Reviewerly/exHarmony huggingface dataset:
Peace, Patience, and Remote Learning: An Origami Lesson

After a full year of remote education, most students have experienced some form of online or hybrid learning. Those who enrolled in Adjunct Lecturer Linda Diekman’s Summer 2021 iSchool course (Information Books and Resources for Youth) at the University of Illinois at Urbana-Champaign, were immersed in learning to locate, evaluate, and select factual print and nonprint materials for youth while being cognizant of the children’s intellectual, emotional, social, and physical needs. Developing thoughtful and intentional lessons to better connect with and support each and every youth is imperative in lesson planning, especially during a world pandemic.
Structure-Aware Reasoning for Knowledge Base Question Answering

Answering question according to knowledge base (i.e. KBQA) has attracted extensive attention recently. Information retrieval is one of the mainstream methods for the KBQA task that first finds the topic entity in the question via entity linking systems, and then selects the most related entities as answers from the subgraph (nodes in it are called candidate answers) of topic entity on the knowledge base (KB). However, existing methods generally separately perform reasoning over every candidate answer by considering the semantic relationships between question and the features extracted from KB, breaking away from the graphical structure of the KB and suffering from long-term dependency problem of entities. To address that, we propose a structure-aware reasoning method, which enables to exploit the graphical structure of entities on KB via Graph Convolutional Network and capture deep semantic relationships between question and candidate answers. Our method reasons about the correct answer by jointly considering information of all candidate answers, and focusing on important components in the question and on KB . We conduct experiments on the WebQuestions dataset, and the results demonstrate the effectiveness of our proposed method.
Annotation References for Facilitation Analysis in Intercultural Collaboration

In an intercultural children collaboration using machine translation, the children of low-resource languages have difficulties in participating and engaging in conversations due to inaccuracy of machine translation for the low-resource languages. In order to facilitate and motivate speech for these children, we have focused on the facilitator’s utterances. To identify the facilitator’s speech patterns that encourage the responses of low-resource language speaking children, we need to annotate the actual conversation logs. However, the existing annotation scheme cannot be applied to facilitation utterances completely because it is a classification of speech patterns for general problem-solving purposes. In this study, we have identified the problems that occurred when applying the existing annotation schemes. In addition, we proposed and evaluated a new annotation rule of the existing annotation scheme for facilitation analysis.
Performance Analysis of Video Streaming Methods

Due to the proliferation of smart devices with high-speed data communication, video streaming is evolving as the most dominating application on the Internet. Upcoming requirements of 5G wireless include Quality of Experience (QoE) for streaming and other multimedia applications. Recently, HTTP adaptive streaming (HAS) solution has gained popularity for streaming applications. However, many challenges still need to be addressed, in order to adapt HAS for upcoming 5G wireless networks. In this paper, we explore video. In day-to-day life, we consume enormous number of videos across various platforms, so knowing the main mechanism behind the technology should be an essential part in figuring out various difficulties that could be faced if various components are not fitted properly. The main objective of this work is to compare the best algorithms(s) or codecs available for video streaming. The primary aim is to test the efficiency of well-known algorithms (MPEG, H.264) that are being currently used.
Oughtred's Circles of Proportion 2.0: A Proof of Concept for Hands-on Science Engagement

This paper presents the development of a functional model of the logarithmic slide rule designed by the mathematician William Oughtred in the 17th century, known as Oughtred’s Circles of Proportion, to be used in educational contexts related to the history of science and the teaching of mathematics. The project consisted in interpreting the original instrument to develop a rigorous three-dimensional model of the slide rule, including its logarithmic scales and friction-tight joint, as well as adapting this artifact for 3D printing to the production of manipulable interactive objects at reduced costs. The paper presents the successive stages of development and collaboration, from the definition of goals and the target audience to the design of functional parts, the iterative testing in different educational contexts from schools to science events and plans to a revised version. The project exemplifies a promising way to engage with material heritage of science. The project constitutes a proof of concept for a generalized approach for the development of inclusive objects in science exhibitions, as a strategy to allow an easier and deeper understanding of the underlying scientific concepts and bringing the public closer to science.
The Necessity to Adopt Big Data Technologies for Efficient Performance Evaluation in the Modern Era

Latest technological advancements in the modern world led to innovations which if tackled properly yields value-added outcomes or may result in disruptions if mishandled. One such technology is NoSQL databases that evolved in hundreds of numbers. Though these support a wide number of features such as consistency, availability, fault-tolerance, scalability, and security, there is no single store that bundles all together. Particularly, due to drastic data rise that accounted up to the big data, it has become essential to compromise with security while focusing on consistency and vice versa in similar directions. Another aspect to be considered lies in the ability to handle streaming data which required a special kind of storage to process data on fly. This gains wide support if integrated with various learning platforms such as deep and machine learning to yield an added outcome. However, the application of pre-processing techniques and the identification of training versus test data split irrespective of the dataset is an essential activity to be carried out to infer better results. Also, the selection of an appropriate algorithm to identify the outliers in voluminous data is essential to quantify the results. Toward this end, an efficient hybrid machine learning algorithm polynomial-Bayesian-support vector machine (PBS) is developed to overcome aforementioned big data analysis-based difficulties and results are evaluated to make an inference about the effectiveness of the proposed work.
The Challenges of the Digital Age: The Problems of Criminal Liability for Cybercrimes in Lithuanian Law

Abstract The spread of digital technology and the convergence of computer and communication devices have changed the way people communicate, do important jobs, and so on. However, this digital age has also opened up a wide range of possibilities of using computer technology for criminal purposes. Therefore, the purpose of this research paper is to specify the main problems of legal regulation and case law of criminal liability for cybercrimes in Lithuanian law. So, the analysis was based on three main questions: the difficulties in legal regulation of cybercrimes in Lithuanian criminal law; the problematic relation between cybercrimes and other related crimes as well as the problems of applying some composition features of cybercrimes in Lithuanian case law. The research was carried out by employing the logical, the systematic, the linguistic, the historical, the comparative and the synthesizing research methods as well as the analysis of legal documents. The analysis shows that the legal regulation of criminal liability for cybercrimes in Lithuanian law has considerable problematics compared to international standards, as well as some composition features of cybercrimes are inappropriate for their application in practice and there many problems with ratio between this kind of crimes and similar crimes in Lithuanian case law.
ML-Wasm Entropy and Plot: Dataframes and Plotting Powered by WebAssembly and Rust

This project aims to create a library for data analysis and visualization of datasets. It will be similar in structure to popular data analysis and manipulation libraries in Python so that the library is easily adaptable for new users. This project builds the basic blocks for creation of series and data frames like structures in rust which can be accessed in JavaScript so as to run it on the browser or any other JavaScript environment like NodeJs. This approach would be faster than its pure JavaScript alternative.
The Effect of Mobile User Interface Design on Comic Reading Performance

For decades, the comic industry has continuously reached out to more readers with the help of smartphones. Many comic books have increasingly transformed from physical to digital versions. Even though the size of smartphones nowadays is expanding, the scanned comic pages must fit the screen. The current text and image size are more difficult to read and require users to zoom in or frequently scroll. In this study, researchers wanted to find out how to redesign the online comic format on the phone to be more efficient. The variables were the number of columns, turning page interactions, and navigations with the arrow button and thumbnail. The researchers also recognized the importance of the finger reach area. Twenty subjects who already had the online comic on the phone reading experience participated in this experiment. The results show that the two-column layout satisfies the participants’ reading size the most. Another result shows that scrolling the page is faster than swiping across the screen. Moreover, the participants interacted with the arrow navigation faster than the thumbnail. These results will benefit the future designs of mobile user interfaces to enhance the comic reading performance and pleasure levels.
Policy-Based Reinforcement Learning

Some of the most successful applications of deep reinforcement learning have a continuous action space, such as applications in robotics, self-driving cars, and real-time strategy games.
Blockchain-Based Model Transaction Platform Design for BIM Trader

In recent years, the Building Information Model (BIM) has been rapidly promoted around the world. The number of projects applying BIM technology in China is increasing year by year. BIM has become an important means to achieve informatization in the construction industry. Many BIM technology application achievements and cases have emerged, which have attracted great attention from the industry, enterprises and the government. However, mature model sharing trading platforms still account for a minority in the whole BIM market. At present, the BIM industry lacks a sharing platform approach, and the large-scale library is scarce, which cannot meet the huge demand for BIM models in the current market. By analyzing the application status of BIM technology, this paper conducts a preliminary market survey on the BIM model trading market, combines BIM technology with blockchain technology, and proposes a BIM model trading sharing platform based on blockchain technology, which has laid a theoretical foundation for building the BIM Trader model trading and sharing platform. Also, it has conceived and designed the building of platform functions combining with blockchain technology, aiming to provide new ideas for the development of BIM model trading industry and promote the positive development of China’s construction industry.
Adapting a Gencyber Teacher Training Camp to Virtual Learning

Summer camps have been used to train teachers and allow them to enhance their knowledge or have experience in certain subjects. Due to convenience, the summer camps can also be organized virtually. Our objective in this study is to investigate the effects of virtual cybersecurity teacher summer camps and how to best ensure its educational experience like face-to-face summer camps. Our result shows that training 20 STEM teachers at a oneweek virtual summer camp for cybersecurity can help teachers to integrate fundamentals of cybersecurity subjects in their classroom subjects and lead 950 K-12 students to have experience on cybersecurity.
Civil Defense Volunteers Calling for Helping in Disasters Situation

This article presents a study on the volunteers calling system for helping the Civil Defense group from the city of Curitiba, Brazil. Traditional calling is done using computer and telephone, what takes a time officials cannot spare. Therefore, volunteers are seldom called. A new calling system based on smartphone and geolocation technology was prototyped and compared with the traditional system. Simulation of the two systems showed that, in the traditional system, it takes 1h45min for two officials to call 96 volunteers. Only one official using the new technology can perform the same task in 7min36s. The results show that the introduction of new and affordable technology can improve the work performed by Civil Defense.
VR Interface for Accumulation and Sharing of Knowledge Database in Neurosurgery

In the field of neurosurgery, a variety of knowledge has been accumulated through a lot of surgery experiences. Due to the difficulty of expression, not all of the points that skilled neurosurgeons pay attention to during neurosurgery and the detailed precautions and procedures obtained from experience have been clearly stated and shared. Postoperative reports, incident reports, and accident reports are effective means of recording and sharing empirical knowledge, but they are costly to analyze. So, new methods of knowledge sharing are needed. In this study, we propose an interface based on Virtual Reality as a new interface. In order to construct a virtual reality interface, we utilize the VR Head Mounted Display. By projecting a model of a human head made by 3D Computer Graphics technology in a virtual reality space, we can grasp the structure more accurately. It is also possible to see at the same time the empirical knowledge that neurosurgeons accumulate through surgery while confirming the surgical procedure. The VR Head Mounted Display makes the experience more immersive. In addition, Head Mounted Display has been put into practical use, and its operating functions have been improved. Therefore, it is easy to operate and can be used as an interface for recording according to neural medical ontology. In order to accumulate and share the empirical knowledge of neurosurgeons, we investigated a 3D brain model interface using Virtual Reality.
Towards Simulating a Global Robust Model for Early Asthma Detection

Asthma is a chronic non-communicable disease that affects the lungs and can cause breathlessness leading to fatal exacerbation. This disease mainly starts developing in childhood and can affect the lungs and lifestyle throughout life. Ignoring asthma at any age can be fatal. Therefore, this disease should be detected as early as possible. So, in this regard, we propose a machine learning model to predict early asthma in children. We simulated the federated learning process to build the model and created four virtual hospitals. We have simulated federated learning to build a global robust model where multiple datasets from the different institutions can take part in the training process, which can be used in various regions of the world for predicting early asthma. We have trained the models using both IID (Independent and Identically Distributed) and non-IID approaches of splitting the dataset. We also checked the performance of the models by measuring the predictive accuracy and AUC (Area Under the Receiver Operating Characteristic Curve) score for test data. We got a predictive accuracy of 91.57% and 93.68% for the IID and non-IID approaches. At the same time, we got the AUC score of 0.895 and 0.918 for the IID approach and non-IID approaches.
Run-Time Dependency Graph Models for Independently Developed Robotic Software Components

Non-functional properties of a system must be parameterized as elemental or compound objects before they can be observed. Dependency objects are entities that encapsulate a distinct system characteristic that is relevant to and influenced by the system’s components and connections. A graph can be drawn where edges represent changes in one or more system-wide properties with software components and communication networks as nodes Dependency graphs are networks of dependencies that emerge from the flow of system-level characteristics, with evidence and business logic controlling them. Functional units are provided by component developers and network experts to describe how the value of a dependency object is transferred across computational or connection nodes. While building or validating a system, run-time dependency graph models for independently developed software components and network connections are integrated, built, and explored. These run-time models are used to graphically and programmatically observe networks of dependencies that emerge from the flow of system-level features. This paper describes the design and engineering anatomy of such a toolchain.
UGQE: Uncertainty Guided Query Expansion

Query expansion is a standard technique in image retrieval, which enriches the original query by capturing various features from relevant images and further aggregating these features to create an expanded query. In this work, we present a new framework, which is based on incorporating uncertainty estimation on top of a self attention mechanism during the expansion procedure. An uncertainty network provides added information on the images that are relevant to the query, in order to increase the expressiveness of the expanded query. Experimental results demonstrate that integrating uncertainty information into a transformer network can improve the performance in terms of mean Average Precision (mAP) on standard image retrieval datasets in comparison to existing methods. Moreover, our approach is the first one that incorporates uncertainty in aggregation of information in a query expansion procedure.
Robustness Optimization Based on Swarm Intelligence

This chapter introduces the application of swarm intelligence in robust topology optimization. Traditional greedy algorithms and evolutionary algorithms are easy to fall into the local optimum and cannot obtain the global optimum. The heuristic algorithm based on swarm intelligence can find an approximate optimal solution in polynomial time, which can meet the requirements of constructing a robust topology. In the swarm intelligence algorithm, ant colony and particle swarm are the two most widely used typical algorithms. Therefore, this chapter will discuss the topology robustness optimization problems of single-sink node network and multi-sink node network based on these two algorithms.
Note on Journal Extracts 2017–2020: Followed by Extracts from Handwritten Journals

Introductory note followed by extracts from handwritten journals 2017–2020. Witnessing and recording environmental destruction, the trauma of fire events, the attempts at recuperative acts via poetry.
Towards Differentially Private Machine Learning Models and Their Robustness to Adversaries

The pervasiveness of modern machine learning algorithms exposes users to new vulnerabilities: violation of sensitive information stored in the training data and wrong model behaviors caused by adversaries. State-of-the-art approaches to prevent such behaviors are usually based on Differential Privacy (DP) and Adversarial Training (AT). DP is a rigorous formulation of privacy in probabilist terms to prevent information leakages that could reveal private information about the users, while AT algorithms empirically increase the system’s robustness, injecting adversarial examples during the training process. Both techniques involve achieving their goal by modeling noise introduced into the system. We propose analyzing the relationship between these two techniques, studying how one affects the other. Our objective is to design a mechanism that guarantees DP and robustness against adversarial attacks, injecting modeled noise into the system. We propose Recommender Systems as an application scenario because of the severe risks to user privacy and system sensitivity to adversaries.
Research on Qiang Culture Redesign Based on AR Technology

In order to study the redesign of Qiang culture linking tradition and fashion, based on summarizing the connotation and design status quo of Qiang culture, combined with the characteristics of augmented reality technology and interactive expression, the AR picture book and IP image design of Qiang culture targeted at modern fashion users are studied. The picture book vividly shows the historical connotation of Qiang culture in a three-dimensional and visual way, and highlights the ethnic characteristics of Qiang by combining ethnic IP to enhance ethnic affinity and communication. For the key culture and rich cultural content of Qiang nationality, AR technology is combined with two interactive ways of 3D data superposition and video superposition to provide vivid digital display content. which stimulates users’ exploration and thirst for knowledge, makes the pop-up book more lifelike, and forms an interactive experience combining the virtual and the real. It improves the exploration of knowledge, the interest and extension of the content, and opens up a new field for the learning and inheritance of Qiang culture.
Classification and Recognition of Traffic Signs Using Deep Learning

The demand for employing Artificial Intelligent and data driven decision making in recognition of traffic signs in autonomous vehicles, self-driving cars, and not only in self-driving cars sometimes the driver who is driving also cannot recognize and follow the traffic signs is expediting. It is essential for autonomous vehicles to understand and follow all the traffic rules. The dynamic environment on which autonomous vehicles operate is risky due to insufficient training data. So by using this traffic signs recognizer, the driver will receive the information about the traffic sign coming ahead which can potentially lead to the minimization of the road accidents. Hence, to improve the accuracy in deep learning technology is employed. The primary reason that the method of deep learning is widely accepted in that the model can autonomously learn the deep characteristics of the image from the samples. In the current work, convolution neural network is employed to create a deep learning architecture that can identify traffic signs with close to 98% accuracy on the test set.
Selecting NLP Classification Techniques to Better Understand Causes of Mass Killings

We perform an analysis of SVM, BERT, and Longformer NLP tools as applied to large volumes of unclassified news articles given small volumes of labeled news articles for training. Analysis of the target machine learning tools is performed through a case study of global trigger events; specifically triggers of state-led mass killings. The goal of the case study is to draw relationships from the millions of machine classified articles to identify trends for the prediction and prevention of future mass killing events. In this paper we focus on the classification one specific trigger, coups, in order to glean insight into the accuracy and complexity of our SVM, BERT, and Longformer models. This study centers on classifying which news articles contain uniquely defined coup events and the temporal placement of those articles. Our performance analysis centers on the comparison of multiple accuracy metrics as applied to specific subsets of the corpus. We also demonstrate that raw accuracy scores are insufficient to fully understand the quality of classification required for specific target use cases.