Reviewerly/exHarmony huggingface dataset:
Wireless Communication based Water Surface Cleaning Boat

It is very dreadful that huge liters of excrement are being deposited into the rivers across our country. The pollutants and toxins are assisting the amassing of the toxicant in water thus affecting aquatic life. Only when the detritus floating above the water bodies become persistent, the governments' plan of action is set about and unfortunately by that time, the life cycle in that water body breaks. This research work focuses on deploying a solution for the unfavorable effects on the floating refuse, and henceforth reduces the pollution caused due to the unsettled wastes including plastics and plant weeds. The project aims to develop a waste collecting boat to scavenge the water body and abolish the floating waste. This is done by employing a conveyor belt connected to the front line of the boat. Boat movement is established using a remote control application by incorporating an RF module along with an encoder-decoder pair. This method will reduce the cost of operation because of less human involvement and labor. Major challenges in implementing the boat are compact mechanical design and reliable power supply. The principle feature of this boat is the collection of refuse and floating weeds from the surface of the water into a basket which is removable [2].
An improved DCT-based JND estimation model considering multiple masking effects

The just noticeable distortion (JND) in the contour and orderly regions is easy to be overestimated and that in the disorderly areas is usually underestimated. In order to estimate the JND threshold more accurately, this paper proposes an improved DCT-based JND estimation model considering multiple masking effects properly. The contributions of this paper are characterized by twofold. On the one hand, a mean absolute difference based (MAD-based) block classification method is developed at first to classify image blocks into plain, contour and texture types accurately and quickly. And the JND model for contrast masking effect (CM-JND) is constructed as a modulation factor based on the MAD of each block. On the other hand, we propose a distance-based disorder evaluation metric to measure the disorder intensity in block level. Then, the JND model for the disorderly concealment effect (DC-JND) is proposed based on our psychological experiment. Finally, the total JND estimation threshold is modeled by fusing the spatial contrast sensitivity function, the luminance adaptation effect, the CM and DC effects. Experimental results show that the proposed DCT-based JND estimation model outperforms existing models in performance and complexity. Specifically, the proposed model shows more tolerance for distortions, lower computational complexity with better perceptual quality than other JND models.
Design for Luxury Front-Seat Passenger Infotainment Systems with Experience Prototyping through VR

This article focuses on empowering front-seat passengers in luxury car journeys through infotainment systems and investigates how luxury can be manifested via new functionalities and interactions. It presents an experience prototyping study in which 27 participants tried out a VR-simulated design proposal for the front-seat passenger infotainment system within a travel scenario and reflected on their UX. The article suggests design recommendations to answer the user concerns and expectations revealed in the results, including the provision of enough margin of error in gestural manipulations, prevention of driver and passenger distractions, keeping up with the technology within car’s life cycle, involvement in the journey, collaboration with driver and relatedness to social network. The article underlines that these expectations expand further in luxury cars with the search for digital upgrades, infotainment content with luxury symbolic value, and subtle technology integration to the luxury car interior. It finally provides a framework with various modes showing that front-seat passengers’ expectations from the infotainment system change based on their relationships with the surroundings and the driver, and it offers design solutions to facilitate each mode.
X-DenseNet: Deep Learning for Garbage Classification Based on Visual Images

Abstract In order to effectively solve the problem of garbage classification, this paper designs a garbage classification model based on deep convolutional neural network. Based on Xception network, combined with the idea of dense connections and multi-scale feature fusion in DenseNet, the X-DenseNet is constructed to classify the garbage images obtained by visual sensors. This paper conducts experiments through the process of “obtaining dataset-preprocessing data-building X-DenseNet model-training and testing model” and the accuracy of the model on the testing set is up to 94.1%, which exceeds some classic classification networks. The X-DenseNet automatic garbage classification model based on visual images proposed in this paper can effectively reduce manual investment and improve the garbage recovery rate. It has the vital scientific significance and application value.
Q-Flink: A QoS-Aware Controller for Apache Flink

Modern stream-data processing platforms are required to execute processing pipelines over high-volume, yet high-velocity, datasets under tight latency constraints. Apache Flink has emerged as an important new technology of large-scale platform that can distribute processing over a large number of computing nodes in a cluster (i.e., scale-out processing). Flink allows application developers to design and execute queries over continuous raw-inputs to analyze a large amount of streaming data in a parallel and distributed fashion. To increase the throughput of computing resources in stream processing platforms, a service provider might be tempted to use a consolidation strategy to pack as many processing applications as possible on the working nodes, with the hope of increasing the total revenue by improving the overall resource utilization. However, there is a hidden trap for achieving such a higher throughput solely by relying on an interference-oblivious consolidation strategy. In practice, collocated applications in a shared platform can fiercely compete with each others for obtaining the capacity of shared resources (e.g., cache and memory bandwidth) which in turn can lead to a severe performance degradation for all consolidated workloads.This paper addresses the shared resource contention problem associated with the auto-resource controlling mechanism of Apache Flink engine running across a distributed cluster. A controlling strategy is proposed to handle scenarios in which stream processing applications may have different quality of service (QoS) requirements while the resource interference is considered as the key performance-limiting parameter. The performance evaluation is carried out by comparing the proposed controller with the default Flink resource allocation strategy in a testbed cluster with total 32 Intel Xeon cores under different workload traffic with up to 4000 streaming applications chosen from various benchmarking tools. Experimental results demonstrate that the proposed controller can successfully decrease the average latency of high priority applications by 223% during the burst traffic while maintaining the requested QoS enforcement levels.
Can augmented reality improve problem-solving and spatial skill?

Abstract Spatial and problem-solving skill are two essential skills that are needed by students to deal with the industrial revolution 4.0. Spatial and problem-solving skills certainly are not genetically obtained but are the result of a long learning process. Augmented reality technology is one of the solutions to improve problem-solving and spatial skills. The purpose of this study is to explain why AR can improve problem-solving and spatial skills based on the study of theory and results from previous research. The method used is a literature review. Data was obtained from scientific journals and books that have been published in reputable publishers. The results show that augmented reality can improve problem-solving and spatial skill because there are interrelated relationships between these two skills and augmented reality which is a technology that combines the real world and cyberspace that has been tested in several studies which can improve both of these skills.
Quasi-exact quantum computation

We study quasi-exact quantum error-correcting codes and quantum computation with them. A quasi-exact code is an approximate code such that it contains a finite number of scaling parameters, the tuning of which can flow it to corresponding exact codes, serving as its fixed points. The computation with a quasi-exact code cannot realize any logical gate to arbitrary accuracy. To overcome this, the notion of quasi-exact universality is proposed, which makes quasi-exact quantum computation a feasible model especially for executing moderate-size algorithms. We find that the incompatibility between universality and transversality of the set of logical gates does not persist in the quasi-exact scenario. A class of covariant quasi-exact codes is defined which proves to support a transversal and quasi-exact universal set of logical gates for SU(d). This work opens the possibility of quantum computation with quasi-exact universality, transversality, and fault tolerance.Received 15 October 2019Accepted 2 July 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.033116Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionTopological quantum computingQuantum Information
Trajectory Optimization and Replanning Framework for a Micro Air Vehicle in Cluttered Environments

In the present study, we propose a trajectory optimization and replanning algorithm for micro air vehicles (MAVs) in cluttered environments. To generate the path of an MAV in a cluttered environment, we first design an offline global path optimization algorithm. This algorithm generates a global trajectory for safe aerial delivery; this trajectory enables an MAV to avoid static obstacles marked in the navigation map and satisfies the MAV's initial and arrival velocities. The MAV's trajectory is replanned by exploiting dynamic movement primitives (DMPs) and a time adjustment algorithm to enable computationally efficient unknown obstacle avoidance in local path planning. To validate the applicability of the proposed algorithm, we compare simulation results with those obtained using an existing approach based on DMPs. Furthermore, an autonomous flight is demonstrated in an outdoor environment using a custom-made MAV driven by the proposed approach.
A taxonomy of cybercrime: Theory and design

This paper develops a theory-based taxonomy for cybercrime. The need for a cybercrime taxonomy stems from the lack of a definition and standards to measure and manage cybercrime. We define cybercrime as an act that uses computer technology to commit a crime. The four building blocks of our taxonomy include mutual exclusivity, structure, exhaustiveness, and well-defined categories, which together provide the theoretical foundation. As a proof-of-concept, we use exemplars and heuristics to validate our taxonomy. We identify our contributions for both theory and practice. We discuss implications of our taxonomy from the perspectives of management, reporting, disclosure, governance, regulation and the judiciary.
Fall detection based on CNN models implemented on a mobile robot

Fall accidents are serious events that need to be addressed. Generally, elderly people could suffer these accidents that may lead injures or even death. The use of Convolutional Neural Networks (CNN) has achieved the state of the art for fall detection, but it requires a high computational cost. In this work, we propose an efficient CNN architecture with a reduced number of parameters, which is applied to fall detection in a service with a mobile robot, equipped with a resource-constrained hardware (Nvidia Jetson TX2 platform). Also, different pre-trained CNN models are compared to measure their performances in real scenarios, in addition with other functions like following people and navigation. Furthermore, fall detection is carried out by extraction of temporal features obtained with an Optical Flow extraction from two consecutive RGB images. The proposed network is confirmed by our results to be faster and more suitable for running on resource-constrained Hardware. Our model achieves 88.55% of accuracy using the proposed architecture and it works at 23.16 FPS on GPU and 10.23 FPS on CPU.
Automation of Recording in Smart Classrooms via Deep Learning and Bayesian Maximum <i>a Posteriori</i> Estimation of Instructor's Pose

Internet of Things is making objects smarter and more autonomous. At the other side, online education is gaining momentum and many universities are now offering online degrees. Content preparation for such programs usually involves recording the classes. In this article, we intend to introduce a deep learning-based camera management system as a substitute for the academic filming crew. The solution mainly consists of two cameras and a wearable gadget for the instructor. The fixed camera is used for the instructor's position and pose detection and the pan-tilt-zoom (PTZ) camera does the filming. In the proposed solution, image processing and deep learning techniques are merged together. Face recognition and skeleton detection algorithms are used to detect the position of instructor. But the main contribution lies in the application of deep learning for instructor's skeleton detection and postprocessing of the deep network output for correction of the pose detection results using a Bayesian Maximum A Posteriori (MAP) estimator. This estimator is defined on a Markov state machine. The pose detection result along with the position info is then used by the PTZ camera controller for filming purposes. The proposed solution is implemented by using OpenPose which is a convolutional neural network for detection of body parts. Feeding a neural network pose classifier with 12 features extracted from the output of the deep network yields an accuracy of 89%. However, as we show, the accuracy can be improved by the Markov model and MAP estimator to reach as high as 95.5%.
Smart Stick For the Blind Using Arduino

Abstract The development of technology requires the innovation of a device that can be used to help the blind as a road guide. This device is kind of the white cane to help blind people to scan their surroundings for obstacles or orientation marks. This device is equipped with an ultrasonic sensor, a water sensor, and a pulse heart sensor that will be mounted on a white cane to determine changes in the enviroments. Ultrasonic sensors are used to detect obstacles in front of it by utilizing ultrasonic wave reflection, water detection sensors to find out if there is a puddle or flooaded ahead, and pulse heart sensors to monitor the condition of the user. It works, If there are obstacles and inundation conditions then the use of electronic modules such as the MP3 module is a component that plays an important role for the user, the use of the MP3 module is used to provide direction with sound output, while the GPS module to provide location information from the user.
Tens-embedding: A Tensor-based document embedding method

A human is capable of understanding and classifying a text but a computer can understand the underlying semantics of a text when texts are represented in a way comprehensible by computers. The text representation is a fundamental stage in natural language processing (NLP). One of the main drawbacks of existing text representation approaches is that they only utilize one aspect or view of a text e.g. They only consider texts by their words while the topic information can be extracted from text as well. The term-document and document-topic matrix are two views of a text and contain complementary information. We use the strength of both views to extract a richer representation. In this paper, we propose three different text representation methods with the help of these two matrices and tensor factorization to utilize the power of both views. The proposed approach (Tens-Embedding) was applied in the tasks of text classification, sentence-level and document-level sentiment analysis and text clustering wherein the conducted experiments on 20newsgroups, R52, R8, MR and IMDB datasets indicated the superiority of the proposed method in comparison with other document embedding techniques.
Data Augmentation for Face Recognition with CNN Transfer Learning

Face recognition is a challenging Computer Vision task. In this paper, we propose a method for face recognition by applying data augmentation and transfer learning in pre-trained Convolutional Neural Networks (CNNs). Our main focus is to analyze the power of data augmentation for face recognition systems with CNN transfer learning. We extracted features from the images and trained a KNN classifier using the VGG-Face CNN. For the input dataset, we applied several transformations to generate 12 different versions of datasets used to evaluate which combination produces better results. We ran experiments using data augmentation on the LFW dataset. We also created a proprietary dataset composed of 12 subjects. The tests have shown that the classifier trained with the dataset Saturation presented the best results with an accuracy of 98.43%. For the proprietary dataset, the best accuracy was 95.41% obtained with the Brightness, Contrast, and Saturation Combined version. In this way, we conclude that the data augmentation is essential for the face recognition task. However, a study needs to be performed for each application, since the augmentation operations that improve the classification results are dependent on the input set. Furthermore, we show that with a few samples available, it is possible to have a face recognition system with high accuracy.
Image Steganography Based on Artificial Immune in Mobile Edge Computing With Internet of Things

Mobile edge computing provides high computing power, data storage capacity and bandwidth requirements for Internet of Things (IoT) through edge servers that process data close to data sources or users. In practical, mobile edge computing can be used to implement image steganography in IoT. Considering imperceptibility, security and capacity are important indicators for image steganography, this paper propose an image steganography based on evolutionary multi-objective optimization (EMOsteg). The EMOsteg preprocesses the image through a high-pass filters bank to find noise and texture regions that are difficult to model. By perturbing the image on noise and texture regions in multiple directions, the embedded capacity is increased. By defining the imperceptibility and security as an antigen, defining the perturbation positions of the cover image as an antibody, the EMOsteg uses the artificial immune principle to heuristically obtain the perturbation population through feature extraction of the perturbation and adaptive evolution operations. And the Pareto optimal is used to find the optimal perturbation in the last generation population. The simulation experiments analyze the convergence of the algorithm and the diversity of the solutions. In simulation experiments, the MSE, PSNR and SSIM were adopted to evaluate the imperceptibility, and the results show that the MSE value of our algorithm is 0.000308, the PSNR is 82.7501 and the SSIM approaches 1, they are better than comparison algorithms. The average detection error $P_{E}$ under SPA was adopted to detect the security, and the results show that our algorithm is more robust against anti-SPA steganalysis. In order to evaluate the performance of real-time, the embedding time of the same secret under different algorithms were compared, and the results show that our algorithm is faster than comparison algorithms in the terminal. In summary, the proposed algorithm can maintain the image quality while resist steganalysis tools, and realize real-time processing.
E-Commerce Product Categorization via Machine Translation

E-commerce platforms categorize their products into a multi-level taxonomy tree with thousands of leaf categories. Conventional methods for product categorization are typically based on machine learning classification algorithms. These algorithms take product information as input (e.g., titles and descriptions) to classify a product into a leaf category. In this article, we propose a new paradigm based on machine translation . In our approach, we translate a product’s natural language description into a sequence of tokens representing a root-to-leaf path in a product taxonomy. In our experiments on two large real-world datasets, we show that our approach achieves better predictive accuracy than a state-of-the-art classification system for product categorization. In addition, we demonstrate that our machine translation models can propose meaningful new paths between previously unconnected nodes in a taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph. We discuss how the resultant taxonomy directed acyclic graph promotes user-friendly navigation, and how it is more adaptable to new products.
Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision

The construction of models of biological networks from prior knowledge and experimental data often leads to a multitude of candidate models. Devising a single model from them can require arbitrary choices, which may lead to strong biases in subsequent predictions.
Temporal Reasoning via Audio Question Answering

Multimodal question answering tasks can be used as proxy tasks to study systems that can perceive and reason about the world. Answering questions about different types of input modalities stresses different aspects of reasoning such as visual reasoning, reading comprehension, story understanding, or navigation. In this article, we use the task of Audio Question Answering (AQA) to study the temporal reasoning abilities of machine learning models. To this end, we introduce the Diagnostic Audio Question Answering (DAQA) dataset comprising audio sequences of natural sound events and programmatically generated questions and answers that probe various aspects of temporal reasoning. We adapt several recent state-of-the-art methods for visual question answering to the AQA task, and use DAQA to demonstrate that they perform poorly on questions that require in-depth temporal reasoning. Finally, we propose a new model, Multiple Auxiliary Controllers for Linear Modulation (MALiMo) that extends the recent Feature-wise Linear Modulation (FiLM) model and significantly improves its temporal reasoning capabilities. We envisage DAQA to foster research on AQA and temporal reasoning and MALiMo a step towards models for AQA.
Making QUIC Quicker With NIC Offload

This paper aims at defining the right set of primitives a NIC shall expose to efficiently offload the QUIC protocol. Although previous work already partially tackled this problem, it has only considered one specific aspect: the crypto module. We instead dissect different QUIC implementations, and perform an in-depth analysis of the cost associated to many of its components. We find that the kernel to userspace communication, the crypto module and the packet reordering algorithm are CPU hungry and often the cause of application performance degradation. We use those findings to define an architecture for offloading QUIC and discuss the associated challenges.