Reviewerly/exHarmony huggingface dataset:
Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data Obfuscation

In this paper, we compare and analyze light-weight approaches for instantaneous smart meter (SM) data obfuscation from a group of consumers.In the literature, the common approach is to use additive Gaussian noise based SM data obfuscation.In order to investigate the effects of different approaches, we consider Gaussian, Rayleigh, generalized Gaussian and chi-square distributions to achieve either additive or multiplicative data obfuscation.For each type of obfuscation approach, we calculate the required parameters to achieve obfuscation such that 50% of the obfuscated data fall outside an interval equalling twice the mean of the instantaneous SM measurements.We also calculate the minimum number of SMs required to estimate the mean of the actual SM measurements, such that the estimate varies within only 0.5% of the actual mean with a 99.5% probability.Simulation results are used to verify the calculations, and it is shown that multiplicative Rayleigh and generalized Gaussian noise require the least number of SMs, which is 90% less than the traditional approach of additive Gaussian noise-based SM data obfuscation.
MF-GAN: Multi-conditional Fusion Generative Adversarial Network for Text-to-Image Synthesis

The performance of text-to-image synthesis has been significantly boosted accompanied by the development of generative adversarial network (GAN) techniques. The current GAN-based methods for text-to-image generation mainly adopt multiple generator-discriminator pairs to explore the coarse/fine-grained textual content (e.g., words and sentences); however, they only consider the semantic consistency between the text-image pair. One drawback of such a multi-stream structure is that it results in many heavyweight models. In comparison, the single-stream counterpart bears the weakness of insufficient use of texts. To alleviate the above problems, we propose a Multi-conditional Fusion GAN (MF-GAN) to reap the benefits of both the multi-stream and the single-stream methods. MF-GAN is a single-stream model but achieves the utilization of both coarse and fine-grained textual information with the use of conditional residual block and dual attention block. More specifically, the sentence and word features are repeatedly inputted into different model stages for textual information enhancement. Furthermore, we introduce a triple loss to close the visual gap between the synthesized image and its positive image and enlarge the gap to its negative image. To thoroughly verify our method, we conduct extensive experiments on two benchmarked CUB and COCO datasets. Experimental results show that the proposed MF-GAN outperforms the state-of-the-art methods.
A Novel Image and Audio-based Artificial Intelligence Service for Security Applications in Autonomous Vehicles

Autonomous Vehicles (AVs) can potentially reduce the accident risk while a human is driving. They can also improve the public transportation by connecting city centers with main mass transit systems. Creating a system that can provide a sense of security to the passenger, when the driver is missing, remains a challenging task. In this work, an image and audio-based approach, supported by novel Artificial Intelligence (AI) algorithms, is proposed as a service to increase the security and trust inside an autonomous shuttle. The two modalities, running in real-time, can detect petty crimes scenarios such as screaming, bag snatching, people fighting and vandalism and enable notifications to authorized personnel for proper actions. The proposed solution is deployed on a Jetson AGX Xavier to favor power efficiency and seamless integration and achieves up to 96% accuracy. Thus, the envisioned system exhibits high potential for transforming security and safety in emerging autonomous public transportation infrastructure.
Zoomer: Boosting Retrieval on Web-scale Graphs by Regions of Interest

We introduce Zoomer, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs. Zoomer is designed for tackling two challenges presented by the massive user data at Taobao: low training/serving efficiency due to the huge scale of the graphs, and low recommendation quality due to the information overload which distracts the recommendation model from specific user intentions. Zoomer achieves this by introducing a key concept, Region of Interests (ROI) in GNNs for recommendations, i.e., a neighborhood region in the graph with significant relevance to a strong user intention. Zoomer narrows the focus from the whole graph and “zooms in” on the more relevant ROIs, thereby reducing the training/serving cost and mitigating the information overload at the same time. With carefully designed mechanisms, Zoomer identifies the interest expressed by each recommendation request, constructs an ROI subgraph by sampling with respect to the interest, and guides the GNN to reweigh different parts of the ROI towards the interest by a multi-level attention module. Deployed as a large-scale distributed system, Zoomer supports graphs with billions of nodes for training and thousands of requests per second for serving. Zoomer achieves up to 14x speedup when downsizing sampling scales with comparable (even better) AUC performance than baseline methods. Besides, both the offline evaluation and online A/B test demonstrate the effectiveness of Zoomer.
Dynamic Fizeau interferometer with assistant defocused source

We report and demonstrate a dynamic Fizeau interferometer (DFI) capable of high-precision measurements under severe vibration. The method involves two interferometric passes: a null-interference pass (NP) and a circular carrier-frequency pass (CP). The NP adopts an on-axis Fizeau design, preserving the common path of the reference and test beams. The CP uses an assistant defocused source to generate circular fringe patterns, assisting the NP in phase extraction and sign correction. The feasibility and performance of the proposed method are demonstrated experimentally. To the best of our knowledge, this is the first proposal of a general asynchronous phase-shifting DFI that can not only demodulate null-fringe patterns and correct phase-sign ambiguity but can also maintain the common-path advantages of the Fizeau interferometer.
Sentiment Analysis of Algerian Dialect Using a Deep Learning Approach

Nowadays the Internet has become an essential tool for exchanging information, both on a personal and professional level. Today, the analysis of sentiment offers us a great interest for research, marketing and industry. With millions of comments and tweeting published every day, the information available on the Internet and in social media has become a gold mine for companies developing in their production, management and distribution. In this article, we propose a novel approach to analyze the sentiments of the Algerian dialect for the benefit of the Algerian Telephone Operator Ooredoo. The proposed approach is based on a CNN deep learning model and the SVM machine learning algorithm, which provides state-of-the-art results on a dataset written in Algerian dialect. In this study, the Facebook comments shared in Modern Standard Arabic (MSA) and Algerian dialect of the customers of the Algerian telephone operator Ooredoo are analyzed in order to allow the operator to retain and satisfy its customers to the maximum. Experimental results show that deep learning approaches outperformed traditional methods of sentiment analysis.
Use of structured light in 3D reconstruction of transparent objects

A simple non-interferometric incoherent light ray propagation model is introduced to perform three-dimensional profiling of transparent objects with typical thicknesses of the order of mm to cm by analyzing the distorted captured image behind the object. A two-dimensional cosine fringe is used as the incident reference image, whose periodicity is markedly altered by the shape of the object. By monitoring the local change in the period, the surface profile is simulated and optimized to achieve minimal error with experimental data and thus determine the final morphology. Our proposed method is simple, robust, straightforward, and single-shot, and can be used with coherent or incoherent illumination. Its feasibility for more complex applications is verified experimentally through rigorous error calculation. Moreover, the application of this technique for arbitrary transparent objects is theoretically attainable and promising.
Gaussian Process-based Spatio-Temporal Predictor

This paper presents a grid-based algorithm using Gaussian Processes to predict outputs using spatially and temporally dependent data.First, independent Gaussian Processes are formulated along space and time axes.Then, these processes are coupled with a common noise in the covariance kernel.This common noise acts as a smoothing parameter, trading off accuracy at knots for extrapolation capabilities.The algorithm can predict timeseries at unmeasured locations.The efficiency of the algorithm is demonstrated in a traffic flow prediction problem.Results suggest that applying a common additive noise term capturing cross covariances improves prediction accuracy when extrapolating outside the dataset.
Reinforcement Learning-Based Auto-scaling Algorithm for Elastic Cloud Workflow Service

Deploying a workflow engine as a service on a container cloud environment can improve its service quality and reliability, but auto-scaling of the elastic cloud workflow service doesn’t attract much study attention. Current auto-scaling algorithms oriented to common microservices consider little about the characteristics of a long time and high cost of starting up workflow service, which can easily cause problems such as untimely scaling and excessive scaling. Given this, based on reinforcement learning and semi-Markov decision process (SMDP) modeling, an auto-scaling algorithm for elastic cloud workflow engine is proposed, which enables the cloud workflow service to scale in time, appropriately allocating resources and ensuring service availability. Simulation comparison experiments show that the algorithm automatically scales instances in advance and adapts to changes in traffic through the reinforcement learning SMDP strategy, so that it reduces the violation rate in Service Level Agreements (SLA), and improves the availability of the cloud workflow service.
DIEL: Interactive Visualization Beyond the Here and Now

Interactive visualization design and research have primarily focused on local data and synchronous events. However, for more complex use cases-e.g., remote database access and streaming data sources-developers must grapple with distributed data and asynchronous events. Currently, constructing these use cases is difficult and time-consuming; developers are forced to operationally program low-level details like asynchronous database querying and reactive event handling. This approach is in stark contrast to modern methods for browser-based interactive visualization, which feature high-level declarative specifications. In response, we present DIEL, a declarative framework that supports asynchronous events over distributed data. As in many declarative languages, DIEL developers specify only what data they want, rather than procedural steps for how to assemble it. Uniquely, DIEL models asynchronous events (e.g., user interactions, server responses) as streams of data that are captured in event logs. To specify the state of a visualization at any time, developers write declarative queries over the data and event logs; DIEL compiles and optimizes a corresponding dataflow graph, and automatically generates necessary low-level distributed systems details. We demonstrate DIEL'S performance and expressivity through example interactive visualizations that make diverse use of remote data and asynchronous events. We further evaluate DIEL'S usability using the Cognitive Dimensions of Notations framework, revealing wins such as ease of change, and compromises such as premature commitments.
Testing quantum nonlocality of two-qubit entangled states under decoherence

The quantum nonlocal correlation of quantum states plays an important role in the quantum information and quantum computing protocols. However, during the transmission of entangled states in the quantum channel, they will inevitably interact with the environment, resulting in the degradation of the coherence and then weakening the quantum nonlocal correlation. Using a high probability quantum nonlocal correlation testing scheme based on Hardy-type paradox, in this paper we investigate the quantum nonlocal correlation testing of two-qubit polarization entangled states when they transmit through amplitude damping channel (ADC), phase damping channel (PDC) and depolarization damping channel (DC). The results show that DC has a great influence on the quantum nonlocal correlation testing, while PDC has little influence on the quantum nonlocal correlation testing of quantum states. Finally, this paper also gives condition for the successful quantum nonlocal correlation testing of ADC under weak measurement and quantum weak measurement reversal operation. The results show that when the intensity of weak measurement increases, the influence of ADC decoherence effect on quantum nonlocal correlation testing can be effectively reduced.
Experience in Design of Artificial Neural Network for Object Detection on Monochromatic Images

The paper considers the design of artificial neural networks (ANN) for the detection and classification of vehicles on monochrome images obtained from unmanned aerial vehicles (UAV). The main problem based on the existence of miscellaneous images, which are obtained for various distances and different object profiles. The aim of this paper is an estimation of the efficiency of artificial neural networks as a function of different metrics. A mathematical statement of the problem in the context of binary decision is given. The results were evaluated using simulation in Matlab2020, where images were marked, and a neural network was implemented based on the YOLO technology. The average precision archives 48% in terms of detection and classification of vehicles for various profiles of objects and distance. As a way to improve the accuracy of vehicle detection, it recommends increasing the volume of the training sample. It is advisable to use the entire number of object profiles in order to achieve the upper limit of recognition. As a way to improve the accuracy of vehicle detection, it recommends increasing the volume of the training sample. It is advisable to use the entire number of object profiles in order to achieve the upper limit of recognition.
Series Photo Selection via Multi-View Graph Learning

Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos. While a great progress has been observed, most of the existing SPS approaches concentrate solely on extracting features from the original image, neglecting that multiple views, e.g, saturation level, color histogram and depth of field of the image, will be of benefit to successfully reflecting the subtle aesthetic changes. Taken multi-view into consideration, we leverage a graph neural network to construct the relationships between multi-view features. Besides, multiple views are aggregated with an adaptive-weight self-attention module to verify the significance of each view. Finally, a siamese network is proposed to select the best one from a series of nearly identical photos. Experimental results demonstrate that our model accomplish the highest success rates compared with competitive methods.
A Proposed Model of a Future University in the Era of the Artificial Intelligence Transformative Society: From Why to How

This paper is an attempt to circumscribe briefly the evolution and the revolution of the Global Society in which we live (usually called the Transformative Society) that embedded a huge change in technologies, digitalization, and knowledge-based economy.We need to develop future oriented curricula for "a new university paradigm" that adjust dynamically with the needs of the society and the employers to make the students straightforwardly ready from an output of the new model of university to a swift productive input for the employers.This will help the economy to be more competitive and improve the wellbeing of the Citizens in terms of health and better quality of life.While a traditional University sells degrees, the New Model of University we are offering sells skills for students and prospective employees who want to be life learners equipped with the cutting edge technologies and knowhow.We will use coop programs, in-field learning, and international internships to exchange with worldwide student through a wealth of network.
Real-Time Monitoring Of Big Data Sports Teaching Data Based On Complex Embedded System

The growing mindset that oversees one of the fundamental difficulties created in the big data austerity tings (population) internet industry. Unpredictable from many parts of the big data life cycle, for example, collecting, for example, cloud packing them and having the latest progress cycle information including, including, Extensible Markup Language (XML) and resource interpretation framework. This management structure that is going on simply because the way manage the system does not change. They have become a basic device in the world of games. Previously, the execution of the game to usually completed by cutting the hours or days after the game — this video has a detailed cycle to the original map with each player developing. The ever-evolving gaming application remote sensor and minor improvements give new doors open for improvement. Sensor gadgets are gradually becoming smarter and more flexible, adjusting to freely linked and approved execution information technology capabilities. The various advantages, organizational capability and usefulness of this phone will allow to use the program, then again, with impossible observations and schedule changes. This paper proposes a simple and generic game system to integrate into the online survey of imagination and standard input strategy to ensure non-exclusivity.
Adaptive coordinated motion constraint control for cooperative multi-manipulator systems

Constrained motion and redundant degrees of freedom control exist in a multi-manipulator collaboration system. In other words, the multi-manipulator collaboration technology must solve the problems of uncertain environment interaction and coordinated control. Few studies have been conducted on the coordination control of a multi-manipulator, and the control effect is not good. To solve the coordinated motion problem of the multi-manipulator cooperative system, this study divides the multi-manipulator coordinated motion into two forms, namely coupled and superimposed motions, and proposes an adaptive coordinated motion constraint scheme under different motion forms. The coupled and superimposed motions are investigated through coordinated handling and coordinated drawing circle tasks, respectively. The proposed coordinated control scheme has a good effect. Without position detection and positioning, the kinematic constraint algorithm can maintain the relative motion relationship between end-effectors. When an external disturbance occurs, the slave manipulator can automatically adjust based on the position of the main manipulator, avoiding error accumulation. The experimental results show a maximum trajectory tracking error of 2.131 mm and maximum attitude error of 0.176°, indicating that the proposed control scheme has strong adaptive ability and high control accuracy.
Pure Frequency-Domain Deep Neural Network for IoT-Enabled Smart Cameras

Although deep neural network (DNN) models have been extensively studied, they are often too complex to directly execute in real time on smart cameras with limited resources. Time-domain (TD) DNN models that incorporate frequency-domain (FD) layers, also known as hybrid-domain models, have been developed to mitigate the above problem; however, these require time/FD transforms. Currently, a pure FD DNN does not exist. Thus, our study proposes the first of such, along with our lightweight time/FD transform. Our model ensures that the networks perform faster on smart cameras and are more memory and energy efficient, both of which are important for smart cameras utilizing edge computing. Unlike existing TD or hybrid-domain studies, our model optimizes several internal neural network layers and implements a lightweight time/FD transform to reduce the number of calculations. More importantly, our study is the first to realize an FD fully connected layer, which can better represent a spectral feature distribution. The experimental results show that our accuracy slightly outperforms that of the existing time and hybrid-domain studies. In addition, our model’s inference speed on the edge computing platform was shown to be faster by a maximum of 52.01% for the MNIST data set and 52.00% for the CIFAR-10 data set. Furthermore, our model can improve frames per second (FPS) by at least 52.00% and memory usage by 43.64%, and save approximately 26.09% of power consumption for the MNIST data set.
Real-Time Self-Supervised Monocular Depth Estimation Without GPU

Single-image depth estimation represents a longstanding challenge in computer vision and although it is an ill-posed problem, deep learning enabled astonishing results leveraging both supervised and self-supervised training paradigms. State-of-the-art solutions achieve remarkably accurate depth estimation from a single image deploying huge deep architectures, requiring powerful dedicated hardware to run in a reasonable amount of time. This overly demanding complexity makes them unsuited for a broad category of applications requiring devices with constrained resources or memory consumption. To tackle this issue, in this paper a family of compact, yet effective CNNs for monocular depth estimation is proposed, by leveraging self-supervision from a binocular stereo rig. Our lightweight architectures, namely PyD-Net and PyD-Net2, compared to complex state-of-the-art trade a small drop in accuracy to drastically reduce the runtime and memory requirements by a factor ranging from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100\times $ </tex-math></inline-formula> . Moreover, our networks can run real-time monocular depth estimation on a broad set of embedded or consumer devices, even not equipped with a GPU, by early stopping the inference with negligible (or no) loss in accuracy, making it ideally suited for real applications with strict constraints on hardware resources or power consumption.