Reviewerly/exHarmony huggingface dataset:
Adaptive Video Data Hiding through Cost Assignment and STCs

With the increasing popularity of digital video communication, video data hiding has become an active research topic in covert communication and privacy protection. Traditional video data hiding methods often use quantized discrete cosine transform (QDCT) coefficients to carry a sufficient payload. However, since QDCT coefficients expose texture features and motion characteristics of the present video frame heavily, data embedding with QDCT coefficients may lead to significant intra-frame distortion and inter-frame distortion drift. To avoid obvious visual artifacts and keep bit-rate within a satisfactory level of the marked video, data embedding in QDCT coefficients should take into account both the intra-frame and inter-frame distortion impacts. It motivates the authors to propose an efficient cost assignment-based video data hiding method in this paper. The proposed cost assignment method aims to accurately evaluate the data embedding distortion. Specifically, the proposed scheme considers intra-frame changes and intra-frame distortion drift, for which the texture and motion changes of frames can be measured. The frame position is also used to reflect a cumulative distortion difference of multiple frames. For data embedding, syndrome-trellis code (STC) is adopted to minimize the overall distortion. Experimental results show that the proposed method significantly outperforms existing works in terms of payload-distortion performance.
Native Language's Effect on Java Compiler Errors

Novice student compilation behaviors are well documented by prior research, but those findings are derived from instructional contexts that are largely in English-dominant locales. Speakers of languages other than English face unique challenges while learning to program like partial localization of applications and programming language syntax based on English-only keywords. This study examines compiler errors of novice programming students from different native language backgrounds to explore potential differences in their error distributions relative to those in English dominant contexts. For example, it is plausible that students from non-English language backgrounds would experience more "unknown identifier" types of errors while programming with English keywords and API methods. Using data from the BlueJ Blackbox database, we analyzed error distributions for users based on country and language group characteristics. Statistical analysis showed a statistically significant difference in error distributions between native language groups; however, effect sizes were very weak indicating that the differences have little practical significance in terms of guiding either language or instructional design. However, these results may support drawing broader inferences from earlier Java compilation behavior studies to global contexts.
Improve Reputation Evaluation of Crowdsourcing Participants Using Multidimensional Index and Machine Learning Techniques

Building a scientific and reasonable reputation evaluation mechanism for crowdsourcing participants is an effective way to solve the problem of transaction fraud, to establish the trust of traders and ensure the quality of task completion.Under the big data environment, machine learning methods have been applied in the domain of e-commerce of physical goods to improve the traditional reputation evaluation methods, and achieved good results.However, few studies have applied machine learning methods to crowdsourcing, a form of service e-commerce, to evaluate the reputation of participants.This paper proposes a reputation evaluation model (i.e.LDA-RF) for crowdsourcing participants of Random Forest based on Linear Discriminant Analysis.The model consists of five steps: firstly, building a multidimensional reputation evaluation index system for crowdsourcing participants, collecting real data sets, and preprocessing data; secondly, data dimensionality reduction methods, including Linear Discriminant Analysis, Principal Component Analysis, Mean Impact Value method and ReliefF feature selection method, are used to eliminate redundant variables; thirdly, data normalization; fourthly, with selected feature subset, five machine learning techniques, Random Forest, Decision Tree, Back propagation Neural Network, Radial Basis Function Neural Network and Support Vector Machine are used to train the model; Fifthly, the validity of the model is tested by four evaluation measures: 10 fold cross validation, confusion matrix, Kruskal-wallis test and dispersion degree.The results show that the LDA-RF model on accuracy, F1-measure, generalization ability and robustness are better than those of other models, and it has better performance and effectiveness.This study represents a new contribution to establish reputation evaluation of crowdsourcing participants under big data environment.
Human Activity Recognition Using 2D Convolutional Neural Networks

Activity recognition represents the task of classifying data derived from different sensor types into one of predefined activity classes. Convolutional neural networks (CNN) as one of the best deep learning methods has recently attracted much attention to the problem of activity recognition, Most convolutional neural networks used for recognition task are built using convolution and pooling layers followed by a few number of fully connected layers. In this paper, we propose a new architecture based on 2D convolutional neural network that consists solely of convolutional layers and find that with removing the pooling layers and instead adding strides to convolution layers, the computation time will decrease notably while the model performance will not change or in some cases will even improve. Also its performance will be compared with some other handcrafted feature based methods. The third point that will be discussed in this paper is the impact of applying fast fourier transform to inputs before training learning algorithm. It will be shown that this preprocessing will enhance the model performance. Experiments on benchmark dataset demonstrate the high performance of proposed 2D CNN model with no pooling layers, achieving an overall accuracy of 95.69% on the test set.
New approach to estimate 5-min global solar irradiation data on tilted planes from horizontal measurement

This paper presents a new methodology to estimate global tilted irradiation in 5-min steps using only global irradiation on the horizontal plane. The methodology is based on a combination of the two well-known conventional Perrin Brichambaut and Liu and Jordan models. Tilted irradiation is of great importance for the design and short-term performance assessment of fixed-tilt flat-plate collectors and photovoltaic (PV) systems. Intermediate key parameters of the state of the sky, referred to here as cloudiness factors, are determined and introduced to transform isotropic models into an anisotropic model. The results of the proposed model for all sky conditions with regard to normalized root mean square error (nRMSE), relative percentage error (RPE), normalized mean absolute error (nMAE) and coefficient of correlation (R2) range from 4.7 to 6.41%, 5.5–5.9%, 3.07–4.73% and 0.97 to 0.99, respectively, which are very accurate results, especially for such a short time step. A comparison with the best conventional models and even artificial neural network (ANN) models described in the literature has confirmed that the developed model has smaller errors.
aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge

The University of Toronto is one of eight teams competing in the SAE AutoDrive Challenge - a competition to develop a self-driving car by 2020. After placing first at the Year 1 challenge, we are headed to MCity in June 2019 for the second challenge. There, we will interact with pedestrians, cyclists, and cars. For safe operation, it is critical to have an accurate estimate of the position of all objects surrounding the vehicle. The contributions of this work are twofold: First, we present a new object detection and tracking dataset (UofTPed50), which uses GPS to ground truth the position and velocity of a pedestrian. To our knowledge, a dataset of this type for pedestrians has not been shown in the literature before. Second, we present a lightweight object detection and tracking system (aUToTrack) that uses vision, LIDAR, and GPS/IMU positioning to achieve state-of-the-art performance on the KITTI Object Tracking benchmark. We show that aUToTrack accurately estimates the position and velocity of pedestrians, in real-time, using CPUs only. aUToTrack has been tested in closed-loop experiments on a real self-driving car, and we demonstrate its performance on our dataset.
Video processing inside embedded devices using SSD-Mobilenet to count mobility actors

The actual number of surveillance cameras and the different methods for counting vehicles originate the question: What is the best place to process video flows? This work performs the implementation of a counting system for mobility actors like cars, pedestrians, motorcycles, bicycles, buses, and trucks in the context of an Edge computing application using deep learning. However, the implementation of Deep Neural Networks for Object Detection in low-capacity embedded devices make it difficult to perform tasks that require high processing or must be carried out in real time. To solve this problem this study presents the analysis and implementation of different techniques based on the use of an additional hardware element as is the case of a Vision Processing Unit (VPU) in combination with methods that affect the resolution, bit rate, and time of video processing. For this purpose we consider the Mobilenet-SSD model with two approaches: a pre-trained model with known data sets and a trained model with images from our specific scenarios. The use of SSD-Mobilenet's model generates different results in terms of accuracy and time of video processing in the system. Results show that the use of an embedded device in combination with a VPU and video processing techniques reach 18.62 Frames per Second (FPS). Thus, video processing time is slightly superior (5.63 minutes) for a video of 5 minutes. Recall and precision values of 91% and 97% are reported in the best case (class car) for the vehicle counting system.
Privacy-preserving based task allocation with mobile edge clouds

With the rapid development of mobile crowdsensing applications, task allocation has emerged as a new problem to be solved. Many task allocation strategies have been proposed to select the proper participants to complete tasks. Those methods need participants to submit their real locations to the platform in order to realize the optimal task assignment. However, those traditional task allocation strategies have two weaknesses. First, centralized task allocations result in high computing and communications loads. Second, the exposure of real locations increases participants' concerns regarding location privacy. To address these problems, in this paper, we propose an optimal geo-indistinguishable task allocation (GITA) mechanism using mobile edge clouds. First, the new task that is received by the remote cloud is sent to the mobile edge cloud that is nearest to the task location. Then, the mobile edge clouds serve as distributed controllers to allocate the assigned tasks to the proper candidates. To protect the candidates' real locations, we utilize a geo-indistinguishable mechanism based on differential privacy to preserve location privacy. Specifically, we obfuscate the participants' real locations as disturbed locations, and realize the optimal task allocation based on these disturbed locations. Furthermore, we apply multiobjective mixed integer nonlinear optimization to solve this problem. Finally, extensive experimental results show that, compared with the traditional Laplace mechanism and another privacy-preserving task allocation strategy, the GITA mechanism that is proposed in this paper can decrease users' moving distances and raise the task completion rate.
Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

This paper presents a framework to accurately and non-intrusively detect the number of people in an environment and track their positions. Different from most of the previous studies, our system setup uses only ambient thermal sensors with low-resolution, using no multimedia resources or wearable sensors. This preserves user privacy in the environment, and requires no active participation by the users, causing no discomfort. We first develop multiple methods to estimate the number of people in the environment. Our experiments demonstrate that algorithm selection is very important, but with careful selection, we can obtain up to 100% accuracy when detecting user presence. In addition, we prove that sensor placement plays a crucial role in the system performance, where placing the sensor on the room ceiling yields to the best results. After accurately finding the number of people in the environment, we perform position tracking on the collected ambient data, which are thermal images of the space where there are multiple people. We consider position tracking as static activity detection, where the user’s position does not change while performing activities, such as sitting, standing, etc. We perform efficient pre-processing on the data, including normalization and resizing, and then feed the data into well-known machine learning methods. We tested the efficiency of our framework (including the hardware and software setup) by detecting four static activities. Our results show that we can achieved up to 97.5% accuracy when detecting these static activities, with up to 100% class-wise precision and recall rates. Our framework can be very beneficial to several applications such as health-care, surveillance, and home automation, without causing any discomfort or privacy issues for the users.
Mine tailing extraction indexes and model using remote-sensing images in southeast Hubei Province

Southeast Hubei province is an important iron–copper production base in China, which has produced a large number of mine tailings from mining activities. Although they contain a certain amount of iron or copper as secondary mineral resources, the mine tailings and related acid wastewater can lead to environmental pollution through sand blowing or seepage. For effective resource utilization and environmentally conscious development, rapid evaluations of the spatial distribution, type, and age of mine tailings are of national importance. Using spectral features, which are determined by the structure and composition of tailings, we develop an all-band tailing index, a modified normalized difference tailing index (MNTI), and a normalized difference tailings index for Fe-bearing minerals (NDTIFe). The all-band tailings index reflect the micro-structure and overall high reflectivity of mine tailings by comprehensively utilizing information from each band of Landsat 8 data. The MNTI and NDTIFe provide enhanced tailings composition information from the perspective of anion (carbanion and hydroxy) and cation (mainly ferric ion) contents, respectively. A tailing extraction model (TEM) is built using these three indexes to extract mine tailing information in Huangshi city. The TEM proposed in this paper can successfully and rapidly extract mine tailings information with an extraction precision of 84% in the research area.
Probabilistic Relational Reasoning via Metrics

The Fuzz programming language by Reed and Pierce uses an elegant linear type system combined with a monad-like type to express and reason about probabilistic sensitivity properties, most notably ε -differential privacy. We show how to extend Fuzz to capture more general relational properties of probabilistic programs, with approximate, or (ε, δ) differential privacy serving as a leading example. Our technical contributions are threefold. First, we introduce the categorical notion of comonadic lifting of a monad to model composition properties of probabilistic divergences. Then, we show how to express relational properties in terms of sensitivity properties via an adjunction we call the path construction. Finally, we instantiate our semantics to model the terminating fragment of Fuzz extended with types carrying information about other divergences between distributions.
Attentive matching network for few-shot learning

Few-shot learning has attracted increasing attention recently due to its broad applications. However, it remains unsolved for the difficulty of modeling under few data. In this paper, we present an effective framework named Attentive Matching Network (AMN) to address few-shot learning problem. Based on metric learning, AMN firstly learns robust representations via an elaborately designed embedding network using only few samples. And then distances between representations of support samples and target samples are calculated using similarity function to form a score vector, according to which classification is conducted. Different from existing algorithms, we propose a feature-level attention mechanism to help similarity function pay more emphasis on the features that better reflect the inter-class differences as well as to help embedding network learn better feature extraction capability. Furthermore, to learn a discriminative embedding space that maximizes inter-class distance and minimizes intra-class distance, we introduce a novel Complementary Cosine Loss, which consists of two parts: a modified Cosine Distance Loss for calculating distance between predicted category similarity and the true one that directly takes advantage of all support samples to compute gradients, and a Hardest-category Discernment Loss for handling the similarity of the hardest incorrect class. Results demonstrate that AMN achieves competitive performances on Omniglot and miniImageNet datasets. In addition, we conduct extensive experiments to discuss the influences of embedding network, attention mechanism and loss function.
Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons

Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although solving DCOPs by evolutionary algorithms has attracted increasing interest in the community of evolutionary computation, the design of benchmark test functions of DCOPs is still insufficient. Therefore, we propose a test suite for DCOPs. A dynamic unconstrained optimization benchmark with good time-varying characteristics, called moving peaks benchmark, is chosen to be the objective function of our test suite. In addition, we design adjustable dynamic constraints, by which the size, number, and change severity of the feasible regions can be flexibly controlled. Furthermore, the performance of three dynamic constrained optimization evolutionary algorithms is tested on the proposed test suite, one of which is presented in this paper, named dynamic constrained optimization differential evolution (DyCODE). DyCODE includes three main phases: 1) the first phase intends to enter the feasible region from different directions promptly via a multi-population search strategy; 2) in the second phase, some excellent individuals chosen from the first phase form a new population to search for the optimal solution of the current environment; and 3) the third phase combines the memory individuals of the first two phases with some randomly generated individuals to re-initialize the population for the next environment. From the experiments, one can understand the strengths and weaknesses of the three compared algorithms for solving DCOPs in depth. Moreover, we also give some suggestions for researchers to apply these three algorithms on different occasions.
Support vector machines resilient against training data integrity attacks

Support Vector Machines (SVMs) are vulnerable to integrity attacks, where malicious attackers distort the training data in order to compromise the decision boundary of the learned model. With increasing real-world applications of SVMs, malicious data that is classified as innocuous may have harmful consequences. This paper presents a novel framework that utilizes adversarial learning, nonlinear data projections, and game theory to improve the resilience of SVMs against such training-data-integrity attacks. The proposed approach introduces a layer of uncertainty through the use of random projections on top of the learners, making it challenging for the adversary to guess the specific configurations of the learners. To find appropriate projection directions, we introduce novel indices that ensure the contraction of the data and maximize the detection accuracy. Experiments with benchmark data sets show increases in detection rates up to 13.5% for OCSVMs and up to 14.1% for binary SVMs under different attack algorithms when compared with the respective base algorithms.
A Study of Efficient Energy Management Techniques for Cloud Computing Environment

The overall performance of the development of computing systems has been engrossed on enhancing demand from the client and enterprise domains. but, the intake of ever-increasing energy for computing systems has commenced to bound in increasing overall performance due to heavy electric payments and carbon dioxide emission. The growth in server’s power consumption is increased continuously; and many researchers proposed, if this pattern repeats continuously, then the power consumption cost of a server over its lifespan would be higher than its hardware prices. The power consumption troubles more for clusters, grids, and clouds, which encompass numerous thousand heterogeneous servers. Continuous efforts have been done to reduce the electricity consumption of these massive-scale infrastructures. To identify the challenges and required future enhancements in the field of efficient energy consumption in Cloud Computing, it is necessary to synthesize and categorize the research and development done so far. In this paper, the authors prepare a taxonomy of huge energy consumption problems and its related solutions. The authors cover all aspects of energy consumption by Cloud Datacenters and analyze many more research papers to find out the better solution for an efficient energy consumption.