Reviewerly/exHarmony huggingface dataset:
Facing Technological Disenfranchisement In The Global Community

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract 1 Session 3538 Facing Technological Disenfranchisement In The Global Community Dr. Mark W. McK. Bannatyne, Professor Robert A. Hall Purdue University/State Dept. of Corrections, New Hampshire Introduction As professionals in what we might be pleased to call the "technologically progressive nations", we have access to data systems and networks that are able to both manage and provide bits and bytes in quantities that just a few years ago were considered a dream of things to come. Indeed, there are many among us who doubtless consider their days a waste of waking hours if they aren't able to receive a daily dose of e-mail, or surf the Net. We have become so concerned about the information that we might receive that even while we sleep we employ computers and other information servants to stand on vigilant duty guarding precious data which are retrieved by fax and answering machines until we can once more join the active world. This daily acquisition, this barrage of information, has become a staple of our careers. It is the very breath that supports our biosphere of intellect. The review of information has become a corner stone of the society in which we work and even in which we seek escape. We are always seeking the latest version of the tools which displays our data, the fastest access time, and the surest storage capacity by which this information may be tenderly safeguarded. To fail in the quest to obtain information, or to lag behind in the pursuit of more and more information, causes some among us to be ranked by our colleagues as being "illiterate" should we be unable to perform this daily acquisition in an efficient and timely manner. This vast amount of accessible information, and the interaction it allows us with of our fellow man, in only moments of time and space, are mere key-stokes away from us in our offices, our homes, and even as we travel. And what of the data that is only waiting to be harvested? The field of information available covers such a breadth of knowledge in such diverse levels of interest that at times the deluge of facts presented for our consumption may seem more of a "a solution in search of a problem" rather than the means of bringing any inquiry to a successful conclusion. However, this world of abundance - this seemingly unfettered access to information, this plethora of technology which obeys our slightest whim for entertainment, or work, or simply distraction - is not available to all who might wish to tap the resources which are just suspended in cyberspace waiting to be summoned. As strange as it may seem, to those who thrive on the acquisition of information, there exists, for lack of a better term, "emerging" nations, societies, and even neighborhoods which long to be ushered into the ranks of the technologically mature. They somehow seem destined to remain in a sort of perpetual simplistic childhood of technological innocence. Who are these groups? What is causing them to postpone their entrance into
Model Eliciting Activities Motivated Problem Solving Process: Solution Path Analysis

Abstract Model Eliciting Activities motivated problem solving process: solution path analysisAs part of a larger NSF funded project focused on developing, incorporating and assessingModel Eliciting Activities (MEAs), we conducted a study to investigate the solution pathsteams used in their problem solving processes. In short, MEAs require the team to develop ageneralizable, mathematical model to solve the problem and to present both the solutionmethod and the results to the “client” in a form of a written report. Our previous researchshows improved student conceptual understanding of topics, improved student problemsolving and improved professional skills using MEAs; however, it was found that a portionof student teams still performed poorly on the activity. This particular experiment is directedat obtaining additional insight into the underlying reasons for student teams’ incorrectsolution paths when solving MEAs. In doing so, we focused on determining where and whyin the problem solving process the teams start to go awry with their solution process. Acontent analysis of the process was performed to identify underlying themes of how groupssolved the problem; a detailed path analysis of the solution process similar to the previousanalysis performed with the written reports was also performed. Data were recorded frommultiple teams solving the problem ‘out-loud,’ transcribed, and the transcripts wereanalyzed using both qualitative and quantitative methods. The qualitative analysis allowedthe research team to have an in-depth understanding of why students selected certainsolution paths. In doing the analysis we focused on six key questions: Where do students gowrong and why? Do they iterate? Do they iterate but still hold on to poor assumptions? Howmany times and do they correct their mistakes? Do they have new ideas? How often do theyhave new ideas and are these ideas right or wrong?
The Engineering Design I And Ii At Kanazawa Institute Of Technology

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 2560 The Engineering Design I and II at Kanazawa Institute of Technology Mary J. Sarmiento, Dr. Shigeo Matsumoto, Dr. Takeshi Kubo IEEE JSEE/JSME ASEE/JSEE 1. Introduction Engineering Design Education (EDE) was introduced in 1995 as a primary focus of educa- tional reform at Kanazawa Institute of Technology (KIT). It consists of two introductory courses: Engineering Design (ED) I and II. Starting in the Fall and Winter Quarters of 1996, ED I and ED II are taught respectively to all sophomore engineering students, comprised of about 2,000 students every year. To expose the students to a variety of ideas, backgrounds and different ways of thinking, the classes are taught not only by Japanese professors, but also by foreign professors with experi- ence working for U.S. universities and with the assistance of American teaching liaisons. EDE is based entirely on a new concept in Japan, hence it is also the first time the Japanese professors have taught these kinds of classes. 2. Objective1 Within the exam-based educational system of Japan, students naturally tend to regard as im- portant the “question and answer system” within which there is only one correct and precise answer. In the ED classes we are determined to reform the students’ perspectives such that they are able to tackle engineering design problems which may have innumerable solutions or for which a solution may not exist. The goal of EDE, which is derived from KIT’s motto ‘From Knowledge to Wisdom’, is to allow students to acquire actual engineering design experience through working on real-life projects in class. In the problem solving activities (projects) that engineers face in society, there are many cases in which the problem is not clearly defined, the problem domain is am- biguous, or the problem does not possess a unique correct answer. Additionally, real-world projects are seldom undertaken by individuals. Most projects are tackled by teams, teams which may be composed of workers from different countries. In an effort to prepare engineering students for their future professional careers, we think it is important to provide them with experience in discovering and solving problems similar to those they will face in society. It is crucial that the students be allowed to develop the skills and abilities for tackling problems independently, rather than following the directions of oth- ers. Engineering Design I and II are courses founded on these basic principles. 3. Course Management 3.1 Engineering Design Process1 In our Engineering Design Curriculum, the Engineering Design Process (EDP) is defined as the process by which the following activities are performed.
Probability And Image Enhancement

and is currently pursuing a Master's degree at Bucknell, also in electrical engineering.His research interests include discrete transforms and efficient hardware implementation of transform algorithms and other operations
Crime authors join police and crime experts at CSI Portsmouth

Five internationally acclaimed crime authors will be at CSI Portsmouth 2 Nov with police and forensic experts, as part of Portsmouth BookFest
Improving the school library for IB students: a case study at The British International School, Jakarta

The background of this research is firstly to evaluate the current services and facilities in the secondary library since there is a plan to extend the library building among the school projects. The outcomes of this research would be good feedback for the library staff to improve its services and facilities. Secondly, some colleagues on ECIS listserv have been discussing the need for IB guidelines especially for those who due to commence the library for IB programme This paper is not intended to be a guideline, but a sharing work to those who are concerned with the IB library to find different perspectives from a secondary school library at The British International School, Jakarta. A survey to gain feedback has been conducted using a qualitative method. The findings and the profile of the library are discussed in the same section to help the reader understand the real situation and the feedback from the respondents. This paper, expectantly, would be a valuable reference for any school libraries particularly those who run or plan to have a library for the IB Diploma programme.
An Attention-based System Approach for Scene Analysis in Driver Assistance

Research on computer vision systems for driver assistance resulted in a variety of isolatedapproaches mainly performing very specialized tasks like, e. g., lane keeping or traffic signdetection. However, for a full understanding of generic traffic situations, integrated and flexible approaches are needed. We here present a highly integrated vision architecture for anadvanced driver assistance system inspired by human cognitive principles. The system usesan attention system as the flexible and generic front-end for all visual processing, allowinga task-specific scene decomposition and search for known objects (based on a short termmemory) as well as generic object classes (based on a long term memory). Knowledge fusion,e. g., between an internal 3D representation and a reliable road detection module improvesthe system performance. The system heavily relies on top-down links to modulate lowerprocessing levels, resulting in a high system robustness.
Conforming Curricula For Software Engineers: Observations From The Australian Experience

Of the 37 universities in Australia offering undergraduate courses in computing, eleven offer courses in Software Engineering which are accredited by the Institute of Engineers, Australia and which may lead the graduate to membership of the Institute.In this way Australia has seized the initiative in the recognition of Software Engineers as professionals and the Institute has plausible claim to being the first national professional engineering body in the world to have accredited four-year undergraduate software engineering degrees as professional qualifications.Traditionally, undergraduate computer courses in Australia have fallen under one of three headings: Computer Science, Information Systems (or Information Technology) and Computer Systems Engineering.Software engineering, it is well known, fits none of these categories.Furthermore, it is long recognised that the education of practitioners in the emerging field of software engineering would require a different approach to that traditionally applied to computer science.Juggling the concurrent requirements of duration and content has required a reshaping of curricula.It is this curricular restructuring which attracted the attention of the authors who instituted a survey of the eleven universities involved in the education of potential professional Software Engineers which has produced graphical evidence confirming the distinct and individual nature of SE as a discipline and demonstrated the willingness of tertiary education institutions to respond to the needs of that discipline.This paper reports on moves in Australia towards the recognition of software engineering as a bona fide profession in its own right and presents the results of the survey showing the changes in curricular definition which have taken place as universities move to support the new discipline.
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract/semantic visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the latest efforts have been focused on increasing the receptive field, through either dilated/atrous convolutions or inserting attention modules. However, the encoder-decoder based FCN architecture remains unchanged. In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task. Specifically, we deploy a pure transformer (i.e., without convolution and resolution reduction) to encode an image as a sequence of patches. With the global context modeled in every layer of the transformer, this encoder can be combined with a simple decoder to provide a powerful segmentation model, termed SEgmentation TRansformer (SETR). Extensive experiments show that SETR achieves new state of the art on ADE20K (50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on Cityscapes. Particularly, we achieve the first position in the highly competitive ADE20K test server leaderboard on the day of submission.
LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities.We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections.In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder.Next, to endow our model with the capability of connecting vision and language semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification), cross-modality matching, and image question answering.These tasks help in learning both intra-modality and cross-modality relationships.After fine-tuning from our pretrained parameters, our model achieves the state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA).We also show the generalizability of our pretrained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR 2 , and improve the previous best result by 22% absolute (54% to 76%).Lastly, we demonstrate detailed ablation studies to prove that both our novel model components and pretraining strategies significantly contribute to our strong results. 1
SpanBERT: Improving Pre-training by Representing and Predicting Spans

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERT large , our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE. 1
Generative Adversarial Networks

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
A survey on the security of blockchain systems

Since its inception, the blockchain technology has shown promising application prospects. From the initial cryptocurrency to the current smart contract, blockchain has been applied to many fields. Although there are some studies on the security and privacy issues of blockchain, there lacks a systematic examination on the security of blockchain systems. In this paper, we conduct a systematic study on the security threats to blockchain and survey the corresponding real attacks by examining popular blockchain systems. We also review the security enhancement solutions for blockchain, which could be used in the development of various blockchain systems, and suggest some future directions to stir research efforts into this area.
A Simple Framework for Contrastive Learning of Visual Representations

This paper presents SimCLR: a simple framework for contrastive learning ofvisual representations. We simplify recently proposed contrastiveself-supervised learning algorithms without requiring specialized architecturesor a memory bank. In order to understand what enables the contrastiveprediction tasks to learn useful representations, we systematically study themajor components of our framework. We show that (1) composition of dataaugmentations plays a critical role in defining effective predictive tasks, (2)introducing a learnable nonlinear transformation between the representation andthe contrastive loss substantially improves the quality of the learnedrepresentations, and (3) contrastive learning benefits from larger batch sizesand more training steps compared to supervised learning. By combining thesefindings, we are able to considerably outperform previous methods forself-supervised and semi-supervised learning on ImageNet. A linear classifiertrained on self-supervised representations learned by SimCLR achieves 76.5%top-1 accuracy, which is a 7% relative improvement over previousstate-of-the-art, matching the performance of a supervised ResNet-50. Whenfine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy,outperforming AlexNet with 100X fewer labels.
Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

The metaverse has the potential to extend the physical world using augmented and virtual reality technologies allowing users to seamlessly interact within real and simulated environments using avatars and holograms. Virtual environments and immersive games (such as, Second Life, Fortnite, Roblox and VRChat) have been described as antecedents of the metaverse and offer some insight to the potential socio-economic impact of a fully functional persistent cross platform metaverse. Separating the hype and “meta…” rebranding from current reality is difficult, as “big tech” paints a picture of the transformative nature of the metaverse and how it will positively impact people in their work, leisure, and social interaction. The potential impact on the way we conduct business, interact with brands and others, and develop shared experiences is likely to be transformational as the distinct lines between physical and digital are likely to be somewhat blurred from current perceptions. However, although the technology and infrastructure does not yet exist to allow the development of new immersive virtual worlds at scale - one that our avatars could transcend across platforms, researchers are increasingly examining the transformative impact of the metaverse. Impacted sectors include marketing, education, healthcare as well as societal effects relating to social interaction factors from widespread adoption, and issues relating to trust, privacy, bias, disinformation, application of law as well as psychological aspects linked to addiction and impact on vulnerable people. This study examines these topics in detail by combining the informed narrative and multi-perspective approach from experts with varied disciplinary backgrounds on many aspects of the metaverse and its transformational impact. The paper concludes by proposing a future research agenda that is valuable for researchers, professionals and policy makers alike.
Graph convolutional networks: a comprehensive review

Abstract Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are often complex and diverse. On the other hand, the representation learning has achieved great successes in many areas. Thereby, a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space, such that the graph properties can be preserved. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. In this survey, despite numerous types of graph neural networks, we conduct a comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models. First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. Then, we categorize different graph convolutional networks according to the areas of their applications. Finally, we present several open challenges in this area and discuss potential directions for future research.