Miracle-dz/UniFAR-FTUs huggingface dataset:
ENHANCING POWER OF SCORE TESTS FOR REGRESSION MODELS VIA FISHER TRANSFORMATION

A simple method is presented to enhance statistical power of score tests for regression models via Fisher transformation (or Fisher’s z-transformation) by exploiting a relationship with the partial correlation coefficient. Simulation studies mimicking marginal association and gene-environment interaction analyses for genome-wide association studies (GWASs) under case-control design demonstrate that the Fisher transformation enhances power of the score tests while maintaining type I error asymptotically. The smaller the sample size is, the more the enhancement is pronounced, at the expense of inflated type I error due to invalidating asymptotic approximation. Accordingly, the proposed method may be applied when sample size is enough for valid asymptotic approximation. An illustration with real GWAS data is also presented. 1. Fisher-transformation of score tests for regression models Suppose that n response variables y = (y1, . . . , yn) T and an n × p design matrix X = (x1, . . . ,xn) T are observed, where xi is a p-dimensional column vector of explanatory variables for subject i ∈ {1, . , n}. Let f(yi | xi) denote the probability distribution of yi conditional on xi for each i. Here, the probability density function of a continuous random variable or the probability mass function of a discrete random variable is referred to as a probability distribution (Dobson, 2002). Assume that a transformed conditional expectation of yi through some differentiable monotone function (i.e. the link function) is written as xi β, in which β is a vector of corresponding p regression coefficients. Then, denote the loglikelihood by l(xi β) = log f(yi | xi) for the ith sample. Throughout, it is assumed that each yi is independently distributed given xi. The above regression framework includes the generalized linear models (McCullagh and Nelder, 1989; Dobson, 2002) and regression with heavy-tailed error distribution (Lange and Sinsheimer, 1993). Suppose thatX is partitioned into two parts as (X1,X2), where X1 is a collection of q (q < p) explanatory variables to be tested for association with y and X2 is a set of p − q covariates to be adjusted for. Correspondingly, let β = (β1 ,β T 2 ) T and xi = (x T 1,i,x T 2,i) T . In this article, X is assumed to be of full column rank. 1.1. Fisher-transformed score test: single parameter case This subsection considers the case of q = 1, and hence the corresponding regression coefficient is written as β1 with a non-bold letter. In genome-wide association study (GWAS) ∗Biostatistics Center, Kurume University, 67 Asahi-machi, Kurume, Fukuoka 830-0011, Japan. Present affiliation is Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan E-mail: uekimrsd@nifty.com
“Stability Control Analysis of the Double Inverted Pendulum using Linear-Quadratic Regulator”

The double inverted pendulum on cart, which is a variation of underactuated mechanical systems has grown popular in the control research community due to its complexity and advantages in various applications. In this paper, the double inverted pendulum on a cart is analyzed using the Euler-Lagrange approach to obtain the equations of motion which are used in Matlab-Simulink to simulate the system. The aim of this study is to use the mathematical model in implementing a linear-quadratic regulator (LQR) control to stabilize the double inverted pendulum in its upright position. A system with such complex dynamics is expected to be coupled, unstable, and highly nonlinear. The simulations demonstrated that the double inverted pendulum on cart can be stabilized using LQR-based control for initial angles less than fifteen degrees from the vertical.
HULA: Dynamic and Scalable Load Balancing Mechanism for Data Plane of SDN

Multi-rooted topologies are used in large-scale networks to provide greater bisectional bandwidth. These topologies efficiently use a higher degree of multipathing, probing, and link utilization. An end-to-end load balancing strategy is required to use the bisection bandwidth effectively. HULA (Hop-by-hop Utilization-aware Load balancing Architecture) monitors congestion to determine the best path to the destination but, needs to be evaluated in terms of scalability. The authors of this paper through artifact research methodologies, stretch the scalability up to 1000 nodes and further evaluate the performance of HULA on software defined network platform over ONOS controller. A detailed investigation on HULA algorithm is analysed and compared with four proficient large-scale load balancing mechanisms including: connection hash, weighted round-robin, Data Plane Devlopment Kit (DPDK) technique, and a Stateless Application-Aware Load-Balancer (SHELL).
Design And Analysis Of Insider Threat Detection And Prediction System Using Machine Learning Techniques

Data is critical for large as well as small organizations as customer trust depends upon the privacy of information maintained. The key tool that every organization uses for assessing resources is the Internet. The use of technology and the Internet comes with a cost. This cost is in the form of cyber-attacks that exist over the Internet. One of the hardest attacks to detect is the insider That occurs from within the organization. The organization must distinguish between the employers as well as the insiders. This paper purposed an optimization-based strategy for the detection of insider threats. Spider monkey optimization is applied to detect the sentiments present within the R4.2 cert data set. This data set has been generated by the university of Carnegie Mellon and is used to detect insider threats. The overall process of insider threat detection Starts with downloading the data set from github. The downloaded files have been compressed so to use them; they must be extracted. A large number of files are contained within the cert dataset. For the proposed work(SMLDA optimization), email and psychometric datasets are shortlisted. After extracting the dataset, pre-processing phase is applied. Within pre-processing, noise in terms of missing values is tackled. This is achieved by rejecting records containing null values within individual cells of the dataset. after pre-processing, dataset features are extracted using the content field of the dataset along with the natural language processing toolbox. Feature selection is performed using the Spyder monkey approach. Selection will be based on the contribution factor calculated with linear discriminant analysis. Using SMO, the highest contribution document built with LDA will be selected. In the end, the polarity of the document is calculated using the TextBlob library. The result of the SMO-driven sentiment analysis (anger, neutral, negative, positive, and sad) is compared with the plain LDA approach. SMO-driven sentiment analysis generates a classification accuracy of 99% and the LDA approach generates a classification accuracy of 90%. Furthermore, it was discovered that negative and sad sentiments most resulted in insider threats.
mmEcho: A mmWave-based Acoustic Eavesdropping Method

Acoustic eavesdropping targeting private or confidential spaces is one of the most severe privacy threats. Soundproof rooms may reduce such risks, but they cannot prevent sophisticated eavesdropping, which has been an emerging research trend in recent years. Researchers have investigated such acoustic eavesdropping attacks via sensor-enabled side-channels. However, such attacks either make unrealistic assumptions or have considerable constraints. This paper introduces mmEcho, an acoustic eavesdropping system that uses a millimeter-wave radio signal to accurately measure the micrometer-level vibration of an object induced by sound waves. Compared with previous works, our eavesdropping method is highly accurate and requires no prior knowledge about the victim. We evaluate the performance of mmEcho under extensive real-world settings and scenarios. Our results show that mmEcho can accurately reconstruct audio from moving sources at various distances, orientations, reverberating objects, sound insulators, spoken languages, and sound levels.
Digitally Programmable CMOS Feedback ASIC for Network of Coupled Electromechanical Oscillators

This paper describes a programmable single-chip feedback ASIC for a network of coupled microelectromechanical systems (MEMS) referenced oscillators in the 0.4-15 MHz range. The chip contains differential low-noise amplifiers (LNAs), variable-gain amplifiers (VGAs) and programmable-gain amplifiers (PGAs) for gain control, second-order active-RC band-pass filters (BPFs) for band selection, all-pass filters (APFs) for phase shifting, an automatic level control (ALC) loop, and output buffers to drive mechanical resonators. The feedback transfer function can be fine-tuned via a three-wire serial peripheral interface (SPI) bus. A compensation path with its own PGAs, programmable attenuator, and APF enables removal of electrical feedthrough within the resonator. The chip has 5 differential input paths with independent gain control, 1–2 of which are used for local feedback (to realize oscillations) while the others accept inputs from other oscillators. The chip has been fabricated in 180 nm CMOS and consumes 4.6 mW at 1.8 V. In initial tests, it is integrated with a 2 MHz quartz resonator $(Q=2000)$ to realize an oscillator with low phase noise (-117 dBc/Hz at 1 kHz offset). Additionally, the chip's ability to synchronize oscillators is validated via a 1:1 injection locking experiment.
A/D Alleviator: Reducing Analog-to-Digital Conversions in Compute-In-Memory with Augmented Analog Accumulation

Compute-in-memory (CIM) has shown great promise in accelerating numerous deep-learning tasks. However, existing analog CIM (ACIM) accelerators often suffer from frequent and energy-intensive analog-to-digital (A/D) conversions, severely limiting their energy efficiency. This paper proposes A/D Alleviator, an energy-efficient augmented analog accumulation data flow to reduce A/D conversions in ACIM accelerators. To make it, switched-capacitor-based multiplication and accumulation circuits are used to connect the bitlines (BLs) of memory crossbar arrays and the final A/D conversion stage. In this way, analog partial sums can be accumulated both spatially across all adjacent BLs that store high-precision weights and temporarily across all input cycles before the final quantization, thereby minimizing the need for explicit A/D conversions. Evaluations demonstrate that A/D Alleviator can improve energy efficiency by 4.9× and 1.9× with a high signal-to-noise ratio, as compared to state-of-the-art ACIM accelerators.
Mobility Management for Novel Cooperative Two-Layer Satellite Network

Only relying on a single satellite cannot ensure the wide coverage, low delay, and high communication quality at the same time. Thus, a novel cooperative two-layer satellite network architecture is proposed in this paper. In the proposed architecture, the centralized unit (CU) control plane that does not require high real-time performance is deployed on the geosynchronous earth orbit satellite, while the CU-user plane and the distributed unit that require low delay and high capacity are deployed on the low earth orbit (LEO) satellite closer to user equipment (UE). The proposed architecture cooperates with the conventional terrestrial communication system to provide large-coverage and high-reliability services for UE. In addition, four mobility management scenarios of the proposed cooperative two-layer satellite network architecture are investigated. And for each scenario, a corresponding handover approach is proposed to ensure the continuity of UE services, and the corresponding signaling overhead is analyzed. Results show that the proposed approach can greatly reduce the signaling overhead compared with the conventional scheme.
A Gain and Bandwidth Individually Tunable ExG Analog Frontend with 516nVrms Noise for Flexible Biomedical Sensors

This paper presents a low-power, low-noise, gain and bandwidth individually tunable analog front-end (AFE) for ExG signals. The proposed three-stage AFE with a bandwidth programmable amplifier enables individually tuning the bandpass cutoff frequencies in the range of 0.4 to 1.9kHz as well as the gain from 40 to 63dB. Designed in a $0.35 \mu\mathrm{m}$ CMOS process with an area of 0.3mm2, the AFE achieves over 120dB CMRR with input referred noise of 516nVrms and a noise efficiency factor of 2.57. The chip consumes $2.5 \mu\mathrm{A}$ at 1.8V supply.
A Study on Mathematics Modeling using Fuzzy Logic and Artificial Neural Network for Medical Decision Making System

The conceptual formulation for diabetes & hepatitis in what seems like a fuzzy & neural network is investigated in this article. Because certain facts in a real-world situation cannot be recognized with precision, the metrics are used as approximate values. The membership functions and set of rules that make up the diabetes framework include fuzzy logic as their beginning premise. Fuzzy logic is just an augmentation of propositional logic which works with uncertainty and ambiguity in systems and offers a powerful mathematical tool to express data in a fashion that mimics naturally occurring human consciousness. ANN is a computational structure with biological inspiration made up of intricately linked adaptable simple processing units that can process information and convey information in a highly comparable manner. Investigators and academics from a diverse range of scientific and engineering disciplines were drawn to the emerging need for responsive intelligent machines because of the integration of Fuzzy Inference algorithms with Artificial Neural Networks. Fuzzy Systems, that can rationalize with imperfect information, are more adept at explaining their choices than artificial neural networks, which struggle to do so. Nevertheless, fuzzy systems are unable to learn the rules they employ to make decisions. Owing to these restrictions, intelligent hybrid systems have been developed to solve the issues using specific methodologies. The much more popular hybrid Neuro-Fuzzy approaches are discussed in this questionnaire survey, along with their benefits and drawbacks.
BGNN-HT: Bidirectional Graph Neural Network for Hardware Trojan Cells Detection at Gate Level

Recently, complex process of production forces Integrated Circuit (IC) to be designed by third-party Electronic Design Automation (EDA) tool or outsourcing, which will create an opportunity for malicious circuits to be inserted into ICs, known as Hardware Trojan (HT). Up to now, there are still challenges in existing researches, such as dependence on the golden model, unclear position of HTs, and difficulty in unknown HT detection. In this paper, a HT detection model called BGNN-HT based on bidirectional graph neural network is proposed, which can detect HT cells by assessing the structure of its surrounding cells at gate level. BGNN-HT can precisely detect HT cells in ICs, and it does not require the golden model or manual feature extraction, which greatly reduces the difficulty of detection and can adapt to unknown HTs. Experiments are conducted on Trust-hub benchmarks including TRIT-TC and TRIT-TS to evaluate our model. The results show that when detecting unknown circuits and HTs, BGNN-HT can reach 96% True Positive Rate (TPR) and 99% True Negative Rate (TNR) in various datasets, and even 99% TPR and TNR in TRIT-TC and TRIT-TS.
Neutrosophic Triangular Fuzzy Number Under Einstein Aggregation Operator with Application for Effective Tap Selection

The proposed method is to combine triangular fuzzy number (TFN) with a Neutrosophic set and define Einstein Aggregation operations to aggregate triangular Neutrosophic fuzzy number and their application to Manufacturing engineering sector. The selection of suitable and economic taps plays an important role in the Manufacturing Engineering sectors. A tap is a thread-cutting tool that is cylindrical or conical in shape and has threads of the required shape on the periphery. The tap cuts or forms the internal thread by combining rotary and axial motion. Also, it is used to make thread for nuts. Leading nut manufacturing industries are struggling to select suitable taps to manufacture their nuts. To address such a problem, this proposed aggregation technique, named the “Neutrosophic Triangular Fuzzy Number Einstein Aggregation Operator,” would select the best taps for manufacturing a mass quantity of nuts with adequate cycle time and tool life.
Fog assisted Visitor Identification Framework with improved Latency and Network Usage

Visitor identification is one of the vital problems of society. The visitor identification process consists of an intelligent security camera that monitors the entrance of the door of a residence, taking pictures of the human when they enter, automated techniques extract the human face from the image and identify them with the help of a database in real-time. However, there is no such existing research that can deal with visitor identification using fog computing. In this paper, a visitor identification framework is proposed that is entirely deployed in the fog computing environment. The proposed prototype is deployed in the Java-based iFogSim simulator. Each simulation uses a different number of cameras for video stream generations. The human object part is trimmed and selected from the raw data for processing. The trimmed data is transferred to the fog node instead of the cloud for processing. This paper deals with how the generated data pass through each module and takes how much amount of time to process the data in distinct modules. Generally, the fog-based framework reduces latency, network usage, and energy consumption. The research finds an improvement in overall latency and network usage of fog computing environments over the cloud environment.
Spike-Predictable Neuron Circuits with Adaptive Threshold for Low-Power SNN Systems

This paper proposes an output spike-predictable comparator based on an adaptive threshold value method (ATVM) for obtaining a low-power neuron circuit. The proposed comparator operates during the predicted time at which the membrane voltage and threshold voltage coincide. This prediction-based power-gating method can help decrease the static power consumption of the comparator. In addition, the A TVM increases the threshold in proportion to the number of output spikes, and thus, the reduced use of the main comparator further decreases the power consumption. With the 28 nm complementary $\mathbf{metal-oxide}$ -semiconductor process, a framework with 144 input layers, 25 hidden layers, and 10 output layers was trained using MATLAB®. Modified National Institute of Standards and Technology (MNIST) classification operations were conducted using 250 synapses and 10 neurons. Using the proposed comparator and ATVM, the total power consumption of the comparator could be reduced by 90.37% with a supply voltage of 1.8 V. The accuracy of the MNIST classification using the A TVM was 95.02 %.
Comparative Study of Amplifiers with High-Frequency and Low-Frequency Chopping

This paper presents a comparative study of high-frequency chopping and low-frequency chopping. Several key parameters of a chopper-stabilized amplifier are analyzed in detail. To verify the effect of chopping frequency, a general-purpose chopper-stabilized amplifier is designed and implemented with a $0.18-\mu \mathrm{m}$ CMOS process. Simulations show that the input biasing current, thermal noise level, and input offset voltage are all related to the chopping frequency.
Simulating Vehicular Mobility: A Comprehensive Framework Using MATLAB SimEvents

As the world becomes increasingly interconnected, vehicles are also becoming an integral part of this networked ecosystem. The study of Vehicular Ad Hoc Networks (VANETs) is critical for ensuring the seamless integration of vehicles into the wider network. To achieve this, it is essential to understand the vehicular mobility and infrastructure and for this purpose, multiple simulation tools have been developed to model vehicle traffic. This study presents the key findings of a basic mobility model that has been developed using Matlab and provides a user-friendly Simulink library to model vehicle traffic. The implementation of this model will help researchers and engineers to analyze and evaluate the performance of VANETs, leading to improvements in their overall efficiency and reliability. The model takes into account various parameters, such as road conditions, traffic density, and vehicle speed to generate realistic simulations of vehicle traffic, making it an ideal tool for various applications in the field of vehicular networking. In addition to its ease of use, the mobility model is highly customizable, allowing users to make changes to the parameters and configurations to meet their specific requirements. This makes it a valuable tool for testing new communication protocols, evaluating the impact of different road designs on traffic flow, and much more. An example and simulation results are presented for a specific case study in Abu Dhabi city.
Gaussian Process for Nonlinear Regression via Memristive Crossbars

Over the last decade, Gaussian processes (GPs) have become popular in the area of machine learning and data analysis for their flexibility and robustness. Despite their attractive formulation, practical use in large-scale problems remains out of reach due to computational complexity. Existing direct computational methods for manipulations involving large-scale $n\times n$ covariance matrices require $O(n^{3})$ calculations. In this work, we present the design and evaluation of a simulated computing platform for exact GP inference, that achieves true model parallelism using memristive crossbars. To achieve a one-shot solution, a linear equation solver and a vector-matrix multiplication solver crossbar configurations are used together, reducing the number of operations from $O(n^{3})$ to $O(n)$. The transistor level op-amps, ADC models for quantization, circuit and interconnect parasitics, together with the finite memristor precision are incorporated into the system simulation. The analog system resulted in %1.51 mean error and %2.93 average variance error in solving a nonlinear regression problem. The proposed method achieved 9× to 144× better energy efficiency compared to TPU and 7× compared to a custom analog linear regression solver.
Sparsity Through Spiking Convolutional Neural Network for Audio Classification at the Edge

Convolutional neural networks (CNNs) have shown to be effective for audio classification. However, deep CNNs can be computationally heavy and unsuitable for edge intelligence as embedded devices are generally constrained by memory and energy requirements. Spiking neural networks (SNNs) offer potential as energy-efficient networks but typically underperform typical deep neural networks in accuracy. This paper proposes a spiking convolutional neural network (SCNN) that exhibits excellent accuracy of above 98 % on a multi-class audio classification task. Accuracy remains high with weight quantization to INT8-precision. Additionally, this paper examines the role of neuron parameters in co-optimizing activation sparsity and accuracy.