Reviewerly/exHarmony huggingface dataset:
On the Feasibility of Hybrid Electrical/Optical Switch Architecture for Large-Scale Training of Distributed Deep Learning

Data parallelism is the dominant method used to train deep learning (DL) model on High-Performance Computing systems such as large-scale GPU clusters. When training a DL model on a large number of nodes, inter-node communication becomes bottle-neck due to its relatively higher latency and lower link bandwidth (than intra-node communication). To cope with this problem, some techniques have been proposed to (a) optimize the collective communication algorithms that take into account the network topology, (b) reduce the message size, and (c) overlap the communication and computation. All of these approaches target to deal with the large message size issue while diminishing the effect of the limitation of the inter-node network. In this study, we investigate the benefit of increasing inter-node link bandwidth by using the hybrid switching systems, i.e., Electrical Packet Switching and Optical Circuit Switching. We found that the typical data-transfer of synchronous data-parallelism training are long-live and rarely changed that can be speed-up with optical switching. Simulation results on Simgrid simulator show that our approach speed-up the training time of deep learning application around 10%.
Spatial movement pattern recognition in soccer based on relative player movements

Knowledge of spatial movement patterns in soccer occurring on a regular basis can give a soccer coach, analyst or reporter insights in the playing style or tactics of a group of players or team. Furthermore, it can support a coach to better prepare for a soccer match by analysing (trained) movement patterns of both his own as well as opponent players. We explore the use of the Qualitative Trajectory Calculus (QTC), a spatiotemporal qualitative calculus describing the relative movement between objects, for spatial movement pattern recognition of players movements in soccer. The proposed method allows for the recognition of spatial movement patterns that occur on different parts of the field and/or at different spatial scales. Furthermore, the Levenshtein distance metric supports the recognition of similar movements that occur at different speeds and enables the comparison of movements that have different temporal lengths. We first present the basics of the calculus, and subsequently illustrate its applicability with a real soccer case. To that end, we present a situation where a user chooses the movements of two players during 20 seconds of a real soccer match of a 2016-2017 professional soccer competition as a reference fragment. Following a pattern matching procedure, we describe all other fragments with QTC and calculate their distance with the QTC representation of the reference fragment. The top-k most similar fragments of the same match are presented and validated by means of a duo-trio test. The analyses show the potential of QTC for spatial movement pattern recognition in soccer.
Based on Improved Edge Detection Algorithm for English Text Extraction and Restoration From Color Images

The superimposed text in color images is an important clue to help understand the content of the image. Extracting and translating it into a familiar language is a hot research topic in the current image field. However, because the background of the image is usually complicated, effective text extraction and restoration is very challenging. In view of the above problems, this paper proposes an English text repair and extraction algorithm for color images based on improved edge detection algorithm. Firstly, the text of the color image is repaired by the sample block-based method. Secondly, the traditional edge-based detection method is improved, and the image is grayed out, and the edge detection is performed to obtain the candidate text area. Then, using the restrictive conditions such as text color and size, the text area is filtered out to extract the target text. Finally, the text information embedded in the extracted processed color image is translated into the target language. The experimental results show that the proposed algorithm has higher accuracy and faster computing speed, and achieves a better comprehensive effect. It further illustrates the extraction of English text by image processing technology, which can facilitate machine translation.
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy.
PoSeg: Pose-Aware Refinement Network for Human Instance Segmentation

Human instance segmentation is a core problem for human-centric scene understanding and segmenting human instances poses a unique challenge to vision systems due to large intra-class variations in both appearance and shape, and complicated occlusion patterns. In this paper, we propose a new pose-aware human instance segmentation method. Compared to the previous pose-aware methods which first predict bottom-up poses and then estimate instance segmentation on top of predicted poses, our method integrates both top-down and bottom-up cues for an instance: it adopts detection results as human proposals and jointly estimates human pose and instance segmentation for each proposal. We develop a modular recurrent deep network that utilizes pose estimation to refine instance segmentation in an iterative manner. Our refinement modules exploit pose cues in two levels: as a coarse shape prior and local part attention. We evaluate our approach on two public multi-person benchmarks: OCHuman dataset and COCOPersons dataset. The proposed method surpasses the state-of-the-art methods on OCHuman dataset by 3.0 mAP and on COCOPersons by 6.4 mAP, demonstrating the effectiveness of our approach.
Design and Implementation of FPGA based Vending Machine for Integrated Circuit (IC)

Vending machine that dispense items like as snacks, coffee, beverages, lottery ticket, consumer products automatically when a customer insert a coin or token are increasing rapidly in metropolitan's cities due to contemporary and fast life style requiring excellent quality products. This paper give the details account of the designing and implementation of a multi select state vending machine using State Machine with which users can select the product and insert the desired token for respective products to dispense the Integrated Circuit (IC) or it will return the inserted token if wrong token is inserted for each Integrated Circuit (IC). We choose FPGA for this vending machine since FPGA based vending give quicker response and absorbed less power and reprogrammed anytime as compared to CMOS vending machine. The intended design is implemented in VHDL and simulated using Xilinx VIVADO 2019.1 and it's implemented on FPGA basy -3 Artix 7 series FPGA (xc7a200tfbg676) development board. Synthesis results and FPGA experimental results are shown in this paper.
Augmenting Spatio-Temporal Human Motion Data for Effective 3D Action Recognition

Action recognition is a fundamental operation in 3D human motion analysis. Existing deep learning classifiers achieve a high recognition accuracy if large amounts of training data are provided. However, such data are difficult to obtain in a variety of application scenarios, mainly due to the high costs of motion capture technologies and an absence of suitable actors. In this paper, we propose augmentation techniques to artificially enlarge existing collections of 3D human skeleton sequences. The proposed techniques are especially useful for datasets distinguishing in a high number of classes, each of them characterized by only a limited number of action samples. We experimentally demonstrate that the augmented data help to significantly increase the recognition accuracy even using a standard deep learning architecture.
Multitarget prediction using an aim-object-based asymmetric neuro-fuzzy system: A novel approach

This paper proposes an aim-object-based asymmetric neuro-fuzzy system that is different from conventional models in two ways. First, this system has an asymmetric structure with different numbers of neurons in the premise and consequent layers. Secondly, with the assistance of the sphere complex fuzzy set, depending on the application, our model can alter the number of outputs. In addition, a hybrid learning algorithm combining the whale optimization algorithm and the recursive least-square estimator is proposed to optimize the proposed model. The results of the experiment show that the proposed model can simultaneously predict multiple targets with fewer parameters and maintain a performance level similar to that of the conventional neuro-fuzzy system.
Effective and scalable causal partitioning based on low-order conditional independent tests

Abstract Recovering causal relationships from observed data is crucial to a variety of applications. Due to the curse of dimensionality, general causal discovery methods such as constraint-based methods and functional model based methods are not quite effective and efficient for large and high-dimensional data sets. Thus, some causal partitioning methods have been proposed to handle this problem. However, existing causal partitioning methods rely on high-order conditional independent (CI) tests, which makes them inefficient in handling dense causal graphs. Therefore, high-dimensionality is still a big challenge to these methods. In this work, we propose a new split-and-merge strategy to enable effective and scalable causality discovery. Different from the existing methods, our method uses only low-order CI tests, can get more accurate results and is applicable to various scenarios. We provide both theoretic analysis and empirical evaluation on the proposed method. Experiments on various real-world causal graphs show that the proposed method outperforms the stat-of-the-art method in terms of accuracy, efficiency and scalability. For high-dimensional cases, our method is much faster than the counterpart by one to three orders of magnitudes.
Uncertain decision tree for bank marketing classification

This study proposes a novel decision tree for uncertain data, called the uncertain decision tree (UDT), based on the uncertain genetic clustering algorithm (UGCA). UDT extends the decision tree to handle data with uncertain information, in which the uncertainty must be considered to obtain high quality results. In UDT, UGCA automatically searches for the proper number of branches of each node, based on the classification error rate and the classification time of UDT. Restated, UGCA reduces both the classification error rate and computing time and, then, optimizes the proposed UDT. Before the UDT is designed using UGCA, an uncertain merging algorithm (UMA) is also developed to reduce the uncertain data set, thereby allowing UGCA to process a large data set efficiently. Importantly, experimental results demonstrate that the proposed UDT outperforms traditional uncertain decision trees.
Detecting Error-Handling Bugs without Error Specification Input

Most software systems frequently encounter errors when interacting with their environments. When errors occur, error-handling code must execute flawlessly to facilitate system recovery. Implementing correct error handling is repetitive but non-trivial, and developers often inadvertently introduce bugs into error-handling code. Existing tools require correct error specifications to detect error-handling bugs. Manually generating error specifications is error-prone and tedious, while automatically mining error specifications is hard to achieve a satisfying accuracy. In this paper, we propose EH-Miner, a novel and practical tool that can automatically detect error-handling bugs without the need for error specifications. Given a function, EH-Miner mines its error-handling rules when the function is frequently checked by an equivalent condition, and handled by the same action. We applied EH-Miner to 117 applications across 15 software domains. EH-Miner mined error-handling rules with the precision of 91.1% and the recall of 46.9%. We reported 142 bugs to developers, and 106 bugs had been confirmed and fixed at the time of writing. We further applied EH-Miner to Linux kernel, and reported 68 bugs for kernel-4.17, of which 42 had been confirmed or fixed.
Recognizing novel patterns via adversarial learning for one-shot semantic segmentation

One-shot semantic segmentation aims to recognize unseen object regions by using the reference of only one annotated example. Many deep convolutional neural networks have achieved enormous success on this task. However, most of the existing methods only use a fixed annotated dataset to train the network. The remaining unannotated examples remain difficult to be leveraged and recognized. In this study, we propose a procedure based on the generative adversarial network to enable the one-shot semantic segmentation model for learning information from previously unknown categories. Our method contains a segmentation network that generates segmentation predictions. We then use a discriminator to differentiate the probability maps of segmentation prediction from the ground truth distribution. Consequently, we can ignore the pixels classified as fake and only use trustworthy regions as the label to train the segmentation network, thus achieving semi-supervised learning. Experimental results demonstrate the effectiveness of the proposed adversarial learning method with an average gain of 49.7% accuracy score on the PASCAL VOC 2012 dataset.
Levels of hate in online environments

Hate speech in online environments is a severe problem for many reasons. The space for reasoning and argumentation shrinks, individuals refrain from expressing their opinions, and polarization of views increases. Hate speech contributes to a climate where threats and even violence are increasingly regarded as acceptable.
Curiosity-Driven Exploration for Off-Policy Reinforcement Learning Methods

Deep reinforcement learning (DRL) has achieved remarkable results in many high-dimensional continuous control tasks. However, the RL agent still explores the environment randomly, resulting in low exploration efficiency and learning performance, especially in robotic manipulation tasks with sparse rewards. To address this problem, in this paper, we intro-duce a simplified Intrinsic Curiosity Module (S-ICM) into the off-policy RL methods to encourage the agent to pursue novel and surprising states for improving the exploration competence. This method can be combined with an arbitrary off-policy RL algorithm. We evaluate our approach on three challenging robotic manipulation tasks provided by OpenAI Gym. In our experiments, we combined our method with Deep Deterministic Policy Gradient (DDPG) with and without Hindsight Experience Replay (HER). The empirical results show that our proposed method significantly outperforms vanilla RL algorithms both in sample-efficiency and learning performance.
Function Interpolation for Learned Index Structures

Range indexes such as B-trees are widely recognised as effective data structures for enabling fast retrieval of records by the query key. While such classical indexes offer optimal worst-case guarantees, recent research suggests that average-case performance might be improved by alternative machine learning-based models such as deep neural networks. This paper explores an alternative approach by modelling the task as one of function approximation via interpolation between compressed subsets of keys. We explore the Chebyshev and Bernstein polynomial bases, and demonstrate substantial benefits over deep neural networks. In particular, our proposed function interpolation models exhibit memory footprint two orders of magnitude smaller compared to neural network models, and 30–40% accuracy improvement over neural networks trained with the same amount of time, while keeping query time generally on-par with neural network models.
SLAT-Calib: Extrinsic Calibration between a Sparse 3D LiDAR and a Limited-FOV Low-resolution Thermal Camera

Accurate estimation of extrinsic parameter (rotation matrix and translation vector) between heterogeneous sensors is important for fusing complementary information. However, the extrinsic calibration between a sparse 3D LiDAR and a thermal camera is challenging, mainly because of the difficulties to accurately extract common features from a sparse point cloud and a thermal image which has limited-FOV and low-resolution. Previous methods either rely on a dense depth sensor or a visual camera to facilitate the feature extraction process. To address the problem, SLAT-Calib (Sparse Lidar And Thermal camera Calibration) is proposed. By observing that circular holes could be detected from both sensors, a specially designed calibration board (a rectangular board with four circular holes) is introduced. Four circle centers in 3D space are used as common features. The benefit is point features are accurate and reliable for feature matching. To extract four circle centers from the thermal camera, three steps are carried out: First, a method is proposed to accurately detect the four circles. Then, the homography matrix of the calibration board can be figured out. Lastly, 3D coordinates of the circle centers are calculated by decomposing the homography matrix. From the LiDAR frame, the four circle centers can be segmented out as long as two laser beams pass through each circle. At last, optimal extrinsic parameter is calculated by minimizing the matching error between the four pairs of 3D circle centers. Quantitative and qualitative experiments are carried out. In simulation, SLAT-Calib outperforms two methods by a large margin. In real environment, it achieves a re-projection error (RMSE) of 0.62 pixel.
Biometric Authentication in Health Care Sector: A Survey

The development of technology has affected the performance of the healthcare system globally. There is no denying the way that security is fundamental in-patient consideration. Health care records are essential to every patient and medical expert as well. There should be a system that only authorised person can get access to the patient record because if any unauthorised person gains access to the patient record, it can lead to the wrong medication or even the death of the patient. Identification of a person is a very challenging problem. The traditional method includes ID cards and password etc. It can be lost, misuse or can share among colleague. To overcome these limitations of traditional ways biometric authentication method introduced. Biometric as the identifier can play a significant role in maintaining security and privacy of healthcare system. Biometric identification is the measurable unique traits of the individual. It divides into two categories: first is physiological that includes the face, fingerprints, iris, retina and hand geometry. Second is behavioural that includes voice recognition, signature verification and keystroke dynamics. In this survey paper, we review different physical and behavioral biometric techniques that described in a different research paper in past decades; then we analyse findings and future direction of different papers separately based on physical and behavioural biometric methods. We provide a comparative analysis of all biometric techniques it will help out readers to get a better idea about different biometric technologies that which one will suit according to their needs and budget. We conclude that for identification, authentication, in healthcare sector there is a need of proper implementation of any of these biometric authentication techniques or the combination of different techniques properly, to provide advanced security and prevention from any security threats to patient records.
An Efficient Location Privacy Preserving Model based on Geohash

With the rapid development of location-aware mobile devices, location-based services have been widely used. When LBS (Location Based Services) bringing great convenience and profits, it also brings great hidden trouble, among which user privacy security is one of them. The paper builds a LBS privacy protection model and develops algorithm depend on the technology of one dimensional coding of Geohash geographic information. The results of experiments and data measurements show that the model the model has reached k-anonymity effect and has good performance in avoiding attacking from the leaked information in a continuous query with the user's background knowledge. It also has a preferable performance in time cost of system process.
Multi-heads Attention Graph Convolutional Networks for Skeleton-Based Action Recognition

Compared with video-based action recognition, skeleton-based methods have more compact and accurate representation. In the recent development, human skeleton is modeled as graph and graph-based deep learning method is applied for recognition. Generally, in actions, few joints are pivotal other than the whole body, like waving hands mostly related with hand and arm joints. Hence, we propose the data-driven multi-head attention model for graph convolutional networks. The attention model identifies key joints of every action by introducing two regularization terms, i.e., spatial diversity and local continuity. Further, we introduce the joint-wise second order motion information as the additional feature on the graph node, which represents the motion variation explicitly. We have tested our methods on the largest popular dataset, NTU-RGB+D, and we reach state-of-the-art performance.
Measuring the accuracy of software vulnerability assessments: experiments with students and professionals

Abstract Assessing the risks of software vulnerabilities is a key process of software development and security management. This assessment requires to consider multiple factors (technical features, operational environment, involved assets, status of the vulnerability lifecycle, etc.) and may depend from the assessor’s knowledge and skills. In this work, we tackle with an important part of this problem by measuring the accuracy of technical vulnerability assessments by assessors with different level and type of knowledge. We report an experiment to compare how accurately students with different technical education and security professionals are able to assess the severity of software vulnerabilities with the Common Vulnerability Scoring System (v3) industry methodology. Our results could be useful for increasing awareness about the intrinsic subtleties of vulnerability risk assessment and possibly better compliance with regulations. With respect to academic education, professional training and human resources selections our work suggests that measuring the effects of knowledge and expertise on the accuracy of software security assessments is feasible albeit not easy.