Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Efficient Routing Protocols and algorithms for Wireless Sensor Networks – A Survey

Wireless Sensor Networks (WSNs) are an emerging technology for monitoring physical world. The sensor nodes are capable of sensing various types of environmental conditions, have some processing capabilities and ability to communicate the sensed data through wireless communication. Routing algorithms for WSNs are responsible for selecting and maintaining the routes in the network and ensure reliable and effective communication in limited periods. The energy constraint of WSNs make energy saving become the most important objective of various routing algorithms. In this paper, a survey of routing protocols and algorithms used in WSNs is presented with energy efficiency as the main goal.
The Effects of Various Frequency Scaling Algorithm on Embedded Linux CPU Power Consumption

In this paper we have tested various frequency scaling algorithm available in Linux and measured the effects of the different algorithms during operation in an actual system. We have tested all the default scaling algorithms included in Linux in a controlled testing environment and measured the actual effects of the scaling algorithms to the CPU power consumption during operation. In order to retain high accuracy and avoid unnecessary components from affecting the results, we have conducted the measurement directly on the CPU power supply line and logged the power usage in real time
A Method for Forecasting The Pork Price Based on Fluctuation Forecasting and Attention Mechanism

With the continuous development of the economy and improvement of people’s living standards, people’s consumption of meat is getting higher and higher, and China has become the largest pork consumer and producer. The price of pork affects not only the quality of life of the residents but also the development of the pig farming industry to a certain extent. Effective pork price forecasting contributes to social stability and unity, not only to ensure farmers’ income, but also to ensure the relation between supply and demand. This paper synthesizes various indicators related to pork prices in the Chinese pork market, and respectively establishes XGboost, SVM and Random Forest models to make preliminary upward and downward forecasts for the samples. The best forecasting results are used to add price forecasting features, and then the LSTM model optimized by the attention mechanism is used to forecast specific prices. The weekly price data of 201501-202106 from the National Bureau of Statistics used in the experiment compared the forecasting effects of three kinds of price increase and decrease forecasting models and eight kinds of numerical price forecasting models. The results show that the Attention-LSTM method of forecasting pork prices based on up and down forecasts is superior to other methods in pork price forecasting accuracy. RMSE = 1.57, MAE = 1.28, MAPE = 2.83%, all belong to a minimum.
Definition of a Stochastic Charging model of EV Aggregation and Estimation of its Flexibility Amount

The massive diffusion of Electric Vehicles in the next years is one of the expected outcomes of the Energy Transition. However, this scenario will impact on Medium and Low Voltage electric distribution systems, due to the installation of a multitude of charging infrastructures. Coupled with the accommodation of a plethora of small generation plants (largely photovoltaics), it may cause huge network investments. On the other hand, if smartly managed, they can become a valuable resource of flexibility for the correct electric system operation. Indeed, Electric Vehicles can be considered as mobile storage units that, coordinated by an Aggregator, provide network support by modulating their slow charging at home or workplace. The knowledge beforehand of this resource behavior (expected combined charging profile, available flexibility to adapt this profile and related uncertainties) is essential for Distribution System Operators to be confident in its use and tailor a sustainable network development plan. In the paper, such models have been obtained on the base of Monte Carlo simulations, for different habits of Electric Vehicles drivers.
Closer look at the fMRI data analysis pipeline and its application in anesthesia resting state experiment

The main focus of the thesis is the resting state fMRI data analysis, with much emphasis on the anesthesia fMRI experiments. Under this central topic, three separate themes are developed: resting state fMRI data analysis overview, improved denoising techniques, and application to the Dexmedetomidine experiment data. In the first part, important and confusing resting state data analysis steps are explored indepth, focusing on how and why the pipeline is different from that of the task-based fMRI. In the second part, the Principal Component Analysis (PCA) based denoising technique is introduced and compared against the conventional fMRI denoising techniques. Finally, with the PCA denoising technique, the functional connectivity of the brainstem with the brain is assessed for the Dexmedetomidine-induced unconscious subjects. We found that the functional connectivity between the Locus Ceruleus (LC) in the brainstem and the Thalamus & Posterior Cingulate Cortex (PCC) is the neural correlates of the Dexmedetomidine-induced unconsciousness. Thesis Supervisor: Emery N. Brown Title: Edward Hood Taplin Professor of Medical Engineering and of Computational Neuroscience
WordNet Exploration and Visualization in Neo4J - A Tag Cloud Based Approach

In the Big Data era, the visualization of large data sets is becoming an increasingly relevant task due to the great impact that data have from a human perspective. Since visualization is the closer phase to the users within the data life cycles phases, there is no doubt that an effective, efficient and impressive representation of the analyzed data may result as important as the analytic process itself. Starting from previous experiences in importing, querying and visualizing WordNet database within Neo4J and Cytoscape, this work aims at improving the WordNet Graph visualization by exploiting the features and concepts behind tag clouds. The objective of this study is twofold: first, we argue that the proposed visualization style is able to put order in the messy and dense structure of nodes and edges of WordNet, showing as much as possible information from the lexical database and in a clearer way; secondly, we think that the tag cloud approach applied to the synonyms rings reinforces the human cognition in recognizing the different usages of words in a language like English. The ultimate goal of this work is, on the one hand, to facilitate the comprehension of WordNet itself and, on the other hand, to investigate techniques and approache s to get more insights from the visual representation and analytics of large graph databases. Keywords–WordNet; Big Data; Data and Information Visualization; Neo4J; Graph Database; NoSQL.
Experimental verification and benchmark of in-memory principal component analysis by crosspoint arrays of resistive switching memory

In-memory computing (IMC) is gaining momentum as the most promising candidate for the upcoming non-von-Neumann, machine learning-optimized computing paradigm. Its intrinsic parallelism is well-suited to accelerate matrix-vector multiplications (MVM), which prove challenging for traditional architectures and are a fundamental operation in principal component analysis (PCA), one of the most renowned algorithms for data classification. Here, we show an experimental demonstration of a novel, IMC-based PCA algorithm by in-memory power iteration and deflation executed in a 4-kbit array of resistive random-access memory (RRAM). Our algorithm achieves 95.25% classification accuracy on the Wisconsin Diagnostic Breast Cancer dataset, matching closely results of a floating-point machine while providing a $250\times$ improvement in energy efficiency.
An improved multi-targets tracking algorithm based on cubature information particle filter

In the traditional bootstrap particle filter, the state transition density is used as the importance sampling function, which brings some problems such as particle degradation and poor tracking accuracy. In this paper, the posterior probability is used as the importance sampling function and its estimation method is proposed. By means of cubature information filtering and Gating technique, the mean and variance of the importance sampling function are estimated, and the importance sampling function is designed. The improved particle filter method is used to estimate the number of targets and the number of targets in the nonlinear situation. The simulation results show that the proposed algorithm has the advantages of high estimation accuracy and good stability in the nonlinear multi-target tracking scenario.
Equipment Maintenance Assistant Technology Based on Augmented Reality

With the development of augmented reality technology, especially the advent of HOLOLENS launched by Microsoft, the equipment maintenance assistant system based on the augmented reality (AR) technology is becoming more mature and excellence. The system takes advantage of the interactive characteristics of AR to make the maintenance process of equipment become more intuitive, which can improve the maintenance efficiency significantly. In this paper, the system is divided into five modules according to their functions, the development of related technologies in these five modules is introduced, and the trend of current research is concluded, provide the corresponding technical reference for the workers who are engaged in the research of augmented reality.
Arabic Speech Act Recognition Using Bootstrapped Rule Based System

This paper presents a rule-based approach to detect Arabic Speech Act types. Our approach is based on the fact that Arabic language is rich in morphology and that its syntax is inherently rule–based. The importance of Arabic Speech Act type recognition comes from its wide variety of application domains, such as: speech synthesis, speech and emotion recognition and conversational analysis. In this work, a rule-based Expert System has been developed in a bootstrapping manner, to classify an utterance speech act type to one of the sixteen types (interrogation, exclamation, negation, etc.). Our system was tested on a corpus of about 1500 sentences and achieved an accuracy rate of 98.92%. The expert system was used to automatically annotate a corpus (sentence-level annotation) by assigning a tag to each sentence according to its speech act type.
Online Network Attack Detection using Statistical Features

In recent years, several supervised intrusion detection systems have been proposed. However, these methods require labeled data for training and cannot automatically adapt to frequently changing network traffic scenarios. It is also required for data to be updated periodically and requires the model to be retrained to detect new attacks. This emphasizes the need for the development of unsupervised detection systems that can target zero-day attacks. In this paper, we propose an unsupervised solution that relies on detecting attacks in a discrete-time sliding window using the distance between statistical features. The proposed algorithm utilizes generated cluster profiles and estimates the distance between statistical features to trigger an attack event if it exceeds the predefined threshold. The proposed method was applied to CICIDS-2018 dataset and tested for FTP Brute Force and HTTP Distributed Denial of Service attacks.
An Automated Compiler for RISC-V Based DNN Accelerator

Multifarious hardware accelerators are developed for the widely used Deep neural networks (DNN). Nowadays the SoCs composed of a general processor and a coupled accelerator are becoming prevalent. Compared to the specialized DNN accelerator for one specific DNN, this kind of coupled architecture is programmable and supports diverse DNNs. However, for the low-level programming interface of the co-processor-like accelerator and the multi-hierarchy memory structure, programming for the DNN accelerator is not easy work. Meanwhile, there are a couple of tensor compilers that deploy the DNN on various hardware. In this work, we combine the flexibility of the tensor compiler and the high efficiency of the hardware accelerator by proposing an automated compiler that can compile tensor programs and generate high-performance programs for programmable DNN accelerators. Our compiler is based on TVM [1] and target at Rocket Chip Coprocessor (RoCC) [2]. The compiler is flexible and supports many kinds of RISC-V instructions. The programmer can define the hardware constraints in the proposed compiler which makes the generated code more efficient. Our compiler can lower the program with the ping-pong strategy and the generated code can achieve 26% speed up compared to the baseline.
A True Time Delay Element using Cascaded Variable Bandwidth All Pass Filters

A Gm-C based variable bandwidth all pass filter (APF) as a true time delay element is presented in this paper. The APF group delay is varied by changing its bias current to modulate its Gm for different delay settings, which avoids the need for high quality varactors. The proposed method emphasizes on designing low order filters to avoid the usage of large range of capacitance values for design simplicity and reduced power consumption. Identical variable Gm APFs are cascaded to extend the delay range. The proposed design is implemented in TSMC 65 nm technology. Simulation results show that the delay element with 3 stages of third order APF maintains uniform magnitude response with the delayrange-bandwidth product of 0.8 and power/delay-range factor of 0.04 mW/ps upto bandwidth of 1 GHz.
Impact of Through Glass Vias Filling on the Performance of Passive Thermal Cooling in Photonic Packages

In this paper, we examine glass as a 2.5D interposer for next generation photonic packaging, where copper vias have been incorporated in the glass to greatly increase its thermal dissipation characteristics. The thermal performance of 110µm thick glass interposers with and without through glass copper vias (20µm diameter and 100µm pitch) as well as silicon interposer have been investigated. A temperature difference of approximately 38°C has been observed in the maximum temperature of the laser die bonded on glass with and without Through Glass Vias (TGVs). These experimental results have been compared with simulation results, which highlights the via filling factor (FF) effect on the thermal dissipation of glass with TGVs. Simulations have been conducted which show the effects of solder thickness, FF, Via Diameter, Via Pitch, glass thickness and via height on the effective thermal conductivity and maximum surface temperature of the laser die. The laser bonded on all three types of 3mm x 3mm interposers (Glass, Glass with TGVs and Silicon) has many factors affecting the heat dissipation such as thickness of gold layer plated on top of copper vias and via shape which have been explored through simulations. The experimental results act as the reference point to propose optimized design parameters. The surface interaction of thin solder preform for packaging laser dies on glass with copper vias has also been demonstrated through an EDX analysis. This analysis offers a design rule set that can then be implemented in the photonic component packaging on glass with TGV substrates.
Optimized Genetic Programming Applications

Optimized Genetic Programming Applications: Emerging Research and Opportunities is an essential reference source that explores the concept of genetic programming and its role in managing engineering problems. It also examines genetic programming as a supervised machine learning technique, focusing on implementation and application. As a resource that details both the theoretical aspects and implementation of genetic programming, this book is a useful source for academicians, biological engineers, computer programmers, scientists, researchers, and upper-level students seeking the latest research on genetic programming.
XFeed PUF: A Secure and Efficient Delay-based Strong PUF Using Cross-Feed Connections

Physical unclonable functions (PUFs) are hardware security primitives that offer a lightweight security solution for constrained devices in the Internet of Things. The challenges facing PUFs security scaling have so far hindered their wide-scale deployment beyond simple key generation primitives. Although physically unclonable, PUFs are vulnerable to soft modeling attacks. Many PUF security enhancements impose significant implementation overhead, which could be problematic for devices operating in a constrained environment. This work introduces the Cross-Feed (XFeed) PUF, a highly efficient PUF circuit resilient against machine learning attacks while requiring a small circuit implementation area. In the XFeed PUF, arbiters feed intermediate race conditions to adjacent PUF rows to increase the non-linearity of the PUF system. A systematic categorization and benchmarking of the possible interconnection strategies are performed to determine the near-optimal connection schemes for the introduced XFeed PUF. The results showed that the XFeed PUF has superior security efficiency and scalability compared to other arbiter PUF-based enhancements.
Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization

Visualization has shown its ability to produce powerful tools for analyzing, understanding, and communicating data and making it accessible for several different tasks and purposes. Impact of visualization to everyday work and personal lives is demonstrated by many successes stories---such as the increasing prevalence of Tableau, the interactive visualizations produced by the New York Times, or toolkits like VTK/Paraview to name just a few. A large community of casual and professional users are increasingly consuming and producing both interactive and static visualizations.While interactive visualizations move from research into practice at an increasing rate, it still remains an important challenge to find appropriate methods to evaluate their utility and usability. There is a growing need in the community to develop special approaches and metrics for evaluation at all stages of the development life cycle that address specific needs in visualization. This need is reflected, for example, in the increasing number of papers on visualization evaluation---not just at BELIV but also in other venues such as the IEEE VIS conferences and EuroVis. The goal of the BELIV workshop is to continue to provide a dedicated event for discussing visualization evaluation and to spread the word on alternative and novel evaluation methods and methodologies in our community.
A 120-160 GHz 28 mW LNA in 70-nm GaAs mHEMT Technology

In this paper, a D-band low noise amplifier (LNA) is designed at the operation frequency of 140 GHz in a 70-nm GaAs metamorphic high electron mobility transistor (mHEMT) process. The three-stage cascode and one-stage common-source LNA is proposed to achieve wide bandwidth, low noise and high gain. Impedance matching is designed by using T-shape and dual T-shapes networks in the input, output and interstage matching. Passive devices are electromagnetically characterized at such a high frequency, including series capacitors and bypass networks. The LNA exhibits simulated average gain of 26.7 dB, global noise Figure below 6.1 dB, and bandwidth of 40 GHz. The input and output return loss are better than 13 dB and 9 dB, respectively. The power consumption is only 28 mW. The layout of the circuit occupies the area of $1.85 \times 1.4 mm^{\mathbf{2}}$.
Study on Sparsity of Recommender System in University Library

Due to the lack of readers’ rating data, university library recommender system is facing the problem of sparsity of data. This study proposes a general logarithmic transformation model which can convert the reader’s implicit feedback data to the rating, thus alleviating the sparsity to a certain extent. Because logarithmic transformation can use different base, several logarithmic transformation methods are analyzed and compared from different angles. The model-based collaborative filtering is used to compare the recall and accuracy of these methods to make full use of the technical advantage based on matrix factorization to further alleviate the data sparsity problem. The experimental results show that the proposed general logarithmic transformation model can play the role of modifying the data skew, compressing the variable scale, reducing the value of the calculation and so on, and the results of the model are interpretable. Moreover, when the suitable k value is selected, the recommended results of different logarithmic transformation methods can approach the optimal solution in a finite experiment.
ANALYSIS OF KNOWLEDGE MANAGEMENT IN KNOWLEDGE SHARING AND ITS EFFECT ON INNOVATION CAPABILITY IN THE OPERATION DEPARTMENT OF PUSRI-III PLANT PUPUK SRIWIDJAJA PALEMBANG

This study aims to analyze the influence between factors of knowledge management (such as individual factors (knowledge self-efficacy), organizational factors (top management support) and technological factors (Information and Communication TechnologyICT Use) on knowledge sharing processes whether more leads to superior firm innovation capability in the Operation Department of Pusri-III Plant of Pupuk Sriwidjaja Palembang. The research is based on a survey of 123 employees from the Operations Department of Pusri-III Plant Pusri Palembang. No probability sampling is used in sampling methods and data analysis using the Partial Least Square (PLS) are employed in this study. The results of this study shows that knowledge management factors consisting of individual factors (knowledge self-efficacy), organizational factors (top management support) and technological factors (Information and Communication Technology-ICT Use) significantly affect both the knowledge donating and knowledge collecting. The result of this study nevertheless shows that in the organizational factors (top management support) does not significantly affect to the knowledge collecting. And in the technological factors (ICT Use) does not significantly affect to knowledge donating. Furthermore, the result shows that both knowledge donating and knowledge collecting significantly affect the innovation capability of the studied in the Operations Department of Pusri-III Plant Pusri in Palembang. Keyword: Knowledge management, Knowledge sharing, Knowledge donating, Knowledge collecting, Innovation capability.
Klasifikasi Emosi Pada Twitter Menggunakan Bayesian Network

Ringkasan. Bahasa digunakan tidak hanya untuk mengutarakan fakta, akan tetapi juga emosi. Emosi tersebut juga dapat terlihat mulai dari perilaku hingga tulisan yang ditulis olehnya. Analisis emosi di dalam teks sendiri dapat dilakukan pada berbagai media, salah satunya adalah Twitter. Pendeteksian emosi bisa memiliki berbagai macam pengaplikasian salah satunya adalah sebagai bahan pertimbangan keputusan politik dalam suatu pemerintahan. Pada tugas akhir ini, penulis meneliti klasifikasi emosi pada twitter menggunakan Bayesian Network. Metode tersebut digunakan karena kemampuannya dalam memodelkan ketidakpastian dan relasi antar fitur. Hasil penelitian menunjukan bahwa, metode yang digunakan untuk melatih jaringan Bayesian Network belum cukup efektif untuk menghasilkan model terbaik, dengan nilai F1-Score tertinggi adalah 53.71%. Model alternatif berbasis Bayesian Network juga dipelajari. Hasil percobaan menunjukan hasil yang lebih baik dibandingkan Multinomial Naive Bayes dengan kompleksitas inferensi yang tidak jauh berbeda. Nilai F1-Score untuk model Multinomial Naive Bayes adalah 51.49%, sedangkan model alternatif berbasis Bayesian Network adalah 52.14%.