Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-stream siamese network with contrastive-center losses for RGB-D action recognition

Abstract. Many fusion methods have been developed to improve the performance of action recognition with RGB and depth data, where learning conjoint representation of heterogeneous modalities by a single network has not been paid enough attention. We present an associated representation method for RGB-D action recognition using the siamese network with contrastive-center loss. First, some samples of each class and modality data are selected as the references to construct positive and negative pairs. Each positive pair consists of a training sample and its class reference, whereas the negative pair only involves different classes reference. Then these pairs are inputted to a two-stream siamese network to learn the collaborative representation of RGB and depth data. Two ranking losses, namely intramodal and cross-modal contrastive-center loss, are developed to impose similarity/dissimilarity metric on those pairs. Specifically, the intramodal contrastive-center loss measures the relationship between samples and references from RGB or depth data. The cross-modal contrastive-center loss measures the relationship of visual and depth features in a same low-dimensional space. Finally, the ranking losses and a softmax loss are jointly optimized for action recognition. The proposed method is evaluated on two large action datasets, LAP IsoGD and NTU RGB+D, and a smaller dataset, Sheffield Kinect gesture. The experimental results demonstrate that the proposed method surpasses most of the state-of-the-art methods.
Clustering user Queries of Search Engine for Restructuring Web Search Results: A Survey

Data Mining refers to mine information from large amounts of data. It also called as knowledge mining from data. Web mining is the application of data mining to mine knowledge from web data including hyperlinks between documents, web document. In today’s internet the search engine is one of the most important applications. For an ambiguous query different users may have different information requirements but the search engine does not satisfy user information requirements properly on the various aspects upon submission of same query. The conclusion and evolution of user search goals can be very useful in improving search engine appropriately. It also improves user knowledge. Therefore a unique approach is used to analyzing user queries from various search engine records. The Feedback sessions are clustered to find out different user search goals for a query. Feedback sessions are constructed from user click-through logs and can efficiently reset the information needs of users. Pseudo-documents are generated through feedback sessions for clustering. Finally Classified Average precision (CAP) algorithm is used to understand the user search goals efficiently. Keywordsuser queries; search engine results, classified average precision; Resturucturing web search results; k-means clustering.
A Survey on Phase Change Memory-Aware Cache Management

PCM (phase change memory) has been regarded as one of the most promising NVRAM. Due to its excellent features including high access speed, good scalability, byte addressability, and low idle power, PCM is expected to be an alternative to DRAM or flash memory in the future. So far, many different kinds of architecture have been proposed to merge PCM into the memory hierarchy of computer systems. For PCM-based computer systems, considering that PCM has various properties differing with DRAM and conventional storage media, traditional cache management policies based on DRAM and disks have to be re-designed. In this paper, we present a survey on the cache management policies for PCM-based systems that employ PCM as main memory or as secondary storage. Specially, for different PCM-based architectures, we explain how PCM-aware cache management policies exploit PCM’s advantages and overcome PCM’s drawbacks like asymmetric read/write latencies.
ID-Softmax: A Softmax-like Loss for ID Face Recognition

The face recognition between photos from identification documents (ID, Citizen Card or Passport Card) and daily photos, which is named FRBID(Zhang et al., 2017), is widely used in real world scenarios. However, traditional Softmax loss of deep CNN usually lacks the power of discrimination for FRBID. To address this problem, in this paper, we first revisit recent progress of face recognition losses, and give the theoretical and experimental analysis on the reason why Softmax-like losses work badly on ID-daily face recognition. Then we propose an novel approach named ID-Softmax, which use ID face features as class ’agent’ to guide the deep CNNs to learn highly discriminative features between ID photos and daily photos. In order to promote the IDdaily face recognition, we collect a large dataset ID74K, which includes 74,187 identities with corresponding ID photos and daily photos. To test our approach, we evaluate the feature distribution and face verification performance on dataset ID74K. In experiments, we achieve the best performance when comparing with other state-of-the-art methods, which verifies the effectiveness of the proposed ID-Softmax loss.
Collusion Trap Against GVW’13 ABE

Attribute-based encryption has broad application prospects. GVW’13 attribute-based encryption (ABE) scheme is one of the major candidates for ABE. The scheme has exquisite structure and proven security, and the crucial component is two-to-one recoding (TOR) algorithm. However, it has security risks in practical applications. In this paper, we describe this security risk, the collusion trap against GVW’13 ABE. In such a scenario: Alice and Bob collude to share decryption ability. In other words, when anyone can decrypt the ciphertext, he will tell the other one the corresponding plaintext. If Bob decrypts Alice’s fake ciphertext and returns “corresponding plaintext” to Alice, Alice can attempt to gradually calculate Bob’s secret key. We use the method of chosen-ciphertext attack of Jaulmes and Joux against NTRU to construct our attack. We show that whether for naked encryption or for calibrated encryption, Alice can obtain the equivalent secret key of Bob in polynomial time, although the size of the attack for calibrated encryption is much larger.
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design

In many high-throughput experimental design settings, such as those common in biochemical engineering, batched queries are more cost effective than one-by-one sequential queries. Furthermore, it is often not possible to directly choose items to query. Instead, the experimenter specifies a set of constraints that generates a library of possible items, which are then selected stochastically. Motivated by these considerations, we investigate \emph{Batched Stochastic Bayesian Optimization} (BSBO), a novel Bayesian optimization scheme for choosing the constraints in order to guide exploration towards items with greater utility. We focus on \emph{site-saturation mutagenesis}, a prototypical setting of BSBO in biochemical engineering, and propose a natural objective function for this problem. Importantly, we show that our objective function can be efficiently decomposed as a difference of submodular functions (DS), which allows us to employ DS optimization tools to greedily identify sets of constraints that increase the likelihood of finding items with high utility. Our experimental results show that our algorithm outperforms common heuristics on both synthetic and two real protein datasets.
Applying Deep Learning Models to Action Recognition of Swimming Mice with the Scarcity of Training Data

Deep learning models have shown their ability to model complicated problems in more efficient ways than other machine learning techniques in many application fields. For human action recognition tasks, the current state-of-the-art models are deep learning models. But they are not well-studied in applying for animal behaviour recognition due to the lack of data required for training these models. Therefore, in this research, we proposed a method to apply deep learning models to recognize the behaviours of a swimming mouse in two mouse forced swim tests with a limited amount of training data. We used deep learning models which are used in human action recognition tasks and fine-tuned them on the largest publicly available mouse behaviour dataset to give the models the knowledge about mouse behaviour recognition tasks. Then we fine-tuned the models one more time using the small amount of data that we have annotated for our swimming mouse behaviour recognition tasks. The good performance of these models in the new tasks proved the efficiency of our approach.
Accuracy improvement of echographic speckle tracking based on analysis of estimation error caused by acoustic pressure field

In echocardiography, blood-flow measurement is important, and several methods of measuring the velocity vector of blood flow have been proposed including echographic speckle tracking. Echographic speckle tracking is typically based on blockmatching algorithms; however, they incur high calculation cost; thus, are time-consuming. To enable real-time blood-flow vector measurement, we applied the Kanade-Lucas-Tomasi (KLT) algorithm to echographic speckle tracking, but the measurement accuracy was low in preliminary trial. This is mainly because echographic speckles deform as speckles move due to the acoustic pressure field of a transmitting beam. The objective of this study was to minimize the estimation error of KLT-based speckle tracking by analyzing error propagation. We analyzed error propagation from the acoustic pressure field to the velocity error by simplifying speckle deformation and formulated the major error factors. From this analysis, we propose a policy of determining the measurement conditions, which are region-of-interest (ROI) size, waveform, and number of ROI divisions, for minimizing estimation error. We verified the proposed policy through numerical simulations. As a result of the analysis and simulations, the gradient of the pressure field, number of ROI divisions, and moving distance of a speckle accounted for most of the estimation error. In addition, optimizing these conditions restricted the mean estimation error to less than 10%. These results indicate that the accuracy of KLT-based speckle tracking can reach a practical level by designing measurement conditions based on the proposed policy.
Robust RGB-D odometry under depth uncertainty for structured environments.

Visual odometry, the process of tracking the trajectory of a moving camera based on its captured video is a fundamental problem behind autonomous mobile robotics and augmented reality applications. Yet, despite almost 40 years of extensive research on the problem, state-of-the-art systems are still vulnerable to several pitfalls that arise in challenging environments due to specific sensor limitations and restrictive assumptions. This thesis, in particular, investigates the use of RGB-D cameras for robust visual odometry in man-made environments, such as industrial plants. These spaces, contrary to natural environments, follow mainly a rectilinear structure made of simple geometric entities. Thus, this work exploits this structure by taking a feature-based approach, where lines, planes and cylinder segments are explicitly extracted as visual cues for egomotion estimation. While the depth captured by RGB-D cameras helps to resolve the ambiguity inherent of passive cameras especially on uniform and low textured surfaces, these active cameras suffer from several limitations, which may deteriorate the performance of RGB-D Odometry, such as, limited operating range, near-infrared light interference and systematic errors, leading to incomplete and noisy depth maps. To address these issues, we have first developed a visual odometry framework that leverages both depth measurements from active sensing and depth estimates from temporal stereo obtained via probabilistic filtering. Our experiments demonstrate that this framework is able to operate on large indoor and outdoor spaces, where the absence and inaccuracy of depth measurements is too high to rely just on RGB-D Odometry. Secondly, this thesis considers the depth sensor error by proposing a depth fusion framework based on Mixture of Gaussians to denoise the depth measurements and model their uncertainties through spatio-temporal observations. Extensive results on RGB-D sequences show that applying this depth model to RGB-D odometry improves significantly its performance and supports our hypothesis that the uncertainty of fused depth needs to be exposed. To fully exploit this probabilistic depth model, the depth uncertainty needs to be propagated throughout the visual odometry pipeline. Therefore, we reformulated the visual odometry system as a probabilistic process by (i) deriving plane and 3D line fitting solutions that model the uncertainties of the feature parameters and (ii) estimating the camera pose by combining different feature-type matches weighted by their respective uncertainties. Lastly, this thesis addresses man-made environments made also of smooth curved surfaces by proposing a curve-aware plane and cylinder extraction algorithm which is shown empirically to be more efficient and accurate than an alternative state-of-the-art plane extraction approach, leading ultimately to better visual odometry performance in scenes made of cylindrical surfaces. To incorporate this feature extractor in visual odometry, the system described above is extended to handle cylinder primitives.
B-SLAM-SIM: A Novel Approach to Evaluate the Fusion of Visual SLAM and GPS by Example of Direct Sparse Odometry and Blender

In order to account for sensor deficiancies, usually a multi-sensor approach is used where various sensors complement each other. However, synchronization of highly accurate Global Positioning System (GPS) and video measurements requires specialized hardware which is not straightforward to set up. This paper proposes a full simulation environment for data generation and evaluation of Visual Simultaneous Localization and Mapping (Visual SLAM) and GPS based on free and open software. Specifically, image data is created by rendering a virtual environment where camera effects such as Motion Blur and Rolling Shutter can be added. Consequently, a ground truth camera trajectory is available and can be distorted via additive Gaussian noise to understand all parameters involved in the use of fusion algorithms such as the Kalman Filter. The proposed evaluation framework will be published as open source online at https://master.kalisz. co for free use by the research community.
Knife Pleated Skirt Multimedia Video Tutorial to Improve Learning Quality

Knife pleated skirt is one of the learning materials in the fashion pattern analysis course, UPI FPTK Fashion Education. Based on a preliminary study of 25 2013 fashion design students who had participated in the knife pleated skirt study, currently the learning used is still conventional, namely by demonstrating and using job sheet, power point, and fragment media. From the results of the preliminary study it was found that there were problems in learning how to determine the amount of fabric needed with the number of pleats and body size. For students who are slow in receiving the material, there needs to be a repetition of learning that can help students without the limited space and time to learn knife pleated skirt material. This study aims to overcome the problems experienced by students, by creating new learning media, namely multimedia knife pleated skirt video tutorials. The method used in this study is the Research and Development (R & D) method, with the stages carried out namely identification, planning, making, validation and testing, and the assessment phase. From the results of validation and field trials, it can be described that the making of knife pleated skirt video tutorials was made into a decent category with the assessment percentage of each validator and user was 95.97% by multimedia experts, 93.21% of material experts, and 91.24% by users. Thus, it can be concluded that the creation of a knife pleated skirt multimedia video tutorial is suitable for use. Keywords—research and development method; multimedia video tutorial; knife pleated skirt
Exploring Microservices as the Architecture of Choice for Network Function Virtualization Platforms

NFV is an emerging key technology that overcomes many challenges facing network service providers, such as reducing the capital and the operating expenses and satisfying the growing demand for mobile services. Integrating NFV with MEC and cloud environments requires an architecture that enables efficient implementations and deployments of NFV entities. Microservices architecture is a promising implementation of service-oriented architecture with recognized advantages in terms of modularity and continuous delivery. This article envisions microservices architecture as the solution of choice for building NFV platforms that are hosted in a dynamic environment ranging from MEC to cloud environments. This article addresses the major challenges and requirements of the microservices architecture to fully-exploit the potential of its adoption in NFV. It also proposes potential solutions that alleviate these issues. The article also discusses the need for agile and modular NFV entities along with MEC to realize various applications. To this end, the article discusses explicitly a novel NFV microservices entities scheduler optimization model. The proposed scheduler aims at minimizing network delays while taking into consideration various functional and non-functional constraints. The evaluation of the simulation results demonstrates that the proposed model minimizes the computational paths' latencies and improves the performance and availability of the NFV service chains.
Learning Discriminative Embeddings for Object Recognition on-the-fly

We address the problem of learning to recognize new objects on-the-fly efficiently. When using CNNs, a typical approach for learning new objects is by fine-tuning the model. However, this approach relies on the assumption that the original training set is available and requires high-end computational resources for training the ever-growing dataset efficiently, which can be unfeasible for robots with limited hardware. To overcome these limitations, we propose a new architecture that: 1) Instead of predicting labels, it learns to generate discriminative and separable embeddings of an object’s viewpoints by using a Supervised Triplet Loss, which is easier to implement than current smart mining techniques and the trained model can be applied to unseen objects. 2) Infers an object’s identity efficiently by utilizing a lightweight classifier in the features embedding space, this keeps the inference time in the order of milliseconds and can be retrained efficiently when new objects are learned. We evaluate our approach on four real-world images datasets used for Robotics and Computer Vision applications: Amazon Robotics Challenge 2017 by MIT-Princeton, T-LESS, ToyBoX, and CORe50 datasets. Code available at [1].
FinSeg: Finger Parts Semantic Segmentation using Multi-scale Feature Maps Aggregation of FCN

Image semantic segmentation is in the center of interest for computer vision researchers. Indeed, huge number of applications requires efficient segmentation performance, such as activity recognition, navigation, and human body parsing, etc. One of the important applications is gesture recognition that is the ability to understanding human hand gestures by detecting and counting finger parts in a video stream or in still images. Thus, accurate finger parts segmentation yields more accurate gesture recognition. Consequently, in this paper, we highlight two contributions as follows: First, we propose data-driven deep learning pooling policy based on multi-scale feature maps extraction at different scales (called FinSeg). A novel aggregation layer is introduced in this model, in which the features maps generated at each scale is weighted using a fully connected layer. Second, with the lack of realistic labeled finger parts datasets, we propose a labeled dataset for finger parts segmentation (FingerParts dataset). To the best of our knowledge, the proposed dataset is the first attempt to build a realistic dataset for finger parts semantic segmentation. The experimental results show that the proposed model yields an improvement of 5% compared to the standard FCN network.
BIM technologies in customer service business processes. Commercial pricing classifier

In the construction industry, implementing the information modeling technologies directly in the business processes of the Customer’s Department will allow improving the following procedures on a deeper scale: designing buildings and structures with the parameters that enable to receive consolidated cost estimates in an automated mode at various stages; monitoring and control over construction and installation works at any stage of the process; and creating a federated model.
Statistical Modelling and Analysis of the Computer-Simulated Datasets

Over the last two decades, the science has come a long way from relying on only physical experiments and observations to experimentation using computer simulators. This chapter focuses on the modelling and analysis of data arising from computer simulators. It turns out that traditional statistical metamodels are often not very useful for analyzing such datasets. For deterministic computer simulators, the realizations of Gaussian process (GP) models are commonly used for fitting a surrogate statistical metamodel of the simulator output. The chapter starts with a quick review of the standard GP-based statistical surrogate model. The chapter also emphasizes on the numerical instability due to near-singularity of the spatial correlation structure in the GP model fitting process. The authors also present a few generalizations of the GP model, reviews methods, and algorithms specifically developed for analyzing big data obtained from computer model runs, and reviews the popular analysis goals of such computer experiments. A few real-life computer simulators are also briefly outlined here.
Gray Standard Deviation Based Ultraviolet Image Segmentation for Electrical Equipment

In this paper, we propose an ultraviolet image segmentation method based on gray standard deviation for electrical equipment. It aims to extract the ultraviolet spot regions which can provide evidence to assess whether there is a discharge on the equipment. We firstly smooth the image to a specific scale space to strengthen the edge we want. Then we compute the standard deviation of each pixel to form a standard deviation image which is quickly achieved by an integral image. After a two-step adaptive segmentation by finding proper threshold and the verification of spot region characteristics, the spot regions can be quickly obtained. From the experiments, it can be seen that the algorithm is of low complexity and can meet the demands of filed detection for electrical equipment.