Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthetic training data for deep neural networks on visual correspondence tasks

In the realm of deep learning for computer vision tasks, the best performing models tend to be trained with supervision, i.e. with a training dataset that contains ground-truth annotations which the model is expected to match. Visual tasks are particularly interesting because humans rely mostly on their eyes for almost everything they do; we attribute great importance to our visual perception of the world, and we have developed methods to produce visually realistic simulations of this world for purposes of entertainment, communication, and research. These same methods enable the creation of synthetic training data: rendered views of virtual worlds with annotations that are more extensive and accurate than anything a human could label with justifiable time and effort. In this thesis, we motivate and describe the making of large synthetic datasets for low-level correspondence matching problems. We used these datasets to train deep neural networks for the fundamental vision tasks of optical flow and stereo disparity estimation, achieving a new state of the art at the time of their publication. We further isolate individual design components that make up an optical flow dataset, and analyze their contributions to the data’s suitability for training. Finally, we use our results to create new datasets for specific real-world scenarios, thereby demonstrating that data engineering is a viable and practicable method for improving the performance of neural networks. Complementary to optimizations that operate on a network itself such as those of architecture, loss function or model capacity, data is a design dimension that can be varied even if the learning algorithm is a black box.
Social Motorics - a predictive processing model for efficient embodied communication

Human communication often seems effortless. We tend to quickly have an idea of our interaction partner’s intentions that enable us to predict their future behavior. How is such efficient communication possible which, despite uncertainty, allows us to quickly attribute beliefs to one another? Also, when and how are beliefs corrected if necessary? By investigating how action and perception influence and are influenced by prior beliefs during non-verbal communication, this work tackles the question of how and when the two subnetworks of the social brain interact. A computational modeling approach is proposed, based on principles of predictive processing and active inference. The model’s hierarchy consists of sensorimotorand mentalizing levels. Their processes influence each other in a way that allows their embodied representations to be used efficiently. It is explored how uncertainty is handled in human communication, before examining the neuroscientific details of social cognition. Both inform the assumptions underlying the proposed model, which is evaluated in a number of simulations. These test the model’s abilities to minimize uncertainty during action and perception, to differentiate between its own and other’s actions, and also its ability to coordinate beliefs between multiple agents in a non-verbal communication game. The simulations not only show that the proposed mechanisms quickly infer action intentions, able to influence future perception and action. Simulations also highlight the importance of weighting new evidence against prior beliefs, so that it is able to detect false beliefs and repair them during social interaction. The proposed computational model demonstrates a mechanistic account of the interplay within the social brain that allows for efficient non-verbal communication between similar agents, with implications for the notion of subjective direct access to other’s minds.
Composition of Visual Feature Vector Pattern for Deep Learning in Image Forensics

In image forensics, to determine whether the image is impurely transformed, it extracts and examines the features included in the suspicious image. In general, the features extracted for the detection of forgery images are based on numerical values, so it is somewhat unreasonable to use in the CNN structure for image classification. In this paper, the extraction method of a feature vector is using a least-squares solution. Treat a suspicious image like a matrix and its solution to be coefficients as the feature vector. Get two solutions from two images of the original and its median filter residual (MFR). Subsequently, the two features were formed into a visualized pattern and then fed into CNN deep learning to classify the various transformed images. A new structure of the CNN net layer was also designed by hybrid with the inception module and the residual block to classify visualized feature vector patterns. The performance of the proposed image forensics detection (IFD) scheme was measured with the seven transformed types of image: average filtered (window size: <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula>), gaussian filtered (window size: <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula>), JPEG compressed (quality factor: {90, 70}), median filtered (window size: {<inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$5\times 5$ </tex-math></inline-formula>}), and unaltered. The visualized patterns are fed into the image input layer of the designed CNN hybrid model. Throughout the experiment, the accuracy of median filtering detection was 98% over. Also, the area under the curve (AUC) by sensitivity (TP: true positive rate) and 1–specificity (FP: false positive rate) results of the proposed IFD scheme approached to ‘1’ on the designed CNN hybrid model. Experimental results show high efficiency and performance to classify the various transformed images. Therefore, the grade evaluation of the proposed scheme is “<italic>Excellent</italic> (<inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula>). ”
IBNSlicing: Intent-Based Network Slicing Framework for 5G Networks using Deep Learning

Network slicing is an important pillar of 5G networks that empowers the network operators to provide the different quality of services (QoS) to the users. It enables network operators to split the physical network into multiple logical networks to meet different QoS requirements. In this research paper, we have designed an intent-based network slicing framework that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just needs to provide higher-level information in the form of intents/contracts for a network slice, and in return our system deploys and configures the requested resources. Moreover, a deep learning model Generative Adversarial Neural Network (GAN) has been used for the management of network resources. Several tests have been performed by creating three slices with our system, which shows better performance in terms of bandwidth and latency.
Design of a 0.6-V, 429-MHz FSK Transceiver Using Q-Enhanced and Direct Power Transfer Techniques in 90-nm CMOS

A 429-MHz ultra low-power frequency-shift keying (FSK) transceiver (TRX) that is operated at 0.6 V at 200 kb/s is presented in this work. The receiver (RX) used in this work uses a proposed frequency-to-time-based technique that demodulates the output data by discriminating the corresponding date period. The proposed RX provides a sensitivity of −85 dBm at a bit error rate of 0.1% and consumes a power of 146 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>. The 429-MHz FSK transmitter (TX) is also reported in this study. For eliminating circuits that consume high power, in particular power amplifier and phase-locked loop, the proposed TX adopts an oscillator that drives the antenna through an impedance matching network for power conversion. The TX consumes 390 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>, supplies an output power of −8.2 dBm, and achieves a global efficiency of 38.7%. The TRX was fabricated in the TSMC 90-nm CMOS process.
Towards understanding habitat use patterns of an endangered galliform subspecies, Pternistis bicalcaratus ayesha: implications for conservation

Determination of factors affecting habitat selection is a major topic in avian ecology, with strong implications for conservation purposes. Studies dealing with the impacts of ecological factors on breeding spurfowls, especially the Critically Endangered Double-spurred Spurfowl Pternistis bicalcaratus ayesha, are scarce. Here, we used auditory detection during transect surveys of calling males to locate breeding birds. Generalised linear models were used to test the relevance of ecological factors in predicting the probability of presence of these spurfowls within two forest reserves in north-western Morocco. The best-supported model of the species’ occurrence included the height of the herbaceous layer together with the interaction effect between the forest reserve and tree cover, tree density, and height of shrubs. This study highlights a common pattern in habitat use in relation to tree cover and height of the herbaceous layer and a differential pattern related to tree density and height of shrubs. Further studies are needed to confirm these patterns established from only two sites. Multi-scale studies will be valuable in improving our understanding of the effects of biotic factors on the processes of habitats selection by this endemic subspecies.
Adaptive Surface Fitting Convolution for 3D Point Cloud Analysis

Traditional Convolutional Neural Networks (CNN) are limited to extract informative local features of point clouds due to the fixed geometric structures in convolution kernel against irregular and unstructured point clouds. It usually requires data transformation such as voxelization or projection, inducing a possible loss of information. Instead of fitting the input points to the kernel by regularization, we choose to fit the kernel to input points to conduct convolution. In this paper, we present a new method to define and compute convolution directly on 3D point clouds by Adaptive Surface Fitting Convolution (ASFConv). The key idea is to utilize a set of kernel points distributed on the tangent plane and project them back to point cloud surface. After adapting to the distribution of input points, ASFConv kernel can better capture local neighborhood geometry and benefit the feature extraction. In the experiments, we evaluate our network on two public datasets: ModelNet40 and ShapeNet for classification and segmentation. The experimental results show that our method obtain competitive performances compared to the state-of-the-art.
Facile Fabrication and High Field Emission Performance of 2-D Ti₃C₂Tₓ MXene Nanosheets for Vacuum Electronic Devices

Although Ti<sub>3</sub>C<sub>2</sub>T<sub><italic>x</italic></sub> has been widely studied in applications such as electrical storage devices, there are very few reports about the field emission properties of Ti<sub>3</sub>C<sub>2</sub>T<sub><italic>x</italic></sub>. In this work, 2-D Ti<sub>3</sub>C<sub>2</sub>T<sub><italic>x</italic></sub> MXene was fabricated by simple etching Al layer from Ti<sub>3</sub>AlC<sub>2</sub> in hydrofluoric acid (HF) at room temperature and employed as a cold cathode for field emission devices. The device presents a high field emission performance with a low turn-on field of 2.7 V/<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> and excellent stability, much lower than the reported value of the former work (around 5 V/<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>), its relative 1-D TiC nanowires (7.1 V/<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>), and most of other 2-D cathode materials, such as MoS<sub>2</sub> and graphene, which is found due to its unique accordion structure with evenly distributed sharp edges and enlarged layer spacing reducing the field screening effect. The results confirmed by numerical simulation demonstrate that the local electric intensity at sharp edges indeed significantly higher than that of elsewhere. In other words, more sharp edges owning to the increased interlayer spacing exposed, which can considerably boost field emission performance. Based on experimental data and simulation analysis, the edge effect was found to correspond well with other former reports. Hence, 2-D MXene can be a promising candidate for vacuum electronic applications other than energy storage and conversion devices.
A Discrete-Time MOS Parametric Amplifier-Based Chopped Signal Demodulator

This article presents a discrete-time parametric amplifier (DTPA) as the signal demodulator for chopper amplifier. Unlike the conventional chopper, the DTPA demodulator features noise-efficient gain augmentation while demodulating the chopped signal. The demodulator also enables low-frequency noise cancellation during the inherent track-and-hold (T/H) process of the charge parametrization. The positive feedback loop and the dc servo loop are implemented for applications requiring larger input impedance and dc input offset cancellation using a bandpass transfer function. Design considerations for the DTPA-based demodulator circuit and the merits and demerits of the chopper–DTPA amplifier are discussed as well. The proposed design has been fabricated in a standard 180-nm CMOS technology node. The complete design occupies 0.127 mm<sup>2</sup> of the die area and consumes 2.4-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power from a 1.5 V of <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\text {DD}}$ </tex-math></inline-formula>. The measurement results show that the DTPA demodulator provides 8-dB gain enhancement while improving on the prior art of T/H-based demodulator and the amplifier achieves input referred noise voltage of <inline-formula> <tex-math notation="LaTeX">$2~ \mu \text {V}_{\text {rms}}$ </tex-math></inline-formula> in 143-Hz bandwidth.
Comparison of SAC and new Pb-free solder alloy for special application

Solder alloy plays a crucial role in the production of electronics devices. Sn-based alloy containing other elements like Pb, Ag and Cu has been the mostly used solder material of electronic circuit in recent years. However, environmental and health concerns over lead toxicity has resulted in recent legislation to limit the use of lead-based solders. Assemblies that work in harsh environment, such as automotive, aerospace, and military have benefited from exemptions for long time. Now due to industry conversion from SnPb to lead free interest in novel solder materials can be observed. Nevertheless, most of these exemptions have expired, and the limited availability of SnPb compatible components have now forced most of these industries into using lead free solders and components. Additionally, the evolution and expansion of electronics into more harsh operating environments, forces the solder alloys to perform more critical functions. This encouraged researchers to discover new leadfree solders. Great amount of effort was dedicated by previous researchers in developing new environmental friendly lead-free solders, such as Sn-Ag- Cu (SAC). Another candidate elements to include in a low melting point lead-free solder include Bi, Au, Tl, Ga, Zn or In.In this paper, the comparison of SAC alloy and new Pb-free solder alloys containing elements such as Bi, Ni and Sb was reported. Their technological properties was compared based on the tests results such as slump test, wetting test as well as spread test. Theoretically, elements contained in the new Pb-free solder alloy offer some benefits. For example, presence of bismuth limits formation of intermetallic compounds between copper surface and tin based alloy. The obtained tests results can be used in the electronics industry for the production of printed circuit boards, in particular for the production of devices for special applications.
The antimicrobial and antioxidant activities of extracts obtained from some moss species in Turkey

Summary Introduction: Pharmaceutical industry is forced to develop new pharmacologically active molecules. Like other plants, mosses are considered to be potential source of new biologically active compounds. Objective: The present study was designed to evaluate the antimicrobial and antioxidant activity of 8 moss species: Hypnum cupressiforme, Homalothecium sericeum, Thuidium delicatulum, Homalothecium lutescens, Homalothecium nitens, Leucodon sciuroides, Ctenidium molluscum, and Eurhynchium striatulum obtained from Turkey. Methods: The antimicrobial activity of extracts was investigated against four Gram (+) and six Gram (−) bacterial strains and three tested fungi. Total phenolic content (TPC), cupric reducing antioxidant capacity (CUPRAC), ferric reducing antioxidant power (FRAP), and DPPH radical scavenging activity assays were applied to determine the antioxidant activity. Results: All moss extacts were found to be active against all the organisms except Homalothecium nitens. Especially, H. sericeum and E. striatulum showed the best antioxidant activity. Conclusion: The obtained results show that mosses may be used as possible natural antioxidant, antimicrobial agents to control various human, animal and plant diseases.
Impact of Dummy Gate Removal and a Silicon Cap on the Low-Frequency Noise Performance of Germanium nFinFETs

The impact of different process options on the low-frequency noise (LFN) performance and reliability of Ge nFinFETs is investigated. The results show that the LFN is mainly determined by carrier number fluctuations, and the density of traps in the high-<inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> dielectric can be reduced by an extended dry clean. In some small-size devices, the Lorentzian-type noise comes from GR centers in the oxide layer or dislocations in the Ge fins. Due to the Coulomb scattering by charged oxide traps, Ge nFinFETs with higher input-referred voltage noise at flat band voltage (<inline-formula> <tex-math notation="LaTeX">$S_{{\text {vgfb}}}$ </tex-math></inline-formula>) usually have a lower mobility. Since the charged traps in the oxide layer are one of the main responsible factors for the shift of the threshold voltage, the transistors with bigger <inline-formula> <tex-math notation="LaTeX">$S_{{\text {vgfb}}}$ </tex-math></inline-formula> show higher PBTI degradation, and <inline-formula> <tex-math notation="LaTeX">$S_{{\text {vgfb}}}$ </tex-math></inline-formula> can be considered as an early indicator for the reliability of Ge nFinFETs.
Quality of Service in Wireless Sensor Networks using Cellular Learning Automata

Wireless Sensor Networks (WSNs) have different Quality of Service (QoS) parameters from those of traditional networks. Several considerations utilized for evaluating QoS include appropriate number of active nodes, network lifetime, network coverage, and resource utilization. One of the features of Cellular Learning Automata (CLA), besides its simple learning structure, is learning in distributed and multi-hop environments with limited communications and incomplete information. CLA benefit show how different problems in WSNs can be overcome. In this paper, the underlying issues of WSNs are discussed, and in order to improve the QoS parameters, efficient solutions have been proposed using CLA. The WSN 's environmental coverage issue is also addressed by turning off redundant nodes and maintaining adequate nodes to conserve resources and enhance network life. In this research, the issue of clustering of WSNs is addressed and the WSNs are clustered by using CLA to efficiently distribute energy to the network and maximize network life. All provided methods are simulated by J-Sim tools showing the overall reduce in WSN energy consumption and also for each node alone. Moreover, we demonstrate the reduce in data communication overhead and maintaining the overall network coverage. Simulation experiments indicate higher performance of the proposed methods than other associated approaches.
Machine Learning-based Optimal VNF Deployment

Network Function Virtualization (NFV) environment can deal with dynamic changes in traffic status with appropriate deployment and scaling of Virtualized Network Function (VNF). However, determining and applying the optimal VNF deployment in consideration of the cost and Quality of Service (QoS) is a complicated and difficult task. In particular, it is necessary to predict the situation at a future point when the deployment decision is applied because it takes processing time to apply the deployment decision to the actual NFV environment. In this paper, we randomly generate service requests in Multiaccess Edge Computing (MEC) topology, then obtain optimal VNF deployment and Service Function Chaining (SFC) result from an Integer Linear Programming (ILP) solution. We use the simulation data to train a machine learning model which predicts the optimal VNF deployment at a predefined future point. The prediction model shows the accuracy over 90% compared to the ILP solution for the 5-minute future time point.
Video Flame Recognition Based on Feature Fusion and Extreme Learning Machine

This paper proposes a video flame detection method based on Extreme Learning Machine (ELM). Visual Background Extractor++(ViBE++) algorithm is used to extract the dynamic foreground features of flame video images, and combined with color histogram threshold analysis, the flame region in the image is segmented. By extracting and processing the flame geometrical features such as roundness, sharpness and gravity center, the dynamic and static features of flame image are fused, thus the suspected flame area is screened out. Using the geometric features of suspected flame area and based on ELM model, the training and classification of sample sets are performed. Experimental results show that this method has higher operating speed and accuracy under the condition of less environmental interference.