nils-herrmann/clean_scirepeval huggingface dataset:
SALES AND MARKETING INTEGRATION : A PROPOSED FRAMEWORK

In response to increased competitive pressures, shortening product life cycles, and heightened customer demands, businesses are increasing their ability to effectively adapt and build competitive advantage by going horizontal—by flattening their organizations, breaking down barriers between functions, and stimulating more teamwork between functional areas. In a marketing context, this organizational trend is reflected in the growing interest in integrated marketing communications (IMC). IMC emphasizes the value of coordinating marketing communications activities—advertising, direct response, Web sites, sales promotion, and publicity. However, these IMC coordination efforts within marketing largely ignore the most significant marketing tool for communicating and influencing relationships with customers—the company’s sales force. Clearly, coordinating the sales and marketing functions can improve the effectiveness of activities undertaken by the functional areas. The Venn diagram in Figure 1 illustrates the need for interaction between sales and marketing. The activities in the circle on the left are primarily undertaken by the marketing function with input from sales, and the activities in the circle on the right are primarily undertaken by the sales function with input from marketing. The activities in the intersection of the two circles can be performed effectively only through a coordinated effort between sales and marketing. Thus, all of the activities shown in Figure 1 require some level of interaction between sales and marketing. Managers recognize this need for greater coordination between marketing and sales. In international surveys of senior executives from a wide range of business-to-business industries, sales and marketing integration was mentioned as one of the organizational changes that would do the most to improve sales performance and as one of the most important issues facing sales and marketing managers (Miller and Gist 2003; Rouziès 2004). Marketing research has examined the relationship between marketing and other functional areas (e.g., research and development, engineering, design, and manufacturing) to successfully develop and launch new products (e.g., Fisher, Maltz, and Jaworski 1997; Olson, Walker, and Ruekert 1995; Tatikonda and Montoya-Weiss 2001). However, research on the sales–marketing interface is limited. Notable exceptions include Cepedes (1996); Dewsnap and Jobber (2000); Strahle, Spiro, and Acito (1996); and Workman, Homburg, and Gruner (1998). Marketing and sales integration is one of the components of market-driven organizations. Narver and Slater (1990) and Slater and Narver (1994) have conceptualized market orientation as consisting of three components—customer orientation, competitor orientation, and interfunctional coordination. Likewise, Day, describing market-driven organizations, emphasizes “the cross-functional coordination and information SALES AND MARKETING  A PROPOSED FRAMEWORK
READ: Reverse Engineering of Automotive Data Frames

Security analytics and forensics applied to in-vehicle networks are growing research areas that gained relevance after recent reports of cyber-attacks against unmodified licensed vehicles. However, the application of security analytics algorithms and tools to the automotive domain is hindered by the lack of public specifications about proprietary data exchanged over in-vehicle networks. Since the controller area network (CAN) bus is the de-facto standard for the interconnection of automotive electronic control units, the lack of public specifications for CAN messages is a key issue. This paper strives to solve this problem by proposing READ: a novel algorithm for the automatic Reverse Engineering of Automotive Data frames. READ has been designed to analyze traffic traces containing unknown CAN bus messages in order to automatically identify and label different types of signals encoded in the payload of their data frames. Experimental results based on CAN traffic gathered from a licensed unmodified vehicle and validated against its complete formal specifications demonstrate that the proposed algorithm can extract and classify more than twice the signals with respect to the previous related work. Moreover, the execution time of signal extraction and classification is reduced by two orders of magnitude. Applications of READ to CAN messages generated by real vehicles demonstrate its usefulness in the analysis of CAN traffic.
Intelligent Guardrails: An IoT Application for Vehicle Traffic Congestion Reduction in Smart City

This paper discusses vehicle traffic congestion which leads to air pollution, driver frustration, and costs billions of dollars annually in fuel consumption. Finding a proper solution to vehicle congestion is a considerable challenge due to the dynamic and unpredictable nature of the network topology of vehicular environments, especially in urban areas. Recent advances in sensing, communication and computing technologies enables us to gather real-time data about traffic condition of the roads and mitigate the traffic congestion via various ways such as Vehicle Traffic Routing Systems (VTRSs), electronic toll collection system (ETCS), and intelligent traffic light signals (TLSs). Regarding this issue, an innovative technology, called Intelligent Guardrails (IGs), is presented in this paper. IGs takes advantages of Internet of Things (IoT) and vehicular networks to provide a solution for vehicle traffic congestion in large cities. IGs senses the roads' traffic condition and uses this information to set the capacity of the roads dynamically.
A DECISION SUPPORT SYSTEM FOR TUBERCULOSIS DIAGNOSABILITY

In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done by comparing expert knowledge and system generated response. This basic emblematic approach using fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and provides support platform to pulmonary researchers in identifying the ailment effectively.
Sentiment Analysis by Capsules

In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis. For a given problem, one capsule is built for each sentiment category e.g., ‘positive’ and ‘negative’. Each capsule has an attribute, a state, and three modules: representation module, probability module, and reconstruction module. The attribute of a capsule is the assigned sentiment category. Given an instance encoded in hidden vectors by a typical RNN, the representation module builds capsule representation by the attention mechanism. Based on capsule representation, the probability module computes the capsule’s state probability. A capsule’s state is active if its state probability is the largest among all capsules for the given instance, and inactive otherwise. On two benchmark datasets (i.e., Movie Review and Stanford Sentiment Treebank) and one proprietary dataset (i.e., Hospital Feedback), we show that RNN-Capsule achieves state-of-the-art performance on sentiment classification. More importantly, without using any linguistic knowledge, RNN-Capsule is capable of outputting words with sentiment tendencies reflecting capsules’ attributes. The words well reflect the domain specificity of the dataset.  Yequan Wang1 Aixin Sun2 Jialong Han3 Ying Liu4 Xiaoyan Zhu1. 2018. Sentiment Analysis by Capsules.  The 2018 Web Conference, April 23–27, 2018, Lyon, France. ACM, New York, NY, USA, 10 pages.
Assessment of the state of food waste treatment in the United States and Canada.

Currently in the US, over 97% of food waste is estimated to be buried in landfills. There is nonetheless interest in strategies to divert this waste from landfills as evidenced by a number of programs and policies at the local and state levels, including collection programs for source separated organic wastes (SSO). The objective of this study was to characterize the state-of-the-practice of food waste treatment alternatives in the US and Canada. Site visits were conducted to aerobic composting and two anaerobic digestion facilities, in addition to meetings with officials that are responsible for program implementation and financing. The technology to produce useful products from either aerobic or anaerobic treatment of SSO is in place. However, there are a number of implementation issues that must be addressed, principally project economics and feedstock purity. Project economics varied by region based on landfill disposal fees. Feedstock purity can be obtained by enforcement of contaminant standards and/or manual or mechanical sorting of the feedstock prior to and after treatment. Future SSO diversion will be governed by economics and policy incentives, including landfill organics bans and climate change mitigation policies.
Aspects of Data Ethics in a Changing World: Where Are We Now?

Ready data availability, cheap storage capacity, and powerful tools for extracting information from data have the potential to significantly enhance the human condition. However, as with all advanced technologies, this comes with the potential for misuse. Ethical oversight and constraints are needed to ensure that an appropriate balance is reached. Ethical issues involving data may be more challenging than the ethical challenges of some other advanced technologies partly because data and data science are ubiquitous, having the potential to impact all aspects of life, and partly because of their intrinsic complexity. We explore the nature of data, personal data, data ownership, consent and purpose of use, trustworthiness of data as well as of algorithms and of those using the data, and matters of privacy and confidentiality. A checklist is given of topics that need to be considered.
Realistic Real-Time Outdoor Rendering in Augmented Reality

Realistic rendering techniques of outdoor Augmented Reality (AR) has been an attractive topic since the last two decades considering the sizeable amount of publications in computer graphics. Realistic virtual objects in outdoor rendering AR systems require sophisticated effects such as: shadows, daylight and interactions between sky colours and virtual as well as real objects. A few realistic rendering techniques have been designed to overcome this obstacle, most of which are related to non real-time rendering. However, the problem still remains, especially in outdoor rendering. This paper proposed a much newer, unique technique to achieve realistic real-time outdoor rendering, while taking into account the interaction between sky colours and objects in AR systems with respect to shadows in any specific location, date and time. This approach involves three main phases, which cover different outdoor AR rendering requirements. Firstly, sky colour was generated with respect to the position of the sun. Second step involves the shadow generation algorithm, Z-Partitioning: Gaussian and Fog Shadow Maps (Z-GaF Shadow Maps). Lastly, a technique to integrate sky colours and shadows through its effects on virtual objects in the AR system, is introduced. The experimental results reveal that the proposed technique has significantly improved the realism of real-time outdoor AR rendering, thus solving the problem of realistic AR systems.
Friend or Foe?: Your Wearable Devices Reveal Your Personal PIN

The proliferation of wearable devices, e.g., smartwatches and activity trackers, with embedded sensors has already shown its great potential on monitoring and inferring human daily activities. This paper reveals a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people accessing key-based security systems. Existing methods of obtaining such secret information relies on installations of dedicated hardware (e.g., video camera or fake keypad), or training with labeled data from body sensors, which restrict use cases in practical adversary scenarios. In this work, we show that a wearable device can be exploited to discriminate mm-level distances and directions of the user's fine-grained hand movements, which enable attackers to reproduce the trajectories of the user's hand and further to recover the secret key entries. In particular, our system confirms the possibility of using embedded sensors in wearable devices, i.e., accelerometers, gyroscopes, and magnetometers, to derive the moving distance of the user's hand between consecutive key entries regardless of the pose of the hand. Our Backward PIN-Sequence Inference algorithm exploits the inherent physical constraints between key entries to infer the complete user key entry sequence. Extensive experiments are conducted with over 5000 key entry traces collected from 20 adults for key-based security systems (i.e. ATM keypads and regular keyboards) through testing on different kinds of wearables. Results demonstrate that such a technique can achieve 80% accuracy with only one try and more than 90% accuracy with three tries, which to our knowledge, is the first technique that reveals personal PINs leveraging wearable devices without the need for labeled training data and contextual information.
Achieving Cooperation Through Deep Multiagent Reinforcement Learning in Sequential Prisoner ’ s Dilemmas

The Iterated Prisoner’s Dilemma has guided research on social dilemmas for decades. However, it distinguishes between only two atomic actions: cooperate and defect. In real world prisoner’s dilemmas, these choices are temporally extended and different strategies may correspond to sequences of actions, reflecting grades of cooperation. We introduce a Sequential Prisoner’s Dilemma (SPD) game to better capture the aforementioned characteristics. In this work, we propose a deep multiagent reinforcement learning approach that investigates the evolution of mutual cooperation in SPD games. Our approach consists of two phases. The first phase is offline: it synthesizes policies with different cooperation degrees and then trains a cooperation degree detection network. The second phase is online: an agent adaptively selects its policy based on the detected degree of opponent cooperation. The effectiveness of our approach is demonstrated in two representative SPD 2D games: the Apple-Pear game and the Fruit Gathering game. Experimental results show that our strategy can avoid being exploited by exploitative opponents and achieve cooperation with cooperative opponents.
A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations

Innovative technology on intelligent processes for smart home applications that utilize Internet of Things (IoT) is mainly limited and dispersed. The available trends and gaps were investigated in this study to provide valued visions for technical environments and researchers. Thus, a survey was conducted to create a coherent taxonomy on the research landscape. An extensive search was conducted for articles on (a) smart homes, (b) IoT and (c) applications. Three databases, namely, IEEE Explore, ScienceDirect and Web of Science, were used in the article search. These databases comprised comprehensive literature that concentrate on IoT-based smart home applications. Subsequently, filtering process was achieved on the basis of intelligent processes. The final classification scheme outcome of the dataset contained 40 articles that were classified into four classes. The first class includes the knowledge engineering process that examines data representation to identify the means of accomplishing a task for IoT applications and their utilisation in smart homes. The second class includes papers on the detection process that uses artificial intelligence (AI) techniques to capture the possible changes in IoT-based smart home applications. The third class comprises the analytical process that refers to the use of AI techniques to understand the underlying problems in smart homes by inferring new knowledge and suggesting appropriate solutions for the problem. The fourth class comprises the control process that describes the process of measuring and instructing the performance of IoT-based smart home applications against the specifications with the involvement of intelligent techniques. The basic features of this evolving approach were then identified in the aspects of motivation of intelligent process utilisation for IoT-based smart home applications and open-issue restriction utilisation. The recommendations for the approval and utilisation of intelligent process for IoT-based smart home applications were also determined from the literature.
Statistical methods for analysing the spatial dimension of changes in land use and farming systems

AIC Akaike information criterion AUC area under the curve COV coefficient of variation GIS geographic information systems GPS Global Positioning System HYV high-yielding variety LUCC land use and land cover change NDVI normalized difference vegetation index OLS ordinary least squares PCA principal component analysis ROC relative or receiver operating characteristic SC Schwartz criterion SSE error sum of squares SSR regression sum of squares SST total sum of squares TLU tropical livestock unit 8 9 Acknowledgements This report is written as part of the project 'Transregional analysis of crop-livestock systems: understanding intensification and evolution across three continents' commissioned by the Ecoregional Fund and implemented by the International Livestock Research Institute (ILRI) in collaboration with the Department of Environmental Sciences of Wageningen University, and the Kenya Agricultural Research Institute. This report reviews a large amount of knowledge from the Land Use and Land Cover Change research community (LUCC, a joint IGBP/IHDP project) and is one of the activities of the LUCC Focus 3 Office hosted by the Department of Environmental Sciences, Wageningen University. Part of the writing was funded by the Foundation for the Advancement of Tropical Research (WOTRO) of the Netherlands Organization for Scientific Research (NWO) within the project 'Integrating macro-modelling and actor-oriented research in studying the dynamics of land use change in NorthEast Luzon, Philippines'. The authors would like to thank all who contributed to this report, especially the contributions of Isabelle Baltenweck, Jeannette van de Steeg and Koen Overmars and the thoughtful reviews of two anonymous referees. 1 Introduction Land use and land cover change (LUCC) has important impacts on the functioning of socioeconomic and environmental systems with important tradeoffs for sustainability, food security, biodiversity and the vulnerability of people and ecosystems to global change impacts. Land cover change refers to the complete replacement of one cover type by another, e.g. deforestation. Land use change includes the modification of land cover types, e.g. intensification of agricultural management or other changes in the farming system. Land use and land cover changes are the result of the interplay between socioeconomic , institutional and environmental factors. Key to understanding LUCC is to recognize the role of individual decision makers bringing about change, through their choices, on land resources and technologies. A unifying hypothesis that links the ecological and social realms, and an important reason for pursuing integrated modelling of LUCC, is that humans respond to cues both from the physical environment and …
Can Blockchain Strengthen the Internet of Things?

This column evaluates blockchain’s roles in strengthening security in the Internet of Things (IoT). Key underlying mechanisms related to the blockchain–IoT security nexus are covered. From a security standpoint, the article highlights how blockchain-based solutions could be, in many aspects, superior to the current IoT ecosystem, which relies mainly on centralized cloud servers. Using practical applications and real-world examples, the article argues that blockchain’s decentralized nature is likely to result in a low susceptibility to manipulation and forgery by malicious participants. Special consideration is given to how blockchain-based identity and access management systems can address some of the key challenges associated with IoT security. The column provides a detailed analysis and description of blockchain’s roles in tracking the sources of insecurity in supply chains related to IoT devices. Using blockchain, it is also possible to contain an IoT security breach in a targeted way after it is discovered. The column also discusses and evaluates initiatives of organizations, interorganizational networks, and industries on the frontlines of blockchain.
Online Controlled Experiments and A / B Tests

The internet connectivity of client software (e.g., apps running on phones and PCs), web sites, and online services provide an unprecedented opportunity to evaluate ideas quickly using controlled experiments, also called A/B tests, split tests, randomized experiments, control/treatment tests, and online field experiments. Unlike most data mining techniques for finding correlational patterns, controlled experiments allow establishing a causal relationship with high probability. Experimenters can utilize the Scientific Method to form a hypothesis of the form “If a specific change is introduced, will it improve key metrics?” and evaluate it with real users. The theory of a controlled experiment dates back to Sir Ronald A. Fisher’s experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, and the topic of offline experiments is well developed in Statistics (Box 2005). Online Controlled Experiments started to be used in the late 1990s with the growth of the Internet. Today, many large sites, including Amazon, Bing, Facebook, Google, LinkedIn, and Yahoo! run thousands to tens of thousands of experiments each year testing user interface (UI) changes, enhancements to algorithms (search, ads, personalization, recommendation, etc.), changes to apps, content management system, etc. Online controlled experiments are now considered an indispensable tool, and their use is growing for startups and smaller websites. Controlled experiments are especially useful in combination with Agile software development (Martin 2008, Rubin 2012), Steve Blank’s Customer Development process (Blank 2005), and MVPs (Minimum Viable Products) popularized by Eric Ries’s Lean Startup (Ries 2011).  Many good resources are available with motivation and explanations about online controlled experiments (Siroker and Koomen 2013, Goward 2012, McFarland 2012, Schrage 2014, Kohavi, Longbotham and Sommerfield, et al. 2009, Kohavi, Deng and Longbotham, et al. 2014, Kohavi, Deng and Frasca, et al. 2013).