Reviewerly/exHarmony huggingface dataset:
Reducing classifier overconfidence against adversaries through graph algorithms

In this work we show that deep learning classifiers tend to become overconfident in their answers under adversarial attacks, even when the classifier is optimized to survive such attacks. Our work draws upon stochastic geometry and graph algorithms to propose a general framework to replace the last fully connected layer and softmax output. This framework (a) can be applied to any classifier and (b) significantly reduces the classifier's overconfidence in its output without much of an impact on its accuracy when compared to original adversarially-trained classifiers. Its relative effectiveness increases as the attacker becomes more powerful. Our use of graph algorithms in adversarial learning is new and of independent interest. Finally, we show the advantages of this last-layer softmax replacement over image tasks under common adversarial attacks.
The Use of Online Tools for Qualitative Data Collection in Educational Research

교육연구 수행에서 질적연구의 활용이 활성화되고 있다. 코로나19 이후 비대면 문화의 확산, 연구에서 활용 가능한 과학기술의 발달, 연구자와 참여자 간의 밀접한 관계 형성이 요구되는 교육연구의 특성은 교육분야 질적연구 수행에서 온라인 도구의 활용을 촉진하고 있으며 확대되고 있다. 이 연구는 온라인 도구를 활용하여 질적데이터를 수집한 연구자들의 경험을 분석하여 질적데이터 수집에 있어서 온라인 도구가 가진 가능성과 과제를 확인하는 데 목적이 있다. 이 연구는 사례연구를 연구방법으로 선정하여성인 대상 그룹 인터뷰, 학생 대상 그룹 인터뷰, 사용성 평가에서 화상회의 플랫폼을 활용한 세 가지 사례를 분석하였다. 연구 결과, 교육연구에서 온라인 도구를 활용한 질적데이터 수집의 강점(참여자 모집 및 일정 수립, 데이터 수집 및 관리의 용이성, 편안한 데이터 수집 환경 조성 등)과 제한점(라포 형성의 어려움, 환경 통제의 제약, 수동적 참여자 및 집중도 유지 문제, 발언 관리 어려움 등)이 확인되었다. 이러한 연구결과는 실제 사례를 바탕으로 질적데이터 수집 방안으로 온라인 도구가 가진 가능성과 활용방안을 제안했다는 의의가 있다. 이를 통해 교육연구자들에게 온라인 도구 활용에 대한 이해를 높이고 연구 대상과 목적에 맞는 질적데이터 수집 전략을 마련하는 데 유용한 자원이 될 수 있을 것이다.Qualitative research is actively used as a main research approach in conducting educational research. The spread of non-face-to-face culture and the characteristics of educational research promote the use of online tools as a way to collect data in conducting qualitative research in the educational field. The purpose of this study is to identify the possibilities and challenges of using online tools in qualitative data collection by analyzing the experiences of researchers who collected qualitative data using online tools. This study adapted a case study as a research method, and analyzed three cases using a video conference platform in online group interview with adults, online group interview with students, and usability test (demonstration, observation, and interview). As a result of the study, the strengths (ease of selection of participants and data collection, creating a comfortable environment in data collection) and limitations (difficulties in forming a lapo and managing speech, constraints of controlling environment, issues of passive participant and maintaining concentration) of qualitative data collection using online tools were identified. The research results suggest the possibility and utilization of online tools as a method for collecting qualitative data in conducting educational research.
A Phenomenological Study on the Experience of Learning Instrumental Music through Media in Youth

본 연구는 청소년이 경험한 미디어 기반 기악학습의 의미를 현상학적 질적 연구 방법을 사용하여 탐색하는 것에 그 목적을 둔다. 이를 위해 미디어를 통한 기악학습 경험이 있는 초등학생 2명, 고등학생 2명, 대학생 1명, 총 5명을 대상으로 리코더, 피아노, 기타, 플루트 관련 YouTube채널 활용에 대한 반구조화된 질문을 Zoom 화상 인터뷰로 진행하였고 수집된 자료를 현상학적 관점으로 분석하였다. 연구 결과 미디어를 통한 기악학습의 의미는 자신만의 목표에 따라 선정된 각기 다른 출발점, 시공간과 학습 수준의 경계를 넘나드는 접근의 편리함, 완성된 예제와의 즉각적인 비교를 통한 실력 향상, 미디어를 통한 기악학습 과정에서의 어려움과 문제해결을 위한 노력, 새로운 과제 도전에 대한 욕구로 나타났다. 이와 같은 연구 결과를 통해 미디어를 통한 기악학습에서는 전통적인 도제식 기악학습의 환경에서와는 차별화 되어 학습자 스스로 학습 목표를 설정하고 적절한 출발점을 직접 선택하며 본인이 처한 환경에 맞추어 능동적인 학습이 가능하며 이를 통한 성장의 가능성을 확인할 수 있었다.This study aims to explore the meaning of media-based instrumental learning experienced by youth using phenomenological qualitative research methods. To this end, a total of five elementary school students, two high school students, and one college student, who had experience in instrumental music learning through media, were asked semi-structured questions about the use of YouTube channels related to recorders, piano, guitar, and flute through Zoom video interviews, and the collected data were analyzed from a phenomenological perspective. As a result of the research, the instrumental music learning through media was shown as meaningful for various starting points specific to one's own goals, convenience of access regardless of space-time or learning level boundaries, improvement of skills through instant comparison with completed examples, efforts to solve difficulties and problems in the process of instrumental music learning through media, and desire to challenge new tasks. Through the results of such research, it was confirmed that instrumental music learning through media, which is different from the traditional apprenticeship instrumental learning environment, can enable learners to set their own learning goals, choose the appropriate starting point, and actively learn according to their environment, and grow through this.
LinkedIn: An Effective Global Publishing Network at Your Fingertips

In the olden days (side note: I am qualified to say the “olden days” now because as of February 19, 2023, I am 50 years old and have declared myself a Scholarly Publishing Elder; capitalization is intentional), professionals had to connect with the rudimentary tools we had available. We relied on business cards, paper address books, snail mail, early iterations of rudimentary email clients, in-person networking events, and (GASP!) telephones with cords to keep in touch. To be linked in, one had to be intentional about it. Furthermore, valuable relationships tended to be localized to an individual’s geographic location. Along came the emergence of the World Wide Web, and we all know the story: EVERYTHING CHANGED. Boom… our previously small orbits became global. The world was at our fingertips. I still remember the novelty of catching up on the Melrose Place episode from the night before in an Internet recap. And I love to reminisce about the first time I heard Google used as a verb. It was the summer of 2000, and I was working at Cadmus in my very first scholarly publishing job. I had a question for my boss, and she didn’t know the answer, either. She suggested: “Why don’t you just Google it?” Around that time, according to https://about.linkedin.com/, LinkedIn founder Reid Hoffman began the platform from his living room, officially launching it in 2003. As stated on their site: “The mission of LinkedIn is simple: connect the world’s professionals to make them more productive and […]
An Intelligent Approach for Blood Cell Detection Employing Faster RCNN

A CBC (complete blood count) is an essential component of a thorough medical evaluation. The practice of medicine has had a significant impact on popular methodologies, such as manual counting and automated analyzers. The shape of blood cells and other cellular characteristics are highly sensitive to contamination levels in the human body. Tiny blood cell images are studied to spot disease and deviations from the norm that may indicate internal contamination. Correct cell segmentation enables more precise and powerful disease detection. Examining blood cells under a microscope is an important part of any pathological investigation. It emphasizes the examination of the correct malady after pinpointing its precise location and then ranking its anomalies, which plays a crucial role in determining the nature of a patient's condition, planning treatment, and evaluating the outcome of that treatment. Initially the complete blood count (CBC) test of patient is carried out if the report suggested that there is abnormality in the blood than that blood is poured on strip and blood is stained out through addition of coagulant. Expert hematologists are in short supply, particularly in underdeveloped nations, and are frequently overwhelmed. To help them with their burden, we provide a unique method for the automated assessment of family disease using artificial intelligence on blood smear images in this paper. For object detection to be useful in real life, it needs to be quick and good at finding different things in an image. There have been many improvements made to object detection, such as Convolutional Neural Network and the family of R-CNN. In our research, we employ Faster R-CNN to look for RBCs, WBCs, and Platelets in the Blood Cell Count (CBC) and Detection dataset. The number, shape, and other meta-information, or any abnormalities in the different parts of blood, could help find problems and/or diseases like leukemia, anemia, lymphoma, sickle cell disease, thrombocytopenia, leukopenia etc., early on. Our model is accurate enough to find a bounding box on the blood parts. A box is drawn along with their name around the various components of the blood smear slide.
Machine Learning-Based Temporal Motion Analysis To Detect Symptomatic Knee Osteoarthritis With Sit-To-Stand Video

Purpose: Knee Osteoarthritis (KOA) is a degenerative disease that causes pain and dysfunction in the knee. Studies have shown that treatment and prevention in the early stage are important as it is the golden window possible for reversing disease progression. Marker-based gait analysis becomes a desirable tool for quantifying the functional outcome of KOA in the early stage and throughout disease progression. Yet, it requires a long walkway and expensive equipment, which hinders its wide application in the community.
Cyber Threat Indicators Association Prediction Based on Weighted Fusion of Semantic and Topological Information

Nowadays, Cyber Threat Intelligence (CTI) has become increasingly important for detecting and defending against cyber threats. Researchers often construct CTI heterogeneous graphs to describe threat indicators and their associations. However, most existing link prediction methods of normal heterogeneous graphs show poor performance on CTI graphs, as they mainly focus on the topological features and ignore the attributes of the threat indicators. To address this limitation, this paper proposes Ctiap, a Cyber Threat Indicators Association Prediction model based on weighted fusion of the semantic and topological information. The model firstly aims at the semantic characteristics of threat indicators and the topology of CTI graph. We collected more than 20,000 samples through open web platforms to construct a real-word heterogeneous graph dataset of threat indicators. The experimental results show that the accuracy of our model reaches 93.08%, which is better than the state-of-the-art baseline methods.
High-Quality-High-Quantity Semantic Distillation for Incremental Object Detection

Model is required to learn from dynamic data stream under incremental object detection task. However, traditional object detection model fails to deal with this scenario. Fine-tuning on new task suffers from a fast performance decay of early learned tasks, which is known as catastrophic forgetting. A promising way to alleviate catastrophic forgetting is knowledge distillation, which includes feature distillation and response distillation. Previous feature distillation methods have not discuss knowledge selection and knowledge transfer at the same time. In this paper, we propose high-level semantic feature distillation and task re-balancing strategy that consider both high-quality knowledge selection and high-quantity knowledge transfer simultaneously. Extensive experiments are conducted on MS COCO benchmarks. The performance of our method exceeds previous SOTA methods under all experimental scenarios. Remarkably, our method reduces the mAP gap toward full-training to 2.58, which is much better than that of the previous SOTA method with a gap of 3.30.
Comparative Analysis of the Selected Geophysical Disaster Process Case Studies

This chapter presents an analytical overview of the selected geophysical disaster case studies in the world that include earthquakes, volcanic eruptions and tsunamis. Within the geophysical disaster natural subcategory, the following post-disaster case studies are analysed: Volcanic eruption of Mt. Pinatubo in the Philippines (1991); Earthquake and Tsunami in Japan (2011) and Earthquake and Landslides in Nepal (2015), based on the use of the urbanscape, through its built and natural elements: the urban pattern of the everyday life and the urban vulnerability during the pre-disaster phase; the direct urban impact during the disaster event phase and the urban rehabilitation during the post-disaster phase.
Investigating the Causes of Non-compliance with Health Protocols during the COVID-19 Pandemic

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FPGA Implementation of Efficient 32-Bit 3-Operand Addition Using Kogge–Stone (KS) Parallel Prefix Adder

For performing efficient modular arithmetic operations, several cryptographic and pseudorandom bit generator (PRBG) algorithms utilize a 3-operand binary adder as the primary functional unit. The Carry Save Adder is the most common adder used for performing the three-operand extension (CS3A). On the other hand, the ripple-carry step of CS3A results in a significant delay while transmitting the output signals. Due to the lengthy delay, it influences the performance of MDCLG architecture. For performing three-operand addition, two-operand adders, such as Kooge Stone (KSA), can be used. This will decrease the critical route latency, delay, and area compared to other parallel prefix adders. The proposed high-speed and space-efficient adder architecture for performing three-operand binary operations includes carry-prefix computation logic after performing the pre-compute bitwise addition. The proposed adder design reduces the adder latency while consuming less area and power. A Kogge–Stone parallel prefix adder has been used to develop a novel architecture for the proposed 8-bit, 16-bit, and 32-bit three-operand adders. The proposed architecture is implemented by using Verilog coding, and further, the power and delay extraction has been performed by using a Xilinx tool. The proposed architecture has been developed by using the MDCLCG method with the three-operand adder, and further, the proposed architecture is proven with respect to delay as well as area and power.
Students’ Errors in E-Learning Discussion in TEFL Course and Their Responses

Lack of knowledge, practice, and forgetfulness can also contribute to typographical errors. Writing errors will impact the information presented and cannot be appropriately channeled. This research aims to analyze the types of mistakes made by students using English and identify their responses to the errors made. This type of research is qualitative. The research method is a qualitative descriptive method. The sample was determined purposively, namely students in class 3A who were categorized as having superior English skills among other classes. The methods used in collecting data are observation and interviews. The instrument used is a questionnaire sheet. The data analysis technique used is qualitative and quantitative analysis. The study's results proved that there were 7554 errors from 1875 conversations. 3332 (44.11%) lexical errors, 474 (6.27%) grammatical errors, and 419 (5.54%) spelling errors. Additional findings include grammar or mechanics errors, as much as 3329. (44%). Student responses varied towards the mistakes made, including limited knowledge, forgetting English grammar concepts, accustomed to thinking in the native language (mother tongue) or translating, lack of using tenses in everyday life, the assumption that one does not need to master grammar, and limited time and rush in writing answers, as well as lack of accuracy.
Copyright in Works Created by Artificial Intelligence

Artificial intelligence (AI) has captured the attention of copyright lawyers fascinated by the thought of machines creating works of art, music and literature. There is no doubt that, as has often happened in the past during previous waves of technological advances, AI platforms – and especially, machine learning – have brought with them new opportunities as well as challenges. Machine learning is an AI application enabling programs to learn and progress automatically from experience. Its main feature is accessing data and often using it for the purpose of creating outputs, including music, literature, movies and art. Amounts of data are observed and analysed by the machine, which enables the latter to learn and then make creative decisions leading to final outputs that, as precise works of art, are often not foreseeable by the people who developed and started the initial program. Such a process is characterised by the absence of substantial human intervention or assistance after the program is operated, and by using algorithms – namely a sequence of instructions aimed at solving a problem or performing a computation. This can be labelled ‘algorithmic creativity’, that is, the way by which AI creates new works.
Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks

Adversarial attacks on a convolutional neural network (CNN)—injecting human-imperceptible perturbations into an input image—could fool a high-performance CNN into making incorrect predictions. The success of adversarial attacks raises serious concerns about the robustness of CNNs, and prevents them from being used in safety-critical applications, such as medical diagnosis and autonomous driving. Our work introduces a visual analytics approach to understanding adversarial attacks by answering two questions: (1) Which neurons are more vulnerable to attacks? and (2) Which image features do these vulnerable neurons capture during the prediction? For the first question, we introduce multiple perturbation-based measures to break down the attacking magnitude into individual CNN neurons and rank the neurons by their vulnerability levels. For the second, we identify image features (e.g., cat ears) that highly stimulate a user-selected neuron to augment and validate the neuron’s responsibility. Furthermore, we support an interactive exploration of a large number of neurons by aiding with hierarchical clustering based on the neurons’ roles in the prediction. To this end, a visual analytics system is designed to incorporate visual reasoning for interpreting adversarial attacks. We validate the effectiveness of our system through multiple case studies as well as feedback from domain experts.
Uptrendz: API-Centric Real-Time Recommendations in Multi-domain Settings

In this work, we tackle the problem of adapting a real-time recommender system to multiple application domains, and their underlying data models and customization requirements. To do that, we present Uptrendz, a multi-domain recommendation platform that can be customized to provide real-time recommendations in an API-centric way. We demonstrate (i) how to set up a real-time movie recommender using the popular MovieLens-100 k dataset, and (ii) how to simultaneously support multiple application domains based on the use-case of recommendations in entrepreneurial start-up founding. For that, we differentiate between domains on the item- and system-level. We believe that our demonstration shows a convenient way to adapt, deploy and evaluate a recommender system in an API-centric way. The source-code and documentation that demonstrates how to utilize the configured Uptrendz API is available on GitHub.
Enticing Local Governments to Produce FAIR Freedom of Information Act Dossiers

Government transparency is central in a democratic society, and increasingly governments at all levels are required to publish records and data either proactively, or upon so-called Freedom of Information (FIA) requests. However, public bodies who are required by law to publish many of their documents turn out to have great difficulty to do so. And what they publish often is in a format that still breaches the requirements of the law, stipulating principles comparable to the FAIR data principles. Hence, this demo is addressing a timely problem: the FAIR publication of FIA dossiers, which is obligatory in The Netherlands since May 1st 2022.
The impact of information technology on retail industry: an empirical study

In recent decades, rapid advances in internet technology have led to numerous innovative smart technologies. This research investigates the employee and customer acceptance of and resistance to smart technologies in the retail sector, by integrating the literature on technology acceptance model, system characteristics, technology readiness, and store reputation. The data was collected from different retail stores in Jordan. The results show that technological readiness, perceived usefulness and adaptability were impacting the performance of the retail stores. The readiness of the technology does influence employee and consumer behaviour. Retail stores should have the resources needed to minimise frustration and uncertainty. Consumers see reputable stores as more trustworthy and therefore show favourable attitudes to SRT.
Shadow Generation for Composite Image with Multi-level Feature Fusion

Shadow generation for composite image aims to generate shadows for the foreground with reference to the background information, reducing the floating feeling of the foreground due to the lack of shadows caused by simple overlays. The existing two-stage methods are not effective in generating shadows for composite images with complex foregrounds. First, in the Shadow Mask Prediction stage, foreground information and background lighting information are not fully extracted, resulting in inaccurate prediction range and shape of foreground shadows. Second, in the Shadow Filling Stage, the amount of shadow parameter information will decrease as the residual network level deepens, and the shadow parameter prediction is inaccurate, which greatly affects the foreground shadow filling effect. Therefore, we add a multi-scale feature enhancement module to obtain a wider range of feature information and improve the mask prediction accuracy. Meanwhile, we propose a multi-level feature fusion module to reduce the loss of information in the process of shadow parameter prediction by multiplexing features at different levels. Our experiments on the public dataset DESOBA show that the method generates shadow regions with more accurate range and shape.
New dimension-independent upper bounds on linear insdel codes

To determine the insdel distances of linear codes is a very challenging problem.The half-Singleton bound on the insdel distances of linear codes due to Cheng-Guruswami-Haeupler-Li is a basic upper bound on the insertion-deletion error-correcting capabilities of linear codes.In this paper we give several new coordinate ordering-free and coordinate ordering-depending upper bounds for the insdel distances of linear codes.These upper bounds do not depend on dimensions of linear codes and only depend on the formations of codewords.It is shown that for many natural well-known linear codes including binary simplex codes, Kasami codes, many linear binary codes with few non-zero weights and some algebraic geometry codes, the new coordinate ordering-free upper bounds on their insdel distances are strictly smaller than the half-Singleton bound and the direct upper bound.On the other hand for many linear binary codes, the ordering-depending upper bound on their insdel distance is 2. We also give ordering-depending upper bounds on the insdel distances of some linear ternary codes with few non-zero weights and some binary and ternary algebraic geometry codes.