Reviewerly/exHarmony huggingface dataset:
MIMO human handwriting model in stochastic environment

The present paper deals with a multi inputs multi outputs (MIMO) human handwriting model to characterise the pen-tip displacement on the plan from two forearm muscles activities, called electromyography signals (EMG). The proposed model takes into account perturbations and incertitude whish can affect the handwriting process (instability of the writing support, psychical state of the writer, brusque movement, etc.). In this sense, an experimental approach was presented to record the displacements of a pen-tip moving on the plane and two EMG signals during the handwriting motion. The velocity of the writing and ARMAX structure are used to characterise this biological act. Recursive extended least square (RELS) algorithm is used to estimate the parameters of the proposed handwriting model. The obtained structure shows good concordance with the experimental recorded data.
Transfer and Online Reinforcement Learning in STT-MRAM Based Embedded Systems for Autonomous Drones

In this paper we present an algorithm-hardware codesign for camera-based autonomous flight in small drones. We show that the large write-latency and write-energy for nonvolatile memory (NVM) based embedded systems makes them unsuitable for real-time reinforcement learning (RL). We address this by performing transfer learning (TL) on metaenvironments and RL on the last few layers of a deep convolutional network. While the NVM stores the meta-model from TL, an on-die SRAM stores the weights of the last few layers. Thus all the real-time updates via RL are carried out on the SRAM arrays. This provides us with a practical platform with comparable performance as end-to-end RL and 83.4% lower energy per image frame
Research on Quality View and Total Quality Management of Higher Education

It is the realistic requirement of the popularization of higher education to set up the quality concept of development, demand-oriented, student-oriented, diversified and characteristic in Colleges and universities. Taking Hunan Agricultural University as an example, introducing the concept of total quality management into teaching management and implementing the quality management of all staff, all process and all elements is the basic premise to improve the quality of talent cultivation in Colleges and universities. Constructing a comprehensive quality management system of teaching consisting of concept system, organization system, method system, information system, evaluation system and guarantee system is to improve talent cultivation in Colleges and universities. Important guarantee of maintenance quality.
Factors of Corruption Perceptions - Implications toward National Crisis Management -

국민들의 부패에 대한 인식은 국가의 부패수준을 간접적으로 가늠하는 의미를 가질 뿐 아니라 국가 재난상황 해결 과정에서 정책순응 확보가 어렵게 되는 결과를 가져올 수 있다. 이러한 문제의식 하에 본 연구에서는 개인의 부패인식에 영향을 미치는 요인을 탐색하기 위해 개인수준변수와 국가 수준변수를 모두 고려하여 분석하는 다수준 분석기법을 활용하였다. 분석결과 사회전반에 대한 부패수준인식과 공공기관의 부패에 대한 인식 모두 개인적 요인과 국가환경적 요인이 고르게 영향을 미치고 있는 것으로 나타났다. 또한 사회 전반에 대한 부패수준인식과 공공기관에 대한 부패인식의 영향요인이 차별화된다는 결과를 제시하고 있다. 이러한 결과를 통해 부패에 대한 인식 역시 사회적 위험과 위기상황을 구성할 수 있다는 점에서 국민들의 부패인식을 완화시킬 수 있는 정부의 정책적 노력이 필요함을 주장하였다.In this study, I used a multi-level analysis method to analyze the factors affecting individual’s perception of corruption, considering both individual level variables and national level ones. As a result of the analysis, it is found that both perception of social and public corruption level are influencing evenly by individual factors and national environmental factors. It also suggests that the perceived levels of corruption in the society as a whole and the factors influencing the perception of corruption by public institutions are differentiated. These results suggest that the perception of corruption may also constitute social risks and crisis situations and that government policy efforts to mitigate the perception of corruption of the public are needed.
Complementary analysis of use potential and opportunities open on-line courses in the process of studying the discipline “Algorithms and Data Structure”

The article is devoted to the analysis of the potential of the use of mass open online courses in the study of the discipline "Algorithms and data structures" in the process of training engineers-programmers and engineers-teachers in the specialty 122 "Computer Science". and 015.10 Vocational education (computer technology) in higher education institutions. Comparatively massive open online courses for the study of algorithms, in particular, Codecademy specialization "Data Structures and Algorithms", course EdX "Algorithms" and course Prometheus "Development and Analysis of Algorithms." The didactic possibilities of using MOOC Prometheus "Development and analysis of algorithms" in the course of studying the discipline "Algorithms and data structures" as additional material are discussed. The use of MOOC as an additional resource of Prometheus and the course "Development and analysis of algorithms" in the course of studying the discipline "Algorithms and Data Structure" in the training of future engineers-programmers will help form competencies such as: knowledge and skills to use the basic algorithms and data structures, the basic concepts of theoretical algorithms, computational issues, solvability and insolubility of mass problems, concepts of time and spatial complexity of algorithms in solving computational problems. It is concluded that the use of mass open online courses on discipline "Algorithms and data structure". At present, it is better to use MOOC as additional learning resources for students and teachers. Introducing these courses as an alternative or complement to individual parts of the training material has certain perspectives. This will diversify the learning process, deepen the knowledge of discipline and consider the practical implementation of algorithms. Keywords: mass open on-line course, program engineers, algorithms and data structure, resource, algorithms on graphs, data structure, algorithms.
Joining Implications in Formal Contexts and Inductive Learning in a Horn Description Logic

A joining implication is a restricted form of an implication where it is explicitly specified which attributes may occur in the premise and in the conclusion, respectively. A technique for sound and complete axiomatization of joining implications valid in a given formal context is provided. In particular, a canonical base for the joining implications valid in a given formal context is proposed, which enjoys the property of being of minimal cardinality among all such bases. Background knowledge in form of a set of valid joining implications can be incorporated. Furthermore, an application to inductive learning in a Horn description logic is proposed, that is, a procedure for sound and complete axiomatization of $${\mathsf {Horn\text {-}}}\mathcal {M} $$ concept inclusions from a given interpretation is developed. A complexity analysis shows that this procedure runs in deterministic exponential time.
LibGuides: Data Resources for International Environmental Policy (ENVS 377): Help with R/SAS/SPSS/Stata

Guides and introductions to using various programs for statistical analysis. No statistical program will provide you with an emotionally-rewarding relationship, but these guides will help make the couples therapy more productive.
Musikskapande med slumpen som inspiration

This thesis is about getting inspiration from and creating with chance, and how I discovered different ways to create random notes and rhythms for the purpose of writing jazz compositions. I have u ...
The General Sieve Kernel and New Records in Lattice Reduction.

We propose the General Sieve Kernel (G6K, pronounced / Open image in new window e.si.ka/), an abstract stateful machine supporting a wide variety of lattice reduction strategies based on sieving algorithms. Using the basic instruction set of this abstract stateful machine, we first give concise formulations of previous sieving strategies from the literature and then propose new ones. We then also give a light variant of BKZ exploiting the features of our abstract stateful machine. This encapsulates several recent suggestions (Ducas at Eurocrypt 2018; Laarhoven and Mariano at PQCrypto 2018) to move beyond treating sieving as a blackbox SVP oracle and to utilise strong lattice reduction as preprocessing for sieving. Furthermore, we propose new tricks to minimise the sieving computation required for a given reduction quality with mechanisms such as recycling vectors between sieves, on-the-fly lifting and flexible insertions akin to Deep LLL and recent variants of Random Sampling Reduction.
Design Studies to Achieve Energy Optimal Attitude for a Solar-Powered Aircraft

For a solar-powered aircraft, it is vital to harvest maximum solar energy and utilize this in an economical way. For a given flight condition if the position of the sun is taken into consideration and the attitude of the aircraft decided accordingly, more solar energy can be harvested. Changing the attitude of the aircraft changes the lift vector. As a result, the aircraft demands more power to maintain lift as the drag increases (either angle of attack is increased, or the velocity is increased). Hence there will be a trade-off between power required and solar power gained, and this trade-off will lead us towards an optimum roll angle for a level flight depending upon the intensity and position of the sun as well as flight conditions. This paper represents the theoretical representations of energy optimum attitude of a solar-powered aircraft. A controller is designed to achieve this attitude of aircraft with optimal control input. A mathematical model is used to estimate azimuth and elevation and hence the solar irradiance condition for given geographical location. Power required for a level and an unbanked flight is analytically estimated and validated from flight data. With the help of this power required, the power required for banked flight is estimated, that is utilized for optimal bank angle calculation. A case study is done for a flight from city Kanpur to New Delhi in India assuming clear sky and no wind conditions. Irradiance conditions are taken for March 20th and takeoff time is 9 AM, +5:30 GMT. The controller is designed for a low altitude long endurance solar-powered aircraft MARAAL. The MARAAL has been developed in the UAV laboratory, Indian Institute of Technology, Kanpur. This methodology can be adapted for any solar-powered aircraft, for all geographical locations and any season of the year for a clear sky day. Although this methodology is implemented here for level flight, it can be extended for climb and descent as well. For energy optimal attitude the roll angle shouldn't very large as larger roll angle will lead to higher lift requirement resulting more power and therefore more power penalty to maintain lift. As the roll angle is not large, it can be assumed that the aircraft response to control surface input is linear for small deflections. For controller design, the aircraft dynamics is linearized about a level trim condition, and PID controller is used for roll control. It is observed, for a given aircraft and irradiance conditions, either endurance of the flight can be increased or time of flight between two places we can be reduced. While changing the attitude, it is also observed, rolling the aircraft to gain more solar power is only advantageous while sun rays are oblique enough either from left or right of flight path. For subtropical countries like India, this methodology is beneficial for solar-powered flights.
The Effects of flipped learning in a college ESP class and learners’ perceptions: Focusing on students’ vocabulary learning

The purpose of this study is to identify the effectiveness of flipped learning on students’ English vocabulary acquisition in ESP (English for Special...
Steps to the Hilbert Book Model

The Hilbert Book Model is a purely mathematical model of physical reality that starts with a solid foundation
PEMANFAATAN SIM PADA MANAJEMEN HUBUNGAN SEKOLAH DAN MASYARAKAT DI KOTA KEFAMENANU

This descriptive qualitative study has three research focuses, namely (1) What is the school background in organizing a school website at Kefamenanu 1 Public Middle School and Putri St. Junior High School. Xaverius Kefamenanu, (2) How are the steps to use the School website as a Public Relations Facility at Kefamenanu 1 Public Middle School and Putri St. Middle School. Xaverius Kefamenanu and (3) How is the management of SIM management in order to support the function of Husemas activities at Kefamenanu 1 Public Middle School and Putri St. Junior High School. Xaverius Kefamenanu. The fourth finding is that of SMP 1 Kefamenanu and Putri St. Junior High School. Xaverius Kefamenanu has similarities in the form of: (a) attitude towards competition, (b) ideals of using school websites for e-learning media, (c) general content and packaging messages on school websites, (d) the information processing mechanism is not yet implemented , (e) opportunities for workers to attend training. For the differences themselves: (a) availability of human resources with relevant formal education background, (b) availability of supporting facilities and funds, (c) relationship between public relations function and website manager, (d) principal evaluation style, (e) renewal data, (f) variations in the content of information on the website, (g) availability of compensation for managers of school websites, and (h) the role of school principals in administering the website
PERANCANGAN SISTEM INFORMASI MONITORING PRESENSI SISWA (STUDI KASUS: SMK XYZ)

Regarding to the research that had been done by Michael Weitzman and colleagues, children who are frequently or persistently absent from school tend to perform poorly in school and are likely to drop out before graduation from school. The parents action for this kind of problems are highly needed, but nowadays, the implemented system by school as the education provider does not good enough to connect the educational institution with parents faster and efficiently, so that the related problems that occur could not handled earlier. Thus, it is important to create an information system based on technology that could be able to connect parents and education institution faster and efficient. The used methods to support this research are designing system, iterate the related researches, and retrieve data from one of National-Vocational High School in Jakarta. As the system flow, the education provider input the presence attendances of their student on the system interface, system automatically record it and then directly send it to the parents by real time. By building this system, the education provider expected to be able to handle this kind of cheats related with student absence faster, easier, and efficient, so that the unexpected problems could be able to minimize either vanished at all.
Implementation of an 8-bit Dynamic Fixed-Point Convolutional Neural Network for Human Sign Language Recognition on a Xilinx FPGA Board

The goal of this thesis work is to implement a convolutional neural network on an FPGA device with the capability of recognising human sign language. The set of gestures that the neural network can identify has been taken from the Swedish sign language, and it consists of the signs used for representing the letters of the Swedish alphabet (a.k.a. fingerspelling). The motivation driving this project lies in the tremendous interest aroused by neural networks in recent years for its ability for solving complex problems and its capacity to learn by example. More specifically, convolutional neural networks are being extensively used for image classification, and this project aims to design a hardware accelerator to compute the convolutional layers of such type of network topology and test its accuracy and performance when dealing with human sign language. The network topology of choice is Zynqnet, proposed by Gschwend in 2016, which is a topology that has already been implemented successfully on an FPGA platform and it has been trained with the large picture dataset provided by ImageNet, for its popular image recognition contest. In this regard, the aim of this work is not to propose a new neural network topology but to re-use an existent one by introducing some improvements like the utilisation of an 8-bit dynamic fixed-point scheme and challenge it with a different but related task, like human sign language recognition. The methodology followed to carry out a successful hardware implementation has consisted, first, of the installation and setup of a reliable framework used for the training of the neural network. Different frameworks were tried out, like MATLAB or Caffe, but finally, DIGITS from NVIDIA was the more convenient due to its graphical environment and because it provides all the compatibility and drivers needed to run together with the GPU used in this project. Then, an image dataset of more than 13,000 pictures of hand gestures has been built up to grant enough input data for the framework to fine-tune ZynqNet for the new task, i.e. to provide the neural network with the ability to classify the different hand-signs into its corresponding alphabet letter. In parallel, the Register-Transfer Level (RTL) abstraction of the hardware architecture has been generated using a High-Level Synthesis tool chain, in which the algorithmic descriptions are written in C/C++. Finally, the validation of the design has been done by means of co-simulation techniques where the golden data obtained with the C test bench is compared with the output data of the RTL implementation, and all of it within the simulation environment provided by the Vivado Design Suite. As a result, the best-performing obtained solution achieved an accuracy of 80.1\% in the inference test and a frame rate of 6.4 FPS with a clock frequency of 250 MHz. (Less)
Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making

Machine learning (ML) is increasingly being used in image retrieval systems for medical decision making. One application of ML is to retrieve visually similar medical images from past patients (e.g. tissue from biopsies) to reference when making a medical decision with a new patient. However, no algorithm can perfectly capture an expert's ideal notion of similarity for every case: an image that is algorithmically determined to be similar may not be medically relevant to a doctor's specific diagnostic needs. In this paper, we identified the needs of pathologists when searching for similar images retrieved using a deep learning algorithm, and developed tools that empower users to cope with the search algorithm on-the-fly, communicating what types of similarity are most important at different moments in time. In two evaluations with pathologists, we found that these refinement tools increased the diagnostic utility of images found and increased user trust in the algorithm. The tools were preferred over a traditional interface, without a loss in diagnostic accuracy. We also observed that users adopted new strategies when using refinement tools, re-purposing them to test and understand the underlying algorithm and to disambiguate ML errors from their own errors. Taken together, these findings inform future human-ML collaborative systems for expert decision-making.
Library Guides: Digital Research Notebooks: How to use the DRN (UQ users)

Use Digital Research Notebooks, via the UQ Research Data Manager system, to organise, store, curate and share all your project's research or laboratory data including working documents, images, observations, and data in any format.
Early Experience with Open Data from CERN’s Large Hadron Collider

This chapter covers perspectives from the various partners who have worked on the release of large volumes of open research data from the Large Hadron Collider via the CERN Open Data Portal. The early experiences mentioned in the title refer to the launch of the Portal in November 2014 with the release of the first batch of high-level research data collected in 2010 by the CMS Collaboration. This chapter covers the motivation for releasing particle physics data openly as well as the challenges faced in doing so and solutions developed to facilitate these efforts. The authors also touch upon the use cases of the open datasets and the impact the first release has had. Caveat lector: The experiences and figures described in this chapter correspond to the year 2016 when this piece was originally written. The reader may want to consult Nature Physics 15 (2019) 113–119 to learn about later developments.
Mathematical Similarity Models: Do We Need Incomparability to Be Precise?

The expanded version of Tversky’s feature model by Geist, Lengnink and Wille (GLW) provides a compelling representation of similarity by adding an incomparability option. However, previous research has found the GLW model lacking when compared with participants’ responses. We present several new models based on the GLW model and validate them on data from previous similarity experiments. The results indicate that human similarity perception is not well described by a partial order.
Study of the Governing issues for Implementation of Cyber Security system and Digital Morals in Pakistan

The scenario of cyber oppression in Pakistan needs to be studied in detail is ironic, even though cyber victimization includes abuse of fundamental rights and gender harassments, hardly any solid step has been taken to curb this. Most ISPs and networking sites adhere to what we call cyber-crimes and cyber rules and regulations which may give rise to opportunities to experiment with personal freedom, especially freedom of speech and expression and right to privacy. In Pakistan, some of such cyber-crimes may give rise to abuse of fundamental rights guaranteed by our constitution. In Pakistan, matured adults who are internet users must understand that what is offensive in the real space, must be maintained as offensive in the cyber space also. Cyber network has opened the gateway to a global village which may form its own rules and ethics but that in no way should encourage abuse of personal rights and freedom.
AED-Net: An Abnormal Event Detection Network

It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a self-supervised framework, abnormal event detection network (AED-Net), which is composed of PCAnet and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, PCAnet is trained to extract high-level semantics of crowd's situation. Next, kPCA,a one-class classifier, is trained to determine anomaly of the scene. In contrast to some prevailing deep learning methods,the framework is completely self-supervised because it utilizes only video sequences in a normal situation. Experiments of global and local abnormal event detection are carried out on UMN and UCSD datasets, and competitive results with higher EER and AUC compared to other state-of-the-art methods are observed. Furthermore, by adding local response normalization (LRN) layer, we propose an improvement to original AED-Net. And it is proved to perform better by promoting the framework's generalization capacity according to the experiments.