Reviewerly/exHarmony huggingface dataset:
Minimax Prediction in Tree Ising Models

Graphical models are often used to facilitate efficient computation of posteriors in order to make predictions. With this objective in mind, we consider the problem of estimating the parameters of a graphical model with known structure from samples such that posteriors computed using the model are accurate. Focusing on tree-structured binary Markov random fields, our main result is a sharp characterization of the dependence on number of samples needed for all pairwise marginals (and hence posteriors of one variable given another) to be accurate: n = Θ(η <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> log p) samples are necessary and sufficient to estimate model parameters such that all marginals of arbitrary order k are accurate to within √kη. The result implies that prediction error is bounded uniformly, with no dependence on the strength of interactions. We will also show that these guarantees are achievable using moment matching techniques.
SecureMLDebugger: A Privacy-Preserving Machine Learning Debugging Tool

The issue of data privacy is uniquely challenging in machine learning which requires large datasets. Privacypreserving machine learning method based on the concept of model training isolated from data scientists have become a hot topic in recent years. In order to protect data privacy, training data is completely isolated from data scientists. Although this method can protect data privacy, data scientists cannot perceive any training information of data nodes during training, and it is difficult to debug machine learning model. Existing works provide data collection APIs to collect and display metadata during training to help data scientists debug machine learning models. Malicious data scientists can obtain private data through these APIs. In this paper, a novel security machine learning debug tool based non-intrusive metadata collection scheme, called SecureMLDebugger(SMLD), is proposed, which automatically collect, store and manage non-privacy metadata during training without any data collection API. Our tool accelerates users in their machine learning experiment while protecting data privacy. We achieve this by transparently tracing each function call in machine learning code and automatically extracting metadata such as hyperparameters of models, training runs, evaluations and layouts of neural networks. SMLD is integrated with popular frameworks such as scikit-learn and PyTorch, and meets the demands of various privacy-preserving training cases in practical.
Personalized Text Summarization Based on Gaze Patterns

With the explosive growth of online content, summarization has become important for users to grasp information quickly. However, existing summarization only provides static and one-size-fits-all results, e.g., a paragraph is summarized to the same short sentence, for different users, failing to satisfy users' diverse preferences. In our subjective study, such preference diversity on content summarization can be quite significant, e.g., the ground-truth summaries provided by different users on the same long text only have an average cosine similarity value of 0.383. This paper fills this gap by a personalized content summarization framework. First, we have collected a Chinese long text summarization dataset with gaze behavior of different users. Based on our measurement study on the dataset, we reveal that people's gaze behavior and their preferred content summary have strong correlation, e.g., the preference of different parts of speech (POS); Second, we propose to incorporate such gaze-based&#x0022;attention patterns&#x0022; into text summarization, by designing a gaze-based key sentence extracting strategy, PRank (short for Perso alRank) which is then integrated with a conventional pointer generator model to satisfy different individuals. Our trace-driven experiments on the dataset verify the effectiveness of our design: our model outperforms baselines by at least 3 ROUGE-1 points.
An Automatic Learning Model for Trajectory Outlier Detection

The rapid development of global positioning system has given birth to a large number of spatial-temporal data, and there are many outliers of points obviously in these trajectory data. It is very important to detect the outliers in the trajectory to improve the data quality and accuracy of trajectory mining. In this paper, we propose a trajectory outlier detection algorithm based on bi-directional long short-term memory model and attention mechanism. Firstly, an eight-dim eigenvector is extracted from each point of trajectory, and then a two-layer bi-directional long short-term memory model is constructed. Finally, representing the trajectory points in an interactive way which is called attention mechanism. The input of the model is the trajectory point with a certain length, and the output is the type of the trajectory point. The model can automatically learn the difference between the normal point and the adjacent abnormal point with motion features. Experimental dataset based on real trajectory data of taxi from Beijing, and results showed that the performance of this algorithm is significantly better than constant speed threshold method or classical machine learning classification. Especially the precision and recall reaches 0.93 and 0.90 separately, which proves the effectiveness of this algorithm.
Improved 2D Human Pose Tracking Using Optical Flow Analysis

In this paper, we propose a novel human body pose refinement method that relies on an existing single-frame pose detector and uses an optical flow algorithm in order to increase quality of output trajectories. First, a pose estimation algorithm such as OpenPose is applied and the error of keypoint position measurement is calculated. Then, the velocity of each keypoint in frame coordinate space is estimated by an optical flow algorithm, and results are merged through a Kalman filter. The resulting trajectories for a set of experimental videos were calculated and evaluated by metrics, which showed a positive impact of optical flow velocity estimations. Our algorithm may be used as a preliminary step to further joint trajectory processing, such as action recognition.
A low-cost camera-based tracking theodolite for large-scale metrology applications

A wide spectrum of Large-Scale Metrology instruments for measuring 3D positions of mobile targets with different levels of accuracy, operational ranges and corresponding cost exist. However, future assembly paradigms and further industrial applications would benefit from a system with ultra low-cost targets and an accuracy in the order of magnitude of 1 mm, which is currently not available. Therefor the authors propose a camera-based tracking theodolite as a novel instrument for measuring 3D positions of printed visual markers. The approach is implemented as a prototype composed of two rotation stages with built-in encoders, a state-of-the-art industrial CMOS-camera and lens with fixed focal length in conjunction with printed chessboard targets. According mathematical models for the calculation of the targets’ position, kinematic error compensation and measurement uncertainty estimation are elaborated. Competitive experiments against a laser tracker show that the position of the marker can be measured with an absolute error below 1 mm and a statistical uncertainty close to 1 mm in a radius of 5 m, confirming the applicability of the developed concept for a novel Large-Scale Metrology instrument.
Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction

High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method-minimum eigenvector collaborative representation discriminant projection-to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation. On the other hand, we maintain the collaborative representation relationship of samples in the projection subspace to enhance the discriminability of the extracted features. Also, the between-class scatter of the reconstructed samples is used to improve the robustness of the projection space. The experimental results on the COIL-20 image object database, ORL, and FERET face databases, as well as Isolet database demonstrate the effectiveness of the proposed method, especially in low dimensions and small training sample size.
Simulating patient matching to clinical trials using a property rights blockchain

Objective: Biomedical data processing generally requires the secure stepwise transfer of sensitive personal information across multiple parties. Mediating such operations using distributed secure digital ledgers, i.e., blockchains, is investigated in this article. Materials and Methods: The bitmark property rights blockchain was used to simulate the process of assessing individuals for enrollment to specific clinical trials. In the scenario presented, a sponsor publishes a recruitment call for a clinical trial and patients signal their willingness to participate in the trial through blockchain transactions. The blockchain creates and maintains digital references of the medical data assets of prospective study participants as well as digital property certificates for assigning access rights to corresponding medical data assets. Trial matching services review the patient blockchain records and recommend study participants that are likely to meet the enrollment criteria of recruiting clinical trials. Digital certificates assign transient access rights to the data assets of the prospective study participants. These certificates are transferred to pertinent matching services and sponsors, allowing these organizations to examine the candidacy of each prospective study participant. Results: The trial matching simulation demonstrates that property rights blockchains can implement complicated multiparty interactions, such as those associated with medical data exchange, without supplemental peer-to-peer communications. Conclusions: Blockchain-based data marketplaces of the type described, when coupled with data-controlled virtual infrastructure environments (i.e., Medical Data Trusts), provide a viable model for managing the transfer, provenance, and processing of individual health information.
Toward just-in-time patching for containerized applications

Containers have become increasingly popular in distributed computing environments. However, recent studies have shown that containerized applications are susceptible to various security attacks. Traditional pre-scheduled software update approaches not only become ineffective under dynamic container environments but also impose high overhead to containers. In this paper, we propose a new on-demand targeted patching framework for containerized applications. OPatch combines dynamic vulnerability exploit identification and targeted vulnerability patching to achieve more efficient security attack containment. We have implemented a prototype of OPatch and evaluated our schemes over 31 real world security vulnerability exploits in 23 commonly used server applications. Results show that OPatch can accurately detect and classify 81% vulnerability exploits and reduce security patching overhead by up to 84% for memory and 40% for disk.
Occlusion Based Discriminative Feature Mining for Vehicle Re-identification

Existing methods of vehicle re-identification (ReID) focus on training robust models on the fixed data while ignore the diversity in the training data, which limits generalization ability of the models. In this paper, it proposes an occlusion based discriminative feature mining (ODFM) method for vehicle re-identification, which increases the diversity of the training set by synthesizing occlusion samples, to simulate the occlusion problem in the real scene. To better train the ReID model on the data with large occlusions, an attention mechanism was introduced in the mainstream network to learn the discriminative features for vehicle images. Experimental results on two public ReID datasets, VeRi-776 and VehicleID verify the effectiveness of the proposed method comparing to the state-of-the-art methods.
Fused Network for View Synthesis

3D movie has become the mainstream for in cinema, and the growing of 3D television and other digital devices is rapid, 3D contents are more demanded in recent years. Producing stereoscopic effect from many existing 2D videos is the most cost-effective under many circumstances. In this paper, we aim to integrate the pipeline into an end-to-end framework, fusing the unsupervised depth estimation with DIBR, the synthesized view generated by the network directly to get the frame for the virtual view angle, and the segmentation model. The fusion makes it possible to exploit both contextual and geometric information better.
A Novel Deep Learning Model by Stacking Conditional Restricted Boltzmann Machine and Deep Neural Network

A real-world system often exhibits complex dynamics arising from interaction among its subunits. In machine learning and data mining, these interactions are usually formulated as dependency and correlation among system variables. Similar to Convolution Neural Network dealing with spatially correlated features and Recurrent Neural Network with temporally correlated features, in this paper we present a novel deep learning model to tackle functionally interactive features by stacking a Conditional Restricted Boltzmann Machine and a Deep Neural Network (CRBM-DNN). Variables with their dependency relationships are organized into a bipartite graph, which is further converted into a Restricted Boltzmann Machine conditioned by domain knowledge. We integrate this CRBM and a DNN into one deep learning model constrained by one overall cost function. CRBM-DNN can solve both supervised and unsupervised learning problems. Compared to a regular neural network of the same size, CRBM-DNN has fewer parameters so they require fewer training samples. We perform extensive comparative studies with a large number of supervised learning and unsupervised learning methods using several challenging real-world datasets, and achieve significant superior performance.
Index group optimization based on automatic clustering using K-Means genetic algorithm

Abstract E-prints UMSIDA is a repository of student and lecturer publication documents at the Muhammadiyah University of Sidoarjo (UMSIDA). The collection of documents is still random and the search can only detect from the title keywords. The increasing culture of writing and research makes it possible for more and more documents as literature. Documents in E-prints are grouped by subject provided by the repository manager and grouped by the admin who uploaded the document. Automatic document grouping can be done by grouping documents based on the contents of the document using the Information Retrieval (IR) approach. The retrieval process is carried out by document processing with tokenisation to obtain data tokens, the data tokens are processed through a stemming process to obtain the stem value of each word. The stem value is processed using the indexing process and word stem to get sentence indexes through the weighting process. The index results stored in the database become document variables that are the features or characteristics of each document. The index of all documents is grouped through Automatic Clustering technique using K-Means Genetic Algorithm.
Robust Square-Root Cubature FastSLAM with Genetic Operators

SUMMARY An improved FastSLAM based on the robust square-root cubature Kalman filter (RSRCKF) with partial genetic resampling is proposed in this paper. In the proposed method, RSRCKF is used to design the proposal distribution of FastSLAM and to estimate environment landmarks. The proposed method does not require a priori knowledge of the noise statistics. In addition, to increase diversity, it uses the genetic operators-based strategy to further improve the particle diversity. In fact, a partial genetic resampling operation is carried out to maintain the diversity of particles. The proposed method is compared with other methods via simulation and experimental data. It can be seen from the results that the proposed method provides significantly more accurate and robust estimation results compared with other methods even with fewer particles and unknown a priori. In addition, the consistency of the proposed method is better than that of other methods.
Small ship detection in SAR images based on modified SSD

Small object detection is a nodus for CNNs (Convolutional Neural Networks) that take advantages of feature pyramid for object detection, and it is also a hotspot which researchers focus on, especially small ship detection in SAR images. Based on SSD (Single Shot Multibox Detector), which is a classical CNN detector and a typical one-stage method for object detection, and inspired by the ideas of object enhancement and feature fusion, the paper uses deconvolution to enhance representation of small object in feature pyramid. Thereby we expect to improve detecting precision of SSD. The feasibility and effectiveness of proposed method are proved by our experiments. Experimental results on SSDD show that the proposed method greatly improves detecting precision of small ships in SAR images.
Enhancing Deep Multimedia Recommendations Using Graph Embeddings

Recently, deep learning techniques are widely used inthe field of multimedia recommender systems, like movierecommendation, news recommendation, music recommen-dation, and short video recommendation. With its power-ful ability in extracting representations and learning non-linear relations, deep learning-based collaborative filteringmodels outperform traditional collaborative filtering meth-ods (e.g., matrix factorization) and brings an immense liftin recommendation performances. However, these existing deep-learning-based recommen-dation methods only learn from binary user-item relation-ships - that is to say, deep models learn from low-level user-item interactions and the collaborative information hiddenin more complex user-item relationships in the data is notsufficiently exploited and thus will lead to inferior perfor-mance. To solve this problem, in this work, we proposea graph embedding enhanced method to exploit collabo-rative information by mining deep-level user-item interactions. Instead of learning from binary user-item relations, our method explicitly offers collaborative signals, which areencoded in embedding, to deep models. We conduct exper-iments on four real datasets from different multimedia rec-ommendation scenarios with different sparsity. We find outthat there are significant benefits using the graph enhancedmethod: converging faster and producing a better recom-mendation result. Results show that the proposed methodoutperforms traditional collaborative filtering methods andstate-of-the-art deep learning-based methods
Location Property of Convolutional Neural Networks for Image Classification

When doing image classification, the core task of convolutional neural network (CNN)-based methods is to learn better feature representation. Our analysis has shown that a better feature representation in the layer before softmax operation (BSM-layer) means a better feature embedding location that has a larger distance to the separating hyperplane. By defining this property “Location Property” of CNN, the core task of CNN-based methods can be regarded as to find out the optimal feature embedding location in the BSM-layer. In order to achieve this, in this work, we first propose two feature embedding directions, principal embedding direction (PE-direction) and secondary embedding direction (SE-direction). And then, we further propose a loss-based optimization framework, location property loss (LP-loss), which can make feature representation move in the PE-direction and the SE-direction simultaneously during the training phase. LP-loss consists of two parts, LP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PE</sub> and LP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE</sub> , where LP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PE</sub> focuses on PE-direction, and LP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE</sub> focuses on SE-direction. Any loss function focusing on these two embedding directions can be chosen as LP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PE</sub> and LP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE</sub> . Based on the analysis that softmax, L-softmax, and AM softmax can make the feature representation move in PE-direction to a different extent, any of them can be chosen as LP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PE</sub> . Since there is no existing works can fulfill the purpose of LP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE</sub> , a novel loss, secondary optimal feature plane loss (S-OFP loss), is developed. S-OFP loss is designed to make feature representations belonging to the same category embed onto their corresponding S-OFP. It is proved that S-OFP loss is the optimal feature plane in the SE-direction. Experiments are done with shallow, moderate, and deep models on four benchmark data sets, including the MNIST, SVHN, CIFAR-10, and CIFAR-100, and results demonstrate that CNN models can obtain remarkable performance improvements with LP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">softmax</sub> , S-OFP and LP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AM softmax</sub> , S-OFP, which verify the effectiveness of location property.
Accuracy Measurement of Deep Neural Network Accelerator via Metamorphic Testing

Deep neural networks (DNNs) have been proved to be popular and successful in our daily life. For mobile devices with the services from DNN, it is needed to equip a DNN accelerator to employ fast and accurate DNN models. Different DNN accelerators may have different accuracy performance, which directly affects the user experience. To measure the accuracy of the DNN accelerators, there only exists indirect approach by comparing to the third-party platform with 32-bit float format. In this paper, metamorphic testing is employed to measure the accuracy of the DNN accelerator for the first time. Specifically, our scheme utilizes the mathematical properties of the operators in deep neural networks to measure the accuracy of the DNN accelerator, getting rid of the dependence on the third-party platform. Besides, we design an objective metric to quantitatively measure the accuracy performance. It can be used not only to facilitate the direct competition between different accelerators but also to detect defects in the implementation of the DNN accelerator. An experiment on two well-known DNN accelerators, HiAI and Snapdragon Neural Processing Engine (SNPE), is conducted, and the results show that the metamorphic distance of the former is smaller than that of the latter in terms of their accuracy performance with 16-bit float format on typical Ops, such as softmax and convolution.