A Survey of RFID Authentication Protocols Based on Hash-Chain Method

Security and privacy are the inherent problems in RFID communications. There are several protocols have been proposed to overcome those problems. Hash chain is commonly employed by the protocols to improve security and privacy for RFID authentication. Although the protocols able to provide specific solution for RFID security and privacy problems, they fail to provide integrated solution. This article is a survey to closely observe those protocols in terms of its focus and limitations.Comment: Third ICCIT 2008 International Conference on Convergence and Hybrid Information Technolog

Systematic analysis of the Hippo pathway organization and oncogenic alteration in evolution.

The Hippo pathway is a central regulator of organ size and a key tumor suppressor via coordinating cell proliferation and death. Initially discovered in Drosophila, the Hippo pathway has been implicated as an evolutionarily conserved pathway in mammals; however, how this pathway was evolved to be functional from its origin is still largely unknown. In this study, we traced the Hippo pathway in premetazoan species, characterized the intrinsic functions of its ancestor components, and unveiled the evolutionary history of this key signaling pathway from its unicellular origin. In addition, we elucidated the paralogous gene history for the mammalian Hippo pathway components and characterized their cancer-derived somatic mutations from an evolutionary perspective. Taken together, our findings not only traced the conserved function of the Hippo pathway to its unicellular ancestor components, but also provided novel evolutionary insights into the Hippo pathway organization and oncogenic alteration

Chatbot Empathy in Customer Service: When It Works and When It Backfires

The advance of artificial intelligence technologies has enabled chatbots to be emotionally responsive. While expressing empathy constitutes a critical component of emotional responsiveness often required for human service employees, the impact of empathy expressions by service chatbots is underexamined. In this research, we investigate the effect of service chatbots’ empathy expressions towards two possible sources for customers’ negative emotions: negative consumption experience and chatbot failure. Drawing on the social perception literature, we propose that chatbot-expressed empathy towards negative consumption experience enhances service evaluations by increasing perceived warmth of a chatbot, but not competence. We further propose that chatbot-expressed empathy towards chatbot failure hurts service evaluations by decreasing perceived competence of a chatbot, but not warmth. Results from laboratory experiments provide suggestive evidence for our arguments. Our theoretical framework and findings illuminate the role of empathy expressed by service chatbots and offer guidance on when to deploy empathic chatbots in practice

Public Policies and Women's Employment after Childbearing

This paper examines how the public policy environment in the United States affects work by new mothers following childbirth. We examine four types of policies that vary across states and affect the budget constraint in different ways. The policy environment has important effects, particularly for less advantaged mothers. There is a potential conflict between policies aiming to increase maternal employment and those maximizing the choices available to families with young children. However, this tradeoff is not absolute since some choice-increasing policies (generous child care subsidies and state parental leave laws) foster both choice and higher levels of employment.public policies, maternal employment, childbearing

Implementing Choices in Chatbot-initiated Service Interactions: Helpful or Harmful?

Chatbots are increasingly equipped to provide choices for customers to click and choose from when communicating with the chatbots. This research investigates when and why implementing choices enhances or impairs customers’ service experience. Based on the concept of fluency, we posit that the implementation of choices is beneficial only after a conversational breakdown occurs because the value of choice provision for facilitating fluency may not be recognizable or realized in the absence of service breakdowns. We further propose that the implementation of choices is counterproductive when the choice set is perceived as incomprehensive because it decreases the perception of fluency. We conducted several experiments to test these hypotheses. By illuminating when and why choice implementation may help or harm customers during a chatbot-initiated service interaction, we augment the current understanding of a chatbot’s role in customers’ service experience and provide insights for the deployment of choice-equipped chatbots in customer service

A pairing-based blind signature scheme with message recovery

Blind signatures enable users to obtain valid signatures for a message without revealing its content to the signer. This paper presents a new blind signature scheme, i.e. identity-based blind signature scheme with message recovery. Due to the message recovery property, the new scheme requires less bandwidth than the identity based blind signatures with similar constructions. The scheme is based on modified Weil/Tate pairings over elliptic curves, and thus requires smaller key sizes for the same level of security compared to previous approaches not utilizing bilinear pairings. Security and efficiency analysis for the scheme is provided in this paper

How volume of water effects in morphological growth and number of endomycorrhizas on P. delicatum [abstract]

Abstract only availableIn alpine tundra plants, water is the most important environmental factor of plant growth. And the endomycorrhiza is the greatest symbiotic organism. Because, it provides Nitrogen and Phosphorus to the plants and it obtains the a Carbohydrate from the plants. Based on the experimental result, I thought about that if the plants had reached the limit, would they effect to the mycorrhiza? For that question, I made a experiment plan.Through controling of water volume, I could get the data of P.delicatum (called Jacob's ladder) about the morphological growths and the increment of the endomycorrhizas for 3 weeks. To control the environment, I built a weather port which can avoid rain, in addition opened the side of the weather port to make sure other variations as equal. After that, I made 4 treatments, which 10 plants have randomly assigned. And using ANOVA-test, I got 0.8615 as probablity. It means they are randomly assigned well. Each treatment has different water volume from 50ml every day to none of water. And then, I collected pre-data and post-data of morphological feature and number of endomycorrhizas. Unfortunately, I can't make a conclusion now. Because, I finished counting og the endomycorrhizas yesterday. So I need time to apply statistical approachs to my data. But I presuppose the result as the more watered plants would be had more endomycorrhizas

Dictionary module and UDC: Two new approaches to enhance embedding capacity of a steganographic channel

In this paper we present a new technique to enhance embedding capacity of a steganographic channel by preprocessing the secret data. Here we limit ourselves to textual data only. By preprocessing we don't mean compression; secret data can be compressed after it has been preprocessed by our technique. We introduce the concept of Scrambled Letters, User Defined Codes and the Dictionary Module to explain our technique. All these concepts when applied together give a phenomenal embedding capacity. Theoretical results show that we can achieve at least 25-30% increase in embedding capacity. We introduce two techniques, one in which we only apply the dictionary concept while the other in which we use the user defined codes along with the dictionary approach. The former technique is generic and can be applied to any form of textual data, whereas the latter can only be applied to pure text, i.e. without any form of graphics like images or graphs. Once the data is preprocessed by using our technique it can be easily embedded in any steganographic cover medium by using any steganographic algorithm

Powerful tool to expand business intelligence: Text mining

With the extensive inclusion of document, especially text, in the business systems, data mining does not cover the full scope of Business Intelligence. Data mining cannot deliver its impact on extracting useful details from the large collection of unstructured and semi-structured written materials based on natural languages. The most pressing issue is to draw the potential business intelligence from text. In order to gain competitive advantages for the business, it is necessary to develop the new powerful tool, text mining, to expand the scope of business intelligence. In this paper, we will work out the strong points of text mining in extracting business intelligence from huge amount of textual information sources within business systems. We will apply text mining to each stage of Business Intelligence systems to prove that text mining is the powerful tool to expand the scope of BI. After reviewing basic definitions and some related technologies, we will discuss the relationship and the benefits of these to text mining. Some examples and applications of text mining will also be given. The motivation behind is to develop new approach to effective and efficient textual information analysis. Thus we can expand the scope of Business Intelligence using the powerful tool, text mining