Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformation-Aware Embeddings for Image Provenance

A dramatic rise in the flow of manipulated image content on the Internet has led to a prompt response from the media forensics research community. New mitigation efforts leverage cutting-edge data-driven strategies and increasingly incorporate usage of techniques from computer vision and machine learning to detect and profile the space of image manipulations. This paper addresses Image Provenance Analysis, which aims at discovering relationships among different manipulated image versions that share content. One important task in provenance analysis, like most visual understanding problems, is establishing a visual description and dissimilarity computation method that connects images that share full or partial content. But the existing handcrafted or learned descriptors — generally appropriate for tasks such as object recognition — may not sufficiently encode the subtle differences between near-duplicate image variants, which significantly characterize the provenance of any image. This paper introduces a novel data-driven learning-based approach that provides the context for ordering images that have been generated from a single image source through various transformations. Our approach learns transformation-aware embeddings using weak supervision via composited transformations and a rank-based Edit Sequence Loss. To establish the effectiveness of the proposed approach, comparisons are made with state-of-the-art handcrafted and deep-learning-based descriptors, as well as image matching approaches. Further experimentation validates the proposed approach in the context of image provenance analysis and improves upon existing approaches.
Multiple Time Series Perceptive Network for User Tag Suggestion in Online Innovation Community

User tag suggestion technique, aiming at learning users’ preferences over knowledge products from their historical behaviors, plays an important role in generating personalized recommendation in online innovation community. However, most current user tagging solutions only utilize a single kind of behavior to predict a single tag for users, resulting in weak generalization of user profile. In this paper, we propose a multiple time series perceptive network (MTSPN) for user tagging tasks in online innovation community. In particular, MTSPN takes multiple kinds of user behaviors into consideration for collaborative perception purpose, in which multi-scale sequential features are extracted from different sequential behaviors, and a multi-label classification module is built-in the proposed MTSPN model to predict multiple tags for users. Our encouraging experimental results on a real-world dataset collected from “Thingiverse” community validate the superiority of the our MTSPN model over several existing user tagging methods.
Design of a Reconfigurable Parallel Continuum Robot With Tendon-Actuated Kinematic Chains

In this letter, a novel spatial parallel continuum robot is proposed. It is composed of three tendon-actuated continuum robots as kinematic chains that are coupled at a common end-effector platform by spherical joints. A modular design approach is used, allowing the reconfiguration of each continuum robot's base position and orientation to adapt the overall structure to application specific constraints and environments. The parallel continuum robot is evaluated in terms of its position and orientation repeatability. Furthermore, comparisons are made to the performance of the single continuum kinematic chains. Overall, average position and orientation repeatabilities of 3.3 mm and 1.2<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> can be achieved for the parallel continuum robot, with a single individual continuum segment showing repeatabilities of 5.5 mm and 3.2<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>. The proposed parallel continuum robot exhibits improved repeatabilities than each of the employed individual continuum segments, improving the average repeatability by 67% for position and 167% for orientation, respectively.
ISOF: Information Scheduling and Optimization Framework for Improving the Performance of Agriculture Systems Aided by Industry 4.0

Industry 4.0 is a promising evolution in the field of smart farming by improving the productivity and reducing human intervention to modernize agriculture. This smart paradigm incorporates different levels of the automation from cropping to production yield through sophisticated techniques. Different intelligent computing techniques and communication technologies are augmented with the industry paradigm for improving the efficiency of agriculture systems. This letter introduces information scheduling and optimization framework (ISOF) for optimizing the communication and information layer process in industry 4.0 architecture. Information scheduling and classification of agriculture information are optimized through this framework for reducing process latency and stagnancy. The control flexibility of a smart farm is determined using the latency and stagnancy at the end of yields. The classification part segregates information based on processing and completion time to reduce backlogs through offloading process. The advantage of this framework is that it inherits the advantages of Internet of Things (IoT) and edge computing (EC) technologies with interoperable feature to aid information processing, information classification, offloading, and periodic updates. The performance of the proposed framework is tested in a corn farm and some common metrics, such as delayed information, processing time, audit data, and information distribution, are analyzed for proving the reliability of the framework.
Ensemble Meteorological Cloud Classification Meets Internet of Dependable and Controllable Things

Advances in Internet of Things (IoT) and cloud/edge computing systems could precisely monitor the meteorological elements and environmental conditions. Remote automated observation system (RAOS) makes the full use of IoT to communicate with other sensors, enabling the active responses from passive devices for smart weather. Cloud observation and classification have been regarded as a successful application that could automatically perform emergency tasks in RAOS. However, with the increasing growth of resource exploitation, the performance of communications among the automatic observation platforms, and the efficiency of task allocation among them has become a critical challenge. In this article, an ensemble learning method and resource allocation scheme are proposed to realize the cloud observation and classification with the help of reliable and controllable infrastructures. On the one hand, several ensemble methods, like Bagging, AdaBoost, and Snapshot are selected as a base classifier to capture the cross-semantic and structure features of cloud, while applying them to the ensemble using convolutional neural networks with different base learners and residual neural networks with different depths. on the other hand, a particular cloud-edge distributed framework is proposed for cloud classification approach based on the intelligent network, to overcome the difficulty in the massive data transmission. The experimental results verify that the proposed ensemble approach achieves high accuracy of cloud classification, and effectively improves the number of allocated tasks. Ensemble methods can generate a more accurate prediction than any single classifier or the majority algorithms. It consistently yields lower error rates than single state-of-the-art models at no additional training cost.
Machine Learning-Based Beamforming in K-User MISO Interference Channels

In this paper, we consider a $K$ -user multiple-input and single-output interference channel and propose a machine learning-based beamforming design. To circumvent the difficulties of beamforming design in interference channels, we consider a beamforming scheme that combines two well-known beamforming schemes, which are maximum ratio transmission and zero-forcing, with one of finite combining factors. However, this problem is still NP-hard, which requires brute-force search for optimal beamforming, so we adopt the machine learning to find the optimal beamforming vectors. Our machine learning design exploits a deep neural network structure, whose input nodes take channel vectors with transmit power, while the output nodes return the combining factors for the transmitters’ beamforming. Our numerical results show that our proposed machine learning-based beamforming achieves a sum rate more than 99% of that with the best combining factors numerically found with brute-force search.
Online Placing and Pricing for Cloud Container Clusters: An Auction Approach

Containers are more lightweight and agile, which are merging as an alternative to virtual machines to provide efficient cloud computing services. For cloud service providers, there are two main challenges for provisioning container clusters: i) Design an optimal placement scheme while considering the communication between containers; ii) Flexible pricing mechanism, which should not only meet the market demand but also maximize the social welfare. To solve the above challenges, this paper proposes an efficient auction that targets truthfulness, efficiency, and social welfare maximization. We first model the problem into a nonconventional integer linear program (ILP), considering both inter-container traffic flow and resource consumed in containers. We then introduce auxiliary variables and reformulate it to a classical packing-type ILP. Second, we apply the primal-dual technique to design an effective online auction algorithm, based on the amount of consumed resources to ensure the flexible supply of resources while obtaining maximum social welfare. We prove that the competitive ratio of the auction is In $\lambda$, and verify that the our auction performs well in various settings through experimental simulations.
Backward Discrete State Event-Driven Approach for Simulation of Stiff Power Electronic Systems

Power electronic systems are intrinsically hybrid systems, consisting of continuous states and discrete events. The hybrid nature makes their accurate and efficient simulation challenging to achieve. A novel approach called discrete state event-driven (DSED) is able to solve such hybrid systems efficiently, but it shows unsatisfying simulation speed when calculating circuits containing parasitic parameters, namely stiff systems. Since the effect of parasitic parameters brought by connection lines can be destructive when they produce voltage peak or resonance, it is crucial to evaluate the impact of parasitic elements during the design phase of the converters by simulation. This paper proposes a backward DSED (BDSED) approach that can solve stiff systems efficiently by cooperating with the event-driven framework. The BDSED adopts a semi-variable-step-variable-order (S-VSVO) mechanism for integrating continuous states and uses interpolation method for dealing with discrete events. With this simulation approach, the effect of parasitic elements in power electronic systems can be analyzed more efficiently compared to other commercial software. In a case study, the proposed approach shows 60 times faster in simulation speed compared with ode15s in Simulink and more than 3 times faster compared with stiff solver in a commercial software called PLECS at the same level of accuracy.
Effect of swift heavy ion on structural and optical properties of undoped and doped nanocrystalline zinc oxide films

Swift heavy ion (SHI) induced modification in structural and optical properties of undoped and doped nanocrystalline (nc) ZnO films deposited by sol-gel method are investigated. These nanocrystalline films were irradiated by MeV ions of Au, Ag and Ni at various ion fluences. The structural properties were studied using X-ray diffraction and it shows that the average crystallite size of ZnO films is observed to increase by the irradiation. The atomic force microscopy (AFM) study of films shows that the roughness of the films varies with increase in the fluence. A maximum transmittance is observed to be 85% in the visible region for doped films. It is also shown that the bandgap of undoped and doped ZnO films is varied using SHI irradiation. The modifications of structural and optical properties are explained in terms of thermal spikes induced by SHIs. Copyright © 2013 VBRI press.
Wireless Power and Data Transmission for Implanted Devices via Inductive Links: A Systematic Review

Wireless power and data communication systems in implantable medical devices (IMDs) are developed to control and report acquired biological data from an implanted device to an external stage in several medical applications. Ultrasonic, capacitive, optical, radio frequency (RF), and inductive links are employed as a wireless power and data transmission technique. Inductive power transfer (IPT) is one of the most commonly used techniques due to its robustness, simplicity, safety, and capability of simultaneous and bidirectional data and power transmission. This paper presents a systematic review and survey of the latest inductive power and data transmission methods for IMDs. The fundamental principles of remote powering through inductive links, the critical parameters of design, and power transfer efficiency calculations are presented. Single and multiple inductive links, the advantages and drawbacks, optimization methods, and comparison by their performances are explored. In addition, modulation schemes, along with improvement techniques reported in the literature, the strengths and limitations, and the measured or the simulated data rate of each are also reviewed. A benchmarking table with summarized design features of systems, essential parameters for wireless powering and data communication, and categorized Figure-of-Merits (FoMs) to compare performances are also provided.
Wide-Band and Wide-Angle Scanning Phased Array Antenna for Mobile Communication System

A wide-band phased array antenna with wide-angle scanning capability for mobile communication system is proposed in this article. An air cavity is properly embedded into the substrate under each array element. This method is very simple and can efficiently enhance wide-angle scanning performance by improving the wide-angle scanning impedance matching (WAIM) and realizing the beam-width of elements in the array. Besides, the operating bandwidth is also extended with the proposed approach. The wide-angle scanning capability is analyzed and verified by both the linear array antennas (on large ground planes in detail) and the planar array. Two $1\times 8$ linear arrays and one $8\times 8$ planar array are demonstrated, which achieve the beam scanning of around ±60° with a realized gain reduction under 3.5 dB in the wide operating bandwidth (37.4%). Furthermore, the beam in the E-plane can scan over ±70° with a realized gain reduction under less than 3 dB. Two linear array prototypes with wide-angle scanning capacity in two planes are fabricated and characterized, yielding good performance within overall operational bandwidth. The measured results align very well with the simulated. The proposed wideband phased array with large scanning coverage is a promising candidate for 5G mobile communications.
Fine-Grained Powercap Allocation for Power-Constrained Systems Based on Multi-Objective Machine Learning

Power capping is an important solution to keep the system within a fixed power constraint. However, for the over-provisioned and power-constrained systems, especially the future exascale supercomputers, powercap needs to be reasonably allocated according to the workloads of compute nodes to achieve trade-offs among performance, energy and powercap. Thus it is necessary to model performance and energy and to predict the optimal powercap allocation strategies. Existing power allocation approaches have insufficient granularity within nodes. Modeling approaches usually model performance and energy separately, ignoring the correlation between objectives, and do not expose the Pareto-optimal powercap configurations. Therefore, this article combines the powercap with uncore frequency scaling and proposes an approach to predict the Pareto-optimal powercap configurations on the power-constrained system for input MPI and OpenMP parallel applications. Our approach first uses the elaborately designed micro-benchmarks and a small number of existing benchmarks to build the training set, and then applies a multi-objective machine learning algorithm which combines the stacked single-target method with extreme gradient boosting to build multi-objective models of performance and energy. The models can be used to predict the optimal processor and memory powercap settings, helping compute nodes perform fine-grained powercap allocation. When the optimal powercap configuration is determined, the uncore frequency scaling is used to further optimize the energy consumption. Compared with the reference powercap configuration, the predicted optimal configurations predicted by our method can achieve an average powercap reduction of 31.35 percent, an average energy reduction of 12.32 percent, and average performance degradation of only 2.43 percent.
Improving delivery conditions by dynamically managing the urban parking system: Parking availability prediction

An adequate dimensioning of delivery bays combined with their effective availability facilitates deliverers' tasks, fluidizes urban traffic and improves living conditions in the city. Despite this marked interest, there is still no formal tool associated with a global approach to design and manage delivery areas network. In this paper, we lay the groundwork for a new vision of urban parking. It consists in sharing, equitably between the different road users, all the parking infrastructures. It considers that parking spaces can switch, in real time, their profile from a classic parking place to a delivery bay and vice versa. It is based mainly on the parking availability prediction. We employ three types of ensemble regressors to forecast the number of free parking slots. We examine their outcomes, evaluate their performance and determine their relative advantages as well as disadvantages. We conduct these tests leveraging parking data collected and published by the City of Melbourne, Australia.
Using Different Features of Signal in EMG Signal Classification

Separation of signal features is the separation of the necessary data by selecting the desired parts of the signal. In this research, a comparative analysis of the classification based on some significant time domain and frequency domain features of the EMG signal is presented. In our research, it became clear analyzed that in the individual and combination application of features in the time and frequency domains, the features in the time domain had a higher rate than the features in the frequency domain with their speed and accuracy.
Performance Evaluation of RPL and DODAG Formations for IoTs Applications

Nowadays the rapid growth in the Internet of Things (IoTs) has change our life to be more intelligent and smart, the development in the Wireless Sensors Networks (WSNs), besides the wide use of the embedded devices in different area like industry, home automation, transport, agriculture and health care, which was led the Routing Over Low-power and Lossy- network (ROLL) working group to introduce the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), therein the RPL nodes have organized topology as a Directed Acyclic Graph (DAG) and terminated at one root to form the Destination Oriented DAGs (DODAGs). In this paper by using InstantContiki3.0 and CoojaGUI we analyze the DODAG formations and evaluate the performance of the RPL based on the Expected Transmission Count (ETX) Objective Function (OF) that enable RPL to select and optimize routes within RPL instance, as well as we evaluate the following metrics: The ETX Reliability Object (ETX), Radio Duty Cycle (RDC), energy consumption, the received packets by the motes and neighbor count. The simulation results show that the DODAG root able to connect to all of the neighbor motes as well as the ETX can essentially take control over DODAG formations and it has an effects in the RDC ratio, furthermore most of the motes show reasonable low power consumption, also the motes show acceptable number of the received packets.
Adversarial Edge-Aware Image Colorization With Semantic Segmentation

It has become a trend in recent years to use deep neural networks for colorization. However, previous methods often encounter problems with edge color leakage and difficulties in obtaining a plausible color output from the Euclidean distance. To solve these problems, we propose a new adversarial edge-aware image colorization method with multitask output combined with semantic segmentation. The system uses a generator with a deep semantic fusion structure to infer semantic clues in a given grayscale image under chroma conditions and learns colorization by simultaneously predicting color information and semantic information. In addition, we also use a specific color difference loss with characteristics of human visual observation that is combined with semantic segmentation loss and adversarial loss for training. The experimental results show that our method is superior to existing methods in terms of different quality metrics and achieves good results in image colorization.
A Conceptual Framework for Detecting Learning Style in an Online Education Using Graph Representation Learning

Online learning has become increasingly popular in recent years. This interest in online education has provided us with many opportunities to research, the behavior of learners as it generates behavior data at large scale. By dynamically adapting learning, the automated identification of learning styles in online learning based on learner activity is a significant issue, in which many researchers have shown their interests as it improves learning output. In this paper, the collection of behavioral characteristics, which is an important step in the automated extraction of learning styles has been considered. We propose a conceptual framework for the identification of learning styles that automatically learns features from the context of online education through graph representation learning. We used the learned features and mapping rules to distinguish learning styles in the Felder-Silverman learning style theory. The alternative approach is expected to provide a better outcome, a more reliable identification of learning style than the previous processes. Future research directions are provided in the study.
Intra-Datacenter Bandwidth Resource Allocation for Bursty Large-Scale Traffic in Polled OLT

This paper aims at the phenomenon of bandwidth resource in short supply which is caused by the sudden large amount of traffic under a certain wavelength in OL T pooling intra-datacenter. From the perspective of bandwidth fragmentation, an ONU optimization migration mechanism based on the greedy-genetic algorithm is proposed under the architecture of Passive Optical Network within the datacenter, so as to reduce the delay problem caused by traffic congestion, and improve the bandwidth utilization efficiency. The simulation results show that the proposed mechanism can effectively improve the satisfaction rate of bandwidth. Compared with the mechanism without defragmentation, this mechanism can increase the satisfaction rate by about 48%, while compared with the random migration mechanism, this mechanism can increase the satisfaction rate by about 68%.