Integration on acceleration signals by adjusting with envelopes

Direct integration of acceleration often causes unrealistic drifts in velocity and displacement. A method of integration on acceleration data to acquire realistic velocity and displacement is proposed. In this approach, drifts are estimated by using the mean of the upper and lower envelopes of signals after integration from acceleration into velocity and displacement. The experimental results obtained by using simulated data and real world signals are presented to demonstrate the effectiveness of the method

Examining the Influencing Factors of Cross-Project Knowledge Transfer: An Empirical Study of IT Service Firms

Despite the significance of knowledge transfer in IT service industry, our understanding of knowledge transfer between projects remains limited. Different from the existing studies mainly examining knowledge transfer at organizational level or at individual level within the same project team, this study examines the factors that influence cross-project knowledge transfer in IT service firms. Based on the process logic of knowledge transfer, we develop an integrated theoretical model that posits that cross-project knowledge transfer is influenced by knowledge, transfer activities, project teams’ transfer capabilities, project team context and project task context. We use the recipient IT project implementation performance to measure the effectiveness of cross-project knowledge transfer. Results of the preliminary test show that the designed questionnaires have scalability for the latent constructs, and the theoretical model has its rationality to some extent. To fully assess our proposed research model, we will collect large data set and perform a complete data analysis to test our model. Our study contributes to existing research by focusing on cross-project knowledge transfer and empirically investigating the performance effect of project-related factors. Results of the study will have important implications for IT practitioners

Research on the Effects of Entrepreneurial Education and Entrepreneurial Self-Efficacy on College Students’ Entrepreneurial Intention

Entrepreneurship is one of the important engines of economic development. Under the influence of policy encouragement and economic situation, college students have become the emerging entrepreneurial subjects. Studying the factors influencing their willingness to innovate is conducive to improving the entrepreneurial status and performance. From the perspective of planned behavior theory, this paper analyzes the effects of college students’ entrepreneurship education and self-efficacy on their entrepreneurial intention. Using a sample of 327 college students in China, we test the hypotheses, and get some results. Firstly, college students’ entrepreneurial education has a significant positive effect on their entrepreneurial intention, but has no obvious effect on the entrepreneurial attitude. Secondly, college students’ entrepreneurial self-efficacy has a significant positive effect on the entrepreneurial attitude and entrepreneurial intention, and the entrepreneurial attitude plays a partial intermediary role in the relationship between entrepreneurial self-efficacy and entrepreneurial intention

Local helioseismology and correlation tracking analysis of surface structures in realistic simulations of solar convection

We apply time-distance helioseismology, local correlation tracking and Fourier spatial-temporal filtering methods to realistic supergranule scale simulations of solar convection and compare the results with high-resolution observations from the SOHO Michelson Doppler Imager (MDI). Our objective is to investigate the surface and sub-surface convective structures and test helioseismic measurements. The size and grid of the computational domain are sufficient to resolve various convective scales from granulation to supergranulation. The spatial velocity spectrum is approximately a power law for scales larger than granules, with a continuous decrease in velocity amplitude with increasing size. Aside from granulation no special scales exist, although a small enhancement in power at supergranulation scales can be seen. We calculate the time-distance diagram for f- and p-modes and show that it is consistent with the SOHO/MDI observations. From the simulation data we calculate travel time maps for surface gravity waves (f-mode). We also apply correlation tracking to the simulated vertical velocity in the photosphere to calculate the corresponding horizontal flows. We compare both of these to the actual large-scale (filtered) simulation velocities. All three methods reveal similar large scale convective patterns and provide an initial test of time-distance methods.Comment: 15 pages, 9 figures (.ps format); accepted to ApJ (tentatively scheduled to appear in March 10, 2007 n2 issue); included files ms.bbl, aabib.bst, aabib.sty, aastex.cl

Unintended Memorization and Timing Attacks in Named Entity Recognition Models

Named entity recognition models (NER), are widely used for identifying named entities (e.g., individuals, locations, and other information) in text documents. Machine learning based NER models are increasingly being applied in privacy-sensitive applications that need automatic and scalable identification of sensitive information to redact text for data sharing. In this paper, we study the setting when NER models are available as a black-box service for identifying sensitive information in user documents and show that these models are vulnerable to membership inference on their training datasets. With updated pre-trained NER models from spaCy, we demonstrate two distinct membership attacks on these models. Our first attack capitalizes on unintended memorization in the NER's underlying neural network, a phenomenon NNs are known to be vulnerable to. Our second attack leverages a timing side-channel to target NER models that maintain vocabularies constructed from the training data. We show that different functional paths of words within the training dataset in contrast to words not previously seen have measurable differences in execution time. Revealing membership status of training samples has clear privacy implications, e.g., in text redaction, sensitive words or phrases to be found and removed, are at risk of being detected in the training dataset. Our experimental evaluation includes the redaction of both password and health data, presenting both security risks and privacy/regulatory issues. This is exacerbated by results that show memorization with only a single phrase. We achieved 70% AUC in our first attack on a text redaction use-case. We also show overwhelming success in the timing attack with 99.23% AUC. Finally we discuss potential mitigation approaches to realize the safe use of NER models in light of the privacy and security implications of membership inference attacks.Comment: This is the full version of the paper with the same title accepted for publication in the Proceedings of the 23rd Privacy Enhancing Technologies Symposium, PETS 202

Use of Cryptography in Malware Obfuscation

Malware authors often use cryptographic tools such as XOR encryption and block ciphers like AES to obfuscate part of the malware to evade detection. Use of cryptography may give the impression that these obfuscation techniques have some provable guarantees of success. In this paper, we take a closer look at the use of cryptographic tools to obfuscate malware. We first find that most techniques are easy to defeat (in principle), since the decryption algorithm and the key is shipped within the program. In order to clearly define an obfuscation technique's potential to evade detection we propose a principled definition of malware obfuscation, and then categorize instances of malware obfuscation that use cryptographic tools into those which evade detection and those which are detectable. We find that schemes that are hard to de-obfuscate necessarily rely on a construct based on environmental keying. We also show that cryptographic notions of obfuscation, e.g., indistinghuishability and virtual black box obfuscation, may not guarantee evasion detection under our model. However, they can be used in conjunction with environmental keying to produce hard to de-obfuscate versions of programs

The audio auditor: user-level membership inference in Internet of Things voice services

With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recognition (ASR) model under strict black-box access. With user representation of the input audio data and their corresponding translated text, our trained auditor is effective in user-level audit. We also observe that the auditor trained on specific data can be generalized well regardless of the ASR model architecture. We validate the auditor on ASR models trained with LSTM, RNNs, and GRU algorithms on two state-of-the-art pipelines, the hybrid ASR system and the end-to-end ASR system. Finally, we conduct a real-world trial of our auditor on iPhone Siri, achieving an overall accuracy exceeding 80%. We hope the methodology developed in this paper and findings can inform privacy advocates to overhaul IoT privacy

Effects of Melanocortin 3 and 4 Receptor Deficiency on Energy Homeostasis in Rats

Melanocortin-3 and 4 receptors (MC3R and MC4R) can regulate energy homeostasis, but their respective roles especially the functions of MC3R need more exploration. Here Mc3r and Mc4r single and double knockout (DKO) rats were generated using CRISPR-Cas9 system. Metabolic phenotypes were examined and data were compared systematically. Mc3r KO rats displayed hypophagia and decreased body weight, while Mc4r KO and DKO exhibited hyperphagia and increased body weight. All three mutants showed increased white adipose tissue mass and adipocyte size. Interestingly, although Mc3r KO did not show a significant elevation in lipids as seen in Mc4r KO, DKO displayed even higher lipid levels than Mc4r KO. DKO also showed more severe glucose intolerance and hyperglycaemia than Mc4r KO. These data demonstrated MC3R deficiency caused a reduction of food intake and body weight, whereas at the same time exhibited additive effects on top of MC4R deficiency on lipid and glucose metabolism. This is the first phenotypic analysis and systematic comparison of Mc3r KO, Mc4r KO and DKO rats on a homogenous genetic background. These mutant rats will be important in defining the complicated signalling pathways of MC3R and MC4R. Both Mc4r KO and DKO are good models for obesity and diabetes research