Investigation of Advanced Energy Saving Stand by Strategies for Production Systems

AbstractSwitching machinery into standby during non-productive phases for saving energy is rarely applied in today's production environments. Frequent reasons for this are lack of information on potential benefits and the uncertainty on resulting, potentially negative effects. Thus, in a recent project an approach for investigating both economical and ecological benefits was developed, integrating a facile definition process of possible standby modes and basic production system simulation for investigation of different switching strategies. The results of this estimation are evaluated both economically and ecologically, providing a clear decision base for strategy selection. In this paper, the approach is introduced along with exemplary results

MoPS: A Modular Protection Scheme for Long-Term Storage

Current trends in technology, such as cloud computing, allow outsourcing the storage, backup, and archiving of data. This provides efficiency and flexibility, but also poses new risks for data security. It in particular became crucial to develop protection schemes that ensure security even in the long-term, i.e. beyond the lifetime of keys, certificates, and cryptographic primitives. However, all current solutions fail to provide optimal performance for different application scenarios. Thus, in this work, we present MoPS, a modular protection scheme to ensure authenticity and integrity for data stored over long periods of time. MoPS does not come with any requirements regarding the storage architecture and can therefore be used together with existing archiving or storage systems. It supports a set of techniques which can be plugged together, combined, and migrated in order to create customized solutions that fulfill the requirements of different application scenarios in the best possible way. As a proof of concept we implemented MoPS and provide performance measurements. Furthermore, our implementation provides additional features, such as guidance for non-expert users and export functionalities for external verifiers.Comment: Original Publication (in the same form): ASIACCS 201

The Negative Side Of ICT-Enabled Communication: The Case Of Social Interaction Overload In Online Social Networks

This research aims to explain the negative side of ICT-enabled communications. Therefore, the perception of users that social interactions on online social networks (OSN) are threatening is suggested as a new variable called social interaction overload. The paper theorizes that individual, OSN-specific, and OSN-specific communication characteristics manifest the extent to which social interaction overload is perceived and how users response to it in a psychological and behavioral manner. Results of an empirical survey with 246 OSN users validate the assumed effects, so that we identify age, number of friends, and communication content as contribution factors of social interaction overload, which in turn has a direct effect on the two outcome variables satisfaction and continuous usage intention. Moreover, results reveal that social interaction overload has higher effects on OSN users’ satisfaction than perceived usefulness or perceived enjoyment

Enterprise resource planning systems induced stress: a comparative empirical analysis with young and elderly SAP users

In this research study we investigate whether and how ERP system characteris-tics cause its users to experience stress. In order to do so, we analyze a research model explaining enterprise resource planning systems induced stress with an empirical study in two organizations (N=227). The results reveal that usefulness, complexity, reliability, and pace of change are important ERP system characteris-tics leading to the perception of stressors and exhaustion. Furthermore, our com-parative empirical analysis with young and elderly ERP users indicate that the el-derly ones perceive ERP characteristics more negatively and are more stressed and exhausted than the younger users

Do We Behave Based on Our Implicit Attitudes? Proposing a Research Model and an Experimental Study to Investigate Their Influence on Behavioral Intentions

Attitudes are one of the three most-frequently studied independent variables to explain user behavior. However, although psychological literature distinguishes between explicit and implicit attitudes, most of the investigations in the research stream of IS acceptance and usage have a pure focus on explicit attitudes and do not consider implicit attitudes. Explicit and implicit attitudes can be contradictory and both might predict behavioral intention. Therefore, the present research-in-progress focuses on closing the research gap of refraining to differentiate attitudes in explicit and implicit attitudes and hence examining the influence of implicit attitudes on user behavior. Based on the Implicit Association Test (IAT) and surveys, we propose an experimental setting that measures explicit and implicit attitudes to validate the research model. The proposed research might contribute to the research stream of IS acceptance and usage by better predicting behavioral intentions by also considering implicit attitudes. Future results might explain distorted predictions of behavior and reduce the intention-behavior gap. Furthermore, the present research-in-progress introduces a suitable method to measure implicit attitudes

The Shady Side Of Facebook: The Influence Of Perceived Information And Network Characteristics On The Attitude Towards Information Overload

This research paper analyzes the impact of information and network characteristics on the affective, cognitive, and behavioral attitude towards information overload (IO) on Facebook. By using an information overload model and the data of 300 active Facebook users it can be shown that the various categories of attitude are influenced by different factors. The level of determination of the behavioral attitude towards IO is lower than the level of determination of the affective and cognitive attitude towards IO. The identified antecedents of IO explain up to 36 per cent of the variance of IO. Results indicate that affective and cognitive attitude towards IO are more influenced by these antecedents as the behavioral attitude towards IO. Furthermore, results reveal that the amount of information an individual receives is the major predictor of all three dimensions of attitude. Several implications for adoption research are discussed

Electronic Human Resource Management: A Literature Analysis of Drivers, Challenges, and Consequences

There have been significant changes in how human resources (HR) are managed in the last decade. Electronic Human Resource Management (e-HRM) systems are implemented to support the HR organization digitally. However, e-HRM projects fail frequently. This requires attention as e-HRM systems are essential for organizations to drive the digitalization of HR and thereby ensure competitiveness. The reasons for project failure are unclearly defined project drivers and improperly handled challenges. Furthermore, the consequences of e-HRM are often not well understood and communicated. Therefore, we assume a lack of comprehensive understanding of drivers, challenges, and consequences of e-HRM concerning the organization and the individual. Thus, we analyze the last ten years of e-HRM research and use a structured way to identify eleven drivers, twelve challenges, and twenty consequences of e-HRM. We cluster these findings in an e-HRM synthesis and contribute to e-HRM research by providing avenues for future research on e-HRM success

Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively