Reviewerly/exHarmony huggingface dataset:
Analysis of key technologies and typical problems in computer assisted proficiency testing and Measurement

Proficiency testing and on-site assessments are the two most commonly used ability evaluation techniques. This article explains the necessity of participating in proficiency testing activities, discusses the key technologies for the implementation of proficiency testing, and summarizes the typical problems found in the implementation of the actual proficiency testing. The reasons for the common unsatisfactory results of proficiency testing are also analyzed. The suggestions and solutions are put forward.
Similarity based Deep Neural Networks

Deep neural networks (DNNs) have recently attracted attention in various areas.Their hierarchical architecture is used to model complex nonlinear relationships in high-dimensional data.DNNs generally require large numbers of data to train millions of parameters.However, the training of a DNN with a small number of high-dimensional data can result in an overfitting.To alleviate this problem, we propose a similarity-based DNN that can effectively reduce the dimensionality of the data.The proposed method utilizes a kernel function to calculate pairwise similarities of observations as input, and the nonlinearity based on the similarities is then explored using a DNN.Experiment results show that the proposed method performs effectively regardless of the dataset used, implying that it can be applied as an alternative when learning a small number of high-dimensional data.
Advances on Chip-Based QKD in Bristol Quantum Network

Quantum Key Distribution (QKD) provides a provably secure approach to share keys used to encrypt secret information by transmitting single photons [1] . This is one of the first commercial quantum technologies available, and Bristol is heavily involved in UK Quantum Communications Hub to develop a chip-based approach and integrate them in our 5G-UK network testbed.
1-Attempt Subfield-Based Parallel Thinning

A widely used skeletonization technique is thinning, which is an iterative layer-by-layer erosion in a topology preserving way. In the conventional implementation of thinning algorithms, the deletability of all border pixels in the actual picture is to be investigated. That is why we introduced the concept of 1-attempt thinning. In the case of a 1-attempt algorithm, if a border pixel is not deletable in an iteration step, it cannot be deleted in the remaining thinning phases. This paper presents a computationally efficient implementation scheme for 1-attempt 2-subfield parallel thinning; proves that a 2-subfield parallel thinning algorithm (acting on the conventional 2D square grid) is 1-attempt; shows that 1-attempt property is useful and algorithms fulfilling this property can be implemented with a remarkable speed up.
LibGuides: Diverse Reads: The Why, How, and Where: Book Finders

A resource for youth services librarians and teachers on diverse read selections and programming.
Television industry and its role in the new media landscape under the system of digital economy

Thesis (Ph.D. (Communication Arts and Innovation))--National Institute of Development Administration, 2020
Understanding your audience

Effective documentation requires you to have empathy for your users, which you can build with user research and its tools: interviews, developer surveys, and reviewing support issues. This chapter describes how to conduct research and condense your research into user personas, user stories, and user journey maps that you can refer to later. Empathize with your users by trying out your own software and documenting your experience in a friction log. Notice the places in your product where you can help your users through documentation or through product improvements.
Cyber crime in Uganda, an analysis of the Legal Framework.

Research report submitted to the School of Law in Partial Fulfillment of the Requirement for the Award of Bachelors Degree in Laws of Kampala International University.
Language effects on source credibility and persuasion among bilingual consumers

espanolEsta investigacion examina los efectos del lenguaje e identificacion con la cultura estadounidense sobre la credibilidad de la fuente de informacion y persuasion entre hispanos bilingues. Los resultados revelaron que participantes que no se identifican con la cultura estadounidense perciben que la fuente tiene mas experiencia, y las actitudes son mas positivas hacia las marcas anunciadas en ingles comparadas con marcas anunciadas en espanol. La experiencia de la fuente y las actitudes no variaron entre participantes que se identifican altamente con la cultura estadounidense. Las fuentes de informacion que mezclan los dos idiomas fueron percibidas menos expertas y las actitudes fueron menos favorables en comparacion con fuentes que usan ingles o espanol. No se reportan efectos significativos para la confiabilidad y el atractivo de la fuente. EnglishThis research investigates the interactive effects of language and the identification with American culture on source credibility and, subsequently, on persuasion among Hispanics. The results revealed that low American identifiers perceived a higher source expertise and greater positive attitudes towards brands advertised in English compared to brands advertised in Spanish. On the other hand, source expertise and attitudes towards the brand did not vary because of the language used among high American identifiers. Sources that communicate with code-switching elicited lower expertise and brand attitudes compared to sources that use English or Spanish. The study results found no significant effects for source trustworthiness and attractiveness.
Activity 1: Stakeholder map

Cybersecurity needs a change in communication. It is time to show the world that cybersecurity is an exciting and diverse field to work in. Cybersecurity is not only about hackers and technical gobbledygook. It is a diverse field of work with a lot of collaboration with other disciplines. Over the years, security professionals have tried different awareness strategies to promote their work and to improve the knowledge of their audience but without much success. Communication problems are holding back advances in in the field. Visual Communication for Cybersecurity explores the possibilities of visual communication as a tool to improve the communication about cybersecurity and to better connect with non-experts. Visual communication is useful to explain complex topics and to solve complex problems. Visual tools are easy to share through social media and have the possibility to reach a wide audience. When applied strategically, visual communication can contribute to a people-centric approach to security, where employees are encouraged to actively engage in security activities rather than simply complying with the policies. Cybersecurity education does not usually include communication theory or creative skills. Many experts think that it is not part of their job and is best left to the communication department or they think that they lack any creative talent. This book introduces communication theories and models, gives practical tips, and shows many examples. The book can support students in cybersecurity education and professionals searching for alternatives to bullet-point presentations and textual reports. On top of that, if this book succeeds in inspiring the reader to start creating visuals, it may also give the reader the pleasure of seeing new possibilities and improving their performance. The book is divided into different parts for readers with different interests. There is no need to read the book from cover to cover; the chapters are organized thematically. Readers that are interested in how to apply communication theory to cybersecurity will enjoy the chapters about learning, the context in which communication takes place, and how people are persuaded. Readers that are looking for inspiration and examples of how to use visuals in their daily tasks go straight to the third section of the book. The last section is a workbook that will help the reader to take the first steps towards using visual communication at work.
Blockchain-Enabled Midday Meal Monitoring System

The Midday meal is a program run by the Government of India. It is planned to strengthen the nutritional level of school-age children. The program encourages attendance in the school nationwide. The government of India reviews the mission on the Midday meal program, comprising members from the central government, state government, UNICEF, and the Supreme Court commissioner. But the management of the existing program has some limitations. It is based on the traditional way of keeping records of the students which can be manipulated. Delay in payment to various schools enrolled with this program is also seen over time. This paper proposes a model to strengthen the management of the existing Midday meal program using the smart contract in the Ethereum environment of blockchain. Decentralization, transparency, and immutability are the three prime features that have helped in overcoming the loopholes of the existing Midday meal management program. Our main purpose is to remove the intermediaries and to bring transparency and trust in the existing system using blockchain. The smart contract here shows how to improve authenticity, efficiency and bring transparency among schools and concerned government authorities in the whole Midday meal management program.
GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids

Multiple Object Tracking is an important task for autonomous vehicles. However, it gets difficult to track objects when it is hard to detect them due to occlusion or distance to the sensors. We propose a method, GridTrack, to overcome this difficulty. We fuse a dynamic occupancy grid map (DOGMa) with an object detector. DOGMa is obtained by applying a Bayesian filter on raw sensor data. This improves the tracking of the partially observed / unobserved objects with the help of the Bayesian filter on raw data, which has a powerful prediction capability. We develop a network to track the objects on the grid and fuse information from previous detections in this network. The experiments show that the multi-object tracking accuracy is high with the usage of the proposed method.
The creative machine

In chapter ten, I revisit copyright standards. I further outline the scholarly originality-creativity debate and argue that two critical changes ought to be implemented (not necessarily linked to AI). One is considering a higher originality and creativity-based standard; the second is bridging the originality gap between jurisdictions. This chapter also considers the possibility of a creative machine.
An optimal segmentation method for processing medical image to detect the brain tumor

In the field of medical physics, detection of brain tumor from computed tomography (CT) or magnetic resonance (MRI) scans is a difficult task due to complexity of the brain hence it is one of the top priority goals of many recent researches. In this article, we describe a new method that combines four different steps including smoothing, Sobel edge detection, connected component, and finally region growing algorithms for locating and extracting the various lesions in the brain. The computational algorithm of the proposed method was implemented using Insight Toolkit (ITK). The analysis results indicate that the proposed method automatically and efficiently detected the tumor region from the CT or MRI image of the brain. It is very clear for physicians to separate the abnormal from the normal surrounding tissue to get a real identification of related areas; improving quality and accuracy of diagnosis, which would help to increase success possibility by early detection of tumor as well as reducing surgical planning time. This is an important step in correctly calculating the dose in radiation therapy later.
PENERAPAN SISTEM PEMBELAJARAN DALAM JARINGAN UNTUK MENINGKATKAN KUALITAS BELAJAR DALAM MENGHASILKAN SISWA 4.0

Schools are educational institutions that are formal, non-formal and informal, where their establishment is carried out by the state or private sector with the aim of providing teaching, managing and educating students through guidance provided by educators or teachers. utilizing information technology for learning systems during the Covid-19 pandemic is not yet available, only using Google Classroom at SMK Negeri 7 Bandar Lampung. Not yet optimal knowledge of teachers and students in using information and communication technology in learning systems in the network. The ability of teachers to utilize e-learning-based learning systems is still not optimal. The ability of students to use e-learning-based learning systems is not yet optimal. the learning process before the Covid-19 pandemic still uses conventional methods, namely teachers are still conducting face-to-face learning with students, problems occur when the teacher is unable to attend, the class taught by the teacher will be empty, so students cannot get the desired learning material, this is clearly detrimental to students because the learning material will be explained late by the teacher. training on the use of this application, teachers in using information and communication technology in making learning media more interesting and better, and will facilitate the learning process for students
CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification

Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of models in prior art is evaluated with standard precision, recall, and F1 measures without regard for the rich hierarchical structure. In this work we argue for hierarchical evaluation of the predictions of neural LMTC models. With the example of the ICD-9 ontology we describe a structural issue in the representation of the structured label space in prior art, and propose an alternative representation based on the depth of the ontology. We propose a set of metrics for hierarchical evaluation using the depth-based representation. We compare the evaluation scores from the proposed metrics with previously used metrics on prior art LMTC models for ICD-9 coding in MIMIC-III. We also propose further avenues of research involving the proposed ontological representation.
Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning

Hebbian meta-learning has recently shown promise to solve hard reinforcement learning problems, allowing agents to adapt to some degree to changes in the environment. However, because each synapse in these approaches can learn a very specific learning rule, the ability to generalize to very different situations is likely reduced. We hypothesize that limiting the number of Hebbian learning rules through a genomic bottleneck can act as a regularizer leading to better generalization across changes to the environment. We test this hypothesis by decoupling the number of Hebbian learning rules from the number of synapses and systematically varying the number of Hebbian learning rules. The results in this paper suggest that simultaneously learning the Hebbian learning rules and their assignment to synapses is a difficult optimization problem, leading to poor performance in the environments tested. However, parallel research to ours finds that it is indeed possible to reduce the number of learning rules by clustering similar rules together. How to best implement a genomic bottleneck algorithm is thus an important research direction that warrants further investigation.
Exploring Twitter Data to Understand the Impact of COVID-19 Pandemic in India Using NLP and Deep Learning

In this research, a framework has been developed to analyze tweets to understand the impact of the COVID-19 pandemic on various sectors in India through natural language processing and deep learning. Topic modeling technique has been used to extract various themes around the COVID-19 pandemic in India from 17 September to 17 November 2020. Majority of the tweets are related to the effect of COVID-19 pandemic on economy, education, deaths, air quality, and awareness. A sentiment analysis has been performed to understand people’s reactions during the pandemic. To perform the sentiment analysis, a bidirectional LSTM model has been developed that yields 0.94 recall for positive tweets and 0.89 precision for negative tweets and 77.52% overall accuracy. This study shows a strong correlation between lagged number of deaths and negative sentiments, which probably indicates that the number of COVID-related death negatively impacts on people’s perception towards various socio-economic and environmental aspects during COVID pandemic in India. This research has developed a novel georeferencing model that can retrieve location context from the ungeotagged tweets to analyze people’s perception towards COVID at different locations in India, which will further help in understanding situational awareness and support various policy planning and strategic decision-making measures.
A Framework to Process Natural Speech in Service Robots Using a Combination of a Speech Recognition System, Universal Dependencies Extracted by Means of the Stanford Parser and an Expert System

One of the most challenging tasks in a service robot is the implementation of a framework to digitally process natural speech and to translate it into meaningful commands that the robot can understand and execute. Here we present an architecture aimed to process natural speech in our service robot Justina. It comprises of 3 parts: 1) A module to detect and analyze natural speech, 2) A module to parse the decoded speech and to extract universal dependencies and 3) A module based on an expert system to translate the universal dependencies into a set of instructions that the robot can execute.
China’s Central Bank Digital Currency and Its Impacts on Monetary Policy and Payment Competition: Game Changer or Regulatory Toolkit?

After carrying out some preparatory work for several years, China is likely to issue central bank digital currency (CBDC). Built upon the relevant experience and research in other countries, China has been carefully designing its own version of digital currency and issuance mechanism with a purpose of not disrupting financial intermediation. Nevertheless, it still leaves some residual issues unanswered, in particular, the narrow bank concern and application scenarios. While this article proposes to use interest rates as a policy solution to mitigate the narrow bank concern, the ultimate success of CBDC still depends on market competition. China’s CBDC, while initially being used as a regulatory toolkit to compete with other digital currencies, has potential to become a game changer once used in a large scale.
The Role of Education and Technology Adaptation in Poverty Reduction in Indonesia

This study examines the role of education in improving technology adoption as reflected in technology inclusion, poverty alleviation and efforts to increase community income which is reflected in economic growth. This study uses secondary data from world banks and processed regression using the moving average autoregression method. We found that education investment and technology inclusion were positively related to economic growth. And,negatively related to probability. This indicates that education plays a role in encouraging technological inclusion which reflects technological adaptation and encourages economic growth which is an indicator of the prosperity of the people in Indonesia which is strengthened by a negative relationship with poverty which indicates that education plays an important role in poverty alleviation
Relating Adversarially Robust Generalization to Flat Minima.

Adversarial training (AT) has become the de-facto standard to obtain models robust against adversarial examples. However, AT exhibits severe robust overfitting: cross-entropy loss on adversarial examples, so-called robust loss, decreases continuously on training examples, while eventually increasing on test examples. In practice, this leads to poor robust generalization, i.e., adversarial robustness does not generalize well to new examples. In this paper, we study the relationship between robust generalization and flatness of the robust loss landscape in weight space, i.e., whether robust loss changes significantly when perturbing weights. To this end, we propose average- and worst-case metrics to measure flatness in the robust loss landscape and show a correlation between good robust generalization and flatness. For example, throughout training, flatness reduces significantly during overfitting such that early stopping effectively finds flatter minima in the robust loss landscape. Similarly, AT variants achieving higher adversarial robustness also correspond to flatter minima. This holds for many popular choices, e.g., AT-AWP, TRADES, MART, AT with self-supervision or additional unlabeled examples, as well as simple regularization techniques, e.g., AutoAugment, weight decay or label noise. For fair comparison across these approaches, our flatness measures are specifically designed to be scale-invariant and we conduct extensive experiments to validate our findings.
ANALISIS MINAT BELAJAR MATEMATIKA SISWA SEKOLAH DASAR MELALUI METODE BLENDED LEARNING DITINJAU DARI PERINGKAT DAN GENDER

Abstract This study aims to analyze and describe the positive effect of the blended learning method on the learning interest of elementary school students in terms of rank and gender. The research subjects consisted of 4 students. This type of research is a mixed methods research with explorary sequential design. Data were collected using observation instruments, interviews and learning outcomes tests. The data analysis used is narrative exploration and descriptive analysis. The results showed that MA subjects with high rankings with male gender had very high interest in learning from the application of blended learning methods, high-ranking SA subjects with female gender had high learning interests from applying blended learning methods, AH subjects with low ratings with the male gender has a high interest in learning from the application of the blended learning method, the low-ranking RA subject with the female gender has a very low interest in learning from the application of the blended learning method. Keywords: Interest in Learning, Blended Learning, Ranking, Gender Abstrak Penelitian ini bertujuan untuk menganalisis dan mendeskripsikan pengaruh positif metode blended learning terhadap minat belajar siswa sekolah dasar ditinjau dari peringkat dan gender. Subjek penelitian terdiri dari 4 siswa. Jenis penelitian ini adalah penelitian campuran dengan desain explorary sequential. Data dikumpulkan menggunakan instrument observasi, wawancara dan tes hasil belajar. Analisis data yang digunakan adalah eksplorasi naratif dan analisis deskriptif. Hasil penelitian menunjukkan subjek MA yang berperingkat tinggi dengan jenis kelamin laki-laki memiliki minat belajar sangat tinggi dari penerapan metode blended learning, subjek SA yang berperingkat tinggi dengan jenis kelamin perempuan memiliki minat belajar tinggi dari penerapan metode blended learning, subjek AH yang berperingkat rendah dengan jenis kelamin laki-laki memiliki minat belajar tinggi dari penerapan metode blended learning, subjek RA yang berperingkat rendah dengan jenis kelamin perempuan memiliki minat belajar sangat kurang dari penerapan metode blended learning. Kata Kunci : Minat Belajar, Blended Learning, Peringkat, Gender