Reviewerly/exHarmony huggingface dataset:
ARNet: Active-Reference Network for Few-Shot Image Semantic Segmentation

To make predictions on unseen classes, few-shot segmentation becomes a research focus recently. However, most methods build on pixel-level annotation requiring quantity of manual work. Moreover, inherent information on same-category objects to guide segmentation could have large diversity in feature representation due to differences in size, appearance, layout, and so on. To tackle these problems, we present an active-reference network (ARNet) for few-shot segmentation. The proposed active-reference mechanism not only supports accurately cooccurrent objects in either support or query images, but also relaxes high constraint on pixel-level labeling, allowing for weakly boundary labeling. To extract more intrinsic feature representation, a category-modulation module (CMM) is further applied to fuse features extracted from multiple support images, thus forgetting useless and enhancing contributive information. Experiments on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> dataset show the proposed method achieves a m-IOU score of 56.5% for 1-shot and 59.8% for 5-shot segmentation, being 0.5% and 1.3% higher than current state-of-the-art method.
Research on the slicing method with equal thickness and low redundancy based on STL files

To preserve characteristics of triangles in random order, with high repetition between the adjacent layers in STL files, a uniform thickness algorithm is proposed to improve the slicing efficiency, which deletes the triangles below the slicing plane gradually to decrease the number of searches for intersecting triangles and establish the dynamic topology based on intersecting triangles to obtain orderly intersections and closed profiles. A slicing program is developed based on a VC++ platform and an OpenGL interface. This slicing program is validated by comparing the slicing efficiency of different slicing methods for three ASCII STL models. The results show that the slicing program developed in this work can significantly improve the slicing efficiency of STL models.
Latent sparse transfer subspace learning for cross-corpus facial expression recognition

Facial expression recognition has become an increasingly important research topic in pattern recognition and affective computing. Most of facial expression recognition methods assume that the training and testing data come from the same corpus. However, in practical situations, this assumption does not hold, in which the data are often collected from different scenarios, e.g., different races, environments, or devices, and the recognition performance will drop significantly. To tackle this problem, in this paper, we propose a novel transfer learning method, called latent sparse transfer subspace learning (LSTSL), for cross-corpus facial expression recognition. Specifically, we aim to learn a common subspace in which the target samples can be linearly represented by a few source samples. By introducing an ℓ 2 , 1 -norm on the reconstructive transformation matrix, the most discriminative features can be well selected. To guide the new representation learning, we design a novel graph in which the local structure information can be well preserved. Furthermore, we introduce the popular distance metric, i.e., maximum mean discrepancy (MMD), to boost the transfer ability. We conduct extensive cross-corpus experiments on four popular facial expression datasets. The results show that our proposed method can outperform several state-of-the-art transfer learning methods for cross-corpus facial expression recognition.
Mob programming: From avant-garde experimentation to established practice

Mob programming is an emergent practice in the industry, attracting increasing attention from the research community. Superficially a simple concept – similar to pair programming, but with more people – many of the same concerns arise as with pair programming, only more so: can it truly be efficient, is it for everybody, and what are the benefits and risks involved? In this paper we show that while research interest in this topic is increasing, much of the published literature to date constitutes experience reports, with a number of implicit differences in how mob programming is practiced. To transform mob programming from an area of avant-garde experimentation into a well understood practice in the toolbox of mainstream software engineering, such implicit differences need to be exposed, analyzed and documented, and the contextual enabling factors must be investigated. In this paper we take an important step in that direction by identifying variations in the practice and providing concrete guidance for creating an organizational and social environment where mob programming can be practiced both effectively and safely.
Stacked Capsule Graph Autoencoders for geometry-aware 3D head pose estimation

The goal of image-based 3D head pose estimation is try to estimate the facial direction with 2D images. It is an important attribute widely used in many applications related to faces. However, accurate estimation is hard due to complicated part and pose absence in facial images. Recently, some improvement has been obtained with methods based on neural networks, but most of them ignore the geometric information of facial parts. In this paper, we try to tackle this issue and propose a novel geometry-aware representation. It is based on Stacked Capsule Graph Autoencoders (SCGAE). Different from current methods, we apply Stacked Capsule Autoencoders (SCAE) to encode the parts and poses of facial images. These parts and poses are used to train templates and reconstruct the original faces in decoders. In addition, we improve SCAE with locality loss, in which the inner relationships of similar samples are utilized. To achieve it, graph regularization is applied. In this way, an improved geometry-aware representation can be computed. It is compatible with existing regression methods and experimental results on commonly-used datasets about head pose estimation validate the effectiveness of SCGAE.
Robust Roadside Physical Adversarial Attack Against Deep Learning in Lidar Perception Modules

As Autonomous Vehicles (AVs) mature into viable transportation solutions, mitigating potential vehicle control security risks becomes increasingly important. Perception modules in AVs combine multiple sensors to perceive the surrounding environment. As such, they have been the focus of efforts to exploit the aforementioned risks due to their critical role in controlling autonomous driving technology. Despite extensive and thorough research into the vulnerability of camera-based sensors, vulnerabilities originating from Lidar sensors and their corresponding deep learning models in AVs remain comparatively untouched. Being aware that small roadside objects can be occasionally incorrectly identified as vehicles through on-board deep learning models, we propose a novel adversarial attack inspired by this phenomenon in both white-box and black-box scenarios. The adversarial attacks proposed in this paper are launched against deep learning models that perform object detection tasks through raw 3D points collected by a Lidar sensor in an autonomous driving scenario. In comparison to existing works, our attack creates not only adversarial point clouds in simulated environments, but also robust adversarial objects that can cause behavioral reactions in state of the art autonomous driving systems. Defense methods are then proposed and evaluated against this type of adversarial objects.
Towards Efficient Medical Image Segmentation Via Boundary-Guided Knowledge Distillation

In recent years, knowledge distillation for semantic segmentation has been extensively studied in order to obtain satisfactory performance while reducing computational costs. Compared with natural images, segmentation targets in medical images have fuzzy boundaries that are difficult to determine, but current knowledge distillation methods fail to explicitly transfer boundary information, resulting in poor boundary discrimination in compact models. Therefore, in this paper, we propose two knowledge distillation modules, namely boundary-guided deep supervision and output space boundary embeddng alignment, to explicitly transfer boundary information. The validity of our knowledge distillation approaches is demonstrated by extensive experiemnts on four public medical image data sets, namely, Montgomery County, CHAOS, GlaS and DRISHTI-GS.
Metaheuristic based auto-scaling for microservices in cloud environment: a new container-aware application scheduling

Purpose The cloud is a network of servers to share computing resources to run applications and data storage that offers services in various flavours, namely, infrastructure as a service, platform as a service and software as a service. The containers in the cloud are defined as “standalone and self-contained units that package software and its dependencies together”. Similar to virtual machines, the virtualization method facilitates the resource on a specific server that could be used by numerous appliances. Design/methodology/approach This study introduces a new Dragon Levy updated squirrel algorithm (DLU-SA) for container aware application scheduling. Furthermore, the solution of optimal resource allocation is attained via defining the objective function that considers certain criteria such as “total network distance (TND), system failure (SF), balanced cluster use (BC) and threshold distance (TD)”. Eventually, the supremacy of the presented model is confirmed over existing models in terms of cost and statistical analysis. Findings On observing the outcomes, the total cost of an adopted model for Experimentation 1 has attained a lesser cost value, and it was 0.97%, 10.45% and 10.37% superior to traditional velocity updated grey wolf (VU-GWO), squirrel search algorithm (SSA) and dragonfly algorithm (DA) models, respectively, for mean case scenario. Especially, under best case scenario, the implemented model has revealed a minimal cost value of 761.95, whereas, the compared models such as whale random update assisted lion algorithm, VU-GWO, SSA and DA has revealed higher cost value of 761.98, 779.46, 766.62 and 766.51, respectively. Thus, the enhancement of the developed model has been validated over the existing works. Originality/value This paper proposes a new DLU-SA for container aware application scheduling. This is the first work that uses the DLU-SA model for optimal container resource allocation by taking into consideration of certain constraints such as TND, SF, BC and TD.
Holdout Validation for Comparison Classfication Naïve Bayes and KNN of Recipient Kartu Indonesia Pintar

Abstract Kartu Indonesia Pintar (KIP) is one of the leading programs in the field of Education in the current government era. At present in the provision of smart Indonesian cards often occur not on target. The selection process for smart Indonesian cards is not transparent. For this reason, a transparent selection process is needed so that it does not cause jealousy among students. One way to make the selection process more objective is by applying existing classification methods to data mining. The study applies two methods to see the comparison, namely naïve bayes and k-nearest neighbor. In addition, the algorithm validation stage uses holdout validation. The purpose of algorithm validation is done so that each data has the same opportunity in the training and testing process, in this study also applied 150 dataset, the results showed that the system with no validation algorithm naïve Bayes accuracy is better that is an average accuracy of 85.66% and an average accuracy of k-nn 84.89%. However, if algorithm validation is applied, the k-nn accuracy is better at 88.7% compared to naïve bayes which is only 81.3%.
A combination of multi-objective genetic algorithm and deep learning for music harmony generation

Automatic Music Generation (AMG) has become an interesting research topic for many scientists in artificial intelligence, who are also interested in the music industry. One of the main challenges in Automatic Music Generation is that there is no clear objective evaluation criterion that can measure the music grammar, structural rules, and audience satisfaction. Also, original music contains different elements that should work together, such as melody, harmony, and rhythm; but in the most of previous works, Automatic Music Generation works only for one element (e.g., melody). Therefore, in this paper, we propose a Multi-Objective Genetic Algorithm (MO-GA) to generate polyphonic music pieces, considering grammar and listener satisfaction. In this method, we use three objective functions. The first objective function is the accuracy of the generated music piece, based on music theory; and the other two objective functions are modeled scores provided by music experts and ordinary listeners. The scoring of experts and listeners separately are modeled using Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks. The proposed music generation system tries to maximize mentioned objective functions to generate a new piece of music, including melody and harmony. The results show that the proposed method can generate pleasant pieces with desired styles and lengths, along with harmonic sounds that follow the grammar.
A New Support Vector Machine Based on Convolution Product

The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. Nonetheless, smaller datasets may be very important, costly, and not easy to obtain in a short time. This paper proposes a novel convolutional SVM (CSVM) that has the advantages of both CNN and SVM to improve the accuracy and effectiveness of mining smaller datasets. The proposed CSVM adapts the convolution product from CNN to learn new information hidden deeply in the datasets. In addition, it uses a modified simplified swarm optimization (SSO) to help train the CSVM to update classifiers, and then the traditional SVM is implemented as the fitness for the SSO to estimate the accuracy. To evaluate the performance of the proposed CSVM, experiments were conducted to test five well-known benchmark databases for the classification problem. Numerical experiments compared favorably with those obtained using SVM, 3-layer artificial NN (ANN), and 4-layer ANN. The results of these experiments verify that the proposed CSVM with the proposed SSO can effectively increase classification accuracy.
Performance Analysis of the Task Scheduling Algorithms in the Cloud Computing Environments

Resource Management is continually assuming a significant part in cloud computing, contrasting with other computing paradigms. Due to the accessibility of finite shared resources, it is extremely challenging for cloud providers to accomplish all the solicitations. Scheduling algorithm plays an imperative part in the resource management system. The scheduling algorithm is responsible for assigning cloud resources to various tasks errands by users. While assigning the resources, sometimes the resources are under-utilized or over-utilized, which affects the performance of the scheduling algorithms. Some recent surveys endeavor to incorporate the various task scheduling techniques proposed for cloud computing environments, however, on some accounts, it seem to be constrained. Subsequently, this paper gives a thorough description of task scheduling and various task scheduling techniques used in the environment of cloud computing. The paper presents a wide survey of numerous scheduling algorithms with regards to their methods and other significant features. A relative analysis of the surveyed algorithms is also included in the paper for homogeneous and heterogeneous cloud computing environments. Based upon the investigation of numerous task scheduling algorithms cited in the present paper as well as involved in the recently published surveys, a computed view of their applicability is included. It is observed that meta-heuristic scheduling algorithms are used for optimizing the performance of the task scheduling algorithms in cloud computing environments.
Revisiting the Iterative Ant-tree for color quantization algorithm

The Iterative Ant-tree for color quantization algorithm has recently been proposed to reduce the colors of an image at a low computational cost. It is a clustering-based method that generates good images compared to several well-known color quantization methods. This article proposes the modification of two features of the original algorithm: the value assigned to the parameter associated with the algorithm and the order in which the pixels of the image are processed. As a result, the new variant of the algorithm generates better images than the original and the results are less sensitive to the value selected for the parameter.
Detecting Advance Fee Fraud Using NLP Bag of Word Model

Advance Fee Fraud (AFF) is a form of Internet fraud prevalent within the Cybercrimes domain in literature. Evidence shows that huge financial assets are stolen from the global economy as a result of AFF. Consequently, this paper presents a fraudulent email classifier (FEC) that detects and classifies an email as fraudulent or non-fraudulent using Natural Language Process (NLP) model referred to as Bag-of-Words (BoW). The classifier is designed and trained to detect and classify AFF that originate from known sources using Nigeria as a Case study. Dataset is obtained and used for the training while testing the classifier logs. Experimentally, the classifier was trained using various machine learning algorithms with BoW generated as predictors. By selecting the best algorithms, the classifier was tested and found to perform satisfactorily.
Discriminative Siamese Complementary Tracker With Flexible Update

The offline generative Siamese trackers are equipped with the pre-defined anchors and the fixed target template. They overlook the target-background discriminative information, and lack the flexible target-specific update strategy. To overcome above drawbacks, we propose an adaptive and discriminative Siamese complementary tracking network with flexible update scheme. It consists of three collaborate subnetworks: anchor-free Siamese attention classification and regression subnetwork, online discriminative learning with multi-attention and multi-peak suppression, classifier guided template update subnetwork. All of them are interdependent and complementary to enhance each other for accurate target location. Specifically, an anchor-free multi-attention Siamese tracking subnetwork directly classifies the corresponding image patches with reliability assessment, and cascaded regresses the bounding boxes to progressively refine the predicting accuracy. Its evaluation is flexible and general with both proposal and anchor free in per-pixel prediction manner. Then, we integrate an online discriminative classifier optimizing module as a complementary subnetwork. It introduces spatial-temporal attention mechanism to fully explore multi-view multi-scale target-specific features, and evaluates multi-peak suppression to obtain a single centered peak response map. Its classified results can be fused with Siamese classification branch for accurate target location. Finally, the template update subnetwork is guided by the online discriminative classification scores. Extensive experiments on recent tracking datasets verify its top-ranked tracking accuracy and robustness against some state-of-the-art trackers.
Process Modelling and Creating Predictive Models of Sensory Networks Using Fuzzy Petri Nets

The current period is evidence of the massive replacement of human power with the results of the mental cognitive work of researchers. This mainly concerns the area of industrial production, where the aim is to apply new technologies on the principle of artificial intelligence, using the possibilities of a new direction in the use of a technology called the Internet of Things (IoT). With this idea, however, the question of the user disappears, who in this chaos of massive information can get into a state, where his activities will no longer be manageable. To cope with this situation, it is necessary to examine the impact of these technologies on the quality of life in the work and home environment. This paper investigates process modelling and the creation of predictive models to obtain the emotional state of users. The created predictive models will later allow the simulation of complex sensory systems, which will be able to predict the emotional states of users based on their physiological states. The aim is to also find connections between physiological states and basic emotions to be able to classify user emotions with the highest possible accuracy in the classification process.
Vovel metrics—novel coupling metrics for improved software fault prediction

Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability.