nils-herrmann/clean_scirepeval huggingface dataset:
Professional Development and Teacher Change

This article describes a model of teacher change originally presented nearly two decades ago (Guskey, 1986) that began my long and warm friendship with Michael Huberman. The model portrays the temporal sequence of events from professional development experiences to enduring change in teachers’ attitudes and perceptions. Research evidence supporting the model is summarized and the conditions under which change might be facilitated are described. The development and presentation of this model initiated a series of professional collaborations between Michael and myself, and led to the development of our co-edited book, Professional Development in Education: new paradigms and practices (Guskey & Huberman, 1995), which was named `Book of the Year’ by the National Staff Development Council in 1996.
Recursive self-organizing network models

Self-organizing models constitute valuable tools for data visualization, clustering, and data mining. Here, we focus on extensions of basic vector-based models by recursive computation in such a way that sequential and tree-structured data can be processed directly. The aim of this article is to give a unified review of important models recently proposed in literature, to investigate fundamental mathematical properties of these models, and to compare the approaches by experiments. We first review several models proposed in literature from a unifying perspective, thereby making use of an underlying general framework which also includes supervised recurrent and recursive models as special cases. We shortly discuss how the models can be related to different neuron lattices. Then, we investigate theoretical properties of the models in detail: we explicitly formalize how structures are internally stored in different context models and which similarity measures are induced by the recursive mapping onto the structures. We assess the representational capabilities of the models, and we shortly discuss the issues of topology preservation and noise tolerance. The models are compared in an experiment with time series data. Finally, we add an experiment for one context model for tree-structured data to demonstrate the capability to process complex structures.
Automatic speech recognition An evaluation of Google Speech

The use of speech recognition is increasing rapidly and is now available in smart TVs, desktop computers, every new smart phone, etc. allowing us to talk to computers naturally. With the use in home appliances, education and even in surgical procedures accuracy and speed becomes very important. This thesis aims to give an introduction to speech recognition and discuss its use in robotics. An evaluation of Google Speech, using Google’s speech API, in regards to word error rate and translation speed, as well as a comparison between Google Speech and Pocketsphinx is made. The results show that Google Speech presented lower error rates on general purpose sentences but, due to the high average translation speed and the inability to specify vocabulary, was not suitable for voice-controlled moving robots.
Determinants of Customer Continuance Intention of Online Shopping

The purpose of this study is to clarify theory and identify factors that could explain the level of continuance intention of e-shopping. A revised technology acceptance model integrates expectation confirmation theory and investigates effects of age differences. An online survey of internet shoppers in Saudi Arabia. Structural equation modelling and invariance analysis confirm model fit. The findings confirm that perceived usefulness, enjoyment and social pressure are determinants of e-shopping continuance. The structural weights are mostly equivalent between young and old but the regression path from perceived usefulness to social pressure is stronger for younger respondents. This research moves beyond e-shopping intentions to factors affecting eshopping continuance, explaining 55% of intention to continue shopping online. Online strategies cannot ignore direct and indirect effects on continuance intentions. The findings contribute to literature on internet shopping and continuance intentions in the context of Saudi Arabia.
Aesthetic lateral canthoplasty.

To report our technique and experience in using a minimally invasive approach for aesthetic lateral canthoplasty.



Retrospective analysis of patients undergoing lateral canthoplasty through a minimally invasive, upper eyelid crease incision approach at Jules Stein Eye Institute by one surgeon (R.A.G.) between 2005 and 2008. Concomitant surgical procedures were recorded. Preoperative and postoperative photographs at the longest follow-up visit were analyzed and graded for functional and cosmetic outcomes.



A total of 600 patients (1,050 eyelids) underwent successful lateral canthoplasty through a small incision in the upper eyelid crease to correct lower eyelid malposition (laxity, ectropion, entropion, retraction) and/or lateral canthal dystopia, encompassing 806 reconstructive and 244 cosmetic lateral canthoplasties. There were 260 males and 340 females, with mean age of 55 years old (range, 4-92 years old). Minimum follow-up time was 3 months (mean, 6 months; maximum, 6 years). Complications were rare and minor, including transient postoperative chemosis. Eighteen patients underwent reoperation in the following 2 years for recurrent lower eyelid malposition and/or lateral canthal deformity.



Lateral canthoplasty through a minimally invasive upper eyelid crease incision and resuspension technique can effectively address lower eyelid laxity and/or dystopia, resulting in an aesthetic lateral canthus.
Starcraft from the stands: understanding the game spectator

Video games are primarily designed for the players. However, video game spectating is also a popular activity, boosted by the rise of online video sites and major gaming tournaments. In this paper, we focus on the spectator, who is emerging as an important stakeholder in video games. Our study focuses on Starcraft, a popular real-time strategy game with millions of spectators and high level tournament play. We have collected over a hundred stories of the Starcraft spectator from online sources, aiming for as diverse a group as possible. We make three contributions using this data: i) we find nine personas in the data that tell us who the spectators are and why they spectate; ii) we strive to understand how different stakeholders, like commentators, players, crowds, and game designers, affect the spectator experience; and iii) we infer from the spectators' expressions what makes the game entertaining to watch, forming a theory of distinct types of information asymmetry that create suspense for the spectator. One design implication derived from these findings is that, rather than presenting as much information to the spectator as possible, it is more important for the stakeholders to be able to decide how and when they uncover that information.
Data aggregation and roadside unit placement for a vanet traffic information system

In this paper we investigate how a VANET-based traffic information system can overcome the two key problems of strictly limited bandwidth and minimal initial deployment. First, we present a domain specific aggregation scheme in order to minimize the required overall bandwidth. Then we propose a genetic algorithm which is able to identify good positions for static roadside units in order to cope with the highly partitioned nature of a VANET in an early deployment stage. A tailored toolchain allows to optimize the placement with respect to an application-centric objective function, based on travel time savings. By means of simulation we assess the performance of the resulting traffic information system and the optimization strategy.
Advancing the state of mobile cloud computing

The capabilities of mobile devices have been improving very quickly in terms of computing power, storage, feature support, and developed applications. However, these mobile applications are still intrinsically limited by a relative lack of bandwidth, computing power, and energy compared to their tethered counterparts. Cloud computing offers abundant computing power that can be tapped easily. Apple iCloud and Amazon Silk browser are two recent mobile applications that leverage the cloud. In this paper, we systematically explore the fundamental research questions when combining mobile and cloud computing. We will highlight some of the challenges we face and some of the solutions we are pursuing.
A survey of content-based image retrieval with high-level semantics

In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested. 2006 Pattern Recognition Society.
Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints

Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks as well as their co-location relationship. We study two problem variants in this paper: budget is constrained for every timestamp, i.e. fixed, and budget is constrained for the entire campaign, i.e. dynamic. For each variant, we study the complexity of its offline version and then propose several heuristics for the online version which exploit the spatial and temporal knowledge acquired over time. Extensive experiments with real-world and synthetic datasets show the effectiveness and efficiency of our proposed solutions.
Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes

High-performance computing systems are moving towards 2.5D and 3D memory hierarchies, based on High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) to mitigate the main memory bottlenecks. This trend is also creating new opportunities to revisit near-memory computation. In this paper, we propose a flexible processor-in-memory (PIM) solution for scalable and energy-efficient execution of deep convolutional networks (ConvNets), one of the fastest-growing workloads for servers and high-end embedded systems. Our co-design approach consists of a network of Smart Memory Cubes (modular extensions to the standard HMC) each augmented with a many-core PIM platform called NeuroCluster. NeuroClusters have a modular design based on NeuroStream coprocessors (for Convolution-intensive computations) and general-purpose RISC-V cores. In addition, a DRAM-friendly tiling mechanism and a scalable computation paradigm are presented to efficiently harness this computational capability with a very low programming effort. NeuroCluster occupies only 8 percent of the total logic-base (LoB) die area in a standard HMC and achieves an average performance of 240 GFLOPS for complete execution of full-featured state-of-the-art (SoA) ConvNets within a power budget of 2.5 W. Overall 11 W is consumed in a single SMC device, with 22.5 GFLOPS/W energy-efficiency which is 3.5X better than the best GPU implementations in similar technologies. The minor increase in system-level power and the negligible area increase make our PIM system a cost-effective and energy efficient solution, easily scalable to 955 GFLOPS with a small network of just four SMCs.
DISTRIBUTED TRAINING

Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD are redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during this compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270× to 600× without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.
Best practices and boundary conditions

The idea of a memory test or of a test of academic achievement is often circumscribed. Tests within the classroom are recognized as important for the assignment of grades, and tests given for academic assessment or achievement have increasingly come to determine the course of children's lives: score well on such tests and you advance, are placed in more challenging classes, and attend better schools. Against this widely acknowledged backdrop of the importance of testing in educational life (not just in the US, but all over the world), it would be difficult to justify the claim that testing is not used enough in educational practice. In fact, such a claim may seem to be ludicrous on the face of it. However, this is just the claim we will make in this chapter: Education in schools would greatly benefit from additional testing, and the need for increased testing probably increases with advancement in the educational system. In addition, students should use self-testing as a study strategy in preparing for their classes. Now, having begun with an inflammatory claim – we need more testing in education – let us explain what we mean and back up our claims. First, we are not recommending increased use of standardized tests in education, which is usually what people think of when they hear the words " testing in education. " Rather, we have in mind the types of assessments (tests, essays, exercises) given in the classroom or assigned for homework. The reason we advocate testing is that it requires students to retrieve information effortfully from memory, and such effortful retrieval turns out to be a wonderfully powerful mnemonic device in many circumstances. Tests have both indirect and direct effects on learning (Roediger & Karpicke, 2006b). The indirect effect is that, if tests are given more frequently, students study more. Consider a college class in which there is only a midterm and a final exam compared to a similar class in which weekly quizzes are given every Friday, in addition to the midterm and the final. A large research program is not required to determine that students study more in the class with weekly quizzes than in the class without them. Yet tests also have a direct effect on learning; many studies have shown that students' retrieval of information on tests greatly improves their later retention of the tested This chapter is for the use of …
Real-time credit card fraud detection using Streaming Analytics

With the growing popularity of the internet, everything is available at our doorstep and convenience. The rapid increase in e-commerce applications has resulted in the increased usage of the credit card for offline and online payments. Though there are various benefits of using credit cards such as convenience, instant cash, but when it comes to security credit card holders, banks, and the merchants are affected when the card is being stolen, lost or misused without the knowledge of the cardholder (Fraud activity). Streaming analytics is a time-based processing of data and it is used to enable near real-time decision making by inspecting, correlating and analyzing the data even as it is streaming into applications and database from myriad different sources. We are making use of streaming analytics to detect and prevent the credit card fraud. Rather than singling out specific transactions, our solution analyses the historical transaction data to model a system that can detect fraudulent patterns. This model is then used to analyze transactions in real-time.
Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning

We use a deep learning model trained only on a patient’s blood oxygenation data (measurable with an inexpensive fingertip sensor) to predict impending hypoxemia (low blood oxygen) more accurately than trained anesthesiologists with access to all the data recorded in a modern operating room. We also provide a simple way to visualize the reason why a patient’s risk is low or high by assigning weight to the patient’s past blood oxygen values. This work has the potential to provide cuttingedge clinical decision support in low-resource settings, where rates of surgical complication and death are substantially greater than in high-resource areas.
Unsupervised Natural Language Generation with Denoising Autoencoders

Generating text from structured data is important for various tasks such as question answering and dialog systems. We show that in at least one domain, without any supervision and only based on unlabeled text, we are able to build a Natural Language Generation (NLG) system with higher performance than supervised approaches. In our approach, we interpret the structured data as a corrupt representation of the desired output and use a denoising auto-encoder to reconstruct the sentence. We show how to introduce noise into training examples that do not contain structured data, and that the resulting denoising auto-encoder generalizes to generate correct sentences when given structured data.
Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers’ traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.
N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders

The proliferation of IoT devices that can be more easily compromised than desktop computers has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for new methods that detect attacks launched from compromised IoT devices and that differentiate between hours- and milliseconds-long IoT-based attacks. In this article, we propose a novel network-based anomaly detection method for the IoT called N-BaIoT that extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic from compromised IoT devices. To evaluate our method, we infected nine commercial IoT devices in our lab with two widely known IoT-based botnets, Mirai and BASHLITE. The evaluation results demonstrated our proposed methods ability to accurately and instantly detect the attacks as they were being launched from the compromised IoT devices that were part of a botnet.
Reversible Architectures for Arbitrarily Deep Residual Neural Networks

Recently, deep residual networks have been successfully applied in many computer vision and natural language processing tasks, pushing the state-of-the-art performance with deeper and wider architectures. In this work, we interpret deep residual networks as ordinary differential equations (ODEs), which have long been studied in mathematics and physics with rich theoretical and empirical success. From this interpretation, we develop a theoretical framework on stability and reversibility of deep neural networks, and derive three reversible neural network architectures that can go arbitrarily deep in theory. The reversibility property allows a memoryefficient implementation, which does not need to store the activations for most hidden layers. Together with the stability of our architectures, this enables training deeper networks using only modest computational resources. We provide both theoretical analyses and empirical results. Experimental results demonstrate the efficacy of our architectures against several strong baselines on CIFAR-10, CIFAR-100 and STL-10 with superior or on-par state-of-the-art performance. Furthermore, we show our architectures yield superior results when trained using fewer training data.
System dynamics modelling in supply chain management: research review

The use of System Dynamics Modeling in Supply Chain Management has only recently re-emerged after a lengthy slack period. Current research on System Dynamics Modelling in supply chain management focuses on inventory decision and policy development, time compression, demand amplification, supply chain design and integration, and international supply chain management. The paper first gives an overview of recent research work in these areas, followed by a discussion of research issues that have evolved, and presents a taxonomy of research and development in System Dynamics Modelling in supply chain management.
Web Mining: Information and Pattern Discovery on the World Wide Web

Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is n o established vocabulary, leading to confusion when comparing research efforts. The t e r m W e b mining has been used in two distinct ways. T h e first, called Web content mining in this paper, is the process of information discovery f rom sources across the World Wide Web. The second, called Web m a g e mining, is the process of mining f o r user browsing and access patterns. I n this paper we define W e b mining and present an overview of the various research issues, techniques, and development e f forts . W e briefly describe W E B M I N E R , a system for Web usage mining, and conclude this paper by listing research issues.