passive auxiliaries and light verbs in the passage from Latin to (Italo-)Romance, focussing on (i) the diachronic relationship between auxiliarization and light verbs, (ii) the direction of the changes and (iii) the pertinacity to change of light verbs. The light verb uses of the verbs under investigation (COME, BECOME), both in Late Latin and in some early Italo-Romance vernaculars, exhibit a different type of decategorialization and desemanticization compared with auxiliaries, attested later than their auxiliary function.
Context-Based Rating Prediction using Collaborative Filtering and Linked Open Data

Linked Open Data (LOD) consists of various knowledgebases, such as DBpedia, Yago, and Freebase, and uses structured data as features to conceptualize a domain of interest. During last few years, many researchers have shown how LOD can be utilized in various applications, including recommender systems that are used to map items and users generally on the basis of interest and similarity parameters. However, contextual features play an important role to improve the effectiveness of the recommender systems. In this paper, we propose a contextual feature-based rating prediction and recommendation technique using item-based collaborative filtering and LOD. To this end, we have generated a RDF graph representing items and their contextual features, which help to determine context-based similar items for recommendation using graph matching techniques. In order to extract contextual features for item profiling, we have used LOD and two famous movie data sources, Rotten Tomatoes and IMDB. We also propose a rating prediction model to predict the rating of the non-rated items with the help of the RDF graph and item-based collaborative filtering. The proposed approach is evaluated using mean absolute error and root mean square error, and performs significantly better in comparison to some of the standard baseline methods.
Seal imprint verification via feature analysis and classifications

Verifying a seal imprint to authenticate the identity is a general and important task in financial industries. The seal imprint attached to a document is often used as a form of authenticity. Therefore, in financial applications, it is often necessary to recognize whether a seal is genuine or forged. In order to simplify the verification, we propose an effective method based on regression analysis to approximate the imprint borders, and use the geometric transformation to adjust the perspective of the detected imprint image. To align the detected image with the original image, the first step is to find four borders and cross each border to obtain four vertices of the rectangular imprints. The Ordinary Least Squares regression is then applied on the outer strong feature points, and the estimated regression line would be taken as the borders. The regression process is repeated several times to estimate border lines as precise as possible. Besides, considering the practical application in taking seal imprints by camera, the imprint is subject to geometric distortion, due to camera perspectives, comparing to the stored genuine imprint image. Therefore, the detected image is geometrically transformed to align its perspective with the genuine imprint. Finally, the SSIM and PSNR are calculated as the decision metrics to check whether the two indexes exceed the predetermined thresholds. Then the imprint is determined as a genuine or forged imprint. The experiments are conducted among various stamping imprints, and the results show the efficiency and effectiveness of the proposed identification approach.
Keystroke Dynamics using Auto Encoders

In the modern day and age, credential based authentication systems no longer provide the level of security that many organisations and their services require. The level of trust in passwords has plummeted in recent years, with waves of cyber attacks predicated on compromised and stolen credentials. This method of authentication is also heavily reliant on the individual user's choice of password. There is the potential to build levels of security on top of credential based authentication systems, using a risk based approach, which preserves the seamless authentication experience for the end user. One method of adding this security to a risk based authentication framework, is keystroke dynamics. Monitoring the behaviour of the users and how they type, produces a type of digital signature which is unique to that individual. Learning this behaviour allows dynamic flags to be applied to anomalous typing patterns that are produced by attackers using stolen credentials, as a potential risk of fraud. Methods from statistics and machine learning have been explored to try and implement such solutions. This paper will look at an Autoencoder model for learning the keystroke dynamics of specific users. The results from this paper show an improvement over the traditional tried and tested statistical approaches with an Equal Error Rate of 6.51%, with the additional benefits of relatively low training times and less reliance on feature engineering.
Creating Public Value by Democratizing the Ecosystem of Human Service Providers

Human service providers play a critical role in improving well–being in the United States. However, little is know about (i) how service seekers find the services they are looking for by navigating among available service providers, and (ii) how such organizations collaborate to meet human needs. In this paper, we report the first outcomes of our ongoing project. Specifically, we first describe a data acquisition engine, designed around the particular challenges of capturing, maintaining, and updating data pertaining to human service organizations from semistructured Web sources. We then proceed to illustrate the potential of the resulting comprehensive repository of human service providers through a case study showcasing a mobile app prototype designed to provide a one–stop shop for human service seekers.
Designing Mobile Geographic Information System for Disaster Management by Utilizing Wisdom of The Crowd

Natural disaster can happen in all region in the world. The disaster can happen anytime and anywhere, the natural disaster is often occurred in the region with a disaster-prone area, especially in Indonesia. Disaster management is needed in Indonesia because the geographic location of Indonesia is a part of the Pacific ring of fire which indicates that there is a potential volcanic activity in the area of Indonesia. Basically, each disaster is treated as a new problem and the response is based on the hastily constructed data. So, even there is a response, a poor decision often caused by inaccurate information. In the digital era, the utilization of information system for daily activity is considered important, especially in disaster management. The purpose of this research is to design a crowd-based geographic information system for disaster management. Simple Additive Weighting Method is used to prioritize accuracy and believable information. Further research can be done to test the system by the different disaster that occurred.
Robust switching technique of impulsive noise removal in color digital images

In this paper a novel method of impulsive noise removal in color images is presented. The proposed filtering design is based on a new measure of pixel similarity, which takes into account the structure of the local neighborhood of the pixels being compared. Thus, the new distance measure can be regarded as an extension of the reachability distance used in the construction of the local outlier factor, widely used in the big data analysis. Using the new similarity measure, an extension of the classic Vector Median Filter (VMF) has been developed. The new filter is extremely robust to outliers introduced by the impulsive noise, retains details and has the unique ability to sharpen image edges. Using the structure of the developed filter, a new impulse detector has been constructed. The cumulated sum of smallest reachability distances in the filtering window serves as a robust measure of pixel outlyingness. In this way, a pixel will be treated as corrupted if a predefined threshold is exceeded and will be replaced by the average of pixels which were found to belong to the original, pristine image; otherwise the processed pixel will be retained. This structure is similar to the Fast Averaging Peer Group Filter, however the incorporation of the reachability measure makes this technique more robust. The new filtering design can be applied in real time scenario, as its computational efficiency is comparable with the standard VMF, which is fast enough to be used for the enhancement of video sequences. The new filter operates in a 3&times;3 filtering window, however the information acquired from a larger window is processed. The source of additional information is the local neighborhood of pixels, which is used for the determination of the novel reachability measure. The experiments performed on a large database of color images show that the new filter surpasses existing designs especially in the case of highly polluted images. The robust reachability measure assures that the clusters of impulses are being removed, as not only the pixels, but also their neighborhoods are considered. The novel measure of dissimilarity can be also used in other tasks whose main goal is the detection of outliers.