Reviewerly/exHarmony huggingface dataset:
An Anti-Pattern Detection Technique Using Machine Learning to Improve Code Quality

Poor software design and coding tend the software programs to be buggy at a massive rate. To enhance the code quality this paper proposes an automatic anti-pattern detection technique, which identifies anti-patterns from source code using Machine Learning (ML) classifiers. Here, four anti-patterns are considered such as Blob, Feature Decomposition (FD), Swiss Army Knife (SAK) and Spaghetti Code (SC) from three open-source Java projects namely ArgoUML, Azureus and Xerces. To improve the accuracy, a data pre-processing technique namely SMOTE is adopted. To locate these anti-patterns, four ML classifiers have been used which are Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF) and Decision Tree (DT). The proposed technique shows a better performance in terms of three evaluation metrics such as precision, recall, f-measure. SVM with SMOTE performs better in terms of precision and recall that are respectively 96.42% and 96.18%.
An Augmented Deep Learning Inference Approach of Vehicle Headlight Recognition for On-Road Vehicle Detection and Counting

Vehicles have been a big part of many lives; from the time it is invented and as it increases popularity in the 20th century. Though they offer the benefit of convenience, they also have certain negative effects as they add to air pollution and global warming, as well as risks when they are not handled properly. In recent years, the number of vehicles on the road is rapidly increasing and it causes different major concerns. One of the major effects of this increasing volume of vehicles is the traffic congestion it caused on our roads especially in the urban areas. This traffic congestion became one of the major problems in many cities in the world including Metro Manila, Philippines. Many options are discussed and implemented by the traffic management but it seems that it is still unsolved. In recent years, traffic congestions became unpredictable, there are parts of the cities that don't experience traffic congestion then suddenly traffic builds up to that area. Also, traffic congestion might happen every hour of the day. With this concern, the study proposed a system for vehicle headlight recognition for on-road vehicle detection and counting. This study focused on the detection of the headlight of every vehicle that will be seen on the perimeter of the installed camera. The system can detect headlight vehicles during daytime and nighttime as we trained the AI to recognized the headlights in these scenarios. Possible applications of this system can be in monitoring the volume of vehicles within the area and it can be used by traffic management authority in monitoring the build-up of traffic or traffic situation in an area so that they can provide an immediate solution, such as re-routing, one-way street conversions, etc.
Aerosol transmittance for clear-sky solar irradiance models: Review and validation of an accurate universal parameterization

Accurate modeling of solar radiation under cloudless situations is important to facilitate the development and exploitation of the renewable solar resource worldwide. However, many existing clear-sky solar radiation models perform inconsistently under varying aerosol conditions. This work presents a worldwide review of some of the most widely known clear-sky solar radiation models and shows that most of these inconsistencies are related to deficiencies in the simulation of the extinction by aerosols. As a remedy, a fast yet highly accurate analytical parameterization of the aerosol transmittance is introduced here. A performance evaluation using worldwide ground observations of aerosol Ångström's parameters has shown that, conversely to the existing parameterizations, the new one performs consistently under all possible aerosol conditions with virtually no bias and no dispersion. At seven arid locations, in particular, it is found that, when the new aerosol parameterization is used, the direct normal irradiance (DNI) predictions of the reviewed clear-sky irradiance models are comparable to those made with the high-performance RRTMG physical model. This fact proves that the deficiencies in the simulation of aerosol extinction produce most of the DNI biases of clear-sky solar irradiance models in arid regions. Thus, the performance differences found in such models in previous benchmarking studies can vanish by using a better parameterization of the aerosol transmittance such as the one proposed here.
A Boundary-aware Distillation Network for Compressed Video Semantic Segmentation

In recent years optical flow is often estimated to reuse features so as to accelerate video semantic segmentation. With addition of optical flow network, however, extra cost may incur and accuracy may thus be degraded because of repeated warping operation. In this paper, we propose a boundary-aware distillation network (BDNet) that replaces optical flow network with block motion vectors encoded in compressed video, resulting in negligible computational complexity. In order to make salient features, an auxiliary boundary-aware stream is added to the main stream to jointly estimate silhouette and segmentation of objects. To further correct warped features, a well-trained teacher network is employed to transfer knowledge to the main stream. Both boundary-aware stream and the teacher network are neglected during inference stage, so that video segmentation network enables to get faster without increasing any computational burden. By splitting the task into three components, our BDNet shows almost 10% time saving as well as 1.6% accuracy improvement over baseline on the Cityscapes dataset.
A Fast Traffic Sign Detection Algorithm Based on Modified YOLOv3

Abstract Traffic sign detection is an important component of both automatic driving systems and driver assistance systems. However, it is still a big challenge to detect traffic signs quickly and accurately. In this paper, in order to solve this problem, a lightweight algorithm based on YOLOv3 is proposed, which can achieve real-time and accurate traffic sign detection. Firstly, a new lightweight backbone network is proposed by modifying the darknet53 network, which is the original backbone network of YOLOv3. Secondly, in order to further speed up the detection time, depthwise separable convolution is introduced into the proposed backbone network to further reduce the amount of parameters and computations of the model. Thirdly, dense connection is adopted to maintain the accuracy. Experiments on CCTSDB dataset illustrate that the algorithm has great real-time performance and accuracy, which is better than other state-of-the-art algorithms. The algorithm can detect a 416*416 image in only 3.4 milliseconds, which is 44.3% faster than YOLOv3. In addition, the mAP and F1 value are 97.2% and 91.6% respectively, which are better than YOLOv3.
Student interaction and satisfaction level with a LMS subject at a higher education institution

This research objective was to know the satisfaction levels from a group of students towards an open source learning management system (LMS) as a complement and support to the classroom activities. The satisfaction variables were the planning and contents of the course, participation and teacher-student interaction, usability, multimedia elements and accessibility to the system. The quantitative approach research was developed with a sample of 83 students enrolled in a subject of the Architecture curriculum at the university level. A previously validated satisfaction questionnaire was used. It is concluded that students reveal to be in agreement in their level of satisfaction and implication with the use of an LMS that collaborates positively in the teaching-learning processes. The categories with the highest acceptance were the contents and planning, while the participation and interactivity of the students with the virtual environment was less accepted.
Applying Patterns to Support Deployment in Cloud-Edge Environments: A Case Study

A major trend followed by IT experts and Software developers in recent years is represented by the "Cloudification" of existing applications, with a strong shift of computations and data from local and centralized servers to remote, distributed data-centers. Indeed, using Cloud resources has reduced, for most SMEs, both the initial investments in hardware and software assets and maintenance costs, making it a viable choice in many situations. On the other hand, Cloud Computing requires to store consistent volumes of data on remote databases, with a series of consequences on data privacy that need to be carefully addressed. Moreover, the advent of the Internet of Things, with the huge quantity of data that smart devices continuously produce and consume, often in real time, renders the transfer of information to and from remote servers too cumbersome, as it relies on network speed and continuous availability. New programming paradigms have thus emerged, such as Cloud-Edge, which tries to combine benefits deriving from the exploitation of the resources offered by Cloud architecture and the need to consume data locally. The Cloud-Edge paradigm requires a careful design of the integration between Cloud and Edge architectures, in order to avoid bottlenecks and efficiently exploit both local and remote resources. In this paper a methodology based on Architectural, Computational and Deployment Patterns will be presented to support the deployment of applications in Cloud-Edge environments, starting from pre-existing software solutions.
Collaborative Multi-Object Tracking as an Edge Service using Transfer Learning

Video cameras have been pervasively deployed in Smart Cities and timely processing of the huge amount of video data brings up new challenges. Each individual camera is only able to cover a limited range due to the effective monitoring distances and the complicated layout of a building or an area. Once the object of interest moves away from the camera field of view, the system may lose object tracking. When the object appears and is captured by another camera, object detection, feature abstraction, and behavior classification require an association with other video data exploitation results. Hence, pragmatic approaches for object re-verification (or re-acquisition) are needed so as to not infer the object as a new track. In this paper, we propose a novel Collaborative Multi-Object Tracking system as an Edge service (CoMOTE) based on transfer learning technology. A transfer learning-based human detection and tracking method is introduced, which allocates most of the computing tasks on edge devices. An edge server is adopted to integrate information from edge cameras for decision-making to enable multi-view surveillance. The CoMOTE scheme not only reduces the dependence on a central server but also leads to a lower communication cost. Implemented on edge nodes (Raspberry Pi 4) and tested with WildTrack multi-view video database, extensive experimental results verify that the CoMOTE prototype achieved the design goal and outperforms multiple state-of-the-art methods.
Atomic Cross-Chain Asset Exchange for Ethereum Public Chains

Blockchain technology is reaching mainstream users due to its adoption by a large number of enterprises. Numerous new blockchain platforms and projects are emerging every year, intending to become the best blockchain. However, blockchain technology is far from reaching its full potential because of siloed blockchain platforms. Hence different blockchains should be able to interact with each other for carrying out data sharing and asset transfer among them. In this paper, we propose an approach for atomic cross-chain transfer among heterogeneous blockchains. We provide the implementation details and working of the proposed approach for different Ethereum blockchains.
Autoencoder-like semi-NMF multiple clustering

Clustering is performed to partition samples into disjoint groups for facilitating the discovery of hidden patterns in the data. Many real-world applications involve various clustering methods , most of which only produce a single clustering. As a response to this issue, multiple clustering that aims to generate diverse and high-quality clustering, has emerged recently. This study proposes a novel autoencoder-like semi-nonnegative matrix factorization (NMF) multiple clustering (ASNMFMC) model that generates multiple non-redundant, high-quality clustering. The nonnegative property of the semi-NMF is utilized by the algorithm to enforce non-redundancy. Extensive experimental results demonstrate that the ASNMFMC is superior to the existing multiple clustering methods and can explore diverse high-quality clustering.
Self-Supervised Approach for Facial Movement Based Optical Flow

Computing optical flow is a fundamental problem in computer vision.However, deep learning-based optical flow techniques do not perform well for non-rigid movements such as those found in faces, primarily due to lack of the training data representing the fine facial motion.We hypothesize that learning optical flow on face motion data will improve the quality of predicted flow on faces.This work aims to: (1) exploring self-supervised techniques to generate optical flow ground truth for face images; (2) computing baseline results on the effects of using face data to train Convolutional Neural Networks (CNN) for predicting optical flow; and (3) using the learned optical flow in micro-expression recognition to demonstrate its effectiveness.We generate optical flow ground truth using facial key-points in the BP4D-Spontaneous dataset.This optical flow is used to train the FlowNetS architecture to test its performance on the Extended Cohn-Kanade dataset and a portion of the generated dataset.The performance of FlowNetS trained on face images surpassed that of other optical flow CNN architectures.Our optical flow features are further compared with other methods using the STSTNet micro-expression classifier, and the results indicate that the optical flow obtained using this work has promising applications in facial expression analysis.
A multi-scale human action recognition method based on Laplacian pyramid depth motion images

Human action recognition is an active research area in computer vision. Aiming at the lack of spatial muti-scale information for human action recognition, we present a novel framework to recognize human actions from depth video sequences using multi-scale Laplacian pyramid depth motion images (LP-DMI). Each depth frame is projected onto three orthogonal Cartesian planes. Under three views, we generate depth motion images (DMI) and construct Laplacian pyramids as structured multi-scale feature maps which enhances multi-scale dynamic information of motions and reduces redundant static information in human bodies. We further extract the multi-granularity descriptor called LP-DMI-HOG to provide more discriminative features. Finally, we utilize extreme learning machine (ELM) for action classification. Through extensive experiments on the public MSRAction3D datasets, we prove that our method outperforms state-of-the-art benchmarks.
Analyzing Semantics of Aggregate Answer Set Programming Using Approximation Fixpoint Theory

Abstract Aggregates provide a concise way to express complex knowledge. The problem of selecting an appropriate formalization of aggregates for answer set programming (ASP) remains unsettled. This paper revisits it from the viewpoint of Approximation Fixpoint Theory (AFT). We introduce an AFT formalization equivalent with the Gelfond–Lifschitz reduct for basic ASP programs and we extend it to handle aggregates. We analyze how existing approaches relate to our framework. We hope this work sheds some new light on the issue of a proper formalization of aggregates.
Inheritance-Guided Hierarchical Assignment for Clinical Automatic Diagnosis

Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. Considering that manual diagnosis could be error-prone and time-consuming, many intelligent approaches based on clinical text mining have been proposed to perform automatic diagnosis. However, these methods may not achieve satisfactory results due to the following challenges. First, most of the diagnosis codes are rare, and the distribution is extremely unbalanced. Second, existing methods are challenging to capture the correlation between diagnosis codes. Third, the lengthy clinical note leads to the excessive dispersion of key information related to codes. To tackle these challenges, we propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis. Specifically, we propose a hierarchical joint prediction strategy to address the challenge of unbalanced codes distribution. Then, we utilize graph convolutional neural networks to obtain the correlation and semantic representations of medical ontology. Furthermore, we introduce multi attention mechanisms to extract crucial information. Finally, extensive experiments on MIMIC-III dataset clearly validate the effectiveness of our method.
An Analytical Survey for Improving Authentication levels in Cloud Computing

Cloud Computing is an on-demand network. Cloud computing is an excellent favourable and quickly developing technology. In this type of network, you can access your data at anytime, anywhere if you are an authentic user of Cloud Computing. To store data on cloud computing environment is safe. There is no restriction of time and place to access your data from Cloud Computing environment if you have a proper internet connection. Authentication is a very important term in Cloud Computing for data security. Cloud Computing is a distributed computing and it is useful for storing and analysing large amounts of data in one place. As per your requirements you can use any type of cloud computing. This paper is focusing on different authentication levels used in cloud computing and survey of users view on these authentication levels.
Accelerating 3D Convolutional Neural Networks Using 3D Fast Fourier Transform

Three-dimensional convolutional neural networks (3D CNNs) have attracted great attention in many complex computer vision tasks. However, it is difficult to deploy 3D CNNs on practical applications due to high algorithmic complexity, imposing the urgent requirement for dedicated accelerators. In this paper, F3D, a fast algorithm for 3D CNNs, is proposed based on 3D Fast Fourier Transform (FFT) and achieves a significant algorithmic strength reduction. We then propose an F3D-based hardware architecture, featuring a flexible FFT module and an efficient partial sum aggregation module. Furthermore, a dataflow for efficient mapping of 3D CNNs is designed, leading to a significant reduction of memory access. To demonstrate the efficiency of the above-mentioned techniques, we implement the widely used 3D CNN model, C3D, as our benchmark on the Xilinx VC709 platform. The experimental result shows that compared with the state-of-the-art accelerator, our work achieves a considerable throughput up to 864.1 GOPs, along with 1.68x and 2.00 x efficiency improvement on energy and DSP utilization, respectively.
Web service quality of service prediction via regional <scp>reputation‐based</scp> matrix factorization

Abstract Quality of Service (QoS) of Web services plays an essential role in selecting Web services by consumers. The dynamic QoS attributes of Web services have different values for different users. Therefore, the value of many Web services' QoS features for many users are undetermined, and these values should be predicted. The collaborative filtering (CF) method is one of the most successful approaches to predict these values. CF‐based methods use the QoS values contributed by the other users for prediction and, consequently, the values contributed by unreliable users can decrease the accuracy of prediction. To utilize the reputation of users can be regarded as one of the conventional approaches to overcome this problem. In this paper, we have defined a concept called regional reputation that represents the reputation of a user for users in each geographical region. Regional reputation has been achieved with the combination of the location information of the users and their reputation. Subsequently, by combining this concept with the matrix factorization, we have proposed a prediction method called regional reputation‐based matrix factorization. This approach has been able to improve the accuracy of prediction and be more persistent to the data contributed by unreliable users.
Integrating ICT in Teaching and Learning at University Level Focusing Education 4.0

In this era of Education 4.0 integration of information and communication technology (ICT) in teaching and learning is gaining more importance than ever before. In this paper, a qualitative study is furnished to suggest an effective approach for the integration of ICT in teaching and learning at university level of Bangladesh addressing the needs of Education 4.0. Besides using secondary data from publications covering different geographical areas, a survey is conducted on 200 students and 15 teachers of public and private universities of Bangladesh. The study is carried out covering the factors related to teachers and students. A set of recommendations are enlisted for the appropriate approach at university level of Bangladesh that will produce skilled workforce to address the global needs.
Hardware Architecture of a Haar Classifier Based Face Detection System Using a Skip Scheme

Face recognition applications are being widely studied owing to their extensive usability in the field of computer vision. However, processing an entire image requires a considerable amount of time. To reduce the processing time, several algorithms that extract only the face from the image during pre-processing are studied. Haar classifiers are extensively used for the hardware implementation of face detection algorithms that improve the processing speed of face classification. This paper proposes a Haar classifier based face detection architecture that removes unnecessary iterations during classification to further improve the processing speed. The proposed architecture improves the processing speed by 4.46% compared to that of conventional Haar classifier based face detection architectures, for face detection using a VGA image with 30 faces. The proposed architecture tends to improve the processing speed as the number of faces in the image increases while matching the detection accuracy of conventional methods. Additionally, this architecture can be widely applied to classification algorithms that are based on iterations.