nils-herrmann/clean_scirepeval huggingface dataset:
Case Study of Online Retailing Fast Fashion Industry

—The study investigates into the fast fashion industry worldwide, specifically on Zara, H&M and UNIQLO with respect to efficient supply chain management, scarce value creation, low costs promotions and positioning strategy, supported by comparisons between several typical well-known fast fashion brands. Through the overall analysis of B2C apparel online retailing in China, statistics show an enormous space for online retailing fast fashion industry to explore but a far way to catch up with the leading enterprises in the world in terms of e-commerce scale. The next main part demonstrates a case of a Chinese fast fashion online retailer-Vancl, analyzing its keys to success in aspects of proper product positioning, brand positioning, business mode, marketing strategy, products and services, user experience, logistics and team management. In addition, relevant suggestions for further prosperity are proposed in the end of the paper.
Big data technologies and Management: What conceptual modeling can do

The era of big data has resulted in the development and applications of technologies and methods aimed at effectively using massive amounts of data to support decision-making and knowledge discovery activities. In this paper, the five Vs of big data, volume, velocity, variety, veracity, and value, are reviewed, as well as new technologies, including NoSQL databases that have emerged to accommodate the needs of big data initiatives. The role of conceptual modeling for big data is then analyzed and suggestions made for effective conceptual modeling efforts with respect to big data.
IQA: Visual Question Answering in Interactive Environments

We introduce Interactive Question Answering (IQA), the task of answering questions that require an autonomous agent to interact with a dynamic visual environment. IQA presents the agent with a scene and a question, like: "Are there any apples in the fridge?" The agent must navigate around the scene, acquire visual understanding of scene elements, interact with objects (e.g. open refrigerators) and plan for a series of actions conditioned on the question. Popular reinforcement learning approaches with a single controller perform poorly on IQA owing to the large and diverse state space. We propose the Hierarchical Interactive Memory Network (HIMN), consisting of a factorized set of controllers, allowing the system to operate at multiple levels of temporal abstraction. To evaluate HIMN, we introduce IQUAD V1, a new dataset built upon AI2-THOR [35], a simulated photo-realistic environment of configurable indoor scenes with interactive objects. IQUAD V1 has 75,000 questions, each paired with a unique scene configuration. Our experiments show that our proposed model outperforms popular single controller based methods on IQUAD V1. For sample questions and results, please view our video: https://youtu.be/pXd3C-1jr98.
Compact stepped-impedance resonator bandpass filter with two wide spread passbands

This paper presents the design method for a compact dual-band bandpass filter with a large ratio of center frequencies. Emphasis is placed on circuit synthesis for simultaneously matching the in-band responses at the two designated passbands. In an interdigital configuration, a 0.9/5.8 GHz bandpass filter is designed and fabricated. It is believed that this is the filter has the largest ratio of the center frequencies in comparison with those in open literature. The measured responses show good agreement with the simulation results.
Biodiversity conservation in tropical agroecosystems: a new conservation paradigm.

It is almost certainly the case that many populations have always existed as metapopulations, leading to the conclusion that local extinctions are common and normally balanced by migrations. This conclusion has major consequences for biodiversity conservation in fragmented tropical forests and the agricultural matrices in which they are embedded. Here we make the argument that the conservation paradigm that focuses on setting aside pristine forests while ignoring the agricultural landscape is a failed strategy in light of what is now conventional wisdom in ecology. Given the fragmented nature of most tropical ecosystems, agricultural landscapes should be an essential component of any conservation strategy. We review the literature on biodiversity in tropical agricultural landscapes and present evidence that many tropical agricultural systems have high levels of biodiversity (planned and associated). These systems represent, not only habitat for biodiversity, but also a high-quality matrix that permits the movement of forest organisms among patches of natural vegetation. We review a variety of agroecosystem types and conclude that diverse, low-input systems using agroecological principles are probably the best option for a high-quality matrix. Such systems are most likely to be constructed by small farmers with land titles, who, in turn, are normally the consequence of grassroots social movements. Therefore, the new conservation paradigm should incorporate a landscape approach in which small farmers, through their social organizations, work with conservationists to create a landscape matrix dominated by productive agroecological systems that facilitate interpatch migration while promoting a sustainable and dignified livelihood for rural communities.
Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection

Object detection is a major challenge in computer vision, involving both object classification and object localization within a scene. While deep neural networks have been shown in recent years to yield very powerful techniques for tackling the challenge of object detection, one of the biggest challenges with enabling such object detection networks for widespread deployment on embedded devices is high computational and memory requirements. Recently, there has been an increasing focus in exploring small deep neural network architectures for object detection that are more suitable for embedded devices, such as Tiny YOLO and SqueezeDet. Inspired by the efficiency of the Fire microarchitecture introduced in SqueezeNet and the object detection performance of the singleshot detection macroarchitecture introduced in SSD, this paper introduces Tiny SSD, a single-shot detection deep convolutional neural network for real-time embedded object detection that is composed of a highly optimized, non-uniform Fire subnetwork stack and a non-uniform sub-network stack of highly optimized SSD-based auxiliary convolutional feature layers designed specifically to minimize model size while maintaining object detection performance. The resulting Tiny SSD possess a model size of 2.3MB (~26X smaller than Tiny YOLO) while still achieving an mAP of 61.3% on VOC 2007 (~4.2% higher than Tiny YOLO). These experimental results show that very small deep neural network architectures can be designed for real-time object detection that are well-suited for embedded scenarios.
Intelligent Word-Based Spam Filter Detection Using Multi-Neural Networks

SPAM e-mails have a direct cost in terms of time, server storage space, network bandwidth consumptions and indirect costs to protect privacy and security breaches. Efforts have been done to create new filters techniques to block SPAM, however spammers have developed tactics to avoid these filters. A constant update to these techniques is required. This paper proposes a novel approach which is a characters-word-based technique. This approach uses a multi-neural networks classifier. Each neural network is trained based on a normalized weight obtained from the ASCII value of the word characters. Results of the experiment show high false positive and low true negative percentages.
Understanding and Designing around Users' Interaction with Hidden Algorithms in Sociotechnical Systems

While today many online platforms employ complex algorithms to curate content, these algorithms are rarely highlighted in interfaces, preventing users from understanding these algorithms' operation or even existence. Here, we study how knowledgeable users are about these algorithms, showing that providing insight to users about an algorithm's existence or functionality through design facilitates rapid processing of the underlying algorithm models and increases users' engagement with the system. We also study algorithmic systems that might introduce bias to users' online experience to gain insight into users' behavior around biased algorithms. We will leverage these insights to build an algorithm-aware design that shapes a more informed interaction between users and algorithmic systems.
Differentially private Bayesian learning on distributed data

Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a learning strategy based on a secure multi-party sum function for aggregating summaries from data holders and the Gaussian mechanism for DP. Our method builds on an asymptotically optimal and practically efficient DP Bayesian inference with rapidly diminishing extra cost.
Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences

Recent work on sequence to sequence translation using Recurrent Neural Networks (RNNs) based on Long Short Term Memory (LSTM) architectures has shown great potential for learning useful representations of sequential data. A oneto-many encoder-decoder(s) scheme allows for a single encoder to provide representations serving multiple purposes. In our case, we present an LSTM encoder network able to produce representations used by two decoders: one that reconstructs, and one that classifies if the training sequence has an associated label. This allows the network to learn representations that are useful for both discriminative and reconstructive tasks at the same time. This paradigm is well suited for semi-supervised learning with sequences and we test our proposed approach on an action recognition task using motion capture (MOCAP) sequences. We find that semi-supervised feature learning can improve state-of-the-art movement classification accuracy on the HDM05 action dataset. Further, we find that even when using only labeled data and a primarily discriminative objective the addition of a reconstructive decoder can serve as a form of regularization that reduces over-fitting and improves test set accuracy.
Ambient intelligence: Technologies, applications, and opportunities

Ambient intelligence is an emerging discipline that brings intelligence to our everyday environments and makes those environments sensitive to us. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence. Because these contributing fields have experienced tremendous growth in the last few years, AmI research has strengthened and expanded. Because AmI research is maturing, the resulting technologies promise to revolutionarize daily human life by making people’s surroundings flexible and adaptive. In this paper we provide a survey of the technologies that comprise ambient intelligence and of the applications that are dramatically affected by it. In particular, we specifically focus on the research that makes AmI technologies “intelligent”. We also highlight challenges and opportunities that AmI researchers will face in the coming years.
Structured Pruning of Deep Convolutional Neural Networks

Real-time application of deep learning algorithms is often hindered by high computational complexity and frequent memory accesses. Network pruning is a promising technique to solve this problem. However, pruning usually results in irregular network connections that not only demand extra representation efforts but also do not fit well on parallel computation. We introduce structured sparsity at various scales for convolutional neural networks: feature map-wise, kernel-wise, and intra-kernel strided sparsity. This structured sparsity is very advantageous for direct computational resource savings on embedded computers, in parallel computing environments, and in hardware-based systems. To decide the importance of network connections and paths, the proposed method uses a particle filtering approach. The importance weight of each particle is assigned by assessing the misclassification rate with a corresponding connectivity pattern. The pruned network is retrained to compensate for the losses due to pruning. While implementing convolutions as matrix products, we particularly show that intra-kernel strided sparsity with a simple constraint can significantly reduce the size of the kernel and feature map tensors. The proposed work shows that when pruning granularities are applied in combination, we can prune the CIFAR-10 network by more than 70% with less than a 1% loss in accuracy.
Estimating PageRank on graph streams

This article focuses on computations on large graphs (e.g., the web-graph) where the edges of the graph are presented as a stream. The objective in the streaming model is to use small amount of memory (preferably sub-linear in the number of nodes <i>n</i>) and a smaller number of passes.
 In the streaming model, we show how to perform several graph computations including estimating the probability distribution after a random walk of length <i>l</i>, the mixing time <i>M</i>, and other related quantities such as the conductance of the graph. By applying our algorithm for computing probability distribution on the web-graph, we can estimate the <i>PageRank</i> <i>p</i> of any node up to an additive error of &sqrt;ε <i>p</i>+ε in <i>Õ</i>(&sqrt;<i>M</i>/α) passes and <i>Õ</i>(min(<i>n</i>α+1/ε&sqrt;<i>M</i>/α+(1/ε)<i>M</i>α, α <i>n</i>&sqrt;<i>M</i>α + (1/ε)&sqrt;<i>M</i>/α)) space, for any α ∈ (0,1]. Specifically, for ε = <i>M</i>/<i>n</i>, α = <i>M</i><sup>−1/2</sup>, we can compute the approximate PageRank values in Õ(<i>nM</i><sup>−1/4</sup>) space and Õ(<i>M</i><sup>3/4</sup>) passes. In comparison, a standard implementation of the PageRank algorithm will take <i>O(n)</i> space and <i>O(M)</i> passes. We also give an approach to approximate the PageRank values in just Õ(1) passes although this requires Õ(<i>nM</i>) space.
The State of the Art in Semantic Representation

Semantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e.g., AMR, UCCA, GMB, UDS) have been put forth. Yet, little has been done to assess the achievements and the shortcomings of these new contenders, compare them with syntactic schemes, and clarify the general goals of research on semantic representation. We address these gaps by critically surveying the state of the art in the
PREPARING FOR CULTURALLY RESPONSIVE TEACHING

In this article, a case is made for improving the school success of ethnically diverse students through culturally responsive teaching and for preparing teachers in preservice education programs with the knowledge, attitudes, and skills needed to do this. The ideas presented here are brief sketches of more thorough explanations included in my recent book, Culturally Responsive Teaching: Theory, Research, and Practice (2000). The specific components of this approach to teaching are based on research findings, theoretical claims, practical experiences, and personal stories of educators researching and working with underachieving African, Asian, Latino, and Native American students. These data were produced by individuals from a wide variety of disciplinary backgrounds including anthropology, sociology, psychology, sociolinguistics, communications, multicultural education, K-college classroom teaching, and teacher education. Five essential elements of culturally responsive teaching are examined: developing a knowledge base about cultural diversity, including ethnic and cultural diversity content in the curriculum, demonstrating caring and building learning communities, communicating with ethnically diverse students, and responding to ethnic diversity in the delivery of instruction. Culturally responsive teaching is defined as using the cultural characteristics, experiences, and perspectives of ethnically diverse students as conduits for teaching them more effectively. It is based on the assumption that when academic knowledge and skills are situated within the lived experiences and frames of reference of students, they are more personally meaningful, have higher interest appeal, and are learned more easily and thoroughly (Gay, 2000). As a result, the academic achievement of ethnically diverse students will improve when they are taught through their own cultural and experiential filters (Au & Kawakami, 1994; Foster, 1995; Gay, 2000; Hollins, 1996; Kleinfeld, 1975; Ladson-Billings, 1994, 1995).
Deep Meta-modelling with MetaDepth

Meta-modelling is at the core of Model-Driven Engineering, where it is used for language engineering and domain modelling. The OMG’s Meta-Object Facility is the standard framework for building and instantiating meta-models. However, in the last few years, several researchers have identified limitations and rigidities in such scheme, most notably concerning the consideration of only two meta-modelling levels at the same time. In this paper we present MetaDepth, a novel framework that supports a dual linguistic/ontological instantiation and permits building systems with an arbitrary number of meta-levels through deep meta-modelling. The framework implements advanced modelling concepts allowing the specification and evaluation of derived attributes and constraints across multiple meta-levels, linguistic extensions of ontological instance models, transactions, and hosting different constraint and action languages.
A thin slice perspective on the accuracy of W rst impressions

The accuracy of Wrst impressions was examined by investigating judged construct (negative aVect, positive aVect, the Big Wve personality variables, intelligence), exposure time (5, 20, 45, 60, and 300 s), and slice location (beginning, middle, end). Three hundred and thirty four judges rated 30 targets. Accuracy was deWned as the correlation between a judge’s ratings and the target’s criterion scores on the same construct. Negative aVect, extraversion, conscientiousness, and intelligence were judged moderately well after 5-s exposures; however, positive aVect, neuroticism, openness, and agreeableness required more exposure time to achieve similar levels of accuracy. Overall, accuracy increased with exposure time, judgments based on later segments of the 5-min interactions were more accurate, and 60 s yielded the optimal ratio between accuracy and slice length. Results suggest that accuracy of Wrst impressions depends on the type of judgment made, amount of exposure, and temporal location of the slice of judged social behavior.
Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles

The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction.
An Evaluation of Open Source Serverless Computing Frameworks

Recent advancements in virtualization and software architecture have led to the new paradigm of serverless computing, which allows developers to deploy applications as stateless functions without worrying about the underlying infrastructure. Accordingly, a serverless platform handles the lifecycle, execution and scaling of the actual functions; these need to run only when invoked or triggered by an event. Thus, the major benefits of serverless computing are low operational concerns and efficient resource management and utilization. Serverless computing is currently offered by several public cloud service providers. However, there are certain limitations on the public cloud platforms, such as vendor lock-in and restrictions on the computation of the functions. Open source serverless frameworks are a promising solution to avoid these limitations and bring the power of serverless computing to on-premise deployments. However, these frameworks have not been evaluated before. Thus, we carry out a comprehensive feature comparison of popular open source serverless computing frameworks. We then evaluate the performance of selected frameworks: Fission, Kubeless and OpenFaaS. Specifically, we characterize the response time and ratio of successfully received responses under different loads and provide insights into the design choices of each framework.
Deep Multimodal Learning: A Survey on Recent Advances and Trends

The success of deep learning has been a catalyst to solving increasingly complex machine-learning problems, which often involve multiple data modalities. We review recent advances in deep multimodal learning and highlight the state-of the art, as well as gaps and challenges in this active research field. We first classify deep multimodal learning architectures and then discuss methods to fuse learned multimodal representations in deep-learning architectures. We highlight two areas of research&#x02013;regularization strategies and methods that learn or optimize multimodal fusion structures&#x02013;as exciting areas for future work.
Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity.

Sentence encoders, which produce sentence embeddings using neural networks, are typically evaluated by how well they transfer to downstream tasks. This includes semantic similarity, an important task in natural language understanding. Although there has been much work dedicated to building sentence encoders, the accompanying transfer learning techniques have received relatively little attention. In this paper, we propose a transfer learning setting specialized for semantic similarity, which we refer to as direct network transfer. Through experiments on several standard text similarity datasets, we show that applying direct network transfer to existing encoders can lead to state-of-the-art performance. Additionally, we compare several approaches to transfer sentence encoders to semantic similarity tasks, showing that the choice of transfer learning setting greatly affects the performance in many cases, and differs by encoder and dataset.