Miracle-dz/UniFAR-FTUs huggingface dataset:
TRANSFER LEARNING AND SMOTE ALGORITHM FOR IMAGE-BASED MALWARE CLASSIFICATION

In recent years, the volume and type of malware is growing, which increases the need of improving a detection and classification malware systems. Nowadays, deep convolutional neural networks (CNNs) have recently proven to be very successful for malware classification due to their performance on images classification. However, their effectiveness is degraded with the unbalanced malware families. In this paper, we propose a malware classification framework using CNN-based deep learning architecture, including a SMOTE technique "Synthetic Minority Oversampling Technique" to balance the dataset (malwares families). Our proposed method consists to converting the binary files into gray scale images and balancing them by the SMOTE method, and then we use them to train the CNN architecture to detect and identify malware families. We use the Transfer Learning technique based on an existing Deep Learning model VGG16 that has previously trained with the ImageNet dataset (≥ 10 million). For evaluations, an extensive experiment was conducted using Microsoft Malware dataset. The Results show that our approach is efficient with an average accuracy of 98%.
Benchmarking Apache Spark and Hadoop MapReduce on Big Data Classification

Most of the popular Big Data analytics tools evolved to adapt their working environment to extract valuable information from a vast amount of unstructured data. The ability of data mining techniques to filter this helpful information from Big Data led to the term ‘Big Data Mining’. Shifting the scope of data from small-size, structured, and stable data to huge volume, unstructured, and quickly changing data brings many data management challenges. Different tools cope with these challenges in their own way due to their architectural limitations. There are numerous parameters to take into consideration when choosing the right data management framework based on the task at hand. In this paper, we present a comprehensive benchmark for two widely used Big Data analytics tools, namely Apache Spark and Hadoop MapReduce, on a common data mining task, i.e., classification. We employ several evaluation metrics to compare the performance of the benchmarked frameworks, such as execution time, accuracy, and scalability. These metrics are specialized to measure the performance for classification task. To the best of our knowledge, there is no previous study in the literature that employs all these metrics while taking into consideration task-specific concerns. We show that Spark is 5 times faster than MapReduce on training the model. Nevertheless, the performance of Spark degrades when the input workload gets larger. Scaling the environment by additional clusters significantly improves the performance of Spark. However, similar enhancement is not observed in Hadoop. Machine learning utility of MapReduce tend to have better accuracy scores than that of Spark, like around 2%-3%, even in small-size data sets.
A cloud computing based intelligent traffic control system for vehicular networks

The number of vehicles on the road increases permanently causing more and more traffic jam and congestion which results in a lot of delay in arriving at destination. Intelligent transport systems (ITS) have merged as an efficient way to overcome particularly this kind of problem in vehicular networks (VANETs) and improve performance of transportation systems. One of the solutions is to put intelligent traffic lights at each intersection of high vehicle density. In this paper, we propose a cloud computing based intelligent traffic light control algorithm, called CITLA. This algorithm aims to schedule the traffic by switching the phases of the traffic lights dynamically according to the state of the road in real time based on the conventional cloud. The Cloud has a global view on the road network since it is responsible for collecting road information via the RSUs placed on each street corner. It commutes thereafter, based on the information collected, the density of each road so as to minimize the waiting time and congestion on the roads. The results of these calculations are transmitted in real time to the RSUs which are responsible for controlling the traffic lights by changing the phases. The feasibility of CITLA was demonstrated through simulations using the discrete event simulator OMNeT++ with Simulation of Urban Mobility (SUMO).
Navigating the White Water of Project Management

To minimize conflict on a project, the manager must be a proactive information gatherer and a nonpareil communicator. The attributes of successful project managers are discussed, with attention also given to the traits of those who are less than successful. The various costs incurred when conflict remains unresolved on a project are summarized. It is stressed that conflict can present opportunities for both personal and organizational growth.
A multi‐pass method for accelerated spectral sampling

Spectral Monte Carlo rendering can simulate advanced light phenomena, such as chromatic dispersion, but typically shows a slow convergence behavior. Properly sampling the spectral domain can be challenging in scenes with many complex spectral distributions. To this end, we propose a multi‐pass approach. We build and store coarse screen‐space estimates of incident spectral radiance and use these to then importance sample the spectral domain. Hereby, we lower variance and reduce noise with little overhead. Our method handles challenging scenarios with difficult spectral distributions, many different emitters, and participating media. Finally, it can be integrated into existing spectral rendering methods for an additional acceleration.
Real‐time Denoising Using BRDF Pre‐integration Factorization

Path tracing has been used for real‐time renderings, thanks to the powerful GPU device. Unfortunately, path tracing produces noisy rendered results, thus, filtering or denoising is often applied as a post‐process to remove the noise. Previous works produce high‐quality denoised results, by accumulating the temporal samples. However, they cannot handle the details from bidirectional reflectance distribution function (BRDF) maps (e.g. roughness map). In this paper, we introduce the BRDF pre‐integration factorization for denoising to better preserve the details from BRDF maps. More specifically, we reformulate the rendering equation into two components: the BRDF pre‐integration component and the weighted‐lighting component. The BRDF pre‐integration component is noise‐free, since it does not depend on the lighting. Another key observation is that the weighted‐lighting component tends to be smooth and low‐frequency, which indicates that it is more suitable for denoising than the final rendered image. Hence, the weighted‐lighting component is denoised individually. Our BRDF pre‐integration demodulation approach is flexible for many real‐time filtering methods. We have implemented it in spatio‐temporal variance‐guided filtering (SVGF), ReLAX and ReBLUR. Compared to the original methods, our method manages to better preserve the details from BRDF maps, while both the memory and time cost are negligible.
Identification of Human Mass Properties From Motion

Abstract We experimentally identify the human body dynamics from motion capture data and contact force measurements. We make use of the 6 base-link equations to write the identification model. We have shown that the obtained system is sufficient to estimate the whole base inertial parameters. The method is computationally efficient; and allows to identify the pure inertial parameters without transmission, friction considerations. However finding persistent exciting trajectories is a drawback of the method. In this paper we put emphasis in describing persistent exciting trajectories that are suitable to obtain accurate results. We propose a method to automatically select the set of motions used for the identification in order to optimize the computation time and the accuracy of the identified parameters. For the experiments we make use of gymnastic motions. Each of the 3 subjects -of different body conformations- perform 29 types of motions. The identification method and the automatic motions selection method are both applied to obtain the inertial parameters of one subject.
Importance of installation practices for corrosion protection of ductile iron pipe

Loose polyethylene encasement is a widely used corrosion protection measure for buried ductile iron pipes. Polyethylene encasement functions as a physical barrier between the pipe and surrounding soil environment. Findings from an international industry survey strongly suggest that performance of polyethylene encasement is governed by installation and repair practices. Common problems reported were inappropriate repairs at tapping connections, pre-installation damage, failure to overlap encasement at joints and improper excavation to accommodate encased joints during installation. To quantify the benefits of improved installation practice, physical and economic models were developed. The physical model predicts corrosion related failures and the economic model calculates the benefits accrued through their avoidance. The model indicates that pipelines in moderate to extremely corrosive soils, better installation practices were beneficial. Extrapolating to the network scale, a moderate 5% reduction in installation damage through improved practice created a benefit of approximately $1M/year benefit after 30 years in-service.
Kinematic analysis of punt kick in football goalkeepers based on the level of kick effort

In the present study, we aimed to investigate the dierences in punt kicks by football goalkeepers based on the level of eort required. Twelve experienced goalkeepers participated in the study. �e participants were instructed to kick the ball as far as possible in the maximum distance trial (100% trial) and to have a more controlled approach for the 80% and 60% trials. Each punt kick was divided into three events: release of the ball from the lehand (BR), pivot foot ground-contact (LFC), and ball impact (IMP). Right lower limb joint veloci- ty, right hip and knee joint angles, �ight distance, ball velocity, and kick angle were calculated. �e 80% and 100% trials yielded almost the same velocity for each part of the right leg; however, in the 60% trial, the level of kicking eort was managed by adjusting the velocity of the right ankle joint, starting from BR, in addition to adjustment of the velocity of the right knee joint at LFC. Compared to punt kicks with a lower level of eort, the punt kicks with a higher level of eort involved an increase in the hip joint extension angle for the right leg during the backswing and the lowering of the knee joint angle of the right leg at the start of the forward swing, thus producing forward swing velocity for the right foot.
Coordinated Control of Large Scale Offshore Wind Farms Integrating into VSC-HVDC System

VSC-HVDC has become prior transmission way in delivering off-shore wind power. This paper studies offshore wind system based on DFIG (Doubly Fed Induction Generator) wind turbine. The control strategy of the system during the normal and fault condition are studied. In the normal condition, the GSVSC are operated by controlling constant frequency, voltage and phases. During the fault condition, the GSVSC converted to limit current model and dc voltage link are controlled by DFIG-RSC converter. These control strategies are validated in good performance in PSCAD/EMTDC platform.
Current-controllable FDNRs using linear transcapacitance amplifiers

A systematic synthesis process is described for the simulation of current-controllable FDNRs using linear transcapacitance amplifiers. The process is used to obtain three circuits; two are believed to be novel. The values of the FDNRs are controllable by adjusting the bias currents of the transcapacitance amplifiers. In order to implement the obtained FDNRs, a new transcapacitance amplifier is designed. Operation principles of designed circuits are presented and experimental results are used to verify theoretical predictions. The results show close agreement between predicted behaviour and experimental performance.
Stabilization of the SARS-CoV-2 Receptor Binding Domain by Protein Core Redesign and Deep Mutational Scanning

Stabilizing antigenic proteins as vaccine immunogens or diagnostic reagents is a stringent case of protein engineering and design as the exterior surface must maintain recognition by receptor(s) and antigen—specific antibodies at multiple distinct epitopes. This is a challenge, as stability-enhancing mutations must be focused on the protein core, whereas successful computational stabilization algorithms typically select mutations at solvent-facing positions. In this study we report the stabilization of SARS-CoV-2 Wuhan Hu-1 Spike receptor binding domain (S RBD) using a combination of deep mutational scanning and computational design, including the FuncLib algorithm. Our most successful design encodes I358F, Y365W, T430I, and I513L RBD mutations, maintains recognition by the receptor ACE2 and a panel of different anti-RBD monoclonal antibodies, is between 1-2°C more thermally stable than the original RBD using a thermal shift assay, and is less proteolytically sensitive to chymotrypsin and thermolysin than the original RBD. Our approach could be applied to the computational stabilization of a wide range of proteins without requiring detailed knowledge of active sites or binding epitopes, particularly powerful for cases when there are multiple or unknown binding sites.
Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures

Plasmodium vivax sporozoites reside in the salivary glands of a mosquito before infecting a human host. Previous transcriptome-wide studies in populations of these forms were limited in their ability to elucidate cell-to-cell variation, thereby masking cellular states potentially important in understanding transmission outcomes. In this study, we performed transcription profiling on 9,947 P. vivax sporozoites to assess the extent to which they differ at single-cell resolution. We show that sporozoites residing in the mosquito’s salivary glands exist in distinct developmental states, as defined by their transcriptomic signatures. Additionally, relative to P. falciparum, P. vivax displays overlapping and unique gene usage patterns, highlighting conserved and species-specific gene programs. Notably, distinguishing P. vivax from P. falciparum were a subset of P. vivax sporozoites expressing genes associated with translational regulation and repression. Finally, our comparison of single-cell transcriptomic data from P. vivax sporozoite and erythrocytic forms reveals gene usage patterns unique to sporozoites. In defining the transcriptomic signatures of individual P. vivax sporozoites, our work provides new insights into the factors driving their developmental trajectory and lays the groundwork for a more comprehensive P. vivax cell atlas.
Propagation of TE Modes in Dielectric Loaded Waveguides

The propagation of TE/sub no/ modes in rectangular wave-guides that contain two dielectric slabs parallel to the narrow wall and extending over the full height of the guide is investigated. Waveguide and dielectric are assumed to be lossless and infinitely long. Apart from these restrictions, the dielectric slabs may have arbitrary thickness, position, and dielectric constant. The analysis is restricted to TE/sub no/ modes with the E-field parallel to the narrow guidewall. The guide containing only one dielectric slab is covered by this analysis. The even modes n = 2, 4, 6, . of the guide with two slabs correspond to the odd modes n' = n/2 = 1, 2, 3, . of the guide with one slab half the width of the guide with two slabs. For six relative dielectric constants (/spl epsilon/= 2.25, 4.00, 9.00, 12.25, 16.00, 25.00) the cutoff frequencies for TE 10, 20, 30, 40, 60 modes and the normalized propagation constants for TE 10 and TE 20 modes between their respective cutoff frequencies and a frequency slightly above the second- and fourth-order mode cutoff frequency for the empty guide, respectively, have been computed for a large range of slab thicknesses and slab positions. Selected results are presented graphically. These results are discussed. The parametric dependence of field distributions, of normalized characteristic impedances, of the ratio of cutoff frequencies (fractional bandwidth), and of the ratio of magnetic field components (ellipticity) are illustrated.
Using Multimedia to Motivate Students in EFL Classrooms: A Case Study of English Master's Students at Jijel University, Algeria

With the development of technology and the boom of digital revolution, foreign-language teachers should think about effective new ways to create better foreign-language teaching or learning milieus that are supported by multimedia technologies. It is widely agreed that language instruction that integrates technology has had a tremendous impact on the language learning process. Moreover, learners are expected to be more motivated in a web-based computer-assisted language learning (CALL) programme and to improve their competencies. As a result, CALL has become increasingly popular in English as a Foreign Language (EFL) teaching. This study focuses on how multimedia can play an important role in motivating EFL students. For this purpose, a questionnaire was administered to a group of Master's degree students at Med Seddik Benyahia University, Algeria, in June 2012. It was found that the availability of multimedia was a dynamic and challenging motivating factor in EFL classrooms, leading to certain suggestions focusing on the achievement of learning objectives.
Antenna Technologies for Beyond-5G Wireless Communication: Challenges and Opportunities

This paper provides an overview of the Sweden-Taiwan collaborative research framework project ‘Antenna Technologies for Beyond-5G Wireless Communication’ that has been recently established to develop robust and energy-efficient integrated antenna solutions for frequencies beyond 100 GHz as future enabling technologies for the 5G+ applications. Technological challenges in manufacturing and integration with electronics as well as available opportunities are discussed.
Multimodal Deep Kernel Learning: Application to Disaster Response in Social Media Network

Recently multimodal learning has become a popular research topic because variety of types of sensors can be used to describe the events, for example, images and texts in the social network for disaster detection and response. It is useful because of different sources of information that data will cause heterogeneous problems. The kernel function can measure the similarity of data, and the fusion of different kernels can handle this problem well. This paper proposes a method based on multimodal deep kernel learning to detect specific categories of content posted on Twitter during a disaster for humanitarian response. Specifically, the convolutional neural network is used to extract image and text features, and obtain multiple kernel functions in each mode. Then a deep neural network is used to fuse multiple basic kernel functions corresponding to the two modalities. Finally, we use Support Vector Machine (SVM) for classification. Applying this method to a real disaster data set, and compared with the baseline, the results prove the effectiveness of our model.
Efficient Probabilistic Packet Marking for AS Traceback

Distributed Denial of Service (DDoS) attacks are among the most perilous attack types in the Internet. In addition to the significant harms for the victim site, intermediate Autonomous Systems (AS) are also unintended victims in DDoS attacks. The main goal of a DDoS defense mechanism is to reduce the attack’s effect as close as possible to their sources to prevent malicious traffic in the Internet. In this work, we proposed an AS traceback scheme to infer AS level forward paths from attacker sites to a victim site. We utilize the 16-bit IP ID field and 6-bit from the ToS field in the IPv4 protocol. In our method, only the ingress routers of ASes probabilistically mark the packet with their AS numbers. We propose an encoding technique to reduce the number of required packets significantly. The destination site can construct the path after receiving enough packets. Our results show that a victim site can construct the forward path from an attacker site after receiving 10.14 packets on the average. Compared to the other techniques, our approach requires fewer packets to construct the paths from attacker sites to a victim site.
Optimized Method for Thermal Tracking in 3D NoC Systems by Using ANN

In this paper, we improve the thermal sensor allocation method based on the correlation between each node in the Network on Chip (NoC) system by tracking temperature under different scenarios. Moreover, an artificial neural network (ANN) is used to estimate the full-chip temperature distribution for the first time. Compared with the state of the art methods, our method can improve the average accuracy of the estimated results under different scenarios by 32.0% to 95.5%.
Trend of Emerging Non-Volatile Memory for AI Processor

Processing unit and memory are key components for artificial intelligent (AI) processor, which are developed by the CMOS technology and static random access memory (SRAM), respectively. As technology scaling down to sub-30 nm, the leakage current has become a critical issue to AI processor. Recently, the emerging non-volatile memory is mature gradually, such as Magnetic RAM (MRAM) and Resistive RAM (RRAM), which can be used to develop AI processor to alternate both processing unit and memory. In this work, we provide a survey on the recent tape-out work on MRAM and RRAM. We discuss the trend of MRAM and RRAM as buffer/Cache, and computingin Memory architecture. Based on the recent developments, we predict both MRAM and RRAM can be a AI product soon.
Applications and Issues in the Fields of Nanotechnology, Information Technology, Neurotechnology, and Biotechnology

Applications and issues in the fields of nanotechnology, information technology, neurotechnology, and biotechnology were discussed. Nanotechnology has been developed fairly recently and regulation is limited, according to some scholars; nonetheless, the scientific community foresees tremendous possibilities with this technology. Advances in information technology are not as recent as nano and neuro technologies; but the field remains transforming the ways in which people communicate and work. Some scholars show concerns in the fashion of regulation and cybercrime; the economic meltdown of 2008 was blamed on the lack of information governance of the banking system. Neurotechnology is highly criticized by some scholars, because of the heavy use of medications to control human behavior; particularly among children. For the purposes of this paper, the focus is on agricultural biotechnology, which has been practiced for decades in the United States. Nevertheless, many other countries still avoid and criticize the use of genetically modified organisms (GMOs).
Digital LDO with reference-less adaptive CLK generation and bit-shifting Coarse–Fine-control

Conventional digital low dropout regulator (DLDO) operating speed is invariable due to constant external reference clock. Fixed DLDO operating speed, regardless of the load state, cause inefficient power consumption for steady-state load or stability problem due to slow transient response time. This paper introduces DLDO with reference-less adaptive clock generation which is optimized by load states. Coarse-fine control and burst-mode are added to the shift register adjusting power transistors (PT) to achieve a fast transient response. The proposed DLDO is designed in 180nm BCDMOS and operates at 1.8V with an output voltage of 1.5V. The maximum and minimum operating frequency is 393MHz, 165MHz respectively. Settling time is 174ns at load current changes from 2mA to 10mA.