Reviewerly/exHarmony huggingface dataset:
An Efficient OpenCL-Based FPGA Accelerator for MobileNet

Abstract Convolutional neural network is widely used in image processing and image recognition. In order to obtain higher accuracy, the computational complexity and the scale of model and parameters are increasing. FPGA has become a good choice because of its low power consumption and high flexibility. MobileNet replaces the standard convolution by depthwise convolution and pointwise convolution, which greatly reduces the computational complexity and parameters of the model in the case of less precision loss, so that it can be applied to the equipment with limited computing resources. In this paper, we propose an efficient OpenCL-based FPGA CNN accelerator to realize inferencing acceleration of MobileNet. We designed the convolution layer with modularization. We used pipeline to design a depth separable convolution parallel acceleration scheme, and made full use of the DSP resources of FPGA. This design finally achieved a good balance of hardware resources, processing speed and power consumption. The experiments show that the accelerator can reach the inferencing speed of 32.56ms, and the energy consumption is 20W. The speedup is 4x compared with CPU and energy efficiency is 3x compared with GPU.
Full-resolution encoder-decoder networks with multi-scale feature fusion for human pose estimation

To achieve more accurate 2D human pose estimation, we extend the successful encoder-decoder network, simple baseline network (SBN), in three ways. To reduce the quantization errors caused by the large output stride size, two more decoder modules are appended to the end of the simple baseline network to get full output resolution. Then, the global context blocks (GCBs) are added to the encoder and decoder modules to enhance them with global context features. Furthermore, we propose a novel spatial-attention-based multi-scale feature collection and distribution module (SA-MFCD) to fuse and distribute multi-scale features to boost the pose estimation. Experimental results on the MS COCO dataset indicate that our network can remarkably improve the accuracy of human pose estimation over SBN, our network using ResNet34 as the backbone network can even achieve the same accuracy as SBN with ResNet152, and our networks can achieve superior results with big backbone networks.
SDAN: Stacked Diverse Attention Network for Video Action Recognition

Recently, deep learning methods have proved exceptional performance in video action recognition. However, it remains a challenging problem to extract discriminative features from videos effectively. Most existing methods mainly focus on spatial-temporal information separately. In this paper, we propose a novel Stacked Diverse Attention Network (SDAN). It uses a Multi-dimensional Attention Module to emphasize informative maps along the channel and spatial-temporal dimension. Besides, a Supervised Attention Module is designed to generate a weighted feature map in a class-supervised way. Compared with previous methods, the proposed method has the following advantages: (1) The Multi-dimensional Attention Module can exploit effective combinations and correlation of attention mechanisms along different dimensions. (2) The Supervised Attention Module improves the network capability supervised directly by action labels which reveal the class-related object and limb information. Extensive experimental evaluation demonstrates the effectiveness of the proposed approach and establishes significant results on Kinetics400, UCF101 and HMDB51 Datasets. Codes are available on https://github.com/jeff62802217/SDAN-Pytorch.
Projected Pólya Tree

One way of defining probability distributions for circular variables (directions in two dimensions) is to radially project probability distributions, originally defined on R2, to the unit circle. Projected distributions have proved to be useful in the study of circular and directional data. Although any bivariate distribution can be used to produce a projected circular model, these distributions are typically parametric. In this article, we consider a bivariate Pólya tree on R2 and project it to the unit circle to define a new Bayesian nonparametric model for circular data. We study the properties of the proposed model, obtain its posterior characterization and show its performance with simulated and real datasets. Supplemental materials for this article are available online.
The Impact of DDoS Attacks on the Power Usage of Virtual Execution Environments

A machine's power consumption relies heavily on the processes being executed within that machine. A Distributed Process Execution Environment is a common process that can have varying degrees of efficiency and usage of computing resources. The efficiency and use of computing resources can depend on the type of Distributed Process Execution Environment and the tasks being executed within the environment. Comparing different types of Distributed Process Execution Environment's effect on the host machine's power consumption, by executing tasks with a varying level of demand within the Distributed Process Execution Environment, will demonstrate the efficiency of each. Distributed Denial of Service, DDoS, attacks are one method of placing stress on different components of the Distributed Process Execution Environment. The experiment was performed by having several compute nodes as Attackers that worked together to perform a DDoS attack on a single machine, the Defender, running the Distributed Process Execution Environments. As the Defender was undergoing the DDoS attacks, its performance was measured in watts.
Monocular 3D Pedestrian Localization Fusing with Bird's Eye View

In recent years, 3D target detection and location methods in the field of autonomous driving have attracted increasing attention, but monocular 3D pedestrian localization research is still facing challenges. In this paper, a monocular pedestrian localization framework and a fine-grained location optimization method which is based on a bird's eye view are proposed. The monocular pedestrian localization framework is divided into three parts, which are coarse-grained localization, depth information reconstruction and fine-grained location optimization. In the stage of coarse-grained location, the human skeleton information is obtained from the original image by using the human skeleton point detection method, and then the pedestrian position is predicted through the method of a light-weight feed-forward neural network. In the stage of depth information reconstruction, the original image is used to reconstruct the corresponding bird's eye view with depth information through a parallel network. Finally, a fine-grained positioning optimization method makes it possible to get a more precise location with the help of the last two stages. The experimental results on the KITTI dataset show that our method has achieved better performance than the state-of-the-art methods.
Structured object tracking with discriminative patch attributed relational graph

Local features have been widely used in visual tracking to improve robustness in the presence of partial occlusion, deformation, and rotation. In this paper, a structured object tracking algorithm, which uses local discriminative color (LoDC) patch representation and discriminative patch attributed relational graph (DPARG) matching, is proposed. Unlike several existing local feature-based algorithms that divide an object into some rectangular patches of fixed sizes while separately locating each patch to track the object, the proposed algorithm relies on a discriminative color model to distinguish the outstanding colors of the given object. Thus, the multimodal color object is represented by multiple unimodal, homogeneous, and discriminative patches. Moreover, these patches are assembled in a structured DPARG, in which vertexes describe the object’s local discriminative patches while encoding the appearance information, and edges express the relations between vertexes while encoding inner geometric structure information. The object tracking is then formulated as inexact matching of the dynamic undirected graph. The changes of DPARG, along with dynamic environments, are used to filter out invalid patches at the current frame, which usually correspond to those abnormal patches emerging from partial occlusion, similar color disturbances, etc. Finally, the valid patches are assembled to locate the object. The experimental results on the popular tracking benchmark datasets exhibit that the proposed algorithm is reliable enough in tracking even in the presence of serious appearance changes, partial occlusion, and background clutter.
Traffic Redundancy in Blockchain Systems: The Impact of Logical and Physical Network Structures

The current designs of Internet-based blockchain systems generate huge traffic redundancy, thereby reducing the efficacy of the system. This paper simulates the Bitcoin blockchain network traffic in a realistic setting and studies the impacts of both logical and physical network structure on the traffic redundancy. Firstly, we construct an Internet AS layer topology graph and map the Bitcoin nodes to the ASes. Then, Bitcoin 's neighbor discovery algorithm is used to connect Bitcoin nodes together logically. Finally, we simulate the Internet traffic generated by Bitcoin 's flood-based broadcast algorithm in the AS layer topology graph. Specifically, the traffic that a message generates is calculated by the number of ASes a message passes through times the message size. The difference between the total and effective traffic (i.e., the theoretical minimum) is considered traffic redundancy. Simulations show that traffic redundancy is positively correlated with the Bitcoin network scale and the number of peer neighbors for each node. Meanwhile, the underlying physical network topology, especially the average routing path length between the Bitcoin nodes, determines the traffic redundancy as well. Our findings imply that one can optimize the blockchain network's throughput by altering the underlying physical network, e.g., migrating from the Internet to a satellite environment.
Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Lynne Bowker and Jairo Buitrago Ciro

With the improvement of the quality of machine translation (MT for short), the use of machine translation has become ubiquitous in real life. Within translation studies, MT has long been viewed as an aid to translation per se and the focus of MT research has been the development of translation system and the improvement of translation quality. Information literacy which entails the use of MT, for example, has been investigated as part of translator’s competence (Pinto and Sales, 2007). Comparatively speaking, the use of MT as part of global researcher’s information literacy has wider social and cultural implications but has received little attention from translation academia. The increasing exchange of global scholarship and the pervasive use of MT call for the integration of MT and global research. This integration is realized by the publication of the book Machine Translation and Global Research – Towards Improved Machine Translation Literacy in...
Exploiting the progress of OO refactoring tools with Android code smells

Mobile applications market is facing a stronger demand continuously, due to the growing popularity of mobile phones. A demand that forces developers to rush the implementation process and shorten the conception phase, leading to poor conception and implementation choices known as code smells. These smells have a negative effect on both device and application's performance, and must therefore be corrected to ensure the quality of mobile applications and the smoothness of their users' experience. This task requires the identification of the infected entities and their refactoring. Most existing refactoring approaches and techniques are focused on object-oriented applications' code smells while only a few of them are destined to Android specific code smells.
Self-organizing hierarchical mayfly optimization algorithm

In this paper, the self-organizing hierarchical method was introduced to improve the capability of optimization for the newly proposed mayfly optimization (MO) algorithm. The weighted current velocities were removed from the updating equations and the re-initialization would be carried out if the updated velocities were turned to zero. Simulation experiments were carried out with the Monte Carlo method. Results verified the better performance of the improved MO algorithm than before.
The effect of rebalancing on LDA in imbalanced classification

In binary classification, class imbalance is undesirable in that it may worsen the performance of a classifier. One of the remedies to handle this problem is rebalancing with the optimal rate. Theoretical derivation of the rate is not usually considered and often empirically detected, because it is complex and depends on the classifier. To simplify this, we used a linear discriminant classifier, deriving the theoretical optimal rate that maximizes the Matthews Correlation Coefficient (MCC) and F 1 score assuming normality. We showed that adjusting the size of each class to be equal is not always the best solution. Instead, we found that there exists the optimal rate depending on the level of class imbalance and the Mahalanobis distance between two classes. Conducting extensive simulation studies and real data analyses, we confirmed that rebalancing with the optimal rate improves the test MCC and F 1 score. These findings suggest that with a careful consideration on the level of class imbalance and the separability between two classes, we can achieve better classification results in presence of class imbalance.
SnapshotSave

Bootstrapping new peers by replaying all transactions on the ledger is not sustainable for ever-growing blockchains. Inspired by the database checkpointing concept, a snapshot synchronization approach is proposed for the fast bootstrapping purpose. The snapshot storage demand is also reduced through making use of the system state database. A prototype was implemented and evaluated based on the Bitcoin Core source code, and the results show that Snapshot-Save reduces the synchronization time by 99.46% at the 600,000th block of Bitcoin's blockchain. In addition, for the snapshot created at the 600,000th block, the prototype requires 43% less disk space compared with storing the whole snapshot.
Study on Improvement of YOLOv3 Algorithm

Abstract In order to optimize the problem of wrong detection and missed detection of small targets in complex environment, a target detection algorithm of YOLOv3-SPP5 was proposed. YOLOv3 in the deep learning algorithm has achieved excellent detection effect in target detection, but it is not perfect in the complex environment. In this paper, YOLO detection heads were added to the overall network of YOLOv3 to improve the extraction of multi-scale information features. In order to avoid the effect of feature extraction being reduced with the increase of network depth, Spatial Pyramid Pooling (SPP) were added to each YOLO detection head to optimize the extraction of deep network features. The optimized algorithm was named YOLOv3-SPP5.With the same setting of YOLOv3-SPP5 and YOLOv3, Under the same setting of YOLOv3-SPP5 and YOLOv3, the experimental results on COCO data set show that the mAP of YOLOv3-SPP5 increases by 7.9, the Inference time and Volume do not increase significantly. The above experimental results show that the optimized YOLOv3-SPP5 algorithm is more suitable for target detection in complex scenes than YOLOv3 algorithm.
Exploring Reusability and Reproducibility for a Research Infrastructure for L1 and L2 Learner Corpora

Up until today research in various educational and linguistic domains such as learner corpus research, writing research, or second language acquisition has produced a substantial amount of research data in the form of L1 and L2 learner corpora. However, the multitude of individual solutions combined with domain-inherent obstacles in data sharing have so far hampered comparability, reusability and reproducibility of data and research results. In this article, we present work in creating a digital infrastructure for L1 and L2 learner corpora and populating it with data collected in the past. We embed our infrastructure efforts in the broader field of infrastructures for scientific research, drawing from technical solutions and frameworks from research data management, among which the FAIR guiding principles for data stewardship. We share our experiences from integrating some L1 and L2 learner corpora from concluded projects into the infrastructure while trying to ensure compliance with the FAIR principles and the standards we established for reproducibility, discussing how far research data that has been collected in the past can be made comparable, reusable and reproducible. Our results show that some basic needs for providing comparable and reusable data are covered by existing general infrastructure solutions and can be exploited for domain-specific infrastructures such as the one presented in this article. Other aspects need genuinely domain-driven approaches. The solutions found for the corpora in the presented infrastructure can only be a preliminary attempt, and further community involvement would be needed to provide templates and models acknowledged and promoted by the community. Furthermore, forward-looking data management would be needed starting from the beginning of new corpus creation projects to ensure that all requirements for FAIR data can be met.
A New Signature Based on Blockchain

For E-commerce scenes like online transaction or online lottery, security issues arises on anonymity protection for legal users and traceability on illegal ones. This paper introduces a new blockchain-based signature scheme to provide both anonymity and traceability. This scheme requires a credible center to distribute initial private key and to add public key for each user, who can sign at any public key belonging to them. Compared to PKI, it enables verifiers checking validity of signers' public keys in certain blocks without looking up entire certificate list. Unlike Identity-based cryptography, this solution also keeps each user's identity anonymous to other users since the identity is only known by the user and the credible center.
Multiplicative angular margin loss for text-based person search

Text-based person search aims at retrieving the most relevant pedestrian images from database in response to a query in form of natural language description. Existing algorithms mainly focus on embedding textual and visual features into a common semantic space so that the similarity score of features from different modalities can be computed directly. Softmax loss is widely adopted to classify textual and visual features into a correct category in the joint embedding space. However, softmax loss can only help classify features but not increase the intra-class compactness and inter-class discrepancy. To this end, we propose multiplicative angular margin (MAM) loss to learn angularly discriminative features for each identity. The multiplicative angular margin loss penalizes the angle between feature vector and its corresponding classifier vector to learn more discriminative feature. Moreover, to focus more on informative image-text pair, we propose pairwise similarity weighting (PSW) loss to assign higher weight to informative pairs. Extensive experimental evaluations have been conducted on the CUHK-PEDES dataset over our proposed losses. The results show the superiority of our proposed method. Code is available at https://github.com/pengzhanguestc/MAM_loss.
Augmented reality based flash card to increase understanding of healthy food concept for children with intellectual disability

Obesity is a common problem experienced by children with intellectual disabilities due to their daily activity or mobility lack, so that, there are many fats accumulated in body fat tissue. For increasing the understanding and awareness of children with intellectual disabilities about obesity, it is needed a learning media appropriated to their curious minds. The development of flashcard learning media about the food types that cause obesity packaged with augmented reality technology with displays in the form of three-dimensional objects (3d) can improve children’s understanding and awareness of school-age children with intellectual disabilities about the importance of healthy food for their growth and development.
Exploration Study to Study the Relationships Between Variables of Secure Development Lifecycle (SDL)

Secure development lifecycle consists of a detailed plan that defines the process organizations use to build an application from inception until decommission. In this research paper, our focus was to conduct a survey with the help of a well-structured questionnaire to understand the relationship between various variables considered for the study with the help of a correlation test. Summary of the relationships is portrayed in the findings with respect to various levels. Data was analysed with the help of SPSS software. Nearest neighbour analysis also confirms the relationships among the variables considered.
Defect Report Severity Prediction Based on Genetic Algorithms and Convolutional Neural Network

In software maintainence, defect report severity prediction is an important task which can effectively help developer judge the urgency of defects. However, manually classifying the severity of defect reports is very time consuming and tedious. Recently, researchers have proposed many advanced methods to automate the severity prediction of defect reports. But there is still room for improvement in performance. Therefore, in this paper, we propose a distinctive method for automatically identifying the severity of software defect reports using convolutional neural network (CNN). We first preprocess defect reports and select textual features by genetic algorithms (GA). Then, the Word2Vec model is employed to generate word vectors for the selected features in each defect report. Finally we train the classifier based on CNN and leverage the trained classifier to predict the severity of defect reports. To validate the performance of our method, we experiment with defect reports from four open source projects and compare our method with three common machine learning methods. The experimental results show that our method achieves 77.38% in terms of precision, 62.09% in terms of recall and 68.76% in terms of Fl-score on average, and outperforms the best baseline method by 11.61%, 7.23% and 9.32%, respectively.
Research on adaptive control method of DC motor speed based on STC MCU

Due to the limitation of traditional controller circuit integration, the traditional DC motor speed control method has the problem of low control accuracy, which greatly affects the stability of DC motor operation. Therefore, an adaptive control method of DC motor speed based on STC MCU is proposed. Combined with the basic structure and working principle of DC motor, the mathematical model of DC motor is established. Under this model, the running law of motor speed is analyzed, and STC single chip microcomputer is used to optimize and refit the traditional controller. The speed value of DC motor is measured in real time, and the adaptive control value of speed is determined according to the control target of speed, which is used as the input of controller to realize the adaptive control of DC motor speed. Through the control test experiment, the conclusion is drawn: compared with the traditional control method, the speed control error of the design method is reduced by 8.75r/min, and the design method has more advantages in the control accuracy.