Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymmetric Recursive Gaussian Filtering for Space-Variant Artificial Bokeh

This paper describes a recursive filter that handles discontinuous space-variant blur. The two-dimensional space-variant blur is approximated by separable Gaussian filtering along the x- and y-dimensions. Within each dimension, the one-dimensional Gaussian filter is further approximated by a pair of forward and backward Infinite Impulse Response (IIR) filters applied in parallel. By modifying the blur sigma's of the IIR filters as they approach a blur discontinuity, smearing artefacts are eliminated. Different modifications of the forward and backward filter sigma's results in an asymmetric space-variant IIR Gaussian filter. Our asymmetric IIR filters produce visually pleasing background blur for a complicated scene whose contents are at different depths to the camera.
STIXGEN - A Novel Framework for Automatic Generation of Structured Cyber Threat Information

A large number of Advanced Persistent Threats (APTs) are being launched by nation-states, organizations and individuals within and across borders. It has been observed that APTs launched against an organization subsequently succeeded with high probability against other similar organizations. Therefore, it has become a need of the time that organizations accumulate and share Cyber Threat Information (CTI) with peers in a structured form for timely prevention and recovery of an attack. Although a large volume of cyber threat data is available on different security blogs, however this data is mostly distributed and unstructured. Presently, there is a lack of easy to use frameworks, which produce and share CTI in a structured form. Furthermore, publicly available structured data is sparse and is mostly redundant, irrelevant and erroneous. Ironically, no method has yet been devised to generate the distinct, meaningful and error-free structured data from text. In this regard, we used the standard "Structured Threat Information eXpression (STIX)". Although, STIX is a comprehensive effort, it is slow in adoption. This is due to a largely manual STIX generation process, which is naturally difficult and produces errors. We take all these deficits as a barrier in STIX utilization and these shortcomings have become a motivation for our research work. We not only proposed the STIXGEN framework, but also developed its prototype for a proof of concept. We perform evaluation of our proposed solution in terms of accuracy and effectiveness. At first, we collected different text reports, generated their STIXs via online tools and by using STIXGEN, then we compared and shared their results with domain experts. It was found that our proposed solution's results are better than other tools and are distinct, threat relevant, and error-free. Subsequently, we presented a comparative analysis of the features provided by different STIX generator tools. At the end, we provide a comprehensive STIX dataset of APTs launched against renowned industries on github, so that researchers and analysts can use it for their research.
Mid-Term Load Power Forecasting Considering Environment Emission using a Hybrid Intelligent Approach

The forecasting of electricity load is considered as an essential instrument, especially in countries with a restructured electricity market. The mid-term prediction is performed for the period within 1 month to 1 or 2 years and it is important for mid-term planning, including planning of repairs and economic exploitation of power systems, which are related to the reliability of the system directly. The forecast horizon in this paper is monthly and on a daily basis (peak load). The combined method of the neural network and the particle optimization algorithm were used to predict the load, and then the maximum amount of environmental pollution caused by the production of electricity required to supply the predicted load was calculated. The applied method was tested on the data of a North American electric company for four months (four seasons) and in comparison to the other methods presented in previous studies, it had an acceptable accuracy.
Characterization and Modeling of Gigarad-TID-Induced Drain Leakage Current of 28-nm Bulk MOSFETs

This paper characterizes and models the effects of total ionizing dose (TID) up to 1 Grad(SiO<sub>2</sub>) on the drain leakage current of <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>MOSFETs fabricated with a commercial 28-nm bulk CMOS process. Experimental comparisons among individual <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>MOSFETs of various sizes provide insight into the TID-induced lateral parasitic devices, which contribute the most to the significant increase up to four orders of magnitude in the drain leakage current. We introduce a semiempirical physics-based approach using only three parameters to model the parallel parasitic and total drain leakage current as a function of TID. Taking into account the gate independence of the drain leakage current at high TID levels, we model the lateral parasitic device as a gateless charge-controlled device by using the simplified charge-based Enz–Krummenaker–Vittoz (EKV) MOSFET model. This approach enables us to extract the equivalent density of trapped charges related to the shallow trench isolation oxides. The adopted simplified EKV MOSFET model indicates the weak inversion operation of the lateral parasitic devices.
Research on Multi-Robot Task Allocation Based on BP Neural Network Optimized by Genetic Algorithm

Performing time and speed in multiple robots search and rescue is the key, requiring multiple robots to quickly complete task assignments. When each robot bid for a task with multiple prices, the BP neural network algorithm is used to fuse these bid prices and determine the robot that wins the bid. However, the traditional BP neural network has the disadvantages that network training can easily fall into local minimums and training errors. It will lead to long time and slow speed for multiple robots to perform tasks. Based on the traditional BP neural network algorithm, this paper proposes a method based on genetic algorithm to optimize BP neural network. This method can effectively deal with the problem of redundant parameters, and the training error is small. The simulation results show that the proposed method can greatly improve the training times and errors of BP network. It also verified that multiple robots can save time when completing tasks.
Optimizing Virtual Resource Management for Consolidated NUMA Systems

Virtualization consolidates multiple virtual machines (VMs) on a single physical server to achieve high resource utilization and energy conservation. However, as current data center server architectures shifting to Non-Uniform Memory Access (NUMA), the complex interplay between data access affinity and shared resource access overloaded still challenge the consolidation efficiency. Existing NUMA-aware virtual resource management policies lack holistic awareness of the data access affinities as well as unable to manage the system loads, which may result in sub-optimal overall system performance. In this paper, we propose a load-aware global resource access management framework (LG-RAM) that aims to optimize VM consolidation performance on NUMA systems. Our real system based evaluations indicate that, compared with state-of-the-art approaches, LG-RAM exhibits an average throughput improvement of 36.5% on our testbed. Besides, LG-RAM only incurs an extra CPU usage of no more than 9% on average when consolidating 32 VMs.
Reasoning about Traffic Signals Controller for Intersection with Contraflow Lanes for Bus Rapid Transit Using Linear-time Temporal Logic

Computational and formal modeling have been used in traffic engineering, mainly to optimize the management of traffic signals and avoid collisions at intersections. One of the problems at the intersection involving the Bus Rapid Transit (BRT) is to reduce the utilization of BRT lanes by non-URT vehicles. This paper proposes a formal model of traffic control system for intersection with contraflow lanes for BRT. Based on the traffic regulations and pertinent data concerning typical intersection and contraflow lanes, we design a generic traffic signal controller for four-armed junction with specialized contraflow lanes for BRT. By using LTL formulas that describe a general traffic scenario and informal traffic specifications to check the compliance, we demonstrate that our proposed formal model complies to the traffic regulations and specifications.
The Facial Stress Recognition Based on Multi-histogram Features and Convolutional Neural Network

The health disorders due to stress and depression should not be considered trivial because it has a negative impact on health. Prolonged stress not only triggers mental fatigue but also affects physical health. Therefore, we must be able to identify stress early. In this paper, we proposed the new methods for stress recognition on three classes (neutral, low stress, high stress) from a facial frontal image. Each image divided into three parts, i.e. pairs of eyes, nose, and mouth. Facial features have extracted on each image pixel using DoG, HOG, and DWT. The strength of orthonormality features is considered by the RICA. The GDA distributes the nonlinear covariance. Furthermore, the histogram features of the image parts are applied at a depth-based learning of ConvNet to model the facial stress expression. The proposed method is used FERET databases for training and validation. The k-fold validation method is used as a validation with k=5. Based on the experiments result, the proposed method accuracy showing outperforms compared with other works.
Bidirectional Pedestrian Counting Based on Virtual Gate Tracking

A method for bidirectional pedestrian counting at entrance and exit of passageways in intelligent video surveillance was proposed. First of all, we detect the pedestrian targets, then set the ROI(region of interest) region and bidirectional counting line as virtual gate. According to the feature information of the detected moving targets that through the virtual gate, the targets are divided into single pedestrian and multiple pedestrians. In this paper, we only track the pedestrian targets that enter the virtual gate area and establish the corresponding tracking array, then use the motion parameters in the target tracking array to perform bidirectional counting.The experimental results show that the method can achieve good effect and real-time performance in the pedestrian counting at the entrance and exit of the passageways.
A Study of Term Frequency toward the Ability to Read an RDF-based Node-Link Diagram Visualization

This paper aims to study the role the term frequency for rearranging triples in an RDF graph diagram from common information to topic-specific information and to evaluate the previous project, RDF4U. Since the analysis cannot be done directly against RDF data due to the lacking of supervised dataset, the text analysis on well-structured articles having well-defined sections about background content and main content is adopted in this study. The result of the experiment demonstrates the feasibility to rearrange triples for different levels of information.
A Novel Coil Design Method for Inductive Power Transfer Avoiding Compensation Circuits

An novel planar Inductive Power Transfer (IPT) system employing two lollipop coils for power coupling, which requires no compensation circuit, is present in this paper. The lollipop coil is a planar coil, which is composed of two planar windings placed at two surfaces of a substrate by use of the principle of dipole antenna, possessing a nature resonant response to achieve the impedance matching of imaginary part. The characteristics of input impedance and current distributions of a 10-turn lollipop coil are investigated. To verify the idea of avoiding the compensation circuits, the proposed IPT system with a pair of 10-turn lollipop coils operating at 13.56 MHz is experimented as an example.
Routing the Pi-Containers in the Physical Internet using the PI-BGP Protocol

The Internet is known for its ability to scale and adapt its routes to every change. These routes are made by every Autonomous System (AS) peering with each other as neighbors with the Border Gateway Protocol (BGP). Unfortunately, this amount of trust, between all these Autonomous Systems allowing protocols like the BGP to function properly, is not found in the Physical world between logistic service providers. The Physical Internet, however, with its standardized PI-Containers and Internet-derived protocols, has every promising aspect to face this challenge. We propose in this paper a new routing approach based on the PI-BGP (Physical Internet-Border Gateway Protocol) equivalent of the BGP in the Internet. We developed this new protocol to offer a new perspective to tackle the problem of routing the PI-containers in the Physical Internet.
State of the Art in Artificial Immune-Based Intrusion Detection Systems for Smart Grids

This paper gives a survey of the state of the art in research considering artificial immune-based intrusion detection systems for smart grids. We considered publications of the last five years (2013–2018). The paper starts with a short introduction to the smart grid and artificial immune systems before presenting the noteworthy publications relevant to artificial immunity of smart grids. Finally we will classify the works into their main topics and show gaps in the current research.
High Speed Image Retrieval Method Executable on Smartphones: Toward Vision Assistance for Blind People

This paper describes a high speed image retrieval method executable on smartphones in real-time. This method will enable us to develop an application of smartphones which assists the visually impaired to shoot a video of an object and to distinguish that object without using the internet. Taking a video makes a visually impaired user to put the object in the camera frame correctly, and the smartphone without the internet protects the user's privacy. Our method is constructed by ORB local features and a data structure of binary decision tree. Under the experimental environment of 3.1 GHz Intel Core i7 platform with Windows 10, our system performs 54.5 ms processing time and 96.1% accuracy. Furthermore, our method achieves 30 fps processing time on a smartphone.
Classification of EEG Signals Using Neural Networks to Predict Password Memorability

Feature extraction and classification is a subject of broad and current interest in the brain-computer interfaces (BCIs) community, and remains a challenging task when working with Electroencephalogram (EEG) data, with no agreed upon optimum features set and classifier algorithm. In this 75-participant lab study, we compare different feature extraction methods and classifiers as we investigate the relationship between users' perceptions of the memorability of a number of passwords and the users' EEG data collected using BCIs when presented with these passwords. We asked the participants to rank the comparative memorability of 15 blocks of 5 passwords each, while recording their EEG data. Features from the EEG signals are extracted in three domains, power spectrum from the frequency domain, statistics from the time domain, and wavelet coefficients from the time-frequency domain. The feature subsets are submitted for classification with two classes, most memorable and least memorable, based on the user's perceived memorability of the passwords. Classification performance of Neural Networks and Support Vector Machine algorithms are compared. Results show distinctive features of EEG signals in the two different classes, achieving a classification accuracy of 89%. Our results indicate that features extracted in the time-frequency domain using wavelet transform, and classified using neural networks, resulted in the highest classification performance.
Evaluation of Human Hand Movement For Office Syndrome

In this paper, we propose a method for human hand movement evaluation for office syndrome. This method consists of 2 main part; hand motion detecting and hand motion evaluation. The hand motion can detect from IMU sensor which is a single unit in the electronics module which collects angular velocity and linear acceleration data. The design application and make database to evaluate for helpful physical therapy. The application consists of 3 main windows: first, the patient information and pain score. Second, the angles data from IMU sensor 6 hand movement. And third, the performance of motion for hand compare with normal ROM. From the result when compare angle hand with goniometer have MD about 0.82, 1.04 and 1.94 in x, y and z axis respectively.
Novel Design of a Dual-Band 5G MM-Wave Antenna Array Integrated with a Metal Frame of a Cellular Phone

A novel dual-band mm-Wave antenna array working at 28 GHz and 39 GHz for the fifth generation mobile communications (5G) is proposed to enable the integrated design with a metal frame of a cellular phone and is also promising to achieve the compelling integration solution of mm-Wave antennas in non-mm-Wave antennas (AiA) especially when the metal frame serves as non-mm-Wave antennas. A model for the exterior and dimensions of a popular cellular phone is simulated with a 5-element antenna array designed and integrated on its external metal frame. Based on the simulation results. , the bandwidth $\pmb{(\vert \text{S}_{11}\vert \leq-10\text{dB})}$ ranges from 27.4 to 29.6 GHz and from 36.9 to 40.2 GHz to cover the 5G mm-Wave bands of n260 and n261 in the third generation partnership project (3GPP). When the array performs the beam scanning at 28.0 GHz and 39.0 GHz. , the peak realized gains of the array are 11.3 dBi and 12.0 dBi. , the lowest antenna array efficiencies are −0.42 dB and −0.33 dB. , respectively. , and the maximum scan angles are both 60°. Based on the performance. , satisfactory user experience on cellular phones or mobile devices with metal exteriors can be attained. The proposed design can be applied to THz antennas.
Proposal for Cognitive Architecture for Software Defined Wireless Networking

Software-defined networking (SDN) not only lessens the burden of network operators but also accelerates the provision of high-quality services to various users by speeding up changes to the network configuration. Recently, Wireless Networking (WN) has gained importance as a social infrastructure to connect a large number of devices and cloud applications. Software definition technology for wireless networks is referred to as software-defined wireless networking (SDWN). We hereby propose a cognitive software-defined wireless networking (CSDWN) to enhance SDWN by applying a cognitive cycle to SDWN in addition to the development of a prototype to validate this concept.
Verifying rLTL formulas: now faster than ever before!

Robust Linear Temporal Logic (rLTL) was crafted to incorporate the notion of robustness into Linear-time Temporal Logic specifications. Robustness is ubiquitous in control systems and translates the intuitive notion that “small” violations of environment assumptions should only lead to “small” violations of system guarantees. This notion was formalized in the logic rLTL via 5 different truth values and it led to an increase in the time complexity of the associated model checking problem. In this paper we identify and analyze a fragment of rLTL for which the model checking problem can be solved using generalized Biichi automata with at most $3^{\vert \varphi\vert }$ states where $\vert \varphi \vert$ denotes the length of an rLTL formula $\varphi$. This is a substantial improvement over the previously known bound of $5^{\vert \varphi \vert}$ and close to the tight upper bound $2^{\vert \varphi\vert }$ for LTL.
Research-Practice Gap in Passive House Standard

Throughout human history there has been occasion where people faced challenges and issues that threatened the normalcy of life on earth. On those occasions, paradigm shifts have occurred in the way people govern and conduct their livelihood, often changing technology and cultural norms. Our generation is unique in this respect as we face both catastrophic challenges alongside hopeful positivity. On one side, the harnessing of energy has increased the sophistication and comfort of our lives via technological advancements. On the other hand, the way we produce and use energy has brought devastation to the environment we live in, and may have the potential to make earth an unlivable place for future generations. The passive house standard has emerged as an encouraging engineering approach to mitigate and solve many enerzgy related issues. Conceptualizing innovative solution techniques and providing modeled evidence for the feasibility of those ideas is unquestionably a major step in the solution process. Equally important is testing these ideas and approaches in practice with physical implementation. Physical evidence often involves many challenges, and may require multidisciplinary teams to resolve these challenges. The contributions in this paper relate to this style of multidisciplinary coordination. What we learned through this work is that the practical implementation of a well modelled system that appears to work in theory can be quite challenging. These systems may have large amounts of theoretical evidence, but often are not tested extensively in practice. These experiences are valuable, and should be shared among both researchers and industry practitioners. We would like to share our experience in one such ongoing project: installing a sensor network system intended to monitor a building constructed to passive house standard in an academic setting.
An Experimentally-Validated Verilog-A SPAD Model Extracted from TCAD Simulation

Single-photon avalanche diodes (SPAD) are photodetectors with exceptional characteristics. This paper proposes a new approach to model them in Verilog-A HDL with the help of a powerful tool: TCAD simulation. Besides, to the best of our knowledge, this is first model to incorporate a trap-assisted tunneling mechanism, a cross-section temperature dependence of the traps, and the self-heating effect. Comparison with experimental data establishes the validity of the model.
Using of Wavelets for Secret Key Generation: A Measurement Based Study

For the physical layer (PHY) key generation, two distant nodes exploit the reciprocal channel coefficients as a shared secret. These nodes may obtain erroneous channel estimates and generate highly correlated but not identical secret keys. In this work, we analyze the key error rate (KER) performances when different wavelet families are utilized as a pre-processing step in PHY key generation. Measurement-based KER results show that selection of the wavelet can significantly change the performance.
Wideband Quarter Wave Transformer Based on Defected Ground and Split Ring Resonator Structures

This article examines a wideband impedance transformer based on a split ring resonator and defected ground structures. The transformer was designed as a 4-section binomial one and it provides a transformation from $\pmb{50} \pmb\Omega$ to $\pmb{100\ \Omega}$. Reversed “Z” shaped defects are formed on the ground plane of the design in order to increase the bandwidth. The design has been simulated using a 3D electromagnetic simulator which utilizes finite integration technique. Three distinct resonances are exhibited at specific frequency spots as proofs of the concept of impedance matching. Theoretical and simulated results are in a good agreement with each other and the proposed design might be tailored in many microwave circuits and antenna applications.