Reviewerly/exHarmony huggingface dataset:
Greedy Nominator Heuristic: Virtual function placement on fog resources

Abstract Fog computing is an intermediate infrastructure between edge devices (e.g., Internet of Things) and cloud systems that is used to reduce latency in real‐time applications. An application can be composed of a collection of virtual functions, between which dependency constraints can be captured in a service function chain (SFC). Virtual functions within an SFC can be executed at different geo‐distributed locations. However, virtual functions are prone to failure and often do not complete within a deadline. This results in function reallocation to other nodes within the infrastructure; causing delays, potential data loss during function migration, and increased costs. We proposed Greedy Nominator Heuristic (GNH) to address these issues. GNH is based on redundant deployment and failure tracking of virtual functions. GNH places replicas of each function at multiple locations—taking account of expected completion time, failure risk, and cost. We make use of a MapReduce‐based mechanism, where Mappers find suitable locations in parallel, and a Reducer then ranks these locations. Our results show that GNH reduces latency by up to 68%, and is more cost effective than other approaches which rely on state‐of‐the‐art optimization algorithms to allocate replicas.
Assembly Action Understanding from Fine-Grained Hand Motions, a Multi-camera and Deep Learning Approach

This article presents a novel software architecture enabling the analysis of assembly actions from fine-grained hand motions. Unlike previous works that compel humans to wear ad-hoc devices or visual markers in the human body, our approach enables users to move without additional burdens. Modules developed are able to: (i) reconstruct the 3D motions of body and hands keypoints using multi-camera systems; (ii) recognize objects manipulated by humans, and (iii) analyze the relationship between the human motions and the manipulated objects. We implement different solutions based on OpenPose and Mediapipe for body and hand keypoint detection. Additionally, we discuss the suitability of these solutions for enabling real-time data processing. We also propose a novel method using Long Short-Term Memory (LSTM) deep neural networks to analyze the relationship between the detected human motions and manipulated objects. Experimental validations show the superiority of the proposed approach against previous works based on Hidden Markov Models (HMMs).
Detection of low-frequency earthquakes by the matched filter technique using the product of mutual information and correlation coefficient

Abstract The matched filter technique is often used to detect microearthquakes such as deep low-frequency (DLF) earthquakes. It compares correlation coefficients (CC) between waveforms of template earthquakes and the observed data. Conventionally, the sum of CC at multiple seismic stations is used as an index to detect the DLF earthquakes. A major disadvantage of the conventional method is drastically reduced detection accuracy when there are too few seismic stations. The new matched filter method proposed in this study can accurately detect microearthquakes using only a single station. It adopts mutual information (MI) in addition to CC to measure the similarity between the template and target waveforms. The method uses the product of MI and CC (MICC) as an index to detect DLF earthquakes. This index shows a distinct peak corresponding to an earthquake signal in a synthetic data set consisting of artificial noise and the waveform of a DLF earthquake. Application of this single-station method to field observations of Kirishima volcano, one of the most active volcanoes in Japan, detected a total of 354 events from the data in December 2010, whereas the catalog of the Japan Meteorological Agency shows only two. Of the detected events, 314 (89%) are likely DLF earthquakes and other events may be false detections. Most of the false detections correspond to surface-wave arrivals from teleseismic events. The catalog of DLF earthquakes constructed here shows similar temporal behavior to that found by the conventional matched filter method using the sum of the CC of the six stations near the volcano. These results suggest that the proposed method can greatly contribute to the accurate cataloging of DLF earthquakes using only a single seismic station. Graphical Abstract
Re‐shooting Resistant Blind Watermarking Framework Based on Feature Separation With Gaussian Mixture Model

Watermarking is a technology to embed digital data into a digital image. In particular, blind watermarking is expected to ensure the authenticity and/or accountability of digital images without compromising the visual impression of viewing. A re‐shooting resistant blind watermarking method is proposed in this study. This method uses the features of a given image to select regions, which are modified to embed digital data. As the proposed embedding process preserves these image features, no markers are required to extract the embedded data from the watermarked image. Furthermore, based on the estimation of the watermarked image region, the method crops the re‐shot image before the extraction. Therefore, the proposed method works, even if the re‐shot image includes the outer area surrounding the watermarked image region. Moreover, the proposed method compensates for the geometric transformation of the re‐shot image based on the definition of the coordinate system, which is based on the distribution of the image feature points. Therefore, the proposed method works when the re‐shot image is taken diagonally in front of the watermarked image. Experimental results validated that the proposed method can extract the embedded digital data from re‐shot images of a watermarked image under considerable conditions of re‐shooting. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Verum Fitness: An AI Powered Mobile Fitness Safety and Improvement Application

At home fitness has rapidly risen recently due to the COVID-19 pandemic and stay-at-home-orders. This also produced a large set of first time users of gym equipment and structured exercise routines. Access to professional fitness trainers to assist beginners in proper exercise form has become increasingly difficult. According to the National Safety Council (NSC), approximately 468,000 injuries occurred due to exercise in 2019 before the pandemic. Without proper guidance, this statistic is bound to increase. Therefore, there is a need for systems to monitor exercise performance for both short term and long term injury prevention. We present a novel mobile app called Verum Fitness which will use the camera from a smart phone to record a user performing an exercise. Then, the app will skeletonize the user, extract angles from specific joints, and feed this data into a Fuzzy Inference System (FIS), an inherently explainable model, to classify exercise performance. With the FIS, we can provide a description of each repetition performed to determine if it could cause injury and how to improve. From our synthetically generated data, we show a training and test Accuracy of 80.42% and 71.67%, respectively, as well as high Sensitivity and Specificity for the goblet squat.
Study on Mask R-CNN with Data Augmentation for Retail Product Detection

In deep learning (DL), object detection is a computer vision technology that can be applied in many fields, including face detection and tracking the soccer ball during a match. Previously, when dealing with detecting objects from small retail stores, most studies used Faster R-CNN with ResNet101 as its backbone. However, this method sometimes incorrectly and ineffectively identifies overlapped objects or objects with parts of their edges cut off from the image. This paper use Mask R-CNN with data augmentation (DA) to perform the object detection in retail product recognition. We seek to minimalize the error caused by overlapped objects or adjacent objects having similar colors. In this experiment result, the proposed method on Snacks dataset can achieve mAP of 98.92% with a superior recognition rate in the recognition task compared to the state-of-the-art methods.
Software Development Process Assessment With MMIS v.2, an ISO/IEC 33000-Based Model

While, historically, software quality focused on identifying and mitigating defects, today, companies that want to digitally transform their products and services are changing the manner and scope of their quality assurance processes. Based on the ISO/IEC 33000 family of standards for process assessment, we have built a new maturity model called MMIS V2.0. This new model has been applied in more than 20 companies. Here, we present the experience of these companies in carrying out the MMIS implantation and certification, evaluating its usefulness, and obtaining feedback to improve it. In addition, during this study, we have identified the processes that are more difficult for companies to implement (based on their own perception and on the number of nonconformities identified during certification audits), as well as the main software tools and frameworks used to support the development lifecycle.
Implementation of Single Shot Detector (SSD) MobileNet V2 on Disabled Patient's Hand Gesture Recognition as a Notification System

The development of smart healthcare system has a potential to improve the quality of health services. Disabled patients need to express their needs non-verbally to their families or medical workers to fulfill their needs immediately. In this study, we developed hand gesture recognition using web camera as a notification system. Image acquisition was done on 12 subjects with various gender and ages. Based on human's basic daily needs, there are 5 gestures as the object of study: need to eat, need to drink, need to go to the toilet, need help and need medicines. The collected images were trained using Single Shot Detector (SSD) algorithm on MobileNet V2 architecture. Among the various deep learning techniques, SSD MobileNet V2 was chosen because it has good ability in object detection and needs low computation. Therefore, it is suitable to be applied on realtime detection. This study results on mean Average Precision (mAP) 44.7% and detection rate 85% which means 85 out of 100 images were well-detected. The mAP showed better result than previous studies. Frame rate per second (FPS) provided in this study was ±2 FPS. The gestures detected also triggered the notification on telegram to notify family or nurse who take care of disabled patients.
Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

Abstract Aspect level sentiment analysis belongs to fine-grained sentiment analysis, w hich has caused extensive research in academic circles in recent years. For this task, th e recurrent neural network (RNN) model is usually used for feature extraction, but the model cannot effectively obtain the structural information of the text. Recent studies h ave begun to use the graph convolutional network (GCN) to model the syntactic depen dency tree of the text to solve this problem. For short text data, the text information is not enough to accurately determine the emotional polarity of the aspect words, and the knowledge graph is not effectively used as external knowledge that can enrich the sem antic information. In order to solve the above problems, this paper proposes a graph co nvolutional neural network (GCN) model that can process syntactic information, know ledge graphs and text semantic information. The model works on the “syntax-knowled ge” graph to extract syntactic information and common sense information at the same t ime. Compared with the latest model, the model in this paper can effectively improve t he accuracy of aspect-level sentiment classification on two datasets.
Data pre-processing pipeline generation for AutoETL

Data pre-processing plays a key role in a data analytics process (e.g., applying a classification algorithm on a predictive task). It encompasses a broad range of activities that span from correcting errors to selecting the most relevant features for the analysis phase. There is no clear evidence, or rules defined, on how pre-processing transformations impact the final results of the analysis. The problem is exacerbated when transformations are combined into pre-processing pipeline prototypes. Data scientists cannot easily foresee the impact of pipeline prototypes and hence require a method to discriminate between them and find the most relevant ones (e.g., with highest positive impact) for their study at hand. Once found, these prototypes can be instantiated and optimized e.g., using Bayesian Optimization. In this work, we study the impact of transformations when chained together into prototypes, and the impact of transformations when instantiated via various operators. We develop and scrutinize a generic method that allows to generate pre-processing pipelines, as a step towards AutoETL. We make use of rules that enable the construction of prototypes (i.e., define the order of transformations), and rules that guide the instantiation of the transformations inside the prototypes (i.e., define the operator for each transformation). The optimization of our effective pipeline prototypes provide results that compared to an exhaustive search, get 90% of the predictive accuracy in the median, but with a time cost that is 24 times smaller.
Reverse Image Search Improved by Deep Learning

Reverse Image Search is also known as Instance Retrieval. This technology comes within applications of content based image retrieval. This technique enables developers and researchers to build scenarios beyond simple keyword search. A similar class of technology under the hood is used on sites to check original or similar photo for different purpose. Even face recognition in several security systems uses a similar concept to ascertain the identity of the person. For accurate and optimized similarity search this is done by feature extraction scaling the large image-databases. The image search operation should examine the contents of the image in featured vector by its pixel resolution ratio. One approach is to compare patches of areas between two images. In this paper Reverse Image Search by Google and many of the same techniques as the aforementioned are analyzed and improved by implementing the Deep Learning algorithms and image classifications. Deep learning method is to extract metadata from an image so that, it can then be scaled, indexed and used in a typical text query-based search. Cosine similarity is used to measure the angles of images in the high dimensional feature space. All test cases are implemented with the concept of deep learning using CNN (Convolutional Neural Network) in Python language with its tools Scikit-Learn, TensorFlow and OpenCV. This proposal has given with improved results.
How, Why, and with Whom Do User Experience (UX) Practitioners Communicate? Implications for HCI Education

While being a good communicator is a key skill for user experience (UX) practitioners, human-computer interaction (HCI) education typically prioritizes the creation of tangible project deliverables, e.g., wireframes, over providing students with a more complete set of communication skills. In this paper, we draw from an analysis of 60 interviews with experienced UX professionals to argue that a nuanced understanding of communication strategies should be explicitly included in HCI education. Specifically, we identify five goals that shape communications between UX practitioners and four distinct audiences and show that they select specific methods (techniques, artifacts, and tools) to achieve these goals. Drawing on theories of situated and authentic learning, we discuss three key implications for HCI educators: embracing rhetorical complexity, simulating real-world communication challenges, and highlighting the performative elements of communication. Educators must embrace these challenges to help students become more effective communicators and prepare them for UX careers.
<i>DISSparse</i>: Efficient Mining of Discriminative Itemsets

We tackle the problem of discriminative itemset mining. Given a set of datasets, we want to find the itemsets that are frequent in the target dataset and have much higher frequencies compared with the same itemsets in other datasets. Such itemsets are very useful for dataset discrimination. We demonstrate that this problem has important applications and, at a same time, is very challenging. We present the DISSparse algorithm, a mining method that uses two determinative heuristics based on the sparsity characteristics of the discriminative itemsets as a small subset of the frequent itemsets. We prove that the DISSparse algorithm is sound and complete. We experimentally investigate the performance of the proposed DISSparse on a range of datasets, evaluating its efficiency and stability and demonstrating it is substantially faster than the baseline method.
Kaleidoscopic structured light

Full surround 3D imaging for shape acquisition is essential for generating digital replicas of real-world objects. Surrounding an object we seek to scan with a kaleidoscope, that is, a configuration of multiple planar mirrors, produces an image of the object that encodes information from a combinatorially large number of virtual viewpoints. This information is practically useful for the full surround 3D reconstruction of the object, but cannot be used directly, as we do not know what virtual viewpoint each image pixel corresponds---the pixel label. We introduce a structured light system that combines a projector and a camera with a kaleidoscope. We then prove that we can accurately determine the labels of projector and camera pixels, for arbitrary kaleidoscope configurations, using the projector-camera epipolar geometry. We use this result to show that our system can serve as a multi-view structured light system with hundreds of virtual projectors and cameras. This makes our system capable of scanning complex shapes precisely and with full coverage. We demonstrate the advantages of the kaleidoscopic structured light system by scanning objects that exhibit a large range of shapes and reflectances.
A Sort-Less FPGA-Based Non-Maximum Suppression Accelerator using Multi-Thread Computing and Binary Max Engine for Object Detection

Non-Maximum Suppression (NMS) algorithm is an important post-processing step in object detection networks for various applications [1]. Standard NMS procedure suffers from poor time complexity and large power consumption due to its iterative and greedy search procedure, making it a bottleneck for object detection networks implemented on various processors [2], [3]. Previous NMS accelerators achieved optimization by stacking arithmetic logical units or computing consecutive iterations simultaneously [4] –[6]. However, several challenges exist, as shown in Fig. 1. First, the highly iterative process of NMS will either cause a high time or space complexity if the hardware resources are not designed properly. Second, the standard NMS process requires sorting of the bounding boxes by the score, and such sorting circuits occupy abundant resources and produce massive data movements. Finally, the Intersection Over Union (IOU) calculation requires hardware unfriendly operations like multiplication and division, taking up loads of valuable hardware resources such as DSPs.
Improving 3D Path Tracking of Unmanned Aerial Vehicles through Optimization of Compensated PD and PID Controllers

The development of quadrotor unmanned aerial vehicles (QUAVs) is a growing field due to their wide range of applications. QUAVs are complex nonlinear systems with a chaotic nature that require a controller with extended dynamics. PD and PID controllers can be successfully applied when the parameters are accurate. However, this parameterization process is complicated and time-consuming; most of the time, parameters are chosen by trial and error without guaranteeing good performance. The originality of this work is to present a novel nonlinear mathematical model with aerodynamic moments and forces in the Newton–Euler formulation, and identify metaheuristic algorithms applied to parameter optimization of compensated PD and PID controls for tracking the trajectories of a QUAV. Eight metaheuristic algorithms (PSO, GWO, HGS, LSHADE, LSPACMA, MPA, SMA and WOA) are reported, and RMSE is used to measure each dynamic performance of the simulations. For the PD control, the best performance is obtained with the HGS algorithm with an RMSE = 0.037247252379126. For the PID control, the best performance is obtained with the HGS algorithm with an RMSE = 0.032594309723623. Trajectory tracking was successful for the QUAV by minimizing the error between the desired and actual dynamics.
Path Planning and Collision Avoidance with Artificial Intelligence for a Quadrotor UAV

Artificial intelligence has brought new features to unmanned aerial vehicles. Path planning, trajectory optimization and obstacle avoidance are some of the features that artificial intelligence brings to drones. In this study, path planning optimization and collision avoidance with artificial intelligence for an unmanned aerial vehicle are emphasized. Path planning and collision avoidance simulations were performed using MATLAB software under ideal conditions and under noisy conditions. It is shown that the unmanned aerial vehicle finds the shortest path on maps with various obstacles.
Attentive occlusion-adaptive deep network for facial landmark detection

To be very specific in this paper, an Attentive Occlusion-adaptive Deep Network, hereafter referred as AODN, is proposed for facial landmark detection, consisting of the geometry-aware module, attention module, and low-rank learning module. Facial Landmark Detection (FLD) is a fundamental pre-processing step of facial related tasks. Occlusion, extreme pose, different expressions and illumination are the main challenges in facial landmark detection related tasks. Convolutional Neural Network (CNN) based FLD methods have attained significant improvement regarding accurate FLD but, to deal with occlusion is still very challenging even for CNN. It is because; probably occlusion misleads CNN on feature representation learning. If faces are partially occluded, the localization accuracy will drop significantly. The role of attention in the human visual system is vital, and researchers proved its significance for the computer vision problem. Taking advantage of geometric relationships among different facial components and attention, we extended our already established Occlusion-adaptive Deep Network (ODN). We introduced the attention module consisting of Channel-wise Attention (CA) and Spatial Attention (SA) to improve its ability to deal with the occlusion and enhance feature representation ability simultaneously. The occlusion probability assists as adaptive weights of high-level features and minimizes the effect of the occlusion and assist in modelling the occlusion. Ablation studies prove the synergistic effect of each module. The summary of our trifold contribution is as follows: i) we introduced attention mechanism in our already established ODN model, to deal with occlusion more precisely, and get the rich feature representation to achieve better performance. ii) As per our best of knowledge, we are the pioneers to introduce CA and SA for FLD to model occlusion. iii) Our proposed methodology reduces the number of entire network parameters, which effectually decreases training time and cost. So, the proposed model is more suitable for scalable data processing. Experimental results prove the better performance of proposed AODN on challenging benchmark datasets.