nils-herrmann/clean_scirepeval huggingface dataset:
Software maintenance and evolution: a roadmap

S AND OBJECTIVES are maintenance and evolution are characterised by huge cost and slow speed of implementation. Yet they nevitable activities – almost all software that is useful successful stimulates user-generated requests for ge and improvements. Our aim is to describe a scape for research in software maintenance and ution over the next ten years, in order to improve the d and accuracy of change while reducing costs, by tifying key problems, promising solution strategies and s of importance. The aims are met, by taking two oaches. Firstly current trends and practices are cted forward using a new model of software evolution d the staged model. Both strategic problems and rch to solve particular tactical problems are described in this framework. Secondly, a longer term, and much radical vision of software evolution is presented. Both ral principles and specific research topics are provided, within an overall strategy of engineering research and nale.
The unsupervised learning of natural language structure

There is precisely one complete language processing system to date: the human brain. Though there is debate on how much built-in bias human learne rs might have, we definitely acquire language in a primarily unsupervised fashio n. On the other hand, computational approaches to language processing are almost excl usively supervised, relying on hand-labeled corpora for training. This reliance is largel y due to unsupervised approaches having repeatedly exhibited discouraging performance. In particular, the problem of learning syntax (grammar) from completely unannotated text has r eceived a great deal of attention for well over a decade, with little in the way of positive results. We argue that previous methods for this task have generally underperformed becaus of the representations they used. Overly complex models are easily distracted by non-sy ntactic correlations (such as topical associations), while overly simple models aren’t r ich enough to capture important first-order properties of language (such as directionality , adjacency, and valence). In this work, we describe several syntactic representation s and associated probabilistic models which are designed to capture the basic character of natural language syntax as directly as possible. First, we examine a nested, distribut ional method which induces bracketed tree structures. Second, we examine a dependency model which induces word-to-word dependency structures. Finally, we demonstrate that these two models perform better in combination than they do alone. With these representations , high-quality analyses can be learned from surprisingly little text, with no labeled exam ples, in several languages (we show experiments with English, German, and Chinese). Our re sults show above-baseline performance in unsupervised parsing in each of these langua ges. Grammar induction methods are useful since parsed corpora e xist for only a small number of languages. More generally, most high-level NLP tasks , uch as machine translation
Visual Relationship Detection with Language Priors

Visual relationships capture a wide variety of interactions between pairs of objects in images (e.g. “man riding bicycle” and “man pushing bicycle”). Consequently, the set of possible relationships is extremely large and it is difficult to obtain sufficient training examples for all possible relationships. Because of this limitation, previous work on visual relationship detection has concentrated on predicting only a handful of relationships. Though most relationships are infrequent, their objects (e.g. “man” and “bicycle”) and predicates (e.g. “riding” and “pushing”) independently occur more frequently. We propose a model that uses this insight to train visual models for objects and predicates individually and later combines them together to predict multiple relationships per image. We improve on prior work by leveraging language priors from semantic word embeddings to finetune the likelihood of a predicted relationship. Our model can scale to predict thousands of types of relationships from a few examples. Additionally, we localize the objects in the predicted relationships as bounding boxes in the image. We further demonstrate that understanding relationships can improve content based image retrieval.
Do social media marketing activities enhance customer equity ? An empirical study of luxury fashion brand ☆

a r t i c l e i n f o Keywords: Luxury brands Perceived social media marketing (SMM) activities Value equity Relationship equity Brand equity Customer equity Purchase intention In light of a growing interest in the use of social media marketing (SMM) among luxury fashion brands, this study set out to identify attributes of SMM activities and examine the relationships among those perceived activities, value equity, relationship equity, brand equity, customer equity, and purchase intention through a structural equation model. Five constructs of perceived SSM activities of luxury fashion brands are entertainment , interaction, trendiness, customization, and word of mouth. Their effects on value equity, relationship equity, and brand equity are significantly positive. For the relationship between customer equity drivers and customer equity, brand equity has significant negative effect on customer equity while value equity and relationship equity show no significant effect. As for purchase intention, value equity and relationship equity had significant positive effects, while relationship equity had no significant influence. Finally, the relationship between purchase intention and customer equity has significance. The findings of this study can enable luxury brands to forecast the future purchasing behavior of their customers more accurately and provide a guide to managing their assets and marketing activities as well. The luxury market has attained maturity, along with the gradual expansion of the scope of its market and a rapid growth in the number of customers. Luxury market is a high value-added industry basing on high brand assets. Due to the increased demand for luxury in emerging markets such as China, India, and the Middle East, opportunities abound to expand the business more than ever. In the past, luxury fashion brands could rely on strong brand assets and secure regular customers. However, the recent entrance of numerous fashion brands into the luxury market, followed by heated competition, signals unforeseen changes in the market. A decrease in sales related to a global economic downturn drives luxury businesses to change. Now they can no longer depend solely on their brand symbol but must focus on brand legacy, quality, esthetic value, and trustworthy customer relationships in order to succeed. A key element to luxury industry becomes providing values to customers in every way possible. As a means to constitute customer assets through effective communication with consumers, luxury brands have tilted their eyes toward social media. Marketing communication using social media such as Twitter, Facebook, and …
New Digital Media Marketing and Micro Business: A UK Perspective

This paper considers innovative marketing within the context of a micro firm, exploring how such firm’s marketing practices can take advantage of digital media. Factors that influence a micro firm’s innovative activities are examined and the development and implementation of digital media in the firm’s marketing practice is explored. Despite the significance of marketing and innovation to SMEs, a lack of literature and theory on innovation in marketing theory exists. Research suggests that small firms’ marketing practitioners and entrepreneurs have identified their marketing focus on the 4Is. This paper builds on knowledge in innovation and marketing and examines the process in a micro firm. A qualitative approach is applied using action research and case study approach. The relevant literature is reviewed as the starting point to diagnose problems and issues anticipated by business practitioners. A longitudinal study is used to illustrate the process of actions taken with evaluations and reflections presented. The exploration illustrates that in practice much of the marketing activities within micro firms are driven by incremental innovation. This research emphasises that integrating Information Communication Technologies (ICTs) successfully in marketing requires marketers to take an active managerial role far beyond their traditional areas of competence and authority.
Authentication Protocols for Internet of Things: A Comprehensive Survey

1Department of Computer Science, Guelma University, BP 401, 24000 Guelma, Algeria 2Networks and Systems Laboratory, Badji Mokhtar University, BP 12, 23000 Annaba, Algeria 3School of Computer Science and Informatics, Cyber Security Centre, De Montfort University, Leicester, UK 4Research Institute for Future Media Computing, Shenzhen University, Shenzhen, China 5Guangdong University of Petrochemical Technology, Guangdong, China 6School of Engineering, University of Lincoln, Lincoln, UK
Mapping to Declarative Knowledge for Word Problem Solving

Math word problems form a natural abstraction to a range of quantitative reasoning problems, such as understanding financial news, sports results, and casualties of war. Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc. In this paper, we develop declarative rules which govern the translation of natural language description of these concepts to math expressions. We then present a framework for incorporating such declarative knowledge into word problem solving. Our method learns to map arithmetic word problem text to math expressions, by learning to select the relevant declarative knowledge for each operation of the solution expression. This provides a way to handle multiple concepts in the same problem while, at the same time, supporting interpretability of the answer expression. Our method models the mapping to declarative knowledge as a latent variable, thus removing the need for expensive annotations. Experimental evaluation suggests that our domain knowledge based solver outperforms all other systems, and that it generalizes better in the realistic case where the training data it is exposed to is biased in a different way than the test data.
Theory and Applications of Natural Language Processing

One of the more novel approaches to collaboratively creating language resources in recent years is to use online games to collect and validate data. The most significant challenges collaborative systems face are how to train users with the necessary expertise and how to encourage participation on a scale required to produce high quality data comparable with data produced by “traditional” experts. In this chapter we provide a brief overview of collaborative creation and the different approaches that have been used to create language resources, before analysing games used for this purpose. We discuss some key issues in using a gaming approach, including task design, player motivation and data quality, and compare the costs of each approach in terms of development, distribution and ongoing administration. In conclusion, we summarise the benefits and limitations of using a gaming approach to resource creation and suggest key considerations for evaluating its utility in different research scenarios.
Image Segmentation and its Different Techniques: An In-Depth Analysis

Image segmentation is the most important part of image processing, separating an image into multiple meaningful parts. In this area of the image segmentation, new technologies emerge day after day. In this paper, an in-depth analysis is carried out on some frequently adopted image segmentation techniques such as thresholding based techniques, edge detection based or boundary based techniques, region-based techniques, clustering etc. and also discuss their advantages and disadvantages.
A Discriminatively Learned CNN Embedding for Person Reidentification

In this article, we revisit two popular convolutional neural networks in person re-identification (re-ID): verification and identification models. The two models have their respective advantages and limitations due to different loss functions. Here, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a Siamese network that simultaneously computes the identification loss and verification loss. Given a pair of training images, the network predicts the identities of the two input images and whether they belong to the same identity. Our network learns a discriminative embedding and a similarity measurement at the same time, thus taking full usage of the re-ID annotations. Our method can be easily applied on different pretrained networks. Albeit simple, the learned embedding improves the state-of-the-art performance on two public person re-ID benchmarks. Further, we show that our architecture can also be applied to image retrieval. The code is available at https://github.com/layumi/2016_person_re-ID.
Cyber Threat Intelligence – Issue and Challenges

2018 Revised Mar 2, 2018 Accepted Mar 18, 2018 Today threat landscape evolving at the rapid rate with much organization continuously face complex and malicious cyber threats. Cybercriminal equipped by better skill, organized and well-funded than before. Cyber Threat Intelligence (CTI) has become a hot topic and being under consideration for many organization to counter the rise of cyber-attacks. The aim of this paper is to review the existing research related to CTI. Through the literature review process, the most basic question of what CTI is examines by comparing existing definitions to find common ground or disagreements. It is found that both organization and vendors lack a complete understanding of what information is considered to be CTI, hence more research is needed in order to define CTI. This paper also identified current CTI product and services that include threat intelligence data feeds, threat intelligence standards and tools that being used in CTI. There is an effort by specific industry to shared only relevance threat intelligence data feeds such as Financial Services Information Sharing and Analysis Center (FS-ISAC) that collaborate on critical security threats facing by global financial services sector only. While research and development center such as MITRE working in developing a standards format (e.g.; STIX, TAXII, CybOX) for threat intelligence sharing to solve interoperability issue between threat sharing peers. Based on the review for CTI definition, standards and tools, this paper identifies four research challenges in cyber threat intelligence and analyses contemporary work carried out in each. With an organization flooded with voluminous of threat data, the requirement for qualified threat data analyst to fully utilize CTI and turn the data into actionable intelligence become more important than ever. The data quality is not a new issue but with the growing adoption of CTI, further research in this area is needed
Analysis and Design of High-Frequency Isolated Dual-Bridge Series Resonant DC/DC Converter

Bidirectional dual-bridge dc/dc converter with high frequency isolation is gaining more attentions in renewable energy system due to small size and high-power density. In this paper, a dual-bridge series resonant dc/dc converter is analyzed with two simple modified ac equivalent circuit analysis methods for both voltage source load and resistive load. In both methods, only fundamental components of voltages and currents are considered. All the switches may work in either zero-voltage-switching or zero-current-switching for a wide variation of voltage gain, which is important in renewable energy generation. It is also shown in the second method that the load side circuit could be represented with an equivalent impedance. The polarity of cosine value of this equivalent impedance angle reveals the power flow direction. The analysis is verified with computer simulation results. Experimental data based on a 200 W prototype circuit is included for validation purpose.
Smart Sensor Systems for Wearable Electronic Devices

Wearable human interaction devices are technologies with various applications for improving human comfort, convenience and security and for monitoring health conditions. Healthcare monitoring includes caring for the welfare of every person, which includes early diagnosis of diseases, real-time monitoring of the effects of treatment, therapy, and the general monitoring of the conditions of people’s health. As a result, wearable electronic devices are receiving greater attention because of their facile interaction with the human body, such as monitoring heart rate, wrist pulse, motion, blood pressure, intraocular pressure, and other health-related conditions. In this paper, various smart sensors and wireless systems are reviewed, the current state of research related to such systems is reported, and their detection mechanisms are compared. Our focus was limited to wearable and attachable sensors. Section 1 presents the various smart sensors. In Section 2, we describe multiplexed sensors that can monitor several physiological signals simultaneously. Section 3 provides a discussion about short-range wireless systems including bluetooth, near field communication (NFC), and resonance antenna systems for wearable electronic devices.
Joint inference of entities, relations, and coreference

Although joint inference is an effective approach to avoid cascading of errors when inferring multiple natural language tasks, its application to information extraction has been limited to modeling only two tasks at a time, leading to modest improvements. In this paper, we focus on the three crucial tasks of automated extraction pipelines: entity tagging, relation extraction, and coreference. We propose a single, joint graphical model that represents the various dependencies between the tasks, allowing flow of uncertainty across task boundaries. Since the resulting model has a high tree-width and contains a large number of variables, we present a novel extension to belief propagation that sparsifies the domains of variables during inference. Experimental results show that our joint model consistently improves results on all three tasks as we represent more dependencies. In particular, our joint model obtains 12% error reduction on tagging over the isolated models.
Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks)

Group equivariant and steerable convolutional neural networks (regular and steerable G-CNNs) have recently emerged as a very effective model class for learning from signal data such as 2D and 3D images, video, and other data where symmetries are present. In geometrical terms, regular G-CNNs represent data in terms of scalar fields (“feature channels”), whereas the steerable G-CNN can also use vector or tensor fields (“capsules”) to represent data. In algebraic terms, the feature spaces in regular G-CNNs transform according to a regular representation of the group G, whereas the feature spaces in Steerable G-CNNs transform according to the more general induced representations of G. In order to make the network equivariant, each layer in a G-CNN is required to intertwine between the induced representations associated with its input and output space. In this paper we present a general mathematical framework for G-CNNs on homogeneous spaces like Euclidean space or the sphere. We show, using elementary methods, that the layers of an equivariant network are convolutional if and only if the input and output feature spaces transform according to an induced representation. This result, which follows from G.W. Mackey’s abstract theory on induced representations, establishes G-CNNs as a universal class of equivariant network architectures, and generalizes the important recent work of Kondor & Trivedi on the intertwiners between regular representations. In order for a convolution layer to be equivariant, the filter kernel needs to satisfy certain linear equivariance constraints. The space of equivariant kernels has a rich and interesting structure, which we expose using direct calculations. Additionally, we show how this general understanding can be used to compute a basis for the space of equivariant filter kernels, thereby providing a straightforward path to the implementation of G-CNNs for a wide range of groups and manifolds. 1 ar X iv :1 80 3. 10 74 3v 2 [ cs .L G ] 3 0 M ar 2 01 8
Rare-earth-doped biological composites as in vivo shortwave infrared reporters

The extension of in vivo optical imaging for disease screening and image-guided surgical interventions requires brightly emitting, tissue-specific materials that optically transmit through living tissue and can be imaged with portable systems that display data in real-time. Recent work suggests that a new window across the short-wavelength infrared region can improve in vivo imaging sensitivity over near infrared light. Here we report on the first evidence of multispectral, real-time short-wavelength infrared imaging offering anatomical resolution using brightly emitting rare-earth nanomaterials and demonstrate their applicability toward disease-targeted imaging. Inorganic-protein nanocomposites of rare-earth nanomaterials with human serum albumin facilitated systemic biodistribution of the rare-earth nanomaterials resulting in the increased accumulation and retention in tumour tissue that was visualized by the localized enhancement of infrared signal intensity. Our findings lay the groundwork for a new generation of versatile, biomedical nanomaterials that can advance disease monitoring based on a pioneering infrared imaging technique.
Discriminative Learning of Deep Convolutional Feature Point Descriptors

Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patch representations and in particular train a Siamese network with pairs of (non-)corresponding patches. We deal with the large number of potential pairs with the combination of a stochastic sampling of the training set and an aggressive mining strategy biased towards patches that are hard to classify. By using the L2 distance during both training and testing we develop 128-D descriptors whose euclidean distances reflect patch similarity, and which can be used as a drop-in replacement for any task involving SIFT. We demonstrate consistent performance gains over the state of the art, and generalize well against scaling and rotation, perspective transformation, non-rigid deformation, and illumination changes. Our descriptors are efficient to compute and amenable to modern GPUs, and are publicly available.