Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Point Cloud Categorization for Rigid and Non-Rigid 3D Objects

In this paper, we address the problem of 3D object categorization for point cloud data. With the availability of inexpensive scanning devices and powerful computational resources, there is a rapid growth of point cloud data. This necessitates efficient classification techniques which form the basis for analysis and processing of 3D point cloud data. In order to address the classification problem, we propose a 3D object categorization framework for both rigid and non-rigid objects. Initially, the proposed approach extracts the feature descriptors using improved wave kernel signature by approximating Laplace-Beltrami operator on point cloud data for non-rigid objects. For rigid objects, our approach uses the geometric features, namely, metric tensor and Christoffel symbols by modifying the geodesic distance computation. These feature descriptors are then represented using bag-of-features and improved Fisher vector encoding techniques. Finally, the support vector machine classifies the 3D objects into predefined set of classes. We also provide an exhaustive performance evaluation of the proposed 3D object categorization framework on state-of-the-art datasets, namely, SHREC'10, SHREC'11, SHREC'12, SHREC'15 and Princeton Shape Benchmark. The evaluation results reveal that the proposed approach outperforms the existing object categorization methods for both rigid and non-rigid 3D objects.
Effects of maceration at mowing on digestibility and ruminal fermentation of timothy hay in steers

Eight ruminally fistulated steers (711 kg ± 72 kg) were used in a crossover experimental design to study the nutritional effects of macerating timothy grass (M) over the conventional conditioning process (C). Maceration was applied at mowing by conditioning the freshly cut forage through eight high-speed metallic grinding rolls with a shredding effect on leaves and stems. Animals were fed an all forage diet consisting of M or C. Feces were collected over a period of 6 d for total digestibility determination. On day 7 of each experimental period, ruminal fluid was sampled at 0, 1, 2, 4, 8 and 12 h after meals. On days 9–11, nylon bags were incubated in the rumen of each steer for 4, 8, 12, 24 and 48 h in order to determine forage dry matter (DM) degradation at the ruminal level. Maceration reduced the field drying time to reach baling moisture by 50% under Southeast Canadian climatic conditions. Results of forage digestibility at the ruminal level showed that the rate of forage DM disappearance was greater.
Image Co-segmentation using Graph Convolution Neural Network

Image co-segmentation is jointly segmenting two or more images sharing common foreground objects. In this paper, we propose a novel graph convolution neural network (graph CNN) based end-to-end model for performing co-segmentation. At the beginning, each input image is over-segmented into a set of superpixels. Next, a weighted graph is formed using the over-segmented images exploiting spatial adjacency and both intra-image and inter-image feature similarities among the image superpixels (nodes). Subsequently, the proposed network, consisting of graph convolution layers followed by node classification layers, classifies each superpixel either into the common foreground or its complement. During training, along with the co-segmentation network, an additional network is introduced to exploit the corresponding semantic labels, and the two networks share the same weights in graph convolution layers. The whole model is learned in an end-to-end fashion using a novel cost function comprised of a superpixel wise binary cross entropy and a multi-label cross entropy. We also use empirical class probabilities in the loss function to deal with class imbalance. Experimental results reflect that the proposed technique is very competitive with the state-of-the-art methods on two challenging datasets, Internet and Pascal-VOC.
Dynamic Hand Gesture Recognition using Convolutional Neural Network with RGB-D Fusion

In this paper, we propose a novel, real-time dynamic hand gesture recognition framework using convolutional neural network with depth and RGB data fusion. Hand gestures are a natural form of communication between humans as well as between human and machine. They also find important applications in areas such as sign language recognition, man-machine interaction and behavior understanding. Natural hand gestures are complex hand movements in space and time and are challenging to recognize. In our proposed framework, we use both RGB and depth data to automatically recognize dynamic hand gestures. Initially, we work with RGB and depth data separately. We find the motion history of the gesture performed with RGB data and independently with depth data to store the motion information of the moving hands. Motion history of the performed gesture stores the rich information of the movement. Then, we use transfer learning on two separate VGG16 networks, where one network is fine-tuned using RGB motion history while the other network is fine-tuned using depth motion history, to configure them for dynamic hand gesture recognition problem. Then, using the two fine-tunned VGG16 networks, we extract the features of both the motion history images obtained from RGB and depth data separately, for each dynamic hand gesture. We then, integrate the features obtained from both the networks using weighted summation, to accurately and robustly recognize the dynamic hand gesture. We perform experiments on standard and the publicly available dynamic hand gesture datasets and show that our method outperforms state of the art methods.
On-Demand Augmentation of Contour Trees

The contour tree represents the topology of level sets of a scalar function. Nodes of the tree correspond to critical level sets and arcs of the tree represent a collection of topologically equivalent level sets connecting two critical level sets. The augmented contour tree contains degree-2 nodes on the arcs that represent regular level sets. The degree-2 nodes correspond to regular points of the scalar function and other critical points that do not affect the number of level set components. The augmented contour tree is significantly larger in size and requires more effort to compute when compared to the contour tree. Applications of the contour tree to data exploration and visualization require the augmented contour tree. Current approaches propose algorithms to compute the contour tree and the augmented contour tree from scratch. Precomputing and storing the large augmented contour tree will not be necessary if the contour tree can be augmented on-demand. This paper poses the problem of computing the augmented contour tree given a contour tree as input. Computational experiments demonstrate that the on-demand augmentation can be computed fast while resulting in good memory savings.
Delegation sketch: a parallel design with support for fast and accurate concurrent operations

Sketches are data structures designed to answer approximate queries by trading memory overhead with accuracy guarantees. More specifically, sketches efficiently summarize large, high-rate streams of data and quickly answer queries on these summaries. In order to support such high throughput rates in modern architectures, parallelization and support for fast queries play a central role, especially when monitoring unpredictable data that can change rapidly as, e.g., in network monitoring for large-scale denial-of-service attacks. However, most existing parallel sketch designs have focused either on high insertion rate or on high query rate, and fail to support cases when these operations are concurrent. In this work we examine the trade-off between query and insertion efficiency and we propose Delegation Sketch, a parallelization design for sketch-based data structures to efficiently support concurrent insertions and queries. Delegation Sketch introduces a domain splitting scheme that uses multiple, parallel sketches to ensure all occurrences of a key fall into the same sketch. We complement the design by proposing synchronization mechanisms that facilitate delegation of insertion and queries among threads, enabling it to process streams at higher rates, even in the presence of concurrent queries. We thoroughly evaluate Delegation Sketch across multiple dimensions (accuracy, scalability, query rate and input skew) on two massively parallel platforms (including a NUMA architecture) using both synthetic and real data. We show that Delegation Sketch achieves from 2.5X to 4X higher throughput, depending on the rate of concurrent queries, than the best performing alternative, while at the same time maintaining better accuracy at the same memory cost.
A Temporal Activity Graph Kernel for Human Activity Classification

Human activities are predominantly spatio-temporal, involving spatial changes over time. Qualitative spatial relations between interacting entities are often used to describe spatial change. To derive such qualitative spatial relations, the interacting entities are approximated as a single bounding box or set of bounding boxes. A set of bounding boxes abstracting a single entity has been termed as an extended object; where each box is bounding a component. Extended object abstraction of spatial entities has been shown to be more effective for representation of human activities [10]. The temporal aspect of an activity is characterized through changing spatial relations between components of interacting extended objects over time. In this paper, we propose Temporal Activity Graph (TAG) based representation model to keep track of the sequences of relations between components of the extended objects. A kernel is designed for classification of spatio-temporal interactions in the TAG based model. The TAG kernel uses concepts of label sequence similarity and interestingness to compute similarity of a pair of TAGs. The TAG kernel is a generic solution that can be used with any kernel based method. Here, the kernel is used within a Support Vector Machine classifier. The TAG kernel based classification of activities is found on par with the state-of-the-art approaches for experiments performed on the Mind's Eye, the UT Interaction, and the SBU Kinect Interaction datasets.
Disguise Face Recognition Based On Spectral Imaging

Despite the challenge in recognizing disguise images on Face Recognition Systems (FRS), it has not received significant attention. Considering the fact that most of the FRS operates in visible spectrum which is not optimal solution for disguise face recognition, we explore spectral imaging and thereby the intrinsic features of spectral imaging for disguise face recognition. The major contribution of this work lies in presenting new approach that extract the histogram features independently for the spectral band images using Histogram Oriented Gradient (HOG) method and learning the unique discriminative features in affine feature space for disguise face recognition problem in robust manner. We present an extensive experimental validation results in terms of verification rate to present the significance of our proposed method. Further, we employ state-of-the-art disguise face recognition methods on individual spectral bands and fused spectral bands. The Proposed method demonstrates best performance in recognizing disguise face images compared to the state-of-the-art methods.
Sparse Signal Recovery for Multiple Measurement Vectors with Temporally Correlated Entries: A Bayesian Perspective

Bayesian Sparse Signal Recovery (SSR) for Multiple Measurement Vectors, when elements of each row of solution matrix are correlated, is addressed in the paper. We propose a standard linear Gaussian observation model and a three-level hierarchical estimation framework, based on Gaussian Scale Mixture (GSM) model with some random and deterministic parameters, to model each row of the unknown solution matrix. This hierarchical model induces heavy-tailed marginal distribution over each row which encompasses several choices of distributions viz. Laplace distribution, Student's t distribution and Jeffery prior. Automatic Relevance Determination (ARD) phenomenon introduces sparsity in the model. It is interesting to see that Block Sparse Bayesian Learning framework is a special case of the proposed framework when induced marginal is Jeffrey prior. Experimental results for synthetic signals are provided to demonstrate its effectiveness. We also explore the possibility of using Multiple Measurement Vectors to model Dynamic Hand Posture Database which consists of sequence of temporally correlated hand posture sequence. It can be seen that by exploiting temporal correlation information present in the successive image samples, the proposed framework can reconstruct the data with less linear random measurements with high fidelity.
BinRec: dynamic binary lifting and recompilation

Binary lifting and recompilation allow a wide range of install-time program transformations, such as security hardening, deobfuscation, and reoptimization. Existing binary lifting tools are based on static disassembly and thus have to rely on heuristics to disassemble binaries. In this paper, we present BinRec, a new approach to heuristic-free binary recompilation which lifts dynamic traces of a binary to a compiler-level intermediate representation (IR) and lowers the IR back to a "recovered" binary. This enables BinRec to apply rich program transformations, such as compiler-based optimization passes, on top of the recovered representation. We identify and address a number of challenges in binary lifting, including unique challenges posed by our dynamic approach. In contrast to existing frameworks, our dynamic frontend can accurately disassemble and lift binaries without heuristics, and we can successfully recover obfuscated code and all SPEC INT 2006 benchmarks including C++ applications. We evaluate BinRec in three application domains: i) binary reoptimization, ii) deobfuscation (by recovering partial program semantics from virtualization-obfuscated code), and iii) binary hardening (by applying existing compiler-level passes such as AddressSanitizer and SafeStack on binary code).
Automated data race bugs addition

A challenge faced by concurrency bug detection techniques is the lack of ground-truth corpora, i.e., a lot of true concurrency bugs, making it difficult to evaluate and verify these technologies and tools, e.g., to precisely measure their false negative and false positive rates. In this paper, we present DRInject, a novel dynamic debugging based technique for producing ground-truth corpora by automatically and quickly injecting lots of realistic data race bugs into program source code. Each data race bug is assured by injecting modifying code to a global variable in two concurrency threads. These bugs are realistic in that they are embedded deep with programs and are triggered by real inputs. We have injected over 600 data race bugs into 10 benchmark or real-world programs, including water-nsquared, X264 and libvips. Moreover, we evaluated four data race detectors using the produced buggy programs and found there are much improvement space for these tools. Preliminary experiments show that DRInject can inject data race bugs in large scale programs and evaluate detect tools with fundamental quantities like false negative and false positive rate, which forms the basis to generate large bug corpora for the future research in concurrency software.
The Effectiveness of Smartphone Application Usability among E-society of Bertam Perdana Penang: A Case Study of Masjid Abdullah Fahim E-Mosque Application

Islam is the official religion of Malaysia and the Muslim population is expected to grow significantly with an estimated 32.7 million Muslims in Malaysia by 2050. This will be closely linked to the mosque's institution as an important medium for the religious center of the Muslim community. The institution of the mosque is not only seen as a religious activity but also as a community center, administrative center, education center, center of economic activity and others. Presently, the mosque's institution is still less than the point of worship, compared to the growing mosque with various designs but its usage is still not impressive. Although various efforts and writings have been mobilized to enhance the role of mosque institutions in Malaysia, there are still room of improvement for the mosque institutions as a complete hub for the Muslim community. Looking at the advance step taken by Masjid Abdullah Fahim in Bertam Seberang Perai Utara, Penang by introducing the E-society smartphone application or E-Mosque among the resident could enhance the resident involvement towards mosque institution. Thus, this study would like determine the usage perception of the resident towards the effective aspects should be embedded in the E-mosque application for Masjid Abdullah Fahim. An increase of mosque institution involvement through the usage of the E-mosque application could leads in building social strength through E-society as well as can compliment the needs of the new age Muslim community in Malaysia.
Quadro citológico vaginal, concentração plasmática de progesterona durante a gestação e medidas fetais em paca (Cuniculus paca Linnaeus, 1766)

The objective of this work was to describe in 27 pacas (Cuniculus paca Linnaeus, 1766) at 30, 60 and 90 days (D) of pregnancy diagnosed by ultrasonography (U.S.) the cell types of the vaginal epithelium by vaginal smears, to report the vulva opening condition and the characteristics of vaginal mucus, to determine the progesterone (P4) serum concentration by radioimmunoassay, and also measure by ultrasound (U.S.) the fetuses biparietal diameter (BPD) at 60 and 90 days of pregnancy. At D30, 40% of the smears exhibited surface cells (with estrogenic characteristics) and of naked nuclei. At D60 and D90, parabasal, intermediate, superficial, and navicular cells were present in the same proportions, but endocervical cells were described in only 73.9% and 69% of those day's smears, respectively. At D30 the highest proportion of navicular and surface cells differed (p < 0.05) compared with other cell types. The vaginal mucus was crystalline and fluid in 100% and 70% of females at D30 and D60, respectively. It was observed the vaginal vestibule open in around 50% of females in every day of exams. Minimum detectable P4 serum concentration was obtained on 72% and 83% of females, while the averages of the BPD were 1.25 cm (± 0.16) and 2.34 cm (± 0.25) on D60 and D90, respectively. Vaginal cytology aspects at D30, D60 and D90 and BPD provide elements to contribute to pregnancy diagnosis in paca. P4 concentration shows the need of further endocrinology of pregnancy studies of paca.
Quantifying the Characteristics of the Local Urban Environment through Geotagged Flickr Photographs and Image Recognition

Urban environments play a crucial role in the design, planning, and management of cities. Recently, as the urban population expands, the ways in which humans interact with their surroundings has evolved, presenting a dynamic distribution in space and time locally and frequently. Therefore, how to better understand the local urban environment and differentiate varying preferences for urban areas has been a big challenge for policymakers. This study leverages geotagged Flickr photographs to quantify characteristics of varying urban areas and exploit the dynamics of areas where more people assemble. An advanced image recognition model is used to extract features from large numbers of images in Inner London within the period 2013–2015. After the integration of characteristics, a series of visualisation techniques are utilised to explore the characteristic differences and their dynamics. We find that urban areas with higher population densities cover more iconic landmarks and leisure zones, while others are more related to daily life scenes. The dynamic results demonstrate that season determines human preferences for travel modes and activity modes. Our study expands the previous literature on the integration of image recognition method and urban perception analytics and provides new insights for stakeholders, who can use these findings as vital evidence for decision making.
Spatial Reliability Assessment of Social Media Mining Techniques with Regard to Disaster Domain-Based Filtering

The data generated by social media such as Twitter are classified as big data and the usability of those data can provide a wide range of resources to various study areas including disaster management, tourism, political science, and health. However, apart from the acquisition of the data, the reliability and accuracy when it comes to using it concern scientists in terms of whether or not the use of social media data (SMD) can lead to incorrect and unreliable inferences. There have been many studies on the analyses of SMD in order to investigate their reliability, accuracy, or credibility, but that have not dealt with the filtering techniques applied to with the data before creating the results or after their acquisition. This study provides a methodology for detecting the accuracy and reliability of the filtering techniques for SMD and then a spatial similarity index that analyzes spatial intersections, proximity, and size, and compares them. Finally, we offer a comparison that shows the best combination of filtering techniques and similarity indices to create event maps of SMD by using the Getis-Ord Gi* technique. The steps of this study can be summarized as follows: an investigation of domain-based text filtering techniques for dealing with sentiment lexicons, machine learning-based sentiment analyses on reliability, and developing intermediate codes specific to domain-based studies; then, by using various similarity indices, the determination of the spatial reliability and accuracy of maps of the filtered social media data. The study offers the best combination of filtering, mapping, and spatial accuracy investigation methods for social media data, especially in the case of emergencies, where urgent spatial information is required. As a result, a new similarity index based on the spatial intersection, spatial size, and proximity relationships is introduced to determine the spatial accuracy of the fine-filtered SMD. The motivation for this research is to develop the ability to create an incidence map shortly after a disaster event such as a bombing. However, the proposed methodology can also be used for various domains such as concerts, elections, natural disasters, marketing, etc.