The impact of mandatory IFRS adoption on accrual anomaly and earning conservatism

This paper investigates the impact of mandatory IFRS adoption on earning management and accounting conservatism by European countries. Using firm-level data of nine European countries within G20 who mandatorily adopted IFRS in 2005, we found that IFRS either increase or decrease accounting conservatism within the sample countries. With Mishkin test to market efficiency at valuation with disaggregated earning components, the results show that the accrual anomaly is not a generalized phenomenon within Europe, especially the Common Law countries. The market seems to be less able to distinguish abnormal accrual from normal accrual estimated by Jones model, which in term cause the mis-valuation of the future earnings forecast. Cross country characteristics examination, including law enforcement, protection of shareholder and accounting structure, etc. suggests that the change of accounting standard itself cannot solely improve the valuation information environment. Relevant commercial law should change to support IFRS to make accounting information informative and comparable

Physiological assessment of operator workload during manual tracking. 1: Pupillary responses

The feasibility of pupillometry as an indicator for assessing operator workload during manual tracking was studied. The mean and maximum pupillary responses of 12 subjects performing tracking tasks with three levels of difficulty (bandwidth of the forcing function were 0.15, 0.30 and 0.50 Hz respectively) were analysed. The results showed that pupillary dilation increased significantly as a function of the tracking difficulty which was reflected by the significant increase of tracking error (RMS). The present study supplies additional evidence that pupillary response is a sensitive and reliable index which may serve as an indicator for assessing operator workload in man-machine systems

Multicast broadcast services support in OFDMA-based WiMAX systems [Advances in mobile multimedia]

Multimedia stream service provided by broadband wireless networks has emerged as an important technology and has attracted much attention. An all-IP network architecture with reliable high-throughput air interface makes orthogonal frequency division multiplexing access (OFDMA)-based mobile worldwide interoperability for microwave access (mobile WiMAX) a viable technology for wireless multimedia services, such as voice over IP (VoIP), mobile TV, and so on. One of the main features in a WiMAX MAC layer is that it can provide'differentiated services among different traffic categories with individual QoS requirements. In this article, we first give an overview of the key aspects of WiMAX and describe multimedia broadcast multicast service (MBMS) architecture of the 3GPP. Then, we propose a multicast and broadcast service (MBS) architecture for WiMAX that is based on MBMS. Moreover, we enhance the MBS architecture for mobile WiMAX to overcome the shortcoming of limited video broadcast performance over the baseline MBS model. We also give examples to demonstrate that the proposed architecture can support better mobility and offer higher power efficiency

Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks

Towards the vision of translating code that implements an algorithm from one programming language into another, this paper proposes an approach for automated program classification using bilateral tree-based convolutional neural networks (BiTBCNNs). It is layered on top of two tree-based convolutional neural networks (TBCNNs), each of which recognizes the algorithm of code written in an individual programming language. The combination layer of the networks recognizes the similarities and differences among code in different programming languages. The BiTBCNNs are trained using the source code in different languages but known to implement the same algorithms and/or functionalities. For a preliminary evaluation, we use 3591 Java and 3534 C++ code snippets from 6 algorithms we crawled systematically from GitHub. We obtained over 90% accuracy in the cross-language binary classification task to tell whether any given two code snippets implement a same algorithm. Also, for the algorithm classification task, i.e., to predict which one of the six algorithm labels is implemented by an arbitrary C++ code snippet, we achieved over 80% precision