Miracle-dz/UniFAR-FTUs huggingface dataset:
An Android Malware Detection Method Using Deep Learning Based on API Calls

With the in-depth development of the Internet industry, the mobile Internet has been effectively integrated into daily work. However, Android has many extremely serious security issues. In our work, we apply text classification technology to the detection of Android malware, based on the deep learning. We extract the Android malware API sequence based on the Cuckoo sandbox, and use the text processing technology to solve the detection problem of Android malware. To evaluating the performance of our system, we compared it with Dalvik based on the Bi-LSTM. The accuracy of API extraction method using Cuckoo is higher than Dalvik, reaching the accuracy of 96.74%. To further verify the effects of different models, we compared it with GRU, BGRU and LSTM using Cuckoo Sandbox as API extraction method. The result demonstrate the Bi-LSTM has the highest accuracy.
Large-Scale 3D Semantic Mapping Using Monocular Vision

In recent years, with the rapid development of artificial intelligence, simultaneous localization and mapping technology to solve robot perception problems has been widely used in automatic driving, robot navigation, augmented reality and other fields. In this paper, a method of reconstructing large outdoor 3D dense semantic map based on monocular vision is proposed. Firstly, the motion trajectory of the camera is estimated by visual odometer, and the depth data is obtained by monocular depth estimation algorithm. Meanwhile, a dense conditional random field (CRF) image segmentation system based on deep learning and super-pixel distribution is used for semantic segmentation. Then 2D image semantics are gradually transferred to 3D point clouds by Bayesian progressive label migration strategy and further optimize 3D labels through a novel CRF model. Super pixels are utilized to enforce smoothness and form robust PN high order potential. Finally, dense 3D semantic maps of urban environment is generated by arbitrary length image sequence. The algorithm is tested on KITTI dataset, and the constructed semantic map shows that the proposed algorithm can reconstruct globally consistent semantic map in large-scale outdoor scenes.
Aerial Image Semantic Segmentation Using Spatial and Channel Attention

With the development of sensor, remote sensing has become an effective way to observe the Earth. Land cover classification is an important application of remote sensing data. Deep convolutional neural networks (DCNN) have good capability to extract effective image feature. In this paper, an automatic land cover classification method using spatial and channel attention was proposed. Encoder-Decoder structure was adopted as the network architecture. We used "channel attention" to enhance the function of effective channels. By using "spatial attention" to combining high-level semantic features with low-level features, better land objects boundaries were obtained. The proposed method was tested on ISPRS Potsdam 2D-Semantic Segmentation Challenge Dataset. The results showed that our proposed method, which uses spatial and channel attention with atrous spatial pyramid attention networks (SA-ASPA) outperforms the Deeplab_v3+ in terms of mean Intersection over Union (mIoU) for 5%.
A Visual Method to study the Error Function of ICP Algorithms

This paper proposes a novel method to study the error function of ICP based algorithms. With our method, we visualize the multidimensional error function of ICP algorithms in one dimension which allows us to compare and quantify the performance of ICP algorithms in an intuitive and descriptive manner. This is motivated by the fact that there are many ICP variants around and for researchers and engineers it is challenging which algorithm they shall choose. New approaches are often only evaluated based on runtime and accuracy. Our visual method allows to gain further insights beyond those metrics. We demonstrate the capability of our method by applying it to the KITTI LIDAR odometry benchmark. Our experiments show evidence for errors in the ground truth data, difficulties in highway scenarios and prove the power of superior error metrics such as the newly emerged symmetric objective function.
Design and Development of Wearable Sensor System using MQTT Protocol for Real Time Kinematics Analysis

Wearable sensors data fusion is the most advanced and robust technology to study the motion of a body and perform kinematics analysis in real time. The main application of such sensor systems is in sports training and performance measurement. Advancements in wearable technology permit continuous observation and analysis of the sports person without causing any discomfort during training sessions. Multiple sensors are required to be mounted on the subject and synchronous data is needed from all the sensors to get a complete picture of the athlete’s performance.The presented design of wearable sensor system involves a wireless sensor network of smart Inertial Measurement Units and implementation of MQTT Protocol for communication. Pi Zero W has been installed with Mosquitto broker. The transmitted data is published in real time on two different MQTT clients – open source software MQTT SPY and a custom android mobile app. This data can be further stored in a data-base to explore various data fusion algorithms or techniques for kinematics analysis of the subject.
Design of Digital Codebook for Precoding in OSTBC-MIMO systems

Designs of digital codebook for precoding mainly include Love-Heath codebook, Lloyd codebook and Grassmannian codebook and so on. But the bit error rate (BER) performance of Lloyd codebook is affected by error threshold. To enhance BER, spectrum efficiency and solve the problem of Lloyd codebook, we proposed a design of codebook by MacQueen algorithm for MIMO systems. We don’t need to set the error threshold by replacing Lloyd algorithm with MacQueen algorithm. And the centroid is updated after each channel matrix H classification, which provides a more accurate classification basis for the classification of the next channel matrix. Simulation results show that the proposed codebook design has better BER performance and spectrum efficiency than Love-Heath codebook and Lloyd codebook in OSTBC-MIMO systems.
Comparison of NBTI kinetics in RMG Si p-FinFETs featuring Atomic Layer Deposition Tungsten (ALD W) Filling Metal Using B2H6 and SiH4 Precursors

Fast characterization methods are used to study negative bias temperature instability (NBTI) kinetics in replacement metal gate Si p-FinFETs filled by atomic layer deposition (ALD) tungsten (W) using B2H6 or SiH4 precursors. The impact of ALD W filling metal process on threshold voltage shift (ΔVT), generated interface traps (ΔNIT), pre-existing hole traps (ΔNHT), stress bias, and temperature dependence of degradation is analyzed. SiH4-based devices show lower ΔVT compared to B2H6-based devices under identical stress conditions due to lower ΔNHT and ΔNIT. More fluoride-passivated interface traps and higher compressive strain along the channel for ALD W using SiH4 are utilized to explain a lower ΔNIT than ALD W using B2H6. The time kinetics of NBTI degradation is then modeled for long-term prediction and the maximum overdrive voltage is determined. Devices filled with ALD W using SiH4 show superior reliability and a higher operation overdrive voltage, and can be adopted in the 16/14-nm node CMOS technology and beyond.
A Learnable-by-Design (LEAD) Model for Designing Experiments for Computer Science Labs

India graduates 1.5 million engineering students every year, majority of them from its Tier 2 and Tier 3 colleges where there is severe shortage of qualified faculty and lab infrastructure, and where English is second or third language for most students. To provide affordable virtual laboratories to these engineering colleges, Government of India runs the Virtual Labs project with a select set of participating institutions to create virtual labs required for entire engineering student population of India. The program suffers from two problems: 1) lack of focus on pedagogy and learnability of labs being created, and 2) little ability to scale to more teachers and subject matter experts to produce labs aligned to local contexts. This paper presents a model (LEAD model) for the design of virtual labs for a data structures and algorithms course in computer science and is intended to address the problems stated above. The model encodes the principles of learning in the design and structure of an experiment so that labs can be learnable by design. The model is based on constructivist theory of learning. It uses Bloom's Taxonomy for defining learning objectives and applies Merrill's first principles and Gagne's 9 events of instruction as instructional design methodologies. To ensure tasks in an experiment indeed aid learning, the model requires a mapping between tasks and learning principles to be created and published along with the experiment. The model uses a pedagogy that lays strong emphasis on conceptual understanding without the use of code or program. To demonstrate the efficacy of this model, we present the data analysis of the feedback from early users of one the experiments. Even though limited in scope, data shows that experiments built using LEAD model can aid understanding for the students.
Geometric Analysis and Systematic Design of Millimeter-Wave Low-Power Frequency Dividers in 65-nm CMOS

Broadband current-mode logic static divide-by-2 and divide-by-4 circuits fabricated in 65-nm CMOS are presented. The low-power frequency dividers are analyzed in a geometric way. The self-oscillation frequency and locking range of current-mode dividers are analyzed based on current vectors. A systematic design methodology is proposed to reduce power consumption and enhance the locking range. The divide-by-2 circuit operates from 8 to 40 GHz with 0 dBm input signal and consumes dc power of 4.6 mW with a 1.0 V supply. The divide-by-4 circuit operates from 12.4 to 38.4 GHz with 0 dBm input signal and consumes dc power of 7.5 mW with a 1.0 V supply. The core areas of divide-by-2/4 circuits are only <inline-formula> <tex-math notation="LaTeX">$25\times 33\,\,\mu \text{m}^{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$47\times 28\,\,\mu \text{m}^{2}$ </tex-math></inline-formula> respectively.
Multiple Sensitive Values-Oriented Personalized Privacy Preservation Based on Randomized Response

In the case where the private data is not equally important, personalized local privacy preservation based on randomized response (RR) is studied in the collection of sensitive data. So far, the existing RR mechanisms for multiple discrete private sources, which are termed as conventional randomized response (CRR) mechanisms, focus on a universal approach that exerts the same amount of privacy preservation for all sensitive values, without catering for their concrete privacy requirements. An immediate consequence is that they may be offering insufficient protection to a subset of data contributors with relatively higher privacy requirements, while applying excessive privacy control to another subset with relatively lower privacy requirements. Motivated by this, a novel perturbation framework, which is termed as personalized randomized response (PRR) mechanism, is proposed to achieve personalized privacy preservation (Personalized-PP) by designing the statistical privatization mechanism for multiple sensitive values. The proposed PRR technique introduces the weights for different sensitive values according to their sensitivity, and then introduces the weights into the decision of PRR by considering the concrete requirements for privacy, and thus, attains a higher data utility with respect to the quality of statistics while guaranteeing Personalized-PP. The estimate error of the private distribution is used to measure the quality of statistics for the two RR mechanisms. Theoretical study shows that the estimate error of PRR mechanism is smaller than that of the CRR mechanism for a certain same subjective privacy leakage degree. In particular, simulation results reveal the circumstances where CRR mechanism fails to provide Personalized-PP, and then establish the superiority of PRR mechanism.
Vegetation Land Use/Land Cover Extraction From High-Resolution Satellite Images Based on Adaptive Context Inference

In this paper, automatic extraction of multi-context and multi-scale land use/land cover vegetation from high-resolution remote sensing images is tackled, aiming to solve typical challenges in classifying remote sensing images at a pixel level. To solve small inter-class differences and large intra-class differences between the vegetation and background, we introduce a vegetation-feature-sensitive focus perception (FP) module. Considering the intrinsic properties of vegetation objects, we established an adaptive context inference (ACI) model under a supervised setting that includes a context model to represent relationships between a center pixel and its neighbors under semantic constraints, as well as the spatial structures of vegetation features. Comparative experiments on the ZY-3 and Gaofen Image Dataset (GID) datasets demonstrate the effectiveness of our proposed automatic vegetation extraction model against the baseline Deeplab v3+ model. Taking precision, kappa coefficient, mean intersection over union (miou), precision rate, and F1-score as the evaluation indexes, the results showed an improvement in the precision by at least 1.44% and miou by 2.47%, over the baseline Deeplab v3+ model. In addition, the ACI module improved the precision and miou by 2% and 3.88%, and the FP module improved the precision and miou by 1.13% and 1.65%. These results and statistics of these comprehensive experiments illustrated that our adaptive and effective vegetation extraction model could satisfy different requirements of land use/land cover mapping applications.
PMU Assisted State Estimation in Distribution System with PV Penetration

The advancement in the field of renewable energy, PV manufacturing technology and power electronics, fast rising energy demand and need of environment friendly energy resources have forced the power industry to switch over for integration of large scale Distributed Generations. This work discusses the effect of large scale solar PV penetration on IEEE-9 bus system. A PV system has been modelled in ETAP and integrated at suitable locations to analyze the effect of penetration on IEEE-9 bus system. Based on steady state bus voltage bus profile and system losses, optimal PV location and maximum PV integration capacity has been identified. Further, a comparative state estimation study is performed on IEEE-9 bus system to show how phasor measurements can enhance the state estimation results over conventional measurements.
Single-ended Resonant Inverter for Active Balancing System

This paper presents research of a single-ended resonant DC-DC converter used for charging and equalizing the voltages across Li-Ion cells connected in series. This converter is a part of active voltage equalization system. Each cell is galvanically isolated from the power side of the converter by means of high frequency transformer. The transformer has one primary and two secondary windings. The main secondary winding supplies synchronous voltage doubler rectifier (SVDR). By using such a rectifier, the efficiency of the converter is improved. Standard half-bridge driver is used to drive the MOSFET transistors of the synchronous rectifier. The power supply for this half-bridge driver needs to be independent. That's why auxiliary winding is needed in the secondary side of the transformer. In this work is also studied the influence of the additional secondary winding, used to supply the half-bridge driver. Experimental data of working of the single ended resonant DC-DC converter for charging of Li-Ion battery are presented.
An IoT Reconfigurable SoC Platform for Computer Vision Applications

The field of Internet of Things (IoT) and smart sensors has expanded rapidly in various fields of research and industrial applications. The area of IoT robotics has become a critical component in the evolution of Industry 4.0 standard. In this paper, we developed an IoT based reconfigurable System on Chip (SoC) robot that is fast and efficient for computer vision applications. It can be deployed in other IoT robotics applications and achieve its intended function. A Terasic Hexapod Spider Robot (TSR) was used with its DE0-Nano SoC board to implement our IoT robotics system. The TSR was designed to provide a competent computer vision application to recognize different shapes using a machine learning classifier. The data processing for image detection was divided into two parts, the first part involves hardware implementation on the SoC board and to provide real-time interaction of the robot with the surrounding environment. The second part of implementation is based on the cloud processing technique, where further data analysis was performed. The image detection algorithm for the computer vision component was tested and successfully implemented to recognize shapes. The TSR moves or reacts based on the detected image. The Field Programmable Gate Array (FPGA) part is programmed to handle the movement of the robot and the Hard Processor System (HPS) handles the shape recognition, Wi-Fi connectivity, and Bluetooth communication. This design is implemented, tested and can be used in real-time applications in harsh environments where movements of other robots are restricted.
Improved Gamma Correction for Visual SLAM in Low-Light Scenes

Visual SLAM in low-light scenes is important for mobile robots to use in complex industrial scenarios. In this paper, based on the RGB-D SLAM model, an improved Gamma adaptive correction algorithm is added. According to the gray average of the acquired image, the Gamma value is adjusted to achieve adaptive correction. The processed image is substituted into the pose estimation based on the ORB feature, then after key frame extraction and pose graph optimization, and finally the positioning and mapping work based on the RGB-D camera is completed. The experiments show that in different low-light environments, this method can effectively improve the accuracy and stability of image matching compared with other methods. It can be applied to the positioning and mapping of mobile robots in low-light scenes in the future.
A Bin-Picking Benchmark for Systematic Evaluation of Robotic Pick-and-Place Systems

Pick-and-place operations constitute the majority of today's industrial robotic applications. However, comparability and reproducibility of results has remained an issue that delays further advances in this field. Evaluation of manipulation systems can be carried out at different levels, but for the final application the performance of the overall system is the critical one. This paper proposes a benchmarking framework for pick-and-place systems, inspired by a typical task in the logistic domain: picking up fruits and vegetables from a container and placing them in an order bin. The framework uses an easy-to-reproduce environment, a publicly available object set, and guidelines for creating scenarios of different complexity. The proposed benchmark is applied to evaluate the performance of four variants of a robotic system with different end-effectors.
LncPred-IEL: A Long Non-coding RNA Prediction Method using Iterative Ensemble Learning

A large number of transcripts have been generated by the development of high throughput sequencing technologies. Predicting lncRNA from transcripts is a challenging and important task. In this paper, we propose LncPred-IEL, an iterative ensemble learning long non-coding RNA prediction method. LncPred-IEL not only considers features widely used for the lncRNA prediction, but also take into account sequence-derived features used in the RNA sequence classification, so as to make use of diverse information. LncPred-IEL builds base predictors based on different groups of features, and employs a supervised iterative way to combine base predictors and build ensemble models. Our studies demonstrate that supervised iterative way can learn the representations that help to separate lncRNA and protein-coding transcripts, and further improve the performances. Experiments demonstrate that LncPred-IEL outperforms several state-of-the-art methods when evaluated by 10-fold cross-validation. The capability of LncPred-IEL for the cross-species prediction is also tested. As complementary to wet experiments, LncPred-IEL is a useful computational tool for lncRNA prediction.
A Review of Various Techniques for Solar Radiation Estimation

21st century is for the exploration of optimum utilization of Renewable Energy due to its great advantages over Non-renewable Energy. Among all other renewable energy like wind, tidal, etc. solar energy is preferred due to its abundancy and potential. Efficient utilization of Solar Energy is a great area of interest of the researchers. Optimum utilization of solar energy is directly related with amount of solar radiation received. Radiation data recorded by the measuring devices available at meteorological stations are the accurate one. But, due to its high cost and unavailability of meteorological stations everywhere, solar radiation is estimated based on geographical and meteorological parameters by the solar radiation estimation models. Modelling is executed mathematically or soft computing based techniques. Some of them are reviewed in this paper. They are compared and their results are analysed. Among Empirical Models, ANN Models, Fuzzy Models, ANN based Models are found to be most suitable one, due to better correlation and lesser statistical error rate like MBE or RMSE. Although, computational requirements are increased in ANN Models. Also, challenges behind solar radiation modelling and statistical evaluation methodologies are addressed.
Unbiased Noise Estimator for Q-Spectral Subtraction based Speech Enhancement

We present a technique for denoising speech using a modification of spectral subtraction method based on Tsallis statistics, q-SS. This technique works by assuming that noise power spectra are unknown but could be estimate. Since the target and interfering signal are not available, power spectra of both signal must be esimate from the mixed noisy signal. In this paper, we estimate the noise power spectra under MMSE criteria. Implementation of soft decision speech presence probability with fixed prior ensure that the estimator to be unbiased. Our experiment showed that the unbiased estimator signicantly improve q-SS filter performance.
A variation and noise tolerant wide fan-in OR-Logic Domino circuit

This paper presents a new wide fan-in domino OR-gate which reduces variability and power dissipation simultaneously at enhanced noise margin. In this work, some crucial modifications in keeper and evaluation network are done to decrease the process variability and leakage current. Primarily, two extra PMOS transistors are stacked at the top of keeper transistor in source degeneration style to reduce the transconductance which reduce the variability. Second, an effective arrangement is added to control the switching activity of keeper transistor which reduce dynamic power consumption and leakage current of the proposed domino. Further, noise immunity is improved because of efficient charging and discharging of dynamic node. The simulations are done under cadence virtuoso environment for wide fan-in (8, 16, 32 and 64-input) OR-gates using 45nm CMOS technology node at 1V power supply and 1GHz clock frequency respectively. The simulation results indicate 46% and 48% reduction in delay and power variability, respectively along with 50% improvement in noise margin of proposed work as compared to conventional domino gate.