Reviewerly/exHarmony huggingface dataset:
FPGA Based Deep Learning Accelerator for RF Applications: A Design Framework

Recently, interest in the use of deep learning technology for RF applications has increased. However, many of these studies are focused on developing deep learning models for a particular RF application. Therefore this master thesis focuses on the implementation of these kinds of deep learning models by using FPGAs such that these deep learning models can be used in an FPGA-based Software Defined Radio. In this master thesis, a custom FPGA accelerator is designed for CNN models using reusable and configurable building blocks. The accelerator employs a streaming architecture and is fully pipelined, such that it accepts new input data every clock cycle. A key design aspect is that all building blocks in the accelerator are designed to be able to work on a portion of its input data. The implication is that the building blocks can produce an output as soon as enough input data is available. As a result, the work that the building blocks have to performis spread out over time and thememory required for storing data is also reduced. Moreover, the precision of the fixed point parameters and operations is configurable. Therefore there is no limitation of only specifically supporting binary or ternary operations. This accelerator has been tested for the automatic modulation classification problem. The result is an accelerator that can process real-time data at 600MHz and consume fewer FPGA resources than other similar initiatives. In a direct comparison with hls4ml, the designed custom accelerator achieves 2.4 times higher throughput and 2.3 times lower latency for the identical CNN, while also achieving the same accuracy and significantly lower resource utilization. In addition, the custom accelerator is compared to a ternary neural network FPGA accelerator formodulation classification as proposed by Tridgell et al. The custom accelerator uses 3.3 times fewer LUTs, 9 times fewer FFs, 4 times fewer DSPs, and uses no BRAM, while the accelerator proposed by Tridgell et al. uses 48.5% of the available BRAM in an RFSoC FPGA.
Application of Augmented Reality Technology for Human Excretion System as Android-Based Learning Media

The excretion system is a process of removing metabolic waste that is no longer used by the body. These remnants of metabolism in the form of compounds that are toxic (poison) so that if not removed can cause disruption of the function of organs in the body. Organs that play a role in the excretion system are the kidneys, lungs, skin and liver. Kidney is the main component making up the human excretion system, namely urine. The lungs produce residual respiratory processes in the form of gas CO2 (carbon dioxide) and H2O (water vapor). The skin is able to produce residual substances in the form of sweat. The liver produces waste products in the form of bile sap from an overhaul of red blood cells that have been damaged and destroyed in the spleen. Augmented Reality Human Excretion System is an application that can facilitate teachers and class XI MA Tanwiriyyah students in learning about the human excretion system. This application is made based on Android by applying augmented reality technology in its implementation. In this application the teacher and students can see 3D objects and know the process of removing residual substances in the excretory system organs. The design of this application uses the MDLC method and also uses UML diagrams, navigation structures and application interface design.
Four square method for improving students’ writing ability in EF Teens & Kids Kalimalang

Background: Children are easily to speak up their idea directly but do not know how to shift it into proper writing. The children, English learners, need the writing aspect due to fulfill every English aspect especially in their school academic Purpose: This research is going to increase the students’ writing ability through eclectic method. Design and methods: This research has another concept with drawing the eclectic method in different ways. The writer tries to put four square writing methods in order to apply the eclectic method in developing students’ writing ability in EF Kids and Teens Kalimalang. This research is a descriptive quantitative research which will be done by field work. In this research, the writer uses the descriptive and quantitative design to analyze the data. The instrument and variable of this research consist of several parts. They are the directive of teaching method, the directive of the test form, and the result test. Results: The combining of Four Square Writing Method and classroom writing performance as Eclectic Method significantly improves the students’ score. It is clearly showed by the increasing of the mean score. The mean score of the pretest is 68.46% while the mean score of the posttest is 79.13% which increased 10.46%.
Analisis Availability Metrics Webserver Penyedia Website Program Studi Teknik Elektro Fakultas Teknik Universitas Udayana

This research aims to observe and find out the availability value of the system that provides website access of the Electrical Engineering Study Program at the Faculty of Engineering, Udayana University. To find the availability values, data collection was carried out on each network devices by using a network monitoring program called CACTI based on Simple Network Management Protocol. The data were in the form of PING Latency from each network devices consisting of one server, two switches, and two routers. From the PING Latency data, then the value of Mean Time to Failure and Mean Time to Recover can be calculated which is used to determine the availability value of the network device. After all network devices had the availability values, then a calculation was made to determine the availability value of the entire website service provider system of the Electrical Engineering Study Program, Faculty of Engineering, Udayana University which from the result of this research found that Electrical Enginering’s Website has availability values of 99.9%.
Future WARFARE and Artificial Intelligence Applications in the ROK Military: Problems and Alternatives

Purpose: This article explores the most likely impacts of Artificial Intelligence(AI) on the character of future warfare, focuses on how AI will change just about the way we fight in the battlefield, analyzes the current status and problems of AI applications in the ROK military, and then suggests several policy alternatives with regard to future development of the ROK military’s war-fighting capabilities with AI and autonomous systems (i.e., robots). Method: This article uses a literature review method that collects and analyzes data through existing research results or literatures. In order to understand the research trends of AI-driven warfare, professional military papers related to future operational concepts and doctrines are analyzed. In addition, data published by the Army, Navy, and Air Force Headquarters is referenced for the current state and problems of AI application of the ROK military. In particular, the latest data is mainly reflected in order not to miss important issues related to the rapidly changing military application of AI. Results: AI-driven future warfare will have three broad trends, that is, the acceleration of multi-domain battle, the generalization of cognitive-centered operations, and the Acceleration of Human-machine Fusion and increased ambiguity of blurring distinction between combatants and non-combatants. AI applications in the ROK military, however, is somewhat behind advanced countries, and currently stays at the conceptual level and exists mostly on paper. It still does not act to convert AI technology for military use, and not adapt it for operational concepts and acquisition process of weapons systems. For this reason, the ROK military needs to actively pursue various AI technology development strategies that can be used in conducting complex war-fighting within the full spectrum of conflicts. Conclusion: The ROK military needs to discard ineffective legacy weapons systems and prioritize investment in advanced technologies including AI, and streamline its cumbersome weapons acquisition and sustainment process and procedure as a means to win the AI race with other countries. With AI, the ROK military will be ableto create more agile, flexible and lethal war-fighting capabilities in strategic, operational, and tactical dimensions.
Impact of Artificial Intelligence on Humans: A Survey

In the contemporary world artificial intelligence is spurring with highly innovative advancements. From advancing small handheld devices for performing small tasks such as facial recognition to advancing difficult and rigid skills such as car driving. There was a time when hardship is involved in doing typical tasks and muscle power tasks that motivated people to develop new technologies and create new inventions, then the development of Artificial Intelligence i.e. AI takes place with the ambitious goal of creating a human like intelligence in the machines. AI’s major objective is to create expert systems and to implement human intelligence in the machines. This review paper covers the concepts, trends, applications and enhancements that vasts our knowledge about making intelligent computers which work and think in a humanely manner. This survey paper showcases behavioral as well as physical impact of artificial intelligence over humans.
Research Guides: Liaisons: Accessing E-Resources

Librarian Liaisons for Divisions and Pathways Areas of Interest Accessing Electronic Resources
Integrated Manoeuvre Detection and Estimation Using Nonlinear Kalman Filters During Orbit Determination of Satellites

We present our approach to automatically detect and characterize satellite manoeuvres during the orbit de termination (OD) process, based on passive-optical measurements of several geostationary (GEO) satellites from the SMARTnet telescope network [4]. Our technique employs a nonlinear Kalman filter (either an Extended Kalman Filter (EKF) or an Unscented Kalman Filter (UKF)) to detect unexpected deviations from the predicted states of a satellite during the OD process, and subsequently estimates the manoeuvre epoch and respective ∆v-components. An immediate benefit of using nonlinear Kalman filters is that the analysis is performed directly on the angle measurements (right ascension and declination) of an object, which are derived from passive-optical observations with a telescope, as an integral part of the object identification and orbit determination / improvement processes. This mitigates the need to first calculate a new orbit, as is the case for more traditional post-orbit-determination techniques that work with time-series of orbital elements. Once a manoeuvre is detected, its epoch is estimated by means of conjunction analysis. This way we can incorporate the covariance information of the object’s state estimates. More specifically, we propagate two state vectors bracketing the manoeuvre forward and backward in time, respectively. The interval between the two epochs is sampled and the encounter probability is calculated for each step. We then identify the manoeuvre epoch as the time at which the encounter probability takes on its maximum value. Once the manoeuvre epoch is known, the ∆v-components of the manoeuvre can be determined by direct comparison of the two propagated states at that epoch, which also yields information about the type of manoeuvre that has occurred. For the analysis in this study we use passive-optical measurements of two GEO satellites (ASTRA-1KR and ASTRA-1L), for which we had access to operator data including manoeuvre information. Observations were taken with the SMARTnet telescope network station near Sutherland, South Africa. We present our results of using a UKF and an EKF. For each filter, several manoeuvre indicators are employed, namely the squared and normalized residuals, the Log-Likelihood, and the Mahalanobis distance. We demonstrate the ability to determine manoeuvre epochs accurate to within less than a minute and ∆v components to the cm/s-level. Eventually, this automated manoeuvre detection and estimation will be applied to all measurements taken with the SMARTnet telescope network.
A Sensitive-Information Hiding Treatment in Quick-Response Codes Based on Error-Correcting Framework

Quick Responding codes, namely QR codes, are widely used in various communication applications and electronic transactions such as electronic payments and information integrations, since they provide excellent characteristics such as large data capacity, widely coding domain, and stronger error correction ability etc. However, as the QR code is transmitted on public channel and can be scanned by any QR reader, one can obtain the data from the encoded QR code. Simultaneously, the encoding and decoding algorithms are public, the sensitive data such as paying account and password will be revealed to the QR reader, which might incubate the risk of privacy leakage. For solving this problem, this paper proposes a novel approach to protect the private data in QR code. In our method, the secret information is embedded in the random position of a QR code matrix by utilizing an error-correcting mechanism, and only authorized user in possession of required keys will be able to retrieve and recover this secret data embedded and hidden in the QR code. The user without the secret key can only decode public information from the QR code. Although our hiding scheme will decrease the rate of error-correctness of QR decoding, we indicate that the analysis shows that scheme is effect om practical applications. Compared with related schemes, the proposed scheme provides higher security that is less likely to attract the attention of potential attackers.
Majority vs. Approximate Linear Sum and Average-Case Complexity Below NC¹

We develop a general framework that characterizes strong average-case lower bounds against circuit classes 𝒞 contained in NC¹, such as AC⁰[⊕] and ACC⁰. We apply this framework to show: - Generic seed reduction: Pseudorandom generators (PRGs) against 𝒞 of seed length ≤ n -1 and error e(n) = n^{-ω(1)} can be converted into PRGs of sub-polynomial seed length. - Hardness under natural distributions: If 𝖤 (deterministic exponential time) is average-case hard against 𝒞 under some distribution, then 𝖤 is average-case hard against 𝒞 under the uniform distribution. - Equivalence between worst-case and average-case hardness: Worst-case lower bounds against MAJ∘𝒞 for problems in 𝖤 are equivalent to strong average-case lower bounds against 𝒞. This can be seen as a certain converse to the Discriminator Lemma [Hajnal et al., JCSS'93]. These results were not known to hold for circuit classes that do not compute majority. Additionally, we prove that classical and recent approaches to worst-case lower bounds against ACC⁰ via communication lower bounds for NOF multi-party protocols [Hastad and Goldmann, CC'91; Razborov and Wigderson, IPL'93] and Torus polynomials degree lower bounds [Bhrushundi et al., ITCS'19] also imply strong average-case hardness against ACC⁰ under the uniform distribution.Crucial to these results is the use of non-black-box hardness amplification techniques and the interplay between Majority (MAJ) and Approximate Linear Sum (SUM) gates. Roughly speaking, while a MAJ gate outputs 1 when the sum of the m input bits is at least m/2, a SUM gate computes a real-valued bounded weighted sum of the input bits and outputs 1 (resp. 0) if the sum is close to 1 (resp. close to 0), with the promise that one of the two cases always holds. As part of our framework, we explore ideas introduced in [Chen and Ren, STOC'20] to show that, for the purpose of proving lower bounds, a top layer MAJ gate is equivalent to a (weaker) SUM gate. Motivated by this result, we extend the algorithmic method and establish stronger lower bounds against bounded-depth circuits with layers of MAJ and SUM gates. Among them, we prove that: - Lower bound: NQP does not admit fixed quasi-polynomial size MAJ∘SUM∘ACC⁰∘THR circuits. This is the first explicit lower bound against circuits with distinct layers of MAJ, SUM, and THR gates. Consequently, if the aforementioned equivalence between MAJ and SUM as a top gate can be extended to intermediate layers, long sought-after lower bounds against the class THR∘THR of depth-2 polynomial-size threshold circuits would follow.
Development of the AI-based Experiential Education Program Connected to the SW Education

이 연구의 목적은 초등교육과정의 SW 교육과 연계한 인공지능 기반의 체험교육 프로그램을 개발하는 것이다. 이를 위하여 인공지능의 다양한 용어와 교육 관련 내용을 살펴보고, 인공지능 관련 내용을 SW 교육과 연계하여 적용할 수 있는 분야를 탐색하였다. 현재 초등교육 과정에서 사용하는 실과 교과서의 로봇 및 SW (관련) 단원에서 인공지능과 연계할 수 있는 내용을 분석하였다. 이를 기초로 현재 초등 실과 교육과정에서 로봇 교육 및 SW 교육과 연계할 수 있는 엔트리 SW의 다양한 인공지능의 관련 내용을 파악하였고, 이를 기반으로 초등 실과교육에서 로봇 교육과 SW 교육과 연계한 인공지능 기반 체험 교육프로그램을 5개 과정으로 선정하였다. 5개 과정의 체험교육 프로그램은 ㈜네오피아와 함께 개발한 자율주행 자동차의 체험교육 프로그램, 생체 인식 체험교육 프로그램, IoT의 생활 체험교육 프로그램, AI 플레이어 체험교육 프로그램, 머신러닝 체험교육 프로그램이다. 이러한 체험 교육프로그램을 로봇 교육 및 SW 교육과 연계한 인공지능 체험교육 프로그램을 구안하여 학습지도안 등과 함께 제시하였고, 다양한 웹 버전의 제공자와 함께 운영될 수 있게 하였다. 이러한 자료는 교육대학의 예비교사와 현직 교사들에게 필요한 인공지능에 대한 기본 지식과 이해에 필요한 내용과 체험교육용 자료로 제공할 수 있을 것이다. 또한 이러한 자료는 초등현장에서 필요한 SW 교육과 연계한 인공지능 관련 교육을 위한 지침으로써 인공지능 교육의 현장 적용이 가능한 수업자료와 체험교육 프로그램으로 제공될 수 있을 것이다. 개발된 프로그램을 예비교사와 현장에 적용하여 인공지능 관련 체험교육을 진행하는데 발생하는 문제점을 수정하고 보완하여 더 완성도 높은 프로그램이 되기를 기대한다.The purpose of this study is to develop an AI-based experiential education program that is connected to the SW education of the elementary school curriculum. To do so, various terms and education-related contents of AI were examined, and the fields that could be applied by linking AI-related contents with SW education were explored. The contents that can be linked to AI in the robot unit and SW unit of the practical arts textbook currently used in the elementary education field were analyzed. Using this knowledge as a foundation, the contents of various AIs provided that can be used by utilizing the entry SW linked to robot and SW education in the current experiential education course were compared. Based on this, an AI-based experiential education program that is linked to robot education and SW education was selected into five courses. The five experiential education programs are as follows: an autonomous vehicle experiential education program developed with Neopia, a biometrics experiential education program, an Internet of Things (IoT), life experiential education program, an AI player experiential education program, and a machine learning experiential education program. These developed programs will be able to provide the necessary basic knowledge and understanding of AI as well as experiential education and practice to the teachers and pre-service teachers of the University of Education. In addition, as a guideline for AI-related education in connection with SW required in the elementary field, it can be provided as instructional materials and experiential education programs that can be applied to the field of AI education. In the future, it is expected that the developed program will be applied to pre-service teachers and on-site to correct and supplement the problems that occur in AI-related experiential education.
How to Select Participants in My Research Study?: Sampling in Quasi-Experiment Research

Sampling Method selection is very important in a research study to answer the research question and to generalize the results to target population. This article presents the steps of selecting participants in my research study in investigating the effect of contextual teaching and learning on reading motivation and reading comprehension at state Islamic institute of Kerinci. The cluster sampling is used as a method of selecting the participants of the study. In this article, the basic elements related to the sampling of the participants of the research study is discussed, including sample frame, sampling, type of sampling, sampling method according to the type of study methodology, and data analysis of my research study in selecting participants. Therefore, the purpose of this article is to provide an overview of the importance of adequate sampling method in a research, especially in English education research and to provide the readers with useful tips and resources for selecting a representative sample.
SMART HOME ANTI-THEFT DETECTION USING IOT AND AI

This paper addresses the problem involved incurrent socio-economic life where in automation using the techniques of IOT and Artificial intelligence (AI) take up the responsibility of providing security to our homes by providing two way authentication to the users.The proposed system verifies the person identity by verifying the person’s face as a key and also asks for inputting the right password to access the door.The owner can even monitor and control the system remotely with the help of IOT. Artificial Intelligence provides framework for real-time decisions in terms of face detection and recognition. The model also alerts the owner by sending an email containing video clip of the intruder. If the intruder tries to breach the security by breaking the lock or damaging the system, an alarm is raised to intimate the neighbors about the activity.Keywords: Artificial Intelligence, IOT, Raspberry pi, Face Recognition Viola-Jones algorithm , LBP( Local binary pattern) .
Marketplace Apps for e-Nelayan Kota Samarahan

Fishermen are one of the most essential part of a society where they supply fresh seafood to be consumed by other part of society such as customer. Most of these fishermen, mainly in Kota Samarahan still practices traditional trading method where they sell their fish at place such as fish market, door to door and others. As we know, our country, Malaysia has reach modernisation in parallel with other developed countries. Almost every member of the society has widely used mobile devices such as a smartphone. The most dominant part of mobiledevices is a mobile application. So, this project will introduce a mobile app which allows the fishermen to sell their fish directly to the consumer. The fishermen will be able to advertise their fish or other types of seafood products in the mobile app.
Analisis Data Ketinggian Permukaan Air Sungai Mahakam Daerah Kutai Kartanegara Tahun 2010-2016 Menggunakan Model Autoregressive Integrated Moving Average (ARIMA) Dengan Efek Outlier

Measurement of water level is useful as a guide for flood events in an area. As a result of global warming it is predicted that rainfall will increase and the water level will be high, so that the chances of flooding will increase. The method often used in forecasting is the method of Autoregressive Integrated Moving Average (ARIMA). ARIMA is one of the time series forecasting methods that has been studied in depth by Box and Jenkins. ARIMA's basic concepts include,identification of models, parameter estimation, diagnostic checks and forecasting. Forecasting results with the ARIMA method are inaccurate, on data that contains outliers. The weakness of the ARIMA method can be overcome using the ARIMA method with outlier detection. The type of outlier detection in this study is additive outlier (AO). The purpose of this study was to determine the ARIMA forecasting model with an outlier effect on the average water level data of the Mahakam River in the Kutai Kartanegara Region in front of the Tenggarong Museum Building from January 2010 - December 2016. The results showed that the best forecasting model was the river Mahakam Kutai Kartanegara Region is ARIMA ([12], 1,0) with the addition of 4 outlier effects and measure of goodness is AIC with a value of 250,0776.