Reviewerly/exHarmony huggingface dataset:
Global relative position space based pooling for fine-grained vehicle recognition

Abstract In the field of fine-grained vehicle recognition, large intra-class variation often affects the final prediction seriously. One of the principal reasons is unstable change of absolute positions for discriminative features, which can not be completely solved by the region of interest (ROI) localization for inevitable deviations. In this paper, we propose a global relative position space based pooling (GRPSP) method to replace the absolute position information with global relative position information, by which the discriminative features can be well aligned and thereby the intra-class variation is reduced effectively. Specifically, the discriminative features containing both absolute position information and appearance information are extracted by a convolution neural network (CNN). We then transform the absolute positions into global relative positions, and estimate position probabilities of locating at discrete representatives of global relative position space. Accordingly, the joint probabilities of different kinds of features locating at different representative positions can be calculated, and constitute the pooling vector. Lastly, the Support Vector Machine (SVM) is trained for classification. Experimental results on public and self-building challenging datasets both show that our proposed method can improve various CNNs and is the state-of-the-art.
Light CNN Architecture Enhancement for Different Types Spoofing Attack Detection

The widely acknowledged vulnerability of automatic speaker verification systems (ASV) to various spoofing attacks requires the development of countermeasures robust to unforeseen spoofing trials. In this paper we consider deep learning approach based on Light CNN architecture and its modification for replay attack detection on the base of ASVspoof2017 V2. The efficiency of Light CNN based approaches for replay attacks detection has already been confirmed during ASVspoof2017 (for ASVspoof V1 corpora) and ASVspoof2019 Challenges. We enhanced Light CNN architecture previously considered by the authors via applying angular margin based softmax activation for training robust deep Light CNN classifier. The proposed system achieved Equal Error Rate of 5.5% on the evaluation part of ASVspoof2017 V2. In addition, we also investigated the possibility of unified LCNN-based approach to detect not only replay spoofing attacks but also attacks of logical level, specifically speech synthesis and voice conversion. The experiment results were obtained for microphone part of PHONESPOOF database.
PSO Based Path Planning of a Six-Axis Industrial Robot

Aristo robot is a 6-axis articulated robot widely used in industries to lift small parts with greater accuracy. The use of robot in production sector depends upon the efficiency with which it performs a given task with shortest possible time. This research presents a proposal, to optimize the path for a particular task by adopting Particle Swarm Optimization (PSO) algorithm. For the analysis of optimal path, total ten numbers of objects have been defined and the robot has to cover up all objects with all possible paths with minimum cycle time. The optimal solution is obtained using robomaster simulation software and the global best solution is obtained using PSO. The program for optimization of path length is written in MATLAB software. The global best result obtained using PSO is a simple method for solving path planning problems for any industrial applications.
Automatic identification and counting of repetitive actions related to an industrial worker

This study aims at proposing an innovative experimental methodology for the automatic recognition and counting of occupational repetitive action during a work cycle of an industrial worker. Through this aim, we want to evaluate the possibility to objectivize the computation of the OCcupational Repetitive Action (OCRA) index. Two healthy subjects were enrolled in the study and they were asked to perform 11 technical actions in two different repetitions. Participants were equipped with 23 inertial sensors place on different body segments in order to evaluate angles related to trunk, shoulder, elbow and wrist in the three anatomical planes. An algorithm based on Support Vector Machines was used for the automatic recognition of the technical actions; a further algorithm was implemented for the counting of the actions after their recognition. The recognition algorithm was tested with both a subject-specific training and a standard training. Results showed an accuracy in the recognition greater than 89.5% and 86.5% for the subject-specific and standard training, respectively. As regards the action counting, algorithm showed an accuracy from 72.2% to 100% based on different actions. This preliminary study opens the possibility to validate an automatic methodology for the automatic detection and counting of repetitive actions in industrial environment based on machine-learning algorithm and inertial sensors.
Impact of Layer Normalization on Single-Layer Perceptron — Statistical Mechanical Analysis

Various normalization methods have emerged since Ioffe et al. developed Batch Normalization in 2015. These methods made it possible to achieve fast and stable training. Layer Normalization, which was proposed by Ba et al. in 2016, is one such method and widely recognized for easily normalizing hidden layers independent of the batch size. Despite its wide use, the impact of Layer Normalization on the global behavior of neural networks is yet to be clarified. In this study, we analyzed the learning dynamics of single-layer perceptrons with Layer Normalization in the student-teacher model by using the statistical mechanical method. We derive differential equations of order parameters, which explains the macroscopic dynamics of the neural networks, and show that single-layer perceptrons with Layer Normalization are robust against a change in the learning rate, initial weight vector, and variance in the inputs to the layer.
Lessons Learnt from Experiments on the Ad Hoc Multilingual Test Collections at CLEF

This chapter describes the lessons learnt from the ad hoc track at CLEF in the years 2000 to 2009. This contribution focuses on Information Retrieval (IR) for languages other than English (monolingual IR), as well as bilingual IR (also termed "cross-lingual"; the request is written in one language and the searched collection in another), and multilingual IR (the information items are written in many different languages). During these years the ad hoc track has used mainly newspaper test collections, covering more than 15 languages. The authors themselves have designed, implemented and evaluated IR tools for all these languages during those CLEF campaigns. Based on our own experience and the lessons reported by other participants in these years, we are able to describe the most important challenges when designing a IR system for a new language. When dealing with bilingual IR, our experiments indicate that the critical point is the translation process. However, currently online translating systems tend to offer rather effective translation from one language to another, especially when one of these languages is English. In order to solve the multilingual IR question, different IR architectures are possible. For the simplest approach based on query translation of individual language pairs, the crucial component is the merging of the intermediate bilingual results. When considering both document and query translation, the complexity of the whole system represents clearly a main issue.
Beyond Error Propagation: Language Branching Also Affects the Accuracy of Sequence Generation

Sequence generation tasks, such as neural machine translation (NMT) and abstractive summarization, usually suffer from exposure bias as well as the error propagation problem due to the autoregressive training and generation. Many previous works have discussed the relationship between error propagation and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accuracy drop</i> problem (i.e., the right part of the generated sentence is often worse than its left part in left-to-right decoding models). In this paper, taking NMT as a typical sequence generation task, we measure the accuracy of the generated sentence with various metrics and conduct a series of analyses to deeply understand the accuracy drop problem. We obtain several interesting findings. First, The role of error propagation on accuracy drop is overstated in the literature, although it is indeed a cause to the accuracy drop problem. Second, Characteristics of a language play a more important role in causing the accuracy drop problem: the left part of the generated sentence in a right-branching language (e.g., English) is more likely to be more accurate than its right part, while the right part is more accurate for a left-branching language (e.g., Japanese). Our discoveries are also confirmed on other generation tasks (e.g., image captioning, abstractive summarization and language modeling) with multiple left/right-branching languages, as well as in various model structures.
Web-Based Interactive 3D Educational Material Development Framework and Its Authoring Functionalities

Recent advances of web technologies besides computer hardware technologies have made web-based applications popular. Similarly, web-based educational materials have become popular because learners can study at anytime, anywhere using such materials on web browsers in their mobile devices like smartphones. This paper treats the development framework for such web-based educational materials. To increase the learning motivation of students, it is necessary to provide the students with attractive educational materials using state-of-the-art ICT like 3D CG. The authors have already proposed a web-based interactive 3D educational material development framework and several educational materials as its examples. However, the proposed framework has not had enough authoring functionalities. In this paper, the authors added several authoring functionalities to the proposed framework in order to make it possible to develop educational materials for standard users who do not have programming knowledge and skills like teachers. This paper introduces the added authoring functionalities to justify the usefulness of the proposed framework.
An Improved Harris Corner Detection Algorithm

The traditional Harris corner detection algorithm is sensitive to noise, and Corner is prone to drift at different image resolution. Combined with the multi-scale features of wavelet transform, propose a corner detection algorithm based on the wavelet transform. The algorithm maintains the advantages of Harris corner detection algorithm in image scaling, rotation or gray scale change, improves its disadvantage of scale invariance, and has strong anti-noise and real-time performance. It has good anti-noise and real-time performance.
A Simulation Framework for Virtualized Resources in Cloud Data Center Networks

Many IT companies are embracing the new softwarization paradigm through the adoption of new architecture models, such as software-defined network and network function virtualization, primarily to limit the costs of maintaining and deploying their network infrastructures, by giving the possibility to service/application providers to reconfigure and programmatically perform actions on the network. Accordingly, the dynamic management of the data center networks requires complex operations to ensure high availability and continuous reliability in order to guarantee full functionality of the virtualized resources. In this context, simulator-based approaches are helpful for planning and evaluating the deployment of the cloud data center networking, but existing cloud simulators have several limitations: they have too high overhead for wide-scale data center networks, complex configuration, and too abstract deployment models. For these motivations, we propose DCNs-2, a novel extension for the Ns-2 simulator, as a valid solution to efficiently simulate a cloud network infrastructure, with all the involved entities, such as switches, physical/virtual machines, and racks. The proposed solution not only makes configuration easier, but through extensive tests, we show that its execution overhead is limited to less than 130 MB of memory and the execution time is acceptable even for very wide-scale and complex deployment environments.
Fuzzy electromagnetic optimisation clustering algorithm for collaborative filtering

In this paper, an attempt is made to improve the performance of fuzzy c-means (FCM) clustering by incorporating in it the concepts of Electromagnetic Force Optimisation (EFO).EFO is a recently proposed metaheuristic for global optimisation. Its integration with FCM helps in minimising the shortcomings of FCM by providing a good initial start and eventually in determining efficient clusters. The proposed method, named fuzzy electromagnetic clustering, is tested on MovieLens data set. Simulation results demonstrate that the proposed algorithm outperforms the conventional methods available in literature in terms of RMSE and MAE metrics.
Segmentation of Breast MRI Scans in the Presence of Bias Fields

Magnetic Resonance Imaging (MRI) is currently considered to be the most sensitive tool for imaging-based diagnostic and presurgical assessment of breast cancer. In addition to their valuable diagnostic contrasts, MRI scans also provide a superb delineation of breast anatomy, which facilitates a number of related clinical applications, among which are breast density estimation and bio-mechanical modeling of breast tissue. Such applications, however, require one to know the disposition of various types of the tissue, thus warranting the procedure of image segmentation. In the case of breast MRI, the latter is known to be a challenging problem due to the relatively complicate nature of measurement noises, which is particularly problematic to deal with in the presence of bias fields. Accordingly, in this work, we introduce a simple method that can be used to “gaussianize” the noise statistic, which allows the problem of image segmentation to be formulated in the form of a simple optimization problem. In this formulation, segmentation of breast MRI scans can be completed in only a few iterations as demonstrated by our experiments involving both in silico and in vivo MRI data.
Dual Refinement Network for Single-Shot Object Detection

Object detection methods fall into two categories, i.e., two-stage and single-stage detectors. The former is characterized by high detection accuracy while the latter usually has a considerable inference speed. Hence, it is imperative to fuse their merits for a better accuracy vs. speed trade-off. To this end, we propose a dual refinement network (DRN) to boost the performance of the single-stage detector. Inheriting from the advantages of two-stage approaches (i.e., two-step regression and accurate features for detection), anchor refinement and feature offset refinement are conducted in a novel anchor-offset detection, where the detection head is comprised of deformable convolutions. Moreover, to leverage contextual information for describing objects, we design a multi-deformable head, in which multiple detection paths with different receptive field sizes devote themselves to detecting objects. Extensive experiments on PASCAL VOC and ImageNet VID datasets are conducted, and we achieve a state-of-the-art detection performance in terms of both accuracy and inference speed.
A non-fuzzy interferometric phase estimation algorithm based on modified Fully Convolutional Network

Classical phase unwrapping methods suffer from layover regions where phases are discontinuous and layover residues occur. To overcome this weakness, we proposed an interferogram-segmentation-assisted phase estimation method to minimize the influence of layovers. The modified Fully Convolutional Networks (FCN) is first applied to classify interferogram pixels into normal pixels and layover residues. By means of only taking normal pixels as the input of phase filtering and unwrapping steps, the optimized non-fuzzy phase is obtained. Results on simulated and real data verify that the proposed algorithm can effectively avoid the error propagation of residues in layovers, and significantly improve the precision of phase unwrapping.
Improved A-search guided tree construction for kinodynamic planning

With node selection being directed by a heuristic cost [1]–[3], A-search guided tree (AGT) is constructed on-the-fly and enables fast kinodynamic planning. This work presents two variants of AGT to improve computation efficiency. An improved AGT (i-AGT) biases node expansion through prioritizing control actions, an analogy of prioritizing nodes. Focusing on node selection, a bi-directional AGT (BAGT) introduces a second tree originated from the goal in order to offer a better heuristic cost of the first tree. Effectiveness of BAGT pivots on the fact that the second tree encodes obstacles information near the goal. Case study demonstrates that i-AGT consistently reduces the complexity of the tree and improves computation efficiency; and BAGT works largely but not always, particularly with no benefit observed for simple cases.
Area-Proportional Visualization for Circular Data

Data visualization is important for statistical analysis, as it helps convey information efficiently and shed lights on the hidden patterns behind data in a visual context. It is particularly helpful to display circular data in a two-dimensional space to accommodate its nonlinear support space and reveal the underlying circular structure which is otherwise not obvious in one-dimension. In this article, we first formally categorize circular plots into two types, either height- or area-proportional, and then describe a new general methodology that can be used to produce circular plots, particularly in the area-proportional manner, which in our opinion is the more appropriate choice. Formulas are given that are fairly simple yet effective to produce various circular plots, such as smooth density curves, histograms, rose diagrams, dot plots, and plots for multiclass data. Supplemental materials for this article are available online.
Impact of Network Load on Direct Acyclic Graph Based Blockchain for Internet of Things

In this paper, we investigate the impact of network load on the Direct Acyclic Graph (DAG) consensus process for the blockchain enabled Internet of Things. Specifically, Markov chain model has been adopted to illustrate the dynamics in the consensus process for DAG based blockchain networks. The key performance metrics including cumulative weight and transaction confirmation delay are analysed under the unsteady state caused by the fluctuation of transaction arrival rate. The analysis is based on one of the most typical DAGs, Tangle, and the results show that the performance will be affected when the transaction arrival rate fluctuates widely.
Lyndon interpolation theorem of instantial neighborhood logic – constructively via a sequent calculus

Instantial neighborhood logic (INL) is classical propositional logic enriched by a two-sorted operator □. In a neighborhood model, a formula like □(α1,...,αj;α0) (for an arbitrary natural number j) means that, the current point has a neighborhood in which α0 universally holds and none of α1,...,αj universally fails. This paper offers to INL: (1) a cut-free sequent calculus, and (2) a constructive proof of its Lyndon interpolation theorem.
SLIC segmentation method for full‐polarised remote‐sensing image

The rapid development of remote-sensing data acquisition technology means that the resolution of remote-sensing image has been continuously improved, resulting in the large scale of remote-sensing image data and the increase of redundant information, which restricts the image processing and analysis. However, super-pixel segmentation method mainly aimed at the general image segmentation algorithm, rarely for remote-sensing image. Therefore, here, the polarisation decomposition of full-polarised remote-sensing images is used to synthesise pseudo-colour images. Then, based on the region, the image segmentation algorithm based on the colour feature of SLIC super-pixel segmentation algorithm is used to make it have important application value in remote-sensing image target extraction. Here, the image of Danjiangkou reservoir obtained by high-resolution space-borne SAR is selected as the research object. Finally, the polarisation decomposition and segmentation of full-polarised remote-sensing images are realised, and the advantages of SLIC super-pixel algorithm are proved.
When Cooperative Co-Evolution Meets Coordinate Descent: Theoretically Deeper Understandings and Practically Better Implementations

Decomposition-based optimizers have shown very promising computational and convergence performance on many large-scale real-parameter optimization problems. Among them, a class of recently proposed cooperative coevolutionary algorithms (CCEAs) and a type of conventional block coordinate descent algorithms (BCDAs) are arguably the two most representative frameworks applied to the minimization of non-differentiable and differentiable objective function, respectively. This paper explores the connections between CCEAs and BCDAs, which can help gain deeper understandings of CCEAs. First, we propose a unified analytical framework for both CCEAs and BCDAs to capture the common game-theoretic nature by combining their respective theoretical advances. Second, many real-world objective functions are non-additively separable, where all decision variables interact with each other in a direct or indirect fashion. However, most of the state-of-the-art decomposition strategies for CCEAs can only capture the simple additive separability and cannot recognize the non-additive separability, but which has been widely studied in the BCDAs context. The performance of CCEAs on such functions is yet to be fully understood since intuitively CCEAs seem to be not suitable for them. We use the proposed framework to confirm and extend CCEAs' applicability to a special class of non-additively separable functions. Finally, based on the proposed framework, we provide two practical suggestions as well as a suite of new test functions to help design practically better CCEAs for large-scale optimization.
New Interactive VR/AR Media

The advent of broadband technology has revolutionized the fruition of multimedia contents. Moreover, the cloud is now moving the computational load from domestic devices to remote servers, enabling the provisioning of new multimedia services through the Internet. To provide a proper user experience, a low E2E latency and a high bandwidth are both required. Unfortunately, this is difficult to achieve in a shared network like the Internet. My PhD project focuses on the study and analysis of modern network protocols to identify viable solutions for a suitable user experience.
How personality traits influences quality of software developed by students

The activity of analyzing personality of software developers has been a topic discussed by many researchers over the past few years. However, their relation to software metrics has hardly been mentioned in the literature. This work aims to identify the influence of human personality on quality of software products. At first, a psychological test was performed using the MBTI model for a set of academy students and, subsequently, CK metrics were applied to individual software developed by members of the same group. As a result, it was evidenced, through statistical analysis, that the Response For a Classe(RFC) and Weighted Methods Per Class (WMC) metric, do not have a significant relationship with MBTI types. In another analysis, taking into account ideal average values for each CK metric, it was evidenced that Depth of Inheritance(DIT) metric have a significant relationship with MBTI types. Therefore, additional studies are needed to determine any deeper connection between personality and software quality.