Miracle-dz/UniFAR-FTUs huggingface dataset:
Balance control of a biped robot on a rotating platform based on efficient reinforcement learning

In this work, we combined the model based reinforcement learning ( MBRL ) and model free reinforcement learning ( MFRL ) to stabilize a biped robot ( NAO robot ) on a rotating platform, where the angular velocity of the platform is unknown for the proposed learning algorithm and treated as the external disturbance. Nonparametric Gaussian processes normally require a large number of training data points to deal with the discontinuity of the estimated model. Although some improved method such as probabilistic inference for learning control ( PILCO ) does not require an explicit global model as the actions are obtained by directly searching the policy space, the overfitting and lack of model complexity may still result in a large deviation between the prediction and the real system. Besides, none of these approaches consider the data error and measurement noise during the training process and test process, respectively. We propose a hierarchical Gaussian processes ( GP ) models, containing two layers of independent GPs, where the physically continuous probability transition model of the robot is obtained. Due to the physically continuous estimation, the algorithm overcomes the overfitting problem with a guaranteed model complexity, and the number of training data is also reduced. The policy for any given initial state is generated automatically by minimizing the expected cost according to the predefined cost function and the obtained probability distribution of the state. Furthermore, a novel Q ( λ ) based MFRL method scheme is employed to improve the policy. Simulation results show that the proposed RL algorithm is able to balance NAO robot on a rotating platform, and it is capable of adapting to the platform with varying angular velocity.
Design and Optimization of an Area-efficient SOT-MRAM

Spin orbit torque magnetic random access memory (SOT-MRAM) has attracted numerous research interests since it promises to overcome the write speed and energy bottlenecks of the conventional STT-MRAM. However, the cell density of SOT-MRAM is constrained due to more access transistors. In this work, we present a NAND-Like architecture for SOT-MRAM with a single transistor and several diodes, as well as a novel adaptive array design based on the proposed cell structure. Compared with the standard SOTMRAM, the proposed SOT-MRAM achieves significant improvement in the cell density by sharing transistors, meanwhile attains a comparable write speed. The overhead of write energy can be compensated by a well-designed write policy.
BOOTABLE: Bioinformatics Benchmark Tool Suite

The interest in analyzing biological data on a large scale has grown over the last years. Bioinformatic applications play an important role when it comes to the analysis of huge amounts of data. Due to the large amount of biological data and/or large problem spaces a considerable amount of computing resources is required to answer the raised research questions. In order to estimate which underlying hardware might be the most suitable for the bioinformatic tools applied, a well-defined benchmark suite is required. Such a benchmark suite can get useful in the case of purchasing hardware and even further for larger projects with the goal to establish a bioinformatics compute infrastructure. With this paper we present BOOTABLE, our bioinformatic benchmark suite. BOOTABLE currently contains six popular and widely used bioinformatic applications representing a broad spectrum of usage characteristics. It further includes an automated installation procedure and all required datasets. BOOTABLE is available from our Github repository (https://github.com/MaximilianHanussek/BOOTABLE) in various formats.
Risks and Audit Objectives for IT Outsourcing

In the recent years, as a result of globalization, internet and IT progress, the outsourcing of IT services has seen an exponential growth. As a result more and more companies decide to outsource, partially or totally, their IT services. Nevertheless, the outsourcing process exposes both clients and service providers to a series of risks that can seriously affect their activities. Managing these risks by improving the quality and efficiency of internal control has made the ITO audit a necessary component for all the companies involved in this process. The goal of this paper is to identify analyze and map the influence areas of ITO risks in order to suggest a series of objectives for ITO audit.
A 0.75-V 32-MHz 181-µW SOTB-65nm Floating-point Twiddle Factor Using Adaptive CORDIC

In this paper, a Silicon On Thin Buried-oxide (SOTB) implementation of the 32-bit floating-point Twiddle Factor (TF) is presented. The architecture was developed based on the adaptive COordinate Rotation DIgital Computer (CORDIC). The CORDIC method is a well-known approach for approximating the complex-number multiplication, also known as TF in Fast Fourier Transform (FFT) designs. The SOTB-65nm TF core layout has the size area of 86.7K-µm2. The measurement results showed that at the best crossing-point of the 0.75-V power supply (VDD), the chip could run at the maximum operating frequency (FMax) of 32-MHz and consumed 181-µW power. At the sleep-mode, the leakage power dropped about 258.6× to 0.7-µW at the 0.75-V VDD.
An Adaptive Ensemble Machine Learning Model for Intrusion Detection

In recent years, advanced threat attacks are increasing, but the traditional network intrusion detection system based on feature filtering has some drawbacks which make it difficult to find new attacks in time. This paper takes NSL-KDD data set as the research object, analyses the latest progress and existing problems in the field of intrusion detection technology, and proposes an adaptive ensemble learning model. By adjusting the proportion of training data and setting up multiple decision trees, we construct a MultiTree algorithm. In order to improve the overall detection effect, we choose several base classifiers, including decision tree, random forest, kNN, DNN, and design an ensemble adaptive voting algorithm. We use NSL-KDD Test+ to verify our approach, the accuracy of the MultiTree algorithm is 84.2%, while the final accuracy of the adaptive voting algorithm reaches 85.2%. Compared with other research papers, it is proved that our ensemble model effectively improves detection accuracy. In addition, through the analysis of data, it is found that the quality of data features is an important factor to determine the detection effect. In the future, we should optimize the feature selection and preprocessing of intrusion detection data to achieve better results.
Predicting the Popularity of Online News using Social Features

Consuming news via social media is an integral part of our lives. Various news agencies use social media as a medium to spread their content. Popularity prediction of news before publication is a challenging task because it depends on a very large user base. Popularity of news on social platform can be represented using number of likes, shares. We have used number of likes as a popularity measure. In this paper, we first find out features on social platform which can affect popularity of an article. These features and content metadata are fed to various machine learning models. These models are used to predict whether an article is going to be popular or not. Tree based models achieve best results for prediction. These models also show that hashtags, usermentions and other social features are important factors which affect popularity of news.
PEACOCK: A Map-Based Multitype Infectious Disease Outbreak Information System

A map-based infectious disease outbreak information system, called PEACOCK, that provides three types of necessary infectious disease outbreak information is presented. The system first collects the infectious disease outbreak statistics from the government agencies and displays the number of infected people and infection indices on the map. Then, it crawls online news articles for each infectious disease and displays the number of mentions of each disease on the map. Users can also search for news articles regarding the disease. Finally, it retrieves the portal search query data and plots the graphs of the trends. It divides the risk into three levels (i.e., normal, caution, and danger) and visualizes them using different colors on the map. Users can access infectious disease outbreak information accurately and quickly using the system. As the system visualizes the information using both a map and various types of graphs, users can check the information at a glance. This system is in live at http://www.epidemic. co.kr/map.
Deep Mimo Detection Using ADMM Unfolding

This paper presents a low-complexity deep neural network (DNN)based multiple-input-multiple-output (MIMO) detector for the BPSK and QPSK constellation cases. We employ deep unfolding, whose idea is to take insight from the structure of an iterative optimization algorithm and attempt to learn a better iterative algorithm. The structure of the network is obtained from an iterative algorithm arising from the application of ADMM to the maximum-likelihood MIMO detection problem. The number of parameters to be learnt in this new design is less than that of DetNet, a recently proposed DNN-based MIMO detector. Our numerical experiments illustrate that the new method outperforms DetNet and several existing MIMO detectors in the large-scale MIMO case. In particular, we show that for a 160×160 MIMO system, our DNN design, with 40 layers, can attain nearly optimal bit-error rate performance.
A Hybrid Active/Passive Wrist Approach for Increasing Virtual Fixture Stiffness in Comanipulated Robotic Minimally Invasive Surgery

In minimally invasive surgery (MIS), the incision point acts as a fulcrum about which the surgical instrument pivots. Most robotic MIS systems foresee procedures to carefully align the robot with the incision in the patient. Such procedures disrupt the normal workflow. This hampers the clinical translation of such assistive technology. In contrast, backdrivable, wristed (BW) robots, especially when operated as comanipulation devices, have a minimal impact on the traditional surgical workflow in MIS, as they are readily equipped with algorithms that automate fulcrum point estimation. To improve safety, accuracy and/or task completion time, virtual fixtures can be implemented at the distal instrument tip, using an assistive BW system. This letter formalizes the problems related to such distal virtual fixtures. It then develops a hybrid active/passive wrist control strategy to improve the performance of virtual fixtures with BW systems while minimizing stability issues. Aside from an analytical example, experimental validation is added to show that the new algorithm increases the achievable stiffness of distal virtual fixtures.
IoT Driven with Big Data Analytics and Block Chain Application Scenarios

The Big Data, Block chain and the Internet of Things (IoT) are the key names in the technology development of these eras. There are undoubtedly differences between the three technologies, but there are also firm relationships that increase the system operations, practicability, and adoptability. As the domain Block chain and the Internet of Things (IoT) are just infant domain and, still it’s undergoing more and further development events, and the Internet of Things(IoT) is producing a larger set of the data and adopted across the different industries such as healthcare, smart cities development, retail, banking and public administration and real time application forecasting, the well-analyzed data is tremendously valuable the Big Data Analytics will suffice this needs, and on the other side the Block chain will provide more secured transaction of the data, and it provides contracts, and more other applications plus integration and Big Data Analytics can be integrated on top of it. In this paper will focus on the incorporation of the all three technologies or domain, such as Big Data Analytics, Block chain and the Internet of Things (IoT). We concluded that all the three technology Big Data Analytics, Block chain and the Internet of Things (IoT) would play a vital role in resolving each other constraints. Based on our analysis and case studies it will help for future work research.
A Probabilistic Approach for Maximizing Travel Journey WiFi Coverage Using Mobile Crowdsourced Services

A public transport journey planning service often yields multiple alternative journeys plans to get from a source to a destination. In addition to journey preferences, such as connecting time and walking distance, passengers can select the optimal plan based on mobile crowdsourced WiFi coverage available along the journey. This requires discovering mobile crowdsourced WiFi services available along the journey path. However, this task is challenging due to the uncertain availability of discovered services. To enhance the availability of WiFi coverage, we propose a probabilistic approach to discover groups of available crowdsourced WiFi services along with the journey segments. We first analyze the log of their trajectories and use a density estimation technique to discover reference spots representing the frequently visited locations. Then, a joint discrete Fourier transform and autocorrelation analysis are applied to mine the periods of the presence of moving crowdsourced services with respect to each reference spot. A low-complexity cluster analysis based on Jensen–Shannon divergence is then used to mine the periodic movement behaviors of services during the identified periods. Finally, mobile crowdsourced WiFi services that are simultaneously available at intersecting reference spots are grouped. The QoS of discovered groups is computed in terms of availability confidence, failover capacity, aggregated bandwidth capacity, and coverage. Additionally, we propose an algorithm to determine the best public transport journey plan offering based on the QoS of available WiFi service groups along the journey path. We conduct a comprehensive comparative study to validate the effectiveness of the proposed framework.
A Conceptual Framework of Cloud-Based Mobile-Retail Application for Textile Cyberpreneurs

Cloud-based m-retail application is commonly used by internet retailers and cyberpreneurs for conducting online business operations. The emergence of third-party e-commerce platforms along with incentives from Malaysian government has easily enabled the utilization of mobile marketplace app among both Malaysian sellers and customers. As m-retail has just started to gain attention in Malaysia, more works must be done to overcome the academic research limitations in terms of technological and non-technological perspectives. Meanwhile, textile and apparel products are among the most popular to be sold via the internet in addition to the growing number of related businesses. The uses of latest technology such as cloud-based mobile-retail application may help business owners to sustain for business survival. In designing a mobile applications for specific users, a thorough investigation must be performed. A conceptual framework of the apps might be helpful in providing a general overview of required elements for developing a successful mobile-retail applications. Extensive literature reviews, qualitative analysis, interviews and questionnaires have been conducted in designing and validating the proposed framework. The analysis results shows mean values from the survey are > 3.5 for the core element variables in this research. Thus, the statistical analysis validation results indicates the importance of the element as the fundamental component for the proposed framework. Therefore, this paper intends to propose a conceptual framework of cloud-based mobile-retail application specifically to be used by textile cyberpreneurs which might be helpful for related key players.
A 56-Gb/s PAM4 Variable Gain Amplifier in 40-nm CMOS Technology

This work presents a 56-Gb/s PAM4 variable gain amplifier (VGA) in 40-nm CMOS process. The proposed implementation achieves a wide bandwidth of 29 GHz by utilizing an inverter-based transimpedance amplifier (TIA) stage and inductive peaking technology. In order to effectively conduct the 7.2-dB variable gain tuning range, a 5-bit control network is applied. The total power consumption is 7.4-mW from a 1-V supply.
Piezo-PUF: Physical Unclonable Functions for Vacuum-Packaged, Piezoelectric MEMS

The cyber and hardware security of constrained edge nodes have become a paramount requirement for the ultimate success of the Internet of Things (IoT). Such security needs to be guaranteed at minimum impact on energy efficiency and autonomy. One important approach to IoT energy autonmoy is the use of energy harvesters. In this paper, we select MEMS piezolelectric energy harvesters to propose a physically unclonable function (Piezo-PUF) which relies on the manufacturing uncertainties of the MEMS fabrication process. The major result of this paper is that several of the MEMS manufacturing uncertainties cannot be easily cloned, which leads to various mechanical and electrical features that can be leveraged for generating cryptographic keys. Thorough security analysis of Piezo-PUF has been conducted using wafer-level simulations and measurements. The security analysis confirms that Piezo-PUF qualifies as weak PUF for secure key generation and authentication.
When RFID Meets Deep Learning: Exploring Cognitive Intelligence for Activity Identification

Cognitive communication and computing have seen deep penetration in many networking areas in the past decades. With the recent advances in big data analysis and deep learning, we have seen great potential toward exploring cognitive intelligence for a wide range of applications. A notable example therein is human activity recognition, especially through RFID. Existing RFID activity identification solutions are mostly designed for static or slowly moving targets, rendering them far from satisfactory. More importantly, we observe that they suffer serious performance degradation in typical indoor environments with multipath interference. In this article, we argue that the recent advance of deep learning brings new cognitive intelligence for human activity identification. We first review the literature and research challenges of multipath effects in indoor environments. Then we introduce an advanced RFID activity identification framework, DeepTag, which uses a deep-learning-based approach for activity identification in multipath-rich environments. DeepTag gathers massive phase information from multiple tags, and preprocesses them to extract such key features as pseudospectrum and periodogram. We feed the preprocessed signal power and angle information into a deep learning architecture that combines a convolutional neural network and long short-term memory (LSTM) network. Our DeepTag framework can well adapt to both tag-attached and tag-free activity identification scenarios. Our extensive experiments further demonstrate its superiority in activity identification in multipath-rich environments.
Dehazing of Natural Images using Non-Linear Wavelet Filter

The outdoor images are captured in bad weather conditions to yield poor visibility conditions, where noise is present due to low contrast of an image, color differentiation which is a major problem for most computer vision applications. Haze is caused when an image is captured by the camera between the image is strike with the haze because of attenuation and air-light. Attenuation reduces the contrast level of the image and air-light increases the brightness of the image. Haze removal techniques recover back the contrast of the scene. The existing de-hazing method on an atmospheric scattering model and therefore it has the common limitation that is this model is valid only when the atmosphere is homogeneous. This paper reveals to estimate the threshold value to reduce the noise in outdoor images by using wavelet thresholding method. By calculating the value of threshold and types of wavelet functions, this is an important issue in de-noising based wavelet approach. It is possible to perform non-linear de-noising by thresholding the wavelet coefficients. In this paper, the result shows the de-noising algorithm based on the wavelet de-hazing method in terms of increasing the visual quality of the image, this proposed algorithm not only exclude the noise but also improve the PSNR to get better image quality.
Output-Capacitor-Less LDO with High PSR

A low-dropout regulator (LDO) with high power supply ripple rejection (PSR) and capacitor-free is presented in this paper. The LDO proposes a PSR enhancement module based on the analysis of the power supply noise transmission path. It uses the Q-reduction circuit to reduce the required on-chip capacitor and the load on the minimum required output. The proposed LDO is implemented in 110nm CMOS process. The simulation results indicate that the total on-chip capacitance required for the designed circuit is 6. 5p. The LDO has a phase margin of more than 6$0^{o}$ in the range of 50uA to 50mA. The optimum PSR of the LDO at low frequency is -109dB. The LDO achieves line and load regulation of 65$\mu$V/V and 0.396$\mu$V/mA respectively.
Comparative Performance Analysis of Linear Precoding in Downlink Multi-user MIMO

This paper investigates the comparative performance of linear precoding schemes. The linear precoding schemes are including block diagonalization (BD), zero forcing (ZF), and maximum ratio transmission (MRT) in downlink multi-user MIMO. This work delivers the performance of linear precoding in term of achievable sum rate and bit error rate (BER) with a variation of the signal to noise ratio (SNR) and the number of transmitter-receiver antennas. We suppose that the transmitters have a complete channel state information. The results show that the MRT precoding yields better bit error rate than both the BD and ZF precoding schemes. However, the ZF precoding generates better achievable sum rate than the MRT precoding. In the other side, the MRT precoding also outperforms when the number of active users is bigger than Kcross while the number of active users is less than Kcross the ZF precoding is still dominant.
A Low-Computational Complexity System for EEG Signals Compression and Classification

Brain-computer interface (BCI) has emerged as a promising technology to explore physiological activities and functional states of the human brain. This paper presents an Electroencephalogram (EEG) signals processing system, which consists of the compression process, reconstruction process and the classification process. The EEG signals is reconstructed with 5.318±0.08 root mean square error and 0.881±0.06 structural similarity index in short CPU time. The experiments on the two classes motor imagery EEG signals reaches up to 92% accuracy with the proposed feature extraction method. Compared with prior works, the proposed work is able to achieve better performance with high fidelity and low computational complexity.