nils-herrmann/clean_scirepeval huggingface dataset:
Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation

We present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extracts temporally consistent object tubes based on an off-the-shelf detector. Besides the class label for each tube, this provides a location prior that is independent of motion. For the final video segmentation, we combine this information with motion cues. The method overcomes the typical problems of weakly supervised/unsupervised video segmentation, such as scenes with no motion, dominant camera motion, and objects that move as a unit. In contrast to most tracking methods, it provides an accurate, temporally consistent segmentation of each object. We report results on four video segmentation datasets: YouTube Objects, SegTrackv2, egoMotion, and FBMS.
A Unified Perspective on Multi-Domain and Multi-Task Learning

In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL). By introducing the concept of a semantic descriptor, this framework unifies MDL and MTL as well as encompassing various classic and recent MTL/MDL algorithms by interpreting them as different ways of constructing semantic descriptors. Our interpretation provides an alternative pipeline for zero-shot learning (ZSL), where a model for a novel class can be constructed without training data. Moreover, it leads to a new and practically relevant problem setting of zero-shot domain adaptation (ZSDA), which is the analogous to ZSL but for novel domains: A model for an unseen domain can be generated by its semantic descriptor. Experiments across this range of problems demonstrate that our framework outperforms a variety of alternatives.
Building the Internet of Things Using RFID: The RFID Ecosystem Experience

At the University of Washington, the RFID ecosystem creates a microcosm for the Internet of Things. The authors developed a suite of Web-based, user-level tools and applications designed to empower users by facilitating their understanding, management, and control of personal RFID data and privacy settings. They deployed these applications in the RFID ecosystem and conducted a four-week user study to measure trends in adoption and utilization of the tools and applications as well as users' qualitative reactions.
Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.
User interface design and evaluation

All modules are designed for practising professionals and managers in the fields of computing, manufacturing and engineering, in related service industries and the public sector, and in educational organisations. Students are expected to have some experience of software development before they start any of these modules. Experience could be as developers, as project managers of developers, or as specifiers of software systems. Students do not require knowledge of any particular programming language, although they need to be comfortable with studying and manipulating symbols and notations. Specific modules make use of SQL and Java. Practical experience is an important part of the modules.
Strategies for Lean Product Development

The author acknowledges the financial support for this research made available by the Lean Aerospace Initiative at MIT sponsored jointly by the US Air Force and a consortium of aerospace companies. All facts, statements, opinions, and conclusions expressed herein are solely those of the author and do not in any way reflect those of the Lean Aerospace Initiative, the US Air Force, the sponsoring companies and organizations (individually or as a group), or MIT. The latter are absolved from any remaining errors or shortcomings for which the author takes full responsibility.  The essence of lean is very simple, but from a research and implementation point of view overwhelming. Lean is the search for perfection through the elimination of waste and the insertion of practices that contribute to reduction in cost and schedule while improving performance of products. This concept of lean has wide applicability to a large range of processes, people and organizations, from concept design to the factory floor, from the laborer to the upper management, from the customer to the developer. Progress has been made in implementing and raising the awareness of lean practices at the factory floor. However, the level of implementation and education in other areas, like product development, is very low. The Lean Aerospace Initiative (LAI) has been producing research in support of the military and industry since 1993 on the topic of lean and its benefits. Implementation of the research has been shown to have significant impact and interest. LAI is in a very unique situation at MIT to influence and educate world-class engineering students we have exposure to every day. This research will take advantage of this situation and produce a strategic framework for educating engineers on the front-end lean product development findings that have been produced through LAI. These include topics of understanding the customer and the product value, evaluating multidimensional risk, organizational impact on program performance, and many others. The research objectives are to: 1) synthesize the findings uncovered by LAI pertaining to non-manufacturing disciplines into a readily usable manner and 2) formulate a strategic approach for educating engineers on the tools and concepts that facilitate early problem synthesis, mission engineering, and front end product development. Overview There are six modules into which the LAI product development research has been organized. Module I is used to provide a fundamental framework of lean and its application to product development. Module II …
Large Margin Few-Shot Learning

The key issue of few-shot learning is learning to generalize. This paper proposes a large margin principle to improve the generalization capacity of metric based methods for few-shot learning. To realize it, we develop a unified framework to learn a more discriminative metric space by augmenting the classification loss function with a large margin distance loss function for training. Extensive experiments on two state-of-the-art few-shot learning methods, graph neural networks and prototypical networks, show that our method can improve the performance of existing models substantially with very little computational overhead, demonstrating the effectiveness of the large margin principle and the potential of our method.
Influence of Online Shopping in Consumer Behaviour

From the past few years, on-line shopping is the prevalent way of doing dealings in the field of E-Business and is unquestionably going to be the future of shopping in the human race. The increasing consumer base, principally of youths, is playing a significant role in the online shopping. Even though there is an increase in the consumer demand, there are some security issues reported regarding the online shopping and its delivery systems. These may resist the new entries into the online shopping. So this paper investigates the influence of online shopping in
Learning a Rotation Invariant Detector with Rotatable Bounding Box

Detection of arbitrarily rotated objects is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. The existing methods are not robust to angle varies of the objects because of the use of traditional bounding box, which is a rotation variant structure for locating rotated objects. In this article, a new detection method is proposed which applies the newly defined rotatable bounding box (RBox). The proposed detector (DRBox) can effectively handle the situation where the orientation angles of the objects are arbitrary. The training of DRBox forces the detection networks to learn the correct orientation angle of the objects, so that the rotation invariant property can be achieved. DRBox is tested to detect vehicles, ships and airplanes on satellite images, compared with Faster R-CNN and SSD, which are chosen as the benchmark of the traditional bounding box based methods. The results shows that DRBox performs much better than traditional bounding box based methods do on the given tasks, and is more robust against rotation of input image and target objects. Besides, results show that DRBox correctly outputs the orientation angles of the objects, which is very useful for locating multi-angle objects efficiently. The code and models are available at https://github.com/liulei01/DRBox.
Service Innovation Through Touchpoints : Development of an Innovation Toolkit for the First Stages of New Service Development

The field of service design is expanding rapidly in practice, and a body of formal research is beginning to appear to which the present article makes an important contribution. As innovations in services develop, there is an increasing need not only for research into emerging practices and developments but also into the methods that enable, support and promote such unfolding changes. This article tackles this need directly by referring to a large design research project, and performing a related practicebased inquiry into the co-design and development of methods for fostering service design in organizations wishing to improve their service offerings to customers. In particular, with reference to a funded four-year research project, one aspect is elaborated on that uses cards as a method to focus on the importance and potential of touch-points in service innovation. Touch-points are one of five aspects in the project that comprise a wider, integrated model and means for implementing innovations in service design. Touch-points are the points of contact between a service provider and customers. A customer might utilise many different touch-points as part of a use scenario (often called a customer journey). For example, a bank’s touch points include its physical buildings, web-site, physical print-outs, self-service machines, bank-cards, customer assistants, call-centres, telephone assistance etc. Each time a person relates to, or interacts with, a touch-point, they have a service-encounter. This gives an experience and adds something to the person’s relationship with the service and the service provider. The sum of all experiences from touch-point interactions colours their opinion of the service (and the service provider). Touch-points are one of the central aspects of service design. A commonly used definition of service design is “Design for experiences that happen over time and across different touchpoints” (ServiceDesign.org). As this definition shows, touchpoints are often cited as one of the major elements of service
CLASSIFICATION OF DATA WAREHOUSE TESTING APPROACHES

Warehouse is a collection of large amount of data which is used by the management for making strategic decisions. The data in a data warehouse is gathered from heterogeneous sources and then populated and queried for carrying out the analysis. The data warehouse design must support the queries for which it is being used for. The design is often an iterative process and must be modified a number of times before any model can be stabilized. The design life cycle of any product includes various stages wherein, testing being the most important one. Data warehouse design has received considerable attention whereas data warehouse testing is being explored now by various researchers. This paper discusses about various categories of testing activities being carried out in a data warehouse at different levels.
Fast Deep Matting for Portrait Animation on Mobile Phone

Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait image. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided filter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers.
Multimodal Human-Human-Robot Interactions ( MHHRI ) Dataset for Studying Personality and Engagement

In this paper we introduce a novel dataset, the Multimodal Human-Human-Robot-Interactions (MHHRI) dataset, with the aim of studying personality simultaneously in human-human interactions (HHI) and human-robot interactions (HRI) and its relationship with engagement. Multimodal data was collected during a controlled interaction study where dyadic interactions between two human participants and triadic interactions between two human participants and a robot took place with interactants asking a set of personal questions to each other. Interactions were recorded using two static and two dynamic cameras as well as two biosensors, and meta-data was collected by having participants fill in two types of questionnaires, for assessing their own personality traits and their perceived engagement with their partners (self labels) and for assessing personality traits of the other participants partaking in the study (acquaintance labels). As a proof of concept, we present baseline results for personality and engagement classification. Our results show that (i) trends in personality classification performance remain the same with respect to the self and the acquaintance labels across the HHI and HRI settings; (ii) for extroversion, the acquaintance labels yield better results as compared to the self labels; (iii) in general, multi-modality yields better performance for the classification of personality traits.
The bilingual adaptation: How minds accommodate experience.

According to some estimates, more than half of the world's population is multilingual to some extent. Because of the centrality of language use to human experience and the deep connections between linguistic and nonlinguistic processing, it would not be surprising to find that there are interactions between bilingualism and cognitive and brain processes. The present review uses the framework of experience-dependent plasticity to evaluate the evidence for systematic modifications of brain and cognitive systems that can be attributed to bilingualism. The review describes studies investigating the relation between bilingualism and cognition in infants and children, younger and older adults, and patients, using both behavioral and neuroimaging methods. Excluded are studies whose outcomes focus primarily on linguistic abilities because of their more peripheral contribution to the central question regarding experience-dependent changes to cognition. Although most of the research discussed in the review reports some relation between bilingualism and cognitive or brain outcomes, several areas of research, notably behavioral studies with young adults, largely fail to show these effects. These discrepancies are discussed and considered in terms of methodological and conceptual issues. The final section proposes an account based on "executive attention" to explain the range of research findings and to set out an agenda for the next steps in this field.
Near Field Communication (NFC) Technology: A Survey

Near Field Communication, NFCis one of the latest short range wireless communication technologies. NFC provides safe communication between electronic gadgets. NFC-enabled devices can just be pointed or touched by the users of their devices to other NFC-enabled devices to communicate with them. With NFC technology, communication is established when an NFC-compatible device is brought within a few centimetres of another i.e. around 20 cm theoretically (4cm is practical). The immense benefit of the short transmission range is that it prevents eavesdropping on NFC-enabled dealings. NFC technology enables several innovative usage scenarios for mobile devices. NFC technology works on the basis of RFID technology which uses magnetic field induction to commence communication between electronic devices in close vicinity. NFC operates at 13.56MHz and has 424kbps maximum data transfer rate. NFC is complementary to Bluetooth and 802.11 with their long distance capabilities. In card emulation mode NFC devices can offer contactless/wireless smart card standard. This technology enables smart phones to replace traditional plastic cards for the purpose of ticketing, payment, etc. Sharing (share files between phones), service discovery i.e. get information by touching smart phones etc. are other possible applications of NFC using smart phones. This paper provides an overview of NFC technology in a detailed manner including working principle, transmission details, protocols and standards, application scenarios, future market, security standards and vendor’s chipsets which are available for this standard. This comprehensive survey should serve as a useful guide for students, researchers and academicians who are interested in NFC Technology and its applications .
A Learning Framework for Winner-Take-All Networks with Stochastic Synapses

Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this letter, we show that a biologically motivated model based on multilayer winner-take-all circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task and a semisupervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.
Fatigue and Bearing Analysis of an Engine Connecting Rod Based on Multi-body Dynamics

This paper has studied the fatigue and friction of big end bearing on an engine connecting rod by combining the multi-body dynamics and hydrodynamic lubrication model. First, the basic equations and the application on AVL-Excite software platform of multi-body dynamics have been described in detail. Then, introduce the hydrodynamic lubrication model, which is the extended Reynolds equation derived from the Navier-Stokes equation and the equation of continuity. After that, carry out the static calculation of connecting rod assembly. At the same time, multi-body dynamics analysis has been performed and stress history can be obtained by finite element data recovery. Next, execute the fatigue analysis combining the Static stress and dynamic stress, safety factor distribution of connecting rod will be obtained as result. At last, detailed friction analysis of the big-end bearing has been performed. And got a good agreement when contrast the simulation results to the Bearing wear in the experiment.
Using Blockchain to Enhance the Trustworthiness of Business Processes: A Goal-Oriented Approach

Using Blockchain seems a promising approach for Business Process Reengineering (BPR) to alleviate trust issues among stakeholders, by providing decentralization, transparency, traceability, and immutability of information along with its business logic. However, little work seems to be available on utilizing Blockchain for supporting BPR in a systematic and rational way, potentially leading to disappointments and even doubts on the utility of Blockchain. In this paper, as ongoing research, we outline Fides - a framework for exploiting Blockchain towards enhancing the trustworthiness for BPR. Fides supports diagnosing trust issues with AS-IS business processes, exploring TO-BE business process alternatives using Blockchain, and selecting among the alternatives. A business process of a retail chain for a food supply chain is used throughout the paper to illustrate Fides concepts.