Miracle-dz/UniFAR-FTUs huggingface dataset:
A 0.003-mm2 440fsRMS-Jitter and −64dBc-Reference-Spur Ring-VCO-Based Type-I PLL Using a Current-Reuse Sampling Phase Detector in 28-nm CMOS

This paper presents a linear current-reuse sampling phase detector for a single-loop type-I phase-locked loop (PLL) to simultaneously achieve a wide loop bandwidth and low control voltage ripple, resulting in low RMS jitter and reference spur, while minimizing the chip area by avoiding an explicit loop filter. Fabricated in 28-nm CMOS, the PLL prototype measures an integrated jitter of 440 fsRMS, and a spur level of −63.9 dBc at 3.296 GHz. It draws 3.3 mW at a 0.9-V supply and scores a jitter-power figure-of-merit (FoM) of −241.9 dB. With a 103-MHz reference input, a bandwidth of ~20 MHz aids suppressing significantly the ring VCO’s phase noise (PN), leading to an in-band PN of −116 dBc/Hz at 1-MHz offset. The die size is 0.003 mm2.
Towards Robust Android Malware Detection Models using Adversarial Learning

Malware analysis and detection is an endless competitive battle between malware designers and the anti-malware community. Recently researchers have proposed state-of-the-art malware detection models built using machine learning and deep learning which are necessary to detect advanced metamorphic malware. But these malware detection models are susceptible to adversarial attacks. Therefore we propose to design robust Android malware detection models against adversarial attacks using reinforcement learning. We propose the Single Policy and Multi Policy based Evasion Attacks for Perfect Knowledge and Limited Knowledge scenario respectively against many malware detection models built using four different sets of classifiers (bagging, boosting and deep neural network). The motivation is to identify the adversarial vulnerability in Android malware detection models and then propose defence against them.
CHALLENGING TASKS – 13 YEARS ZINGY-MATHEMATICS-OLYMPIAD

The Zingy-Math-Olympiad [ZMO] was held over 13 years with a total of 2102 third graders (~49.43% girls) in the context of university seminars on mathematical giftedness. The almost achieved gender parity is ensured by the requirement that one girl and one boy per participating class should be sent to the ZMO as their mathematics representatives. The tasks, which vary in their degree of difficulty, are divided into 7 rubrics: R1 simple arithmetical introductory tasks, R2 & R3 more demanding arithmetical skills tasks, R4 combinatorically solvable tasks, R5 word problems, R6 pattern and geometric figure tasks, R7 final tasks. The students’ performance in the seminar consists in developing task sets and the evaluation with final scoring of the task completions, all based on research and discussions on relevant literature. In accordance with previous research, there is a tendency for the participating boys to be (slightly) superior to the participating girls, especially in the peak performance. In addition to an analysis of the tasks and their processing, a key question for the future is which cognitive abilities are influencing factors for successful mathematical problem solving in order to be able to address them specifically in mathematics lessons in a further step. So far, there are only meager approaches to this. *) Keywords: functional-/predicative-logical thinking, gender differences, mathematical giftedness, primary school, problem solving
Pruning Deep Networks using Partial Least Squares

Modern pattern recognition methods are based on convolutional networks since they are able to learn complex patterns that beneﬁt the classiﬁcation. However, convolutional networks are computationally expensive and require a considerable amount of memory, which limits their deployment on low-power and resource-constrained systems. To handle these problems, recent approaches have proposed pruning strategies that ﬁnd and remove unimportant neurons (i.e., ﬁlters) in these networks. Despite achieving remarkable results, existing pruning approaches are ineffective since the accuracy of the original network is degraded. In this work, we propose a novel approach to efﬁciently remove ﬁlters from convolutional networks. Our approach estimates the ﬁlter importance based on its relationship with the class label on a low-dimensional space. This relationship is computed using Partial Least Squares (PLS) and Variable Importance in Projection (VIP). Our method is able to reduce up to 67% of the ﬂoating point operations (FLOPs) without penalizing the network accuracy. With a negligible drop in accuracy, we can reduce up to 90% of FLOPs. Additionally, sometimes the method is even able to improve the accuracy compared to original, unpruned, network. We show that employing PLS+VIP as the criterion for detecting the ﬁlters to be removed is better than recent feature selection techniques, which have been employed by state-of-the-art pruning methods. Finally, we show that the proposed method achieves the highest FLOPs reduction and the smallest drop in accuracy when compared to state-of-the-art pruning approaches.
Topological insulator Bi2Te3 fi lms synthesized by metal organic chemical vapor deposition

Topological insulator (TI) materials such as Bi2Te3 and Bi2Se3 have attracted strong recent interests. Large scale, high quality TI thin films are important for developing TI-based device applications. In this work, structural and electronic properties of Bi2Te3 thin films deposited by metal organic chemical vapor deposition (MOCVD) on GaAs (001) substrates were characterized via X-ray diffraction (XRD), Raman spectroscopy, angle-resolved photoemission spectroscopy (ARPES), and electronic transport measurements. The characteristic topological surface states (SS) with a single Dirac cone have been clearly revealed in the electronic band structure measured by ARPES, confirming the TI nature of the MOCVD Bi2Te3 films. Resistivity and Hall effect measurements have demonstrated relatively high bulk carrier mobility of ~350 cm^2/Vs at 300K and ~7,400 cm^2/Vs at 15 K. We have also measured the Seebeck coefficient of the films. Our demonstration of high quality topological insulator films grown by a simple and scalable method is of interests for both fundamental research and practical applications of thermoelectric and TI materials.
Antidepressant and nootropic activity of aqueous extract of Bougainvillea glabra

The aim of this analysis was to assess antidepressant & nootropic capacity of Bougainvillea Glabra aqueous extract (BGAE). Forced swim test, tail suspension test, & tetrabenazine mediated catalepsy & ptosis models were used to assess BGAE's antidepressant efficacy in mice at doses of 250 & 500 mg/kg orally. Morris water maze with elevated plus maze (EPM) was used to test nootropic behaviour. Normal medications for antidepressant & nootropic function were imipramine (25 mg/kg) & piracetam (300 mg/kg), respectively. Antioxidant assays such as DPPH & TBARS were used to validate antidepressant & nootropic function. When comparison to car, pre-treatment with BGAE resulted in substantial dose-dependent decrease in immobility time in both FST & TST. (P0.005) Tetrabenazine-induced catalepsy & ptosis were both decreased dramatically. Furthermore, in MWM & EPM, BGAE demonstrated dose-dependent cognitive enhancing behaviour. In DPPH assay, BGAE had IC50 of 17.39 g/ ml, & in TBARS assay, it had IC50 of 398.71 gm/ ml. BGAE has antidepressant & nootropic activities that are equivalent to imipramine & piracetam at doses of 250 & 500 mg/kg, respectively, which may be due to its effect on neurotransmitters & antioxidant function.
An Information Theoretic Framework to Predict Lithological Properties from Multiple Seismic Attributes

This paper represents multiple seismic attributes such as seismic amplitude, frequency and impedance using information filtering and machine learning to improve the prediction of sand fractions. The conjugate-gradient learning algorithm with artificial neural network (ANN) has been used to model the sand fraction. The data available from well logs have very high-resolution than the low-resolution seismic attributes. Therefore, Fourier transform (FT) as a regularization scheme has been proposed to shape the high-resolution sand fraction data for machine learning. Then input data sets have been segregated into validation, testing and training sets. The results used to check different network structures and function performances. Once the test passes with an acceptable performance, the validation phase follows. Then prediction model is tested against unseen data. The network gives satisfactory performance in the stage validation is used to predict lithological properties from seismic volume. Finally a 3-D spatial filtering is implemented using post-processing scheme for smoothing the sand fraction in the volume. This gives the predictions of lithological properties for reservoir characterization.
Performance Analysis of Applying Load Balancing Strategies on Different SDN Environments

Software-Defined Network (SDN) is considered a breakthrough to the global network. It plays an important role in performance improvement and network optimization. SDN is a new mechanism for managing and designing networks rather than the current traditional network system which does not afford more services and higher data rates; therefore, we analyze the effect of applying load balancing techniques and its importance in different SDN environments. In this paper, we propose a dynamic server load balancing technique in SDN architecture. Hence, we implement a server Connection-based load balancing technique and evaluate its performance with a static Round-robin and Random-based in both mininet emulation environment and Raspberry Pi OpenFlow-enabled switch using Ryu OpenFlow controller. The performance of the proposed algorithm is compared with Round-robin and random distribution of clients' requests. The results show that the proposed technique achieves more reliability and higher resource utilization than the Round-robin and Random-based load balancing strategies. In addition, the proposed scheme exhibits more scalability and low-cost characteristics.
Corporate Governance and Quality of Financial Reporting of Listed Firms: Evidence from Saudi Arabia

This paper examines the impact of applying corporate governance (CG) mechanisms on the quality of financial reports of listed financial firms in Saudi Arabia. The focus is on how quality of financial reporting (QFR) is affected by board independence, board size, audit committee independence, audit committee size, and ownership structure of listed financial firms. The study uses financial statistics from the annual reports and accounts of a sample of 18 financial firms listed in the Saudi Stock Exchange over the period 2012 to 2017. QFR is measured using discretionary accruals of the modified Jones model (Dechow et al., 1995). The findings revealed a statistically positive significant impact of board independence, audit committee independence and ownership structure on QFR. On the other hand, results showed a statistically insignificant impact of the size of the board and audit committee on QFR. Results support arguments of the stakeholder theory that having high institutional shares in listed firms motivates internal stakeholders to disclose high quality financial reports. The Saudi economy is being opened for large foreign investments in line with its Vision 2030; as a result, potential foreign investors would want to know more about the Saudi CG practice in relation to board governance and quality of financial reports of the listed firms on the SSE. Findings from this study contribute to the literature on the application of CG in Saudi Arabia by encouraging policy makers and regulators to improve the CG code and ensure compliance by the listed firms.
Secondary school students’ perception according to their learning style of a mathematics Flipped Classroom

The Flipped Classroom (FC) has been gaining prominence over the past few years. Many articles have shown that the FC increases the interaction between students and between the teacher and students, allowing different learning rhythms, facilitating access to materials, increasing collaboration between students and enhancing active learning. The present research looks at how students perceive the FC based on their learning style. The research was carried out with a sample consisting of two groups of third-year compulsory secondary education students (37 students in total) in the subject of mathematics. A quantitative methodology was used. At the beginning of the term, all students were given a questionnaire (CHAEA) to determine their learning style. At the end of term they were given a questionnaire (Driscoll) on their perception of the FC . The results show that students with a theoretical style of learning value the uses of the FC more positively and perceive greater interaction with peers and the teacher. However, students with a reflective learning style rate the FC lowest and have a the worse perception of the different key characteristics of the FC. In all statements, most students answered that they agree or very agree. These results are in line with those obtained by other authors in other contexts. This study brings certain nuances to the existing literature on the perception of the FC based on the students’ learning style.
Beware of the ‘Bad Guys’: Economic Inequality, Perceived Competition, and Social Vigilance

Previous studies have shown that economic inequality is associated with macrosocial outcomes and psychological processes. However, the role of economic inequality in social cognition is poorly understood. In this article, we propose that perceived economic inequality increases social vigilance. Three experiments were conducted to examine this idea by manipulating the perception of economic inequality. The results revealed that participants in high-inequality environments exhibited higher levels of social vigilance than in low-inequality environments. Furthermore, perceived competition could mediate the link between economic inequality and social vigilance (Experiments 2–3). The findings expand the literature regarding the effect of economic inequality on social cognition and help people better understand the consequences of economic inequality.
Lessons learned developing a massive open online course in implementation research in infectious diseases of poverty in low-and middle-income countries

This study uses a case study approach to examine the development of a massive open online course (MOOC) on intervention and implementation research in infectious diseases of poverty for learners in low- and middleincome countries (LMICs). Implementation research (IR) seeks to understand and address barriers to effective implementation of health interventions, strategies, and policies. In recent years, IR has attracted increased interest, and corresponding demand for training, however, current training opportunities are not easily accessible to learners in LMICs. In 2017, the MOOC was introduced to a diverse range of learners to enhance access to training materials and has been offered yearly since. Findings are based on the experiences of the MOOC working group which included developers and facilitators, and on interpretations of data such as forum discussion activity and Facebook posts. The use of material from local contexts and in local languages, and professional facilitation of discussion forums was identified by the working group to be key considerations in developing the MOOC. Other findings include the importance of using clear instructions and preparing discussion questions to stimulate learner engagement. These findings add to the limited knowledge of MOOCs developed for LMICs and are of value to others developing professional development MOOCs in LMIC health contexts.
The Relationship Between Psychological Well-Being, Discipline and Online Gaming Addiction Among High School Students in Central Java

This study aimed to explore the relationship between online game addiction, psychological well-being and selfdiscipline. The research methodology is a quantitative approach with a correlational descriptive type. The samples used were 1046 high school students in Central Java. The Instruments used were the Psychological Well-Being Scale, Neo-PI Self-Discipline Scale, and Game Online Addiction Scale. Data were analysed using descriptive statistical techniques and multiple regression. The results showed psychological well-being and self-discipline simultaneously had a negative correlation with online game addiction. The analysis result also shows the percentage effect of psychological well-being and self-discipline on online game addiction. Then it is known psychological well-being has a negative effect on online game addiction. The same results were also shown for self-discipline variables which negatively affected online game addiction. An interesting profile has emerged from the results of this study, suggesting that certain psychological characteristics such as psychological well-being and selfdiscipline may predispose some individuals to become addicted to online games. This result will provide basic information that can contribute to developing a prevention program for students who are addicted to online games. Keywords—psychologichal well-being, discipline, game online
Nucleation and growth dynamics of vapor bubbles

Mirko Gallo, Francesco Magaletti, Davide Cocco and Carlo Massimo Casciola1† 1 Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza Università di Roma, Via Eudossiana 18, 00184 Roma Italy 2 School of Computing, Engineering and Mathematics, University of Brighton, Lewes Road, Brighton, UK 3 Dipartimento di Scienze di Base e Applicate per l’Ingegneria, Sapienza Università di Roma, via A. Scarpa 14, 00161 Roma, Italy
SelectiveTaint: Efficient Data Flow Tracking With Static Binary Rewriting

Taint analysis has been widely used in many security applications such as exploit detection, information ﬂow tracking, malware analysis, and protocol reverse engineering. State-of-the-art taint analysis tools are usually built atop dynamic binary instrumentation, which instruments at every possible instruction, and rely on runtime information to decide whether a particular instruction involves taint or not, thereby usually having high performance overhead. This paper presents S ELECTIVE - T AINT , an efﬁcient selective taint analysis framework for binary executables. The key idea is to selectively instrument the instructions involving taint analysis using static binary rewriting instead of dynamic binary instrumentation. At a high level, S ELECTIVE T AINT statically scans taint sources of interest in the binary code, leverages value set analysis to conservatively determine whether an instruction operand needs to be tainted or not, and then selectively taints the instructions of interest. We have implemented S ELECTIVE T AINT and evaluated it with a set of binary programs including 16 coreutils (focusing on ﬁle I/O) and ﬁve network daemon programs (focusing on network I/O) such as nginx web server. Our evaluation results show that the binaries statically instrumented by S E - LECTIVE T AINT has superior performance compared to the state-of-the-art dynamic taint analysis frameworks (e.g., 1.7x faster than that of libdft ).
Preparation of low-dimensional carbon material-based metal nanocomposites using a polarizable organic/water interface

Single wall carbon nanotubes (SWCNTs) and liquid-phase exfoliated multilayer graphene (MLG) material thin films were assembled at a polarizable organic/water interface. A simple, spontaneous route to functionalize/decorate the interfacial assembly of MLG and SWCNTs with noble metal nanoparticles, at the interface between two immiscible electrolyte solutions (ITIES), is reported. The formation of MLG- or SWCNT-based metal nanocomposites was confirmed using various microscopic (scanning electron, transmission electron, and atomic force microscopy) and several spectroscopic (energy dispersive x-ray and Raman spectroscopy) techniques. Increasing the interfacial deposition time of the metal nanoparticles on the assembled low-dimensional carbon material increased the amount of the metal particles/structures, resulting in greater coverage of the MLG or SWCNTs with metal nanoparticles. This low-cost and convenient solution chemistry based impregnation method can serve as a means to prepare nanoscale carbonaceous material-based metal nanocomposites for their potential exploitation as electro-active materials, e.g. , new generation catalysts or electrode materials.
A Unity-norm Preserving Quaternion Extended Kalman Filter

: Preserving the quaternion unity norm has significant importance when combined with the extended Kalman filter (EKF) for state estimation of nonlinear systems perturbed by noise using noisy measurements. Although this unity challenge is solved numerically on the estimated quaternion, it is normalized out of the EKF algorithm. Projecting this constraint on the derivation of the EKF algorithm to preserve the quaternion unity norm is the purpose of this paper. The quaternion unity norm constraint is derived in two forms and projected on the EKF gain derivation. Then this gain is used to update the quaternion vector keeping its unity norm. The results show that the unity norm is preserved using the proposed constrained quaternion EKF (CQEKF) even though sudden changes occur.
Assessments of genetic variations of freshwater fishes Malopterus electricus, Synodontis shall, Labeo niloticus and Lates niloticus

Fishes are the most abundant group of vertebrates with over 33,000 organisms living in aquatic environments and performing a wide range of biological functions in ecosystems (Helfman et al., 2009; Martinez et al., 2018). Heterozygosity (i.e., the proportion of diploid individuals with 2 separate alleles at a single locus) and the total number of alleles present in a population are also significant (Frankham et al., 2014). Following screened blunt snout bream with 88 SRAP primer combinations, of which 13 primer pairs produced stable and reproducible amplification patterns, Ji et al. (2014) reported significant genetic differentiation in the studied five species. Oreochromis niloticus collected from freshwater rivers, streams and canals were investigated using RAPD-PCR against Five random decamer primers and showed varying gene polymorphism (Mahboob et al., 2019). ARTICLE INFO ABSTRACT Article History: Received: April 4, 2021 Accepted: April 13, 2021 Online: April 15, 2021 _______________
Power Sharing and Control by Droop Controller with Advanced Filter Design: A Case Study in Lock-down Periods

In the lock-down period, the islanding mode of operation with droop controllers has several advantages in the alternating current grid. This study focuses on an improvised droop controller. It consists of an advanced filtering segment embedded with a conventional droop controller, which overcomes the drawback of droop controllers of the non-handling of non-linear loads in an ordinary situation. A selective harmonic elimination technique in grid-connected mode and lock-down mode and an advanced filter embedded with droop control are used so that the proposed controller can also work as an Active Harmonic Filter (AHF). The simulation results in different cases show that the proposed controller can control the active and reactive power in the lock-down period as well as the harmonics in the normal period up to an extent.