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关键词： Computer science   Cyber peace   Cyber war   Interdisciplinarity   Teaching  

摘要： Interdisciplinary research and teaching between computer science and peace and security studies is indispensable against the background of the significance of possible conflicts in cyberspace. Even though numerous established courses and textbooks exist in both the one and the other discipline, this does not apply to their intersection. This article reflects on the introduction of the interdisciplinary course "Information Technology for Peace and Security" for students of Computer Science, Cyber Security and Information Systems at Technical University of Darmstadt and Peace and Conflict Research at the TU Darmstadt in cooperation with the Goethe University Frankfurt. The challenges and solutions of interdisciplinary teaching are presented and the importance of this teaching is emphasised.

关键词： Capital punishment   Death penalty   Evangelium vitae   Human dignity   Catholic church  

摘要： Throughout the twentieth and twenty-first centuries, there has been a significant controversy within the Catholic Church regarding the moral permissibility of capital punishment. In light of Evangelium Vitae and recent magisterial documents, the death penalty has been condemned by Pope Francis as a deliberate attack on the dignity of the human person. This movement towards condemning the death penalty as illegitimate in all cases is in opposition to the teachings of Scripture, of the Church Fathers, and of Tradition that protect the state’s right to exercise just and proportionate punishments. The purpose of this essay is to demonstrate the progression of the doctrine on capital punishment from its Scriptural roots, as demonstrated by Aquinas, Augustine, and other prominent Catholic figures, to its current form by underlining its moral implications as defined by Pope John Paul II, Pope Francis, and the United States bishops. From this demonstration, I conclude that the teachings encouraged by Pope Francis must be read only in light of prudential judgement, not as a revision or development of doctrine. It is vital for the union of Catholics that the teachings and traditions of the Church are protected and accurately interpreted so that Catholics may properly advocate for the sanctity of human dignity and create a culture of life under the Church’s guidance.

关键词： Training   Task analysis   Semantics   Shape   Representation learning   Generative adversarial networks   Computer science   Person re-identification   cloth-changing   data augmentation  

摘要： This paper studies the challenging person re-identification (Re-ID) task under the cloth-changing scenario, where the same identity (ID) suffers from uncertain cloth changes. To learn cloth- and ID-invariant features, it is crucial to collect abundant training data with varying clothes, which is difficult in practice. To alleviate the reliance on rich data collection, we reinforce the feature learning process by designing powerful complementary data augmentation strategies, including positive and negative data augmentation. Specifically, the positive augmentation fulfills the ID space by randomly patching the person images with different clothes, simulating rich appearance to enhance the robustness against clothes variations. For negative augmentation, its basic idea is to randomly generate out-of-distribution synthetic samples by combining various appearance and posture factors from real samples. The designed strategies seamlessly reinforce the feature learning without additional information introduction. Extensive experiments conducted on both cloth-changing and -unchanging tasks demonstrate the superiority of our proposed method, consistently improving the accuracy over various baselines.

关键词： Systematics   Bibliographies   Data integrity   Data mining   Computer science   STEM   Market research   Systematic literature review   data imputation   data mining   missingness   data preprocessing   data quality  

摘要： Missing data are a universal data quality problem in many domains, leading to misleading analysis and inaccurate decisions. Much research has been done to investigate the different mechanisms of missing data and the proper techniques in handling various data types. In the last decade, machine learning has been utilized to replace conventional methods to address the problem of missing values more efficiently. By studying and analyzing recently proposed methods using machine learning approaches, vital adoptions in accuracy, performance, and time consumed can be highlighted. This study aimed to help data analysts and researchers address the limitations of machine learning imputation methods by conducting a systematic literature review to provide a comprehensive overview of using such methods to impute missing values. Novel proposed machine learning approaches used for data imputation are analyzed and summarized to assist researchers in selecting a proper machine learning method based on several factors and settings. The review was performed on research studies published between 2016 and 2021 on adopting machine learning to impute missing values, focusing on their strengths and limitations. A total of 684 research articles from various scientific databases were analyzed using search engines, and 94 of them were selected as primary studies. Finally, several recommendations were given to guide future researchers in applying machine learning to impute missing values.

关键词： Cloud computing   Encryption   Social networking (online)   Protocols   Keyword search   Servers   Computer science   Social graph search   versatile queries   searchable encryption   secure computation  

摘要： With the widespread adoption of cloud computing, it is increasingly popular for online social network (OSN) service providers to leverage the public cloud as a back-end to manage their services for the cloud's well-understood benefits. However, the cloud is also notoriously subject to a wide attack surface, making it an imperative need to embed security in the cloud-backed OSN service from the very beginning. In light of this, in this paper, we design, implement, and evaluate PeGraph, the first system simultaneously allowing private, efficient, and rich queries over encrypted social graphs. PeGraph is aimed at safeguarding the confidentiality of the social graph at the cloud, while preserving the functionality of social search, a key enabler for quality OSN services like friend discovery and user targeting. PeGraph is built from a delicate synergy of insights from social graph modelling and lightweight cryptography such as searchable encryption and additive secret sharing, supporting rich social search queries like exact queries, fuzzy queries, and mixed queries. PeGraph also allows the cloud to obliviously render the encrypted social search results in a ranked order according to their importance, as per users' preferences. Extensive experiments demonstrate that PeGraph can securely process a wide range of practical social search queries within 1 second, over a real-world social graph consisting of millions of entities.

ISBN: (数字)9780262371841

摘要： Coding for a purpose: helping young people combine journalism, data, design, and code to make media that makes a *** are urged to teach “code for all”—to make a specialized field accessible for students usually excluded from it. In this book, Clifford Lee and Elisabeth Soep instead ask the question, “Code for what?” What if coding were a justice-driven medium for storytelling rather than a narrow technical skill? What if “democratizing” computer science went beyond the usual one-off workshop and empowered youth to create digital products for social impact? Lee and Soep answer these questions with stories of a diverse group of young people in Oakland, California, who combine journalism, data, design, and code to create media that makes a *** teenage and young adult producers created interactive projects that explored gendered and racialized dress code policies in schools; designed tools for LBGTQ+ youth experiencing discrimination; investigated facial recognition software and what can be done about it; and developed a mobile app to promote mental health through self-awareness and outreach for support, and more, for distribution to audiences that could reach into the millions. Working with educators and media professionals at YR Media, an award-winning organization that helps young people from underserved communities build skills in media, journalism, and the arts, these teens found their own vibrant answers to “why code?” They code for insight, connection and community, accountability, creative expression, joy, and hope.

关键词： Barriers   Black police officers   Diversity   Occupational   police culture  

摘要： The success of an organization is directly dependent upon its the culture. Research was scarce regarding historical police culture and its impacts on Black law enforcement officers. The purpose of the study was to explore how occupational barriers of historical police culture have impacted Black law enforcement officers who policed at least 20 years in Florida. A phenomenological qualitative approach guided the collection and analysis of data from semi-structured interviews with 30 purposefully selected retired law enforcement officers in Central and South Florida. Ten themes: (a) police culture, (b) industry expectations (c) occupational barriers, (d) systematic inequality, (e) same race competition, (f) racial disparity, (g) diversity, inclusion, and equity efforts (h) internal conflict, (i) affects, and (j) coping mechanisms emerged from bracketing followed by manual coding. The experiences and perspectives of the 30 participants provided new knowledge, ideas, and insight that may prove helpful to other law enforcement and their perspectives on police culture. Findings indicate that shattering occupational barriers is critical to police culture. Most participants recognized that occupational barriers from police culture highly impacts career experiences, goals, and personal drive. The officers expressed a desire to see change and improvement in police culture through diversity, equity, and inclusion practices. The study results were consistent with earlier research which indicated that a diverse, equitable, and inclusive culture is positive for employees and organizations (Parikh, 2021). As law enforcement agencies reform and progress, they should introduce streamline policies that ensure diversity, equity, and inclusion (DEI) are included in job assignments, discipline, and promotional processes. As culture evolves, formal programs should also incorporate discussions to address any questions or concerns about DEI.

关键词： Network intrusion detection   Monitoring   Deep learning   Convolutional neural networks   Computer science   Systematics   Switches   Flow information export   network traffic sampling   intrusion detection   machine learning   deep learning   CNN  

摘要： Machine Learning (ML) based Network Intrusion Systems (NIDSs) operate on flow features which are obtained from flow exporting protocols (i.e., NetFlow). Recent success of ML and Deep Learning (DL) based NIDS solutions assume such flow information (e.g., avg. packet size) is obtained from all packets of the flow. However, often in practice flow exporter is deployed on commodity devices where packet sampling is inevitable. As a result, applicability of such ML based NIDS solutions in the presence of sampling (i.e., when flow information is obtained from sampled set of packets instead of full traffic) is an open question. In this study, we explore the impact of packet sampling on the performance and efficiency of ML-based NIDSs. Unlike previous work, our proposed evaluation procedure is immune to different settings of flow export stage. Hence, it can provide a robust evaluation of NIDS even in the presence of sampling. Through sampling experiments we established that malicious flows with shorter size (i.e., number of packets) are likely to go unnoticed even with mild sampling rates such as 1/10 and 1/100. Next, using the proposed evaluation procedure we investigated the impact of various sampling techniques on NIDS detection rate and false alarm rate. Detection rate and false alarm rate is computed for three sampling rates (i.e., 1/10, 1/100, 1/1000), for four different sampling techniques and for three (two tree-based, one deep learning based) classifiers. Experimental results show that systematic linear sampler - SketFlow performs better compared to non-linear samplers such as Sketch Guided and Fast Filtered sampling. We also found that random forest classifier with SketchFlow sampling was a better combination. The combination showed higher detection rate and lower false alarm rate across multiple sampling rates compared to other sampler-classifier combinations. Our results are consistent in multiple sampling rates, exceptional case is observed for Sketch Guided Samp

关键词： Computer science   Data collection   Data pipeline   Data science   Mathematics  

摘要： Temporal sentiment labels are used in various multimedia studies. They are useful for numerous classification and detection tasks such as video tagging, segmentation, and labeling. However, generating a large-scale sentiment dataset through manual labeling is usually expensive and challenging. Some recent studies explored the possibility of using online Time-Sync Comments (TSCs) as the primary source of their sentiment maps. Although the approach has positive results, existing TSCs datasets are limited in scale and content categories. Guidelines for generating such data within a constrained budget are yet to be developed and discussed. This dissertation tries to address the above issues by leveraging existing live comments from a popular video distributed platform, YouTube, as a primary time-synchronized data source and exploring efficient strategies for generating TSCs with a constrained budget. An automatic data mining system was first developed and deployed across multiple platforms. Then, long-period experiments were conducted to test the efficiency of the framework. Additionally, two large-scale TSCs datasets were created through the proposed data framework and analyzed for their characteristics. Finally, the outcomes were tested against the original temporal Automatic Speech Recognition (ASR) sentiment labeling to validate their accuracy. The experiment shows the potential of automatically generating temporal sentiment datasets through the proposed mapping system. This project also provides valuable tools for future multimedia research.

关键词： Chatbots   COVID-19   Artificial intelligence   Medical services   Machine learning   Computer science   Analytical models   chatbot   LSTM algorithm   machine learning   natural language processing   query processing  

摘要： The purpose of this paper is to show concisely how we can promote chatbots in the medical sector and cure infectious diseases. We can create awareness through the users and the users can get proper medical solutions to prevent disease. We created a preliminary training model and a study report to improve human interaction in databases in 2021. Through natural language processing, we describe the human behaviors and characteristics of the chatbot. In this paper, we propose an AI Chatbot interaction and prediction model using a deep feedforward multilayer perceptron. Our analysis discovered a gap in knowledge about theoretical guidelines and practical recommendations for creating AI chatbots for lifestyle improvement programs. A brief comparison of our proposed model concerning the time complexity and accuracy of testing is also discussed in this paper. In our work, the loss is a minimum of 0.1232 and the highest accuracy is 94.32%. This study describes the functionalities and possible applications of medical chatbots and explores the accompanying challenges posed by the use of these emerging technologies during such health crises mainly posed by pandemics. We believe that our findings will help researchers get a better understanding of the layout and applications of these revolutionary technologies, which will be required for continuous improvement in medical chatbot functionality and will be useful in avoiding COVID-19.