Miracle-dz/UniFAR-FTUs huggingface dataset:
A Metaheuristic Relying on Random Walk on a Graph for Binary Optimization Problems

Recently, evolutionary algorithms that can solve decomposable binary problems very efficiently have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by a machine learning technique using a population. Their central procedure is linkage detection that reveals a problem structure, that is, how the entire problem consists of sub-problems. However, the model-based evolutionary algorithms have been shown to be ineffective against problems that are hard to identify their structures. Therefore, metaheuristics including evolutionary algorithms that can solve both problems quickly, reliably, and accurately are required. In this paper, toward realizing such algorithms, we propose a new model-based metaheuristic. It initially forms a graph in which a pair of a position and a value on the string of a solution candidate is a vertex and directed edges are randomly made between vertexes, and then repeats the following three steps: (1) conducting random walk on the graph, (2) producing solution candidates, and (3) reconstructing the topology of the graph. The simulation results show that the proposed metaheuristic is inferior to conventional algorithms against decomposable problems, but superior to conventional ones against problems that are hard to identify their structures.
Molecular Macroscopic Characterization of EPDM's Aging Used for Outdoor High Voltage Insulators

The Ethylene-Propylene-Diene Monomer (EPDM) is a material used for the housing of the high voltage composite insulators. The stress conditions related to that outdoor exposure causes alteration of the material's properties. Several characterization techniques were used in this study for the evaluation of the thermal aging of the EPDM. The FTIR technique was used to evaluate the molecular behaviors of the material. The Carbonyl Index measurement was used to assess the oxidation rate of the material along the aging by means of FTIR spectroscopy. The crosslink density was measured using the solvent swell method. For the macroscopic characterization, the surface roughness of the aged samples was measured. This study showed that both crosslinking and oxidation were observed within the studied samples with a linear variation along the aging time. The correlation between the molecular and morphological properties was investigated. This study showed that for each structural scale a phenomenon of degradation is observed during the aging. The erosion of the material observed by the use of the surface roughness measurement has an impact on the oxidation of the material which was monitored by the use of the FTIR spectroscopy. Both of these measured parameters increase along the aging time. This increase is more important when the aging temperature became higher.
Size-Invariant Attention Accuracy Metric for Image Captioning with High-Resolution Residual Attention

Spatial visual attention mechanisms have achieved significant performance improvements for image captioning. To quantitatively evaluate the performances of attention mechanisms, the "attention correctness" metric has been proposed to calculate the sum of attention weights generated for ground truth regions. However, this metric cannot consistently measure the attention accuracy among the element regions with large size variance. Moreover, its evaluations are inconsistent with captioning performances across different fine-grained attention resolutions. To address these problems, this paper proposes a size-invariant evaluation metric by normalizing the "attention correctness" metric with the size percentage of the attended region. To demonstrate the efficiency of our size-invariant metric, this paper further proposes a high-resolution residual attention model that uses RefineNet as the Fully Convolutional Network (FCN) encoder. By using the COCO-Stuff dataset, we can achieve pixel-level evaluations on both object and "stuff" regions. We use our metric to evaluate the proposed attention model across four high fine-grained resolutions (i.e., 27×27, 40×40, 60×60, 80×80). The results demonstrate that, compared with the "attention correctness" metric, our size-invariant metric is more consistent with the captioning performances and is more efficient for evaluating the attention accuracy.
Analysis of High Efficiency Video Coding (HEVC) Test Model HM-16.18 for P Frame Processing

This paper seeks to provide performance analysis for High Efficient Video Coding (HEVC) HM-16.18 reference coder for different resolutions test sequences. Two test sequences (resolutions) i.e., Jockey (3840×2160) and Kimonol (1920×1080) are considered. The HEVC coder is tested for the fixed Quantization Parameter (QP) value in lowdelay_P configuration, with Main profile when P pictures are processed in IPPP format, respectively. Comparisons were performed with respect to the change of Signal-to-Noise Ratio (SNR), the change of data bit-rate and the change of encoding time saving, respectively. Simulation results have shown small differences in SNR values and obvious differences for bit rate and encoding time values. Beside objective results, subjective video assessments for all tested sequences are presented, as well as present of intra and inter prediction and transform in total amount encoder compression values. Also, there is comparison with segment of our previous experimental work.
Approximate Optimal Distributed Control of Nonlinear Interconnected Systems Using Nonzero-Sum Games

In this paper, continuous and event-sampled approximate optimal distributed control schemes for an interconnected system, with nonlinear subsystem dynamics and strong interconnections, are presented. The control design problem for the interconnected system is reformulated as an N-player cooperative nonzero-sum differential game wherein the control policy of each subsystem is treated as a player in the game. The Nash solution of this game is used to design the control policy for each subsystem to optimize the performance of the interconnected system. Approximate dynamic programming (ADP), with critic neural networks, is utilized to approximate the solutions of the coupled Hamilton-Jacobi equations, for continuous and event-sampled control implementation. Event-sampling conditions are designed to asynchronously orchestrate the sampling and transmission at each subsystem. Finally, simulation results are included to substantiate the theoretical claims.
A Convolution Neural Network Based Nursing-Care Text Classification Model with a New Filter for Expressing Dependency Relations of Words

In this paper, a convolution neural network (CNN) based text classification method is proposed. CNNs show strong performance for computer vision and speech recognition applications. Recently, in some researches, CNNs have been applied to sentence classification applications. Currently, we have studied nursing-care text classification to improve nursing-care quality in Japan. In our former works, several types of feature definitions have been proposed and examined by some classification models like SVMs. In this paper, a single layer CNN is used for classifying nursing-care texts. Each nursing-care text is represented as a concatenated word vectors. Each word is represented as a fixed length word vector which is obtained by the word2vec. Then, nursing-care texts are classified using a two-dimensional CNN-based classification method. The proposed CNN has a new kind of filters which extracts dependency relation between words. From our experimental results, the proposed CNN-based method obtained better performance than our former works.
EEG Based Motor Imagery Classification Using Instantaneous Phase Difference Sequence

Brain-Computer Interfaces (BCI) are systems that enable users to use neural signals, typically Electroencephalogram (EEG) to direct an application or an external device. Motor imagery (MI) based BCI detects subject motor intentions which could be further used as control signals. Due to spatial lateralization of different MI tasks, spatial filtering followed by band power extraction is the most commonly used algorithm for classification tasks in MI-based BCIs. Unfortunately, the spatial filtering approach significantly incorporates Amplitude characteristics when compared to Phase characteristics of the EEG signal. Single trial Phase locking value (sPLV) has been a popular statistics to extract phase based information for classification task in MI-based BCI. To utilize the phase characteristics for MI classification, this paper proposes a novel approach based on instantaneous phase difference (IPD) sequence to extract phase features that explicitly use the phase synchronization information between EEG sensors. We maximized the discriminability of IPD sequence using linear transformation calculated from common spatial pattern algorithm (CSP) on the IPD sequence. An evaluation of our method on BCI competition dataset led to around 15% increase in mean classification accuracies compared to sPLV approach and comparable accuracies to power feature based CSP algorithm. Furthermore, incorporating phase features from our method and power features from traditional algorithms using sparse feature selection technique increased the classification accuracy over both CSP and CSP on the IPD sequence.
Context-Awareness and Anticipation in a Tennis Video Game AI System

This paper is dedicated to the problem of using case-based reasoning AI in a commercial mobile game of lawn tennis. We discuss the unavoidable manual game analysis stage, aimed to represent user intentions accurately and supply them to the machine learning procedure. We show how the right combination of machine learning and manual effort helps to construct a solid game AI system, able to play in human-like manner. Our experience shows that the key factor of the successful decision making and reasonable resource consumption in mobile tennis is careful representation of context-aware behavior and anticipation of opponents' actions, exhibited by real players.
Binary and Ternary True Random Number Generators Based on Spin Orbit Torque

In this work, we have experimentally demonstrated the binary- and ternary-True Random Number Generators (B-TRNG and T-TRNG) based on the stochastic switching characteristics of the nano-scale Ta/CoFeB/MgO heterostructures with perpendicular magnetization anisotropy. For the first time, the random code generation utilizes the spin orbit torque (SOT) induced by current flowing in the heavy metal underneath the CoFeB layer. The 3-XOR post-processed random binary codes have passed the NIST SP800-22 test. Furthermore, the T-TRNG in the same ferromagnetic heterostructure with dual magnetic domains are also demonstrated, which provides a higher security level than its B-TRNG counterpart.
On Stability Analysis Based on Stochastic D-Scaling

In the deterministic systems case, D-scaling is known to be effective for reducing conservativeness in stability analysis of closed -loop systems consisting of two subsystems. This paper considers discrete-time stochastic systems and proposes a new approach called stochastic D-scaling. In this approach, we deal with not only a deterministic scaling element but also a stochastic scaling element having the nature relevant to the randomness behind stochastic systems. The scaling elements are assumed to be given in this paper. Then, the effectiveness of the stochastic D-scaling is demonstrated through numerical examples.
The Impact of a Wet  $S$ -Band Radome on Dual-Polarized Phased-Array Radar System Performance

This paper discusses the impact of a wet flat radome on the performance of a line replaceable unit active phased-array antenna developed for a dual-polarized phased-array weather radar. Water formations, such as film and droplets, were fully characterized over flat and curved radome surfaces using an analytical model as a function of the precipitation rate. Numerical simulations and experimental results validated the proposed analytical model. An active dual-polarized phased-array of <inline-formula> <tex-math notation="LaTeX">$8\times 2$ </tex-math></inline-formula> elements was used to evaluate the degradation of the cross-polarization component as well as the mismatch and the phase of scanned antenna radiation patterns for vertical and horizontal polarizations. In addition, a radome panel of an <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-band weather radar (WSR-88D) was characterized in the far field using a single radio-frequency probe. It is demonstrated that even at the <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-band, the water formation on the radome affects both copolarization and cross-polarization components, degrading the overall performance of the dual-polarized radar system under rain conditions.
An Algorithm to Design Pareto Optimal Controllers for Linear Systems

In this paper, an algorithm is proposed for the multi-objective optimal design of controllers with fixed structure, but tunable parameters, for linear time-invariant systems. Differently from other tools available in the literature, the proposed method allows one to determine an exact solution to a multi-objective minimization problem without eliminating variables. Several applications of the proposed method, which span from the computation of a Pareto optimal solution for a differential game to the fast stabilization of a closed-loop system, are reported to corroborate the theoretical results.
Internet Traffic Analysis of an Educational Network using Bro IDS

Today, educational and research institutes heavily rely on the Internet connectivity. The bandwidth demand in these institutes is growing exponentially as students, faculty members, and research labs need Internet to find content on the world wide web. As a result, bandwidth congestion cases are frequently reported in such environments causing user dissatisfaction about Internet services. In order to mitigate such congestion problem, it is crucial to perform network traffic analysis on these networks. Such studies allow us to improve quality of service and better network planning. Moreover, it provides insights of user behavior and detect malicious activities done by students. Furthermore, network traffic analysis allows us to monitor usage of multimedia and social sites by users in order to conserve bandwidth to avoid congestion. In this paper, we have analyzed network traffic of Al-Khawarizmi Institute of Computer Science (KICS), UET, Lahore. We have carried out measurements across application, transport and network layer to find out different characteristics. Our results show that multimedia sites like "YouTube" consume considerable bandwidth throughout the working hours and it goes up to 75% of total traffic in peak hours. Similarly social media sites are the second most accessed sites during working hours and "Facebook" is the most used among them. About 91% of requested sites are hosted outside of Pakistan especially in USA. Our findings provide guidelines for network administrators to enhance network performance and bandwidth strategies at educational networks.
Assessment of Self-Heating Effects Under Lateral Scaling of GaN HEMTs

The impact on self-heating mechanisms observed in GaN HEMTs fabricated on Si substrates is studied by means of a cellular Monte Carlo particle-based device simulator. Within this framework, the thermal effects are included through an energy-balance equation for phonons allowing for self-consistently coupling the charge and heat transport. First, the advanced electrothermal model of an experimental device is developed and calibrated to measured dc characteristics, showing an accurate description throughout the <inline-formula> <tex-math notation="LaTeX">${I}_{\textsf {DS}}$ </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {GS}}-{V}_{\textsf {DS}}$ </tex-math></inline-formula>) space, as a result of capturing the temperature dependence of the scattering processes that modify the charge transport. Then, the model is used to assess the effect of lateral scaling, i.e., reducing the source-to-gate <inline-formula> <tex-math notation="LaTeX">${L}_{\textsf {SG}}$ </tex-math></inline-formula> and gate-to-drain <inline-formula> <tex-math notation="LaTeX">${L}_{\textsf {GD}}$ </tex-math></inline-formula> dimensions, in terms of detailed temperature maps obtained for the acoustic and optical phonon modes as well as the electric field and carrier velocity profiles. It is found that the hot spot in the channel is not located at the peak electric field as predicted by previous methods, but instead, it is shifted toward the drain up to 32 nm. Furthermore, it is shown that, while scaled devices offer improved dc and small-signal ac performance, they are subjected to temperatures up to 15% higher in the channel as compared to the original nonscaled device when dissipating the same dc power, and the temperature distribution throughout the device shows a strong correlation with the scaled layout.
User Performance of VR-Based Dissection: Direct Mapping and Motion Coupling of a Surgical Tool

Robot-assisted surgical systems aim at enhancing surgeon's skills. Nonetheless, the learning curve for mastering such systems is very slow due to the motion-coupling mode that is usually presented in these systems for manipulating a surgical tool. This mode has limitations compared to the direct mapping mode used in open surgery for manipulating a tool. Virtual reality (VR) surgical simulators may reduce the learning time for transferring the surgeon's skills from direct mapping to motion coupling of tool manipulation. This may be accomplished by adding two features to the simulator. First, force models of tool-tissue interaction can be implemented in the haptic interface of the simulator. Second, VR-based surgical tasks can be designed to recreate directmapping mode and motion coupling mode of tool manipulation, as in open and robot-assisted surgeries, respectively. This may permit to transfer the surgeon's skills from open surgery to robotassisted surgery in a timely manner. This work presents a preliminary study on the effect of direct mapping mode and motion coupling mode of tool manipulation on the performance of naïve participants for VR-based brain tissue dissection. An Analytic force model of soft-tissue dissection was implemented in the simulator along with visual feedback of a predefined tool speed of 1 mm/s, which is observed in neurosurgery. The outcomes indicated that the motion quality of the tool via direct mapping was significantly better than with motion coupling. Thus, the study might serve as a first step toward the assessment of user's skills for VR-based robot-assisted dissection.
Example-Based Skin Wrinkle Displacement Maps

We present an algorithm for generating procedural displacement maps for wrinkle patterns measured from photographs or scans. These displacement maps can contain wrinkle patterns that appear at the meso-and microscale, and are modeled using several spatially varying parameters such as the size, shape and distribution of each individual skin wrinkle. We present an algorithm to measure the parameters of skin wrinkle patterns, and show how to adapt the measured parameters to generate displacement maps with similar properties for 3D models other than the one measured. Lastly, we evaluate the quality of the generated maps by comparing them to maps acquired by scanning human skin.
A Framework for Managing Flexible Waste-to-Energy Systems Based on Decision Rules

This paper presents a framework for managing flexible waste-to-energy (WTE) systems based on decision rules. In the operation of a WTE system, the decision rule is used for decision-makers to adaptively select flexible strategies in response to a changing environment. Compared to the existing methods that use either fixed plans or manually defined rules, our framework adopts an evolutionary approach to automatically optimize decision rules based on the simulation of WTE operation environment. The proposed framework is applied to a case study of an anaerobic digestion WTE system and the decision rule is generated for providing an intuitive guidance to exercise capacity expansion strategy in the given system. The experimental results show that the decision rule being generated based on our framework can help to improve the WTE system's expected net present value compared to the fixed expansion design.
Exploring Multi-level Design to Mitigate Variability and Radiation Effects on 7nm FinFET Logic Cells

This work evaluates the advantages of adopting multi-level design on logic cells instead of complex gates to mitigate process variability and radiation effects. At nominal conditions, as expected, the best choice is to use complex gate topology to minimize the power consumption and increase the performance. On the other hand, the use of multi-level arrangements became the functions up to 50% less sensitive to the transient faults and at least 30% more robust to the process variability effects. For designs with the emphasis on area constraints, a trade-off needs to be done.
Lane Detection Under Adverse Conditions Based on Dual Color Space

A high level of situational awareness is essential to an advanced driver assistance system. One of the most important duties of such a system is the detection of lane markings on the road and to distinguish them from the road and other objects such as shadows, traffic, etc. A robust lane detection algorithm is critical to a lane departure warning system. It must determine the relative lane position reliably and rapidly using captured images. The available literature provides some methods to solve problems associated with adverse conditions such as precipitation, glare and blurred lane markings. However, the reliability of these methods can be adversely affected by the lighting conditions. In this paper, a new method is proposed that combines two distinct color spaces to reduce interference in a pre-processing step. The method is adaptive to different lighting situations. The directional gradient is used to detect the lane marking edges. The method can detect lane markings with different complexities imposed by shadows, rain, reflection, strong sources of light such as headlights and tail lights.
Apparel Goods Recommender System Based on Image Shape Features Extracted by a CNN

Recommender system is an information-filtering tool used in solving the problem that the user's preference in information overload. In recent years, some algorithms have been combined with some side information (i.e., item description documents, user reviews, and social networks), and rating prediction accuracy has been significantly improved. However, for fashionable goods, such as apparel and shoes that are important for designing, the contextual information of items is insufficient, and their image shape feature should be considered. Currently, no such recommender system is available to use this feature of image shape. This study proposes a novel probabilistic model using the image shape feature that integrates a convolutional neural network into the probabilistic matrix factorization. The experiment conducted on two real-world datasets corroborates that our model outperforms the other recommendation models.