Reviewerly/exHarmony huggingface dataset:
A Cloud Computing Based Deep Compression Framework for UHD Video Delivery

Ultra-high-definition (UHD) videos are enjoying increased popularity in people's daily usage because of the good visual experience. However, the data size of UHD videos is 4-16 times larger of HD videos. This will bring many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. In this article, we propose a cloud computing based deep compression framework named Pearl, which utilizes the power of deep learning and cloud computing to compress UHD videos. Pearl compresses UHD videos from two respects: the frame resolution and the colorful information. In pearl, an optimal compact representation of the original UHD video is learned with two deep convolutional neural networks (DCNNs): super resolution CNN (SR-CNN) and colorization CNN (CL-CNN). SR-CNN is used to reconstruct a high resolution video from a low resolution video while CL-CNN is adopted to preserve the color information of the video. Pearl focuses on video content compression in two new directions. Thus, it can be integrated with any existing video compression system. With Pearl, the data size of UHD videos can be significantly reduced. We evaluate the performance of Pearl with a wide variety of network conditions, quality of experience (QoE) metrics, and video properties. In all considered scenarios, Pearl can further compress 84% of video size and reduce 73% of network transmission latency.
From Bitcoin to Solana – Innovating Blockchain Towards Enterprise Applications

This survey presents a comprehensive study of recent advances in blockchain technologies, focusing on how issues that affecting the enterprise adoption were progressively addressed from the original Bitcoin system to Ethereum, to Solana etc. Key issues preventing the wide adoption are scalability and performance, while recent advances in Solana has clearly demonstrated that it is possible to significantly improve on those issues by innovating on data structure, processes and algorithms by consolidating various time-consuming algorithms and security enforcements, and differentiate and balance users and their responsibilities and rights, while maintaining the required security and integrity that blockchain systems inherently offer.
An Adaptive Low-illumination Image Enhancement Algorithm based on Weighted Least Squares Optimization

Abstract An adaptive low-illumination image enhancement algorithm based on the weighted least squares optimization is proposed to solve the difficulty of detailed feature recognition in low-illumination images that collected by visible light imaging equipment. First, the image is converted from RGB channel to LAB channel. Second, we use an edge-preserving smoothing operator based on the weighted least squares optimization to coarsen smooth base layer and extract multi-scale details in brightness channel. Then, an adaptive weight is proposed and applied to the weighted fusion of smooth base and detail features. Finally, the Retinex enhancement is performed to obtain a ultimate enhanced image. Experiments result show that the image enhanced by this method has suitable visual brightness and clear details. In terms of objective indicators, it has good and stable performance in NIQE, TMQI, and information entropy.
Brain MR Atlas Construction Using Symmetric Deep Neural Inpainting

Modeling statistical properties of anatomical structures using magnetic resonance imaging is essential for revealing common information of a target population and unique properties of specific subjects. In brain imaging, a statistical brain atlas is often constructed using a number of healthy subjects. When tumors are present, however, it is difficult to either provide a common space for various subjects or align their imaging data due to the unpredictable distribution of lesions. Here we propose a deep learning-based image inpainting method to replace the tumor regions with normal tissue intensities using only a patient population. Our framework has three major innovations: 1) incompletely distributed datasets with random tumor locations can be used for training; 2) irregularly-shaped tumor regions are properly learned, identified, and corrected; and 3) a symmetry constraint between the two brain hemispheres is applied to regularize inpainted regions. Henceforth, regular atlas construction and image registration methods can be applied using inpainted data to obtain tissue deformation, thereby achieving group-specific statistical atlases and patient-to-atlas registration. Our framework was tested using the public database from the Multimodal Brain Tumor Segmentation challenge. Results showed increased similarity scores as well as reduced reconstruction errors compared with three existing image inpainting methods. Patient-to-atlas registration also yielded better results with improved normalized cross-correlation and mutual information and a reduced amount of deformation over the tumor regions.
Predicting Hand-Object Interaction for Improved Haptic Feedback in Mixed Reality

Accurately detecting when a user begins interaction with virtual objects is necessary for compelling multi-sensory experiences in mixed reality. To address inherent sensing, computation, display, and actuation latency, we propose to predict when a user will begin touch interaction with a virtual object before it occurs. We hypothesize that the sequence of hand poses when approaching an object, combined with object pose, contain sufficient information to predict when the user will begin contact. By leveraging this information, we could reduce or eliminate latency in providing haptic feedback during virtual object interaction. We focus on small time horizons, on the order of 100 ms, to overcome sense-to-actuation latency for haptic feedback in mixed reality systems. We use a time series of tracked hand poses, along with virtual object geometry to perform our prediction. By calculating minimum hand-object distance and feeding those along with hand poses to a self-attention-based network, we achieve approximately 52.8 ms of timing error for a 100 ms prediction horizon. Additionally, we test our system against different levels of tracking and hand-object alignment noise, finding minimal change in timing error. By contrast, when only extrapolating joint and hand velocities, we find that timing error consistently exceeds the prediction horizon.
Path Planning Method of Mobile Robot Based on Q-learning

Abstract With the progress of science and technology, mobile robots gradually play an increasingly important role in the industry, military, science and technology and other fields. Aiming at the core problem of path planning in the path planning of mobile robots, this paper studies and designs a path planning method based on a Q-learning algorithm. Q-learning is widely used in robot path planning, as it only needs the interaction between the current state and the environment to make rewards and punishments for robot actions, to make decisions on the next action. Aiming at the problems of low efficiency and slow convergence in the original Q-learning algorithm, this paper improved the algorithm to enable the robot to quickly complete the planning and get the optimal and shortest path. The grid method was used to establish the environment running program to visualize the convergence process and obtain data. Finally, software simulation is used to establish the environment and code the robot to simulate the real environment, which proves the practical value of the algorithm.
A data‐driven adversarial examples recognition framework via adversarial feature genomes

Adversarial examples pose many security threats to convolutional neural networks (CNNs). Most defense algorithms prevent these threats by finding differences between the original images and adversarial examples. However, the found differences do not contain features about the classes, so these defense algorithms can only detect adversarial examples without recovering the correct labels. In this regard, we propose the Adversarial Feature Genome (AFG), a novel type of data that contains both the differences and features about classes. This method is inspired by an observed phenomenon, namely the Adversarial Feature Separability (AFS), where the difference between the feature maps of the original images and adversarial examples becomes larger with deeper layers. On top of that, we further develop an adversarial example recognition framework that detects adversarial examples and can recover the correct labels. In the experiments, the detection and classification of adversarial examples by AFGs has an accuracy of more than 90.01\% in various attack scenarios. To the best of our knowledge, our method is the first method that focuses on both attack detecting and recovering. AFG gives a new data-driven perspective to improve the robustness of CNNs. The source code is available at https://github.com/GeoX-Lab/Adv_Fea_Genome.
Predictive analytics and the creation of the permanent present

ABSTRACTABSTRACTThis article examines the work of predictive analytics in shaping the social worlds in which they thrive, and in particular the world of the first year of Great State University's student success initiative. Specifically, this article investigates the following research paradox: predictive analytics, as driven by a logic premised on predicting the future, lock Great State University into a never-ending present. Through Brian Massumi's concept of a logic of preemption, this paper explores how a quest for proper predictive analytics locked this student success initiative into a hamster wheel of a never-ending present, where the only space and time of action was here and now. It is argued that the function of predictive analytics in higher education is to produce space and time through dividuation and continuous algorithmic variation, freezing both past and future and restricting spacetimes of change to the present.KEYWORDS: Predictive analyticspreemptionstudent successhigher educationcollege impact AcknowledgementsParts of this paper are adapted from my dissertation as well as a talk given at Florida International University in April 2019. My thanks to Dan Saunders, Ben Baez, and the students and faculty of FIU for the invitation and rich conversations. Thanks also to Paul Eaton and Maureen Flint for their insight on previous versions of this draft, as well as to the two anonymous reviewers for their valuable suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).
Joint Optimal Quantization and Aggregation of Federated Learning Scheme in VANETs

Vehicular ad hoc networks (VANETs) is one of the most promising approaches for the Intelligent Transportation Systems (ITS). With the rapid increase in the amount of traffic data, deep learning based algorithms have been used extensively in VANETs. The recently proposed federated learning is an attractive candidate for collaborative machine learning where instead of transferring a plethora of data to a centralized server, all clients train their respective local models and upload them to the server for model aggregation. Model quantization is an effective approach to address the communication efficiency issue in federated learning, and yet existing studies largely assume homogeneous quantization for all clients. However, in reality, clients are predominantly heterogeneous, where they support different quantization precision levels. In this work, we propose FedDO – Federated Learning with Double Optimization. Minimizing the drift term in the convergence analysis, which is a weighted sum of squared quantization errors (SQE) over all clients, leads to a double optimization at both clients and server sides. In particular, each client adopts a fully distributed, instantaneous (per learning round) and individualized (per client) quantization scheme that minimizes its own squared quantization error, and the server computes the aggregation weights that minimize the weighted sum of squared quantization errors over all clients. We show via numerical experiments that the minimal-SQE quantizer has a better performance than a widely adopted linear quantizer for federated learning. We also demonstrate the performance advantages of FedDO over the vanilla FedAvg with standard equal weights and linear quantization.
Context-based navigation for ground mobile robot in semi-structured indoor environment

There is a growing demand for mobile robots to operate in more variable environments, where guaranteeing safe robot navigation is a priority, in addition to time performance. To achieve this, current solutions for local planning use a specific configuration tuned to the characteristics of the application environment. In this paper, we present an approach for developing quality models that can be used by a self-adaptation framework to adapt the local planner configuration at run-time based on the perceived environment. We contribute a definition of safety model that predicts the safety of a navigation configuration given the perceived environment. Experiments have been performed in a realistic navigation scenario for a retail application to validate the obtained models and demonstrate their integration in a self-adaptation framework.
A Cerebellum-Inspired Network Model and Learning Approaches for Solving Kinematic Tracking Control of Redundant Manipulators

Tracking control of redundant manipulators is always a basic and important issue in robotics. Existing studies have indicated that the pivotal region of the brain associated with human motion control is the cerebellum. This motivates us to devise a model-based cerebellum-inspired (MBCI) scheme and a model-free cerebellum-inspired (MFCI) scheme for the tracking control of redundant manipulators in this article. The MBCI scheme solves the inverse kinematics problem with a cerebellum model. By using the parameters and Jacobian matrix of the manipulator, the task space error is transformed into joint space error, which is taken as the teaching signal to train the cerebellum model designed based on the echo state network. The MFCI scheme is formed by coupling a cerebellum model and a multilayer perceptron (MLP). The MLP is able to generate approximate joint angle commands to the manipulator and the cerebellum model is utilized to fine-tune the MLP controller, thereby improving the tracking accuracy. In addition, leaky integrator neurons (LINs) are integrated into the cerebellum model to further improve the performance of the proposed schemes. Finally, comparative simulations and physical experiments on different types of redundant manipulators are conducted to verify the efficacy and merits of the proposed cerebellum-inspired schemes.
Virtual Reality-Integrated Immersion-Based Teaching to English Language Learning Outcome

Globalization and informatization are reshaping human life and social behaviors. The purpose is to explore the worldwide strategies to cultivate international talents with a global vision. As a global language with the largest population, English, and especially its learning effect, have always been the major concerns of scholars and educators. This work innovatively studies the combination of immersion-based English teaching with virtual reality (VR) technology. Then, based on the experimental design mode, 106 students from a Chinese school were selected for a quasi-experimental study for 16 weeks (3 h a week, and 48 h in total). The collected data were analyzed by computer statistical software, and hypotheses are verified. The results showed that there is a significantly positive correlation between VR and immersion-based language teaching (0.851, p < 0.01). There is a significantly positive correlation between immersion-based language teaching and academic achievement (0.824, p < 0.01), and VR is positively correlated with learning outcome (LO) (0.836, p < 0.01). Compared with other state-of-art research methods, this work modifies the students' oral test through the analysis and comparison with the system database, and the students' learning effect is greatly improved. Finally, some suggestions are put forward according to the research results to provide an experimental reference for English teachers and future linguistics teaching.
EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm

In Human activity recognition (HAR) research, it is a common practice to use wearable sensors to acquire the signals for human daily activities. In this study, an experimental data from electromyography (EMG) wireless sensors is analysed for six different activities recognition. This paper aims to compare EMG signals of left and right of upper leg muscles by using Random Forest (RF) Machine Learning Classifier. The HAR processing comprises of data filtering and segmentation, data feature extraction, feature selection of the data, and classification. Model evaluation of holdout method is implemented for classification assessment. The performance of all human daily activities is evaluated according to the comparison of precision and recall for each activity. The results show combined muscles obtained the highest precision and recall on running activity with 89.2% and 88.3%. The highest overall accuracy of classification was 82.08% on the bicep femoris left and right (BF-Left & Right).
An Intelligent Recommendation System for Real Estate Commodity

Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms, whereas consumers often rely on websites to search and compare prices when purchasing real property.In addition to being time consuming, this search process renders it difficult for agents and consumers to understand the status changes of objects.In this study, Python is used to write web crawler and image recognition programs to capture object information from the web pages of real estate agents; perform data screening, arranging, and cleaning; compare the text of real estate object information; as well as integrate and use the convolutional neural network of a deep learning algorithm to implement image recognition.In this study, data are acquired from two business-to-consumer real estate agency networks, i.e., the Sinyi real estate agent and the Yungching real estate agent, and one consumer-toconsumer real estate agency platform, i.e., the, FiveNineOne real estate agent.The results indicate that text mining can reveal the similarities and differences between the objects, list the number of days that the object has been available for sale on the website, and provide the price fluctuations and fluctuation times during the sales period.In addition, 213,325 object amplification images are used as a database for training using deep learning algorithms, and the maximum image recognition accuracy achieved is 95%.The dynamic recommendation system for real estate objects constructed by combining text mining and image recognition systems enables developers in the real estate industry to understand the differences between their commodities and other businesses in approximately 2 min, as well as rapidly determine developable objects via comparison results provided by the system.Meanwhile, consumers require less time in searching and comparing prices after they have understood the commodity dynamic information, thereby allowing them to use the most efficient approach to purchase real estate objects of their interest.
Attention in Crowd Counting Using the Transformer and Density Map to Improve Counting Result

With the vigorous development of CNN, most crowd counting methods have approached using CNN to estimate the density map and then infer the count. However, these methods face many limitations due to limited receptive fields, background noise, etc. With the advent of Transformer in natural language processing, it is possible to utilize this model for the crowd counting problem. The Transformer can model the global context, so it helps to solve the problem of receptive fields. On the other hand, with the attention mechanism, the model can focus on areas of concentration of people, helping to solve the problem of background noise. In this paper, we propose a Crowd counting model combining Transformer and Density map (TDCrowd) to estimate the number of people in a crowd. With the use of a Transformer, TDCrowd can still be trained so that it does not need information about the location of people in the crowd, but only information about the count. Experiments on three datasets ShanghaiTech, UCF_QNR, and JHU-Crowd++, show that TDCrowd gives better results when compared to regression-based methods (need only the count information) and density map-based (need the count information and location information).
A Code Rate-Compatible High-Throughput Hardware Implementation Scheme for QKD Information Reconciliation

For high-speed quantum key distribution (QKD) post processing, information reconciliation is the most computational step, which usually acts as the system speed bottleneck. Reconciliation efficiency and operation throughput are the two most important performance parameters, but they are often compromised to each other in actual realization due to the available hardware resources. Another characteristic of the information reconciliation is that its channel is time-varying, and in order to guarantee high efficiency, some rate-compatible error correction scheme should be adopted. To cope with the above problems, this paper proposes a rate-compatible high-efficiency reconciliation algorithm based on quasi-cyclic (QC) low-density parity-check (LDPC) codes and puncturing algorithms. On the other hand, in order to obtain high throughput while maintaining low implementation complexity, the normalized min-sum algorithm (NMSA) is realized and optimized by using the fast prototyping tool of high-level synthesis (HLS). The proposed information reconciliation module was implemented on the Xilinx Zynq Ultrascale+ ZCU102 development board. Experimental results show that the maximum throughput rate of the implemented reconciliation module in this paper can reach 136 Mbps, while the efficiency factor is kept lower than 1.32 across the error rate range of 1.7%∼10.6% under the remaining frame error rate level of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . The comprehensive good performance obtained by our design can strongly support the development of modern high-speed QKD systems.
Improving Neural Machine Translation for Low Resource Algerian Dialect by Transductive Transfer Learning Strategy

This study is the first work on a transductive transfer learning approach for low-resource neural machine translation applied to the Algerian Arabic dialect. The transductive approach is based on a fine-tuning transfer learning strategy that transfers knowledge from the parent model to the child model. This strategy helps to solve the learning problem using limited parallel corpora. We tested the approach on a sequence-to-sequence model with and without the Attention mechanism. We first trained the models on a parallel multi-dialects Arabic corpus and then switch them to a low-resource of the Algerian dialect. Transductive transfer learning raises the BLEU score for the Seq2Seq model from 0.3 to more than 34, and for the Attentional-Seq2Seq model from less than 17 to more than 35. The obtained results prove the validity of this approach.