EEG data during 'peaceful' auditory processing at different tempi

This dataset accompanies the publication by Nicolaou et al. (2017), "Directed motor-auditory EEG connectivity is modulated by music tempo", Front Hum. Neurosci., doi: 10.3389/fnhum.2017.00502. The purpose of the research activity in which the data were collected was to investigate how tempo affects the EEG connectivity between different electrodes. For this purpose the participants listened to 'peaceful' music clips at 4 different tempi (50, 100, 150 and 200 beats per minute). To isolate changes related to tempo from changes related to acoustic stimulation, the participants also listened to noise clips generated from the original music clips. Differences in connectivity from a resting state were also studied to isolate the effect of acoustic stimulation. The dataset contains the EEG data while the participants listened to the music and noise clips, and the EEG data from resting state

Algorithm for Using Codes In Place Of Facial Images during Image Processing In Large Databases and Data Warehouses to Reduce Storage, Enhance Efficiency and Processing Speed

The main purpose of this work is to assign codes to the facial image stored in large databases / Data warehouses, the images require large amount of storage as compared to a numeric code. Moreover, when images are compared to images, the process is more time consuming as compared to the numeric codes. It is proposed to keep up all images in a separate database file along with their codes. The codes of facial images may be stored in master Database/ Data warehouse for all records. The search queries can be processed using the numeric codes. In this manner the Time Complexity and Space Complexity are reduced considerably. Whenever, any image is received for searching its record, first its code is obtained by proposed algorithm and then this code is used to search the record from database/Data Warehouse making entire procedure faster and efficient

Maternal and fetal risk factors for stillbirth : population based study

Objective: To assess the main risk factors associated with stillbirth in a multiethnic English maternity population. Design: Cohort study. Setting: National Health Service region in England. Population: 92 218 normally formed singletons including 389 stillbirths from 24 weeks of gestation, delivered during 2009-11. Main outcome measure: Risk of stillbirth. Results: Multivariable analysis identified a significant risk of stillbirth for parity (para 0 and para ≥3), ethnicity (African, African-Caribbean, Indian, and Pakistani), maternal obesity (body mass index ≥30), smoking, pre-existing diabetes, and history of mental health problems, antepartum haemorrhage, and fetal growth restriction (birth weight below 10th customised birthweight centile). As potentially modifiable risk factors, maternal obesity, smoking in pregnancy, and fetal growth restriction together accounted for 56.1% of the stillbirths. Presence of fetal growth restriction constituted the highest risk, and this applied to pregnancies where mothers did not smoke (adjusted relative risk 7.8, 95% confidence interval 6.6 to 10.9), did smoke (5.7, 3.6 to 10.9), and were exposed to passive smoke only (10.0, 6.6 to 15.8). Fetal growth restriction also had the largest population attributable risk for stillbirth and was fivefold greater if it was not detected antenatally than when it was (32.0% v 6.2%). In total, 195 of the 389 stillbirths in this cohort had fetal growth restriction, but in 160 (82%) it had not been detected antenatally. Antenatal recognition of fetal growth restriction resulted in delivery 10 days earlier than when it was not detected: median 270 (interquartile range 261-279) days v 280 (interquartile range 273-287) days. The overall stillbirth rate (per 1000 births) was 4.2, but only 2.4 in pregnancies without fetal growth restriction, increasing to 9.7 with antenatally detected fetal growth restriction and 19.8 when it was not detected. Conclusion: Most normally formed singleton stillbirths are potentially avoidable. The single largest risk factor is unrecognised fetal growth restriction, and preventive strategies need to focus on improving antenatal detection

MPJ Express meets YARN:towards Java HPC on Hadoop systems

AbstractMany organizations—including academic, research, commercial institutions—have invested heavily in setting up High Performance Computing (HPC) facilities for running computational science applications. On the other hand, the Apache Hadoop software—after emerging in 2005— has become a popular, reliable, and scalable open-source framework for processing large-scale data (Big Data). Realizing the importance and significance of Big Data, an increasing number of organizations are investing in relatively cheaper Hadoop clusters for executing their mission critical data processing applications. An issue here is that system administrators at these sites might have to maintain two parallel facilities for running HPC and Hadoop computations. This, of course, is not ideal due to redundant maintenance work and poor economics. This paper attempts to bridge this gap by allowing HPC and Hadoop jobs to co-exist on a single hardware facility. We achieve this goal by exploiting YARN—Hadoop v2.0—that de-couples the computational and resource scheduling part of the Hadoop framework from HDFS. In this context, we have developed a YARN-based reference runtime system for the MPJ Express software that allows executing parallel MPI-like Java applications on Hadoop clusters. The main contribution of this paper is provide Big Data community access to MPI-like programming using MPJ Express. As an aside, this work allows parallel Java applications to perform computations on data stored in Hadoop Distributed File System (HDFS)

Artificial Intelligence A Byproduct of Natural Intelligence and Their Salient Features

This paper mainly focuses on the creation of Artificial Intelligence (AI) using natural intelligence but the question to be considered whether the natural intelligence can be created using artificial intelligence or not. The Artificial intelligence is the outcome of functionality and capabilities of human brain called neural Network. In this paper, it is presumed that the artificial intelligence is a byproduct of natural intelligence and then we discuss some relationship between both of these, especially the working of natural intelligence. Some other important questions are raised to understand a deep linkage between natural and artificial intelligence. There exists lot of non-material phenomenon created by dint of natural intelligence (not created by human) causing to produce systems run by artificial intelligence theorems and algorithms working at backend. The software based on Knowledge Based Systems (KBS) derives its power from human wisdom and natural intelligence. There are several limitations on artificial intelligence. In creation of natural intelligence there is a great role of spirituality.Humans are creator of artificial intelligence with limited abilities. Actually AI started with invention of machines. The applications of creation of natural  intelligence are vastly and abundantly known to humans of 21st Century, which are incorporated in the areas of Space Science, Anatomy, and motion ofPlants, spin of electron, Electronics, plant intelligence and Neural Science etc. The working of machines depending upon the artificial intelligence doesn't provide creativity or self-motivated innovations, within the meaning of natural intelligence

Konsep Penyelesaian Ta’arud Al-Adillah Pada Lembaga Bahtsul Masa’il (LBMNU Sulawesi-Selatan)

Dari hasil penelitian ini menunjukkan bahwa terdapat beberapa cara dalam menyelesaikan ta’arud al-adillah (pertentangan dalil) pada lembaga Bahtsul Masa’il pengurus wilayah Sulawesi Selatan yaitu: yang pertama dilakukan adalah al-jam’u wa al-taufiq (mengumpulkan semua dalil dari berbagai sudut pandang) lalu di tarik kesimpulan, yang kedua dengan cara menguatkan salah satu dalil dengan memeriksa sanad, perawinya, dan tingkan kesahihhanny

Reversible Data Hiding in Encrypted Text Using Paillier Cryptosystem

Reversible Data Hiding in Encrypted Domain (RDHED) is an innovative method that can keep cover information secret and allows the data hider to insert additional information into it. This article presents a novel data hiding technique in an encrypted text called Reversible Data Hiding in Encrypted Text (RDHET). Initially, the original text is converted into their ASCII values. After that, the Paillier cryptosystem is adopted to encrypt all ASCII values of the original text and send it to the data hider for further processing. At the data hiding phase, the secret data are embedded into homomorphically encrypted text using a technique that does not lose any information, i.e., the homomorphic properties of the Paillier cryptosystem. Finally, the embedded secret data and the original text are recovered at the receiving end without any loss. Experimental results show that the proposed scheme is vital in the context of encrypted text processing at cloud-based services. Moreover, the scheme works well, especially for the embedding phase, text recovery, and performance on different security key sizes

Coded DNN Watermark: Robustness against Pruning Models Using Constant Weight Code

Deep Neural Network (DNN) watermarking techniques are increasingly being used to protect the intellectual property of DNN models. Basically, DNN watermarking is a technique to insert side information into the DNN model without significantly degrading the performance of its original task. A pruning attack is a threat to DNN watermarking, wherein the less important neurons in the model are pruned to make it faster and more compact. As a result, removing the watermark from the DNN model is possible. This study investigates a channel coding approach to protect DNN watermarking against pruning attacks. The channel model differs completely from conventional models involving digital images. Determining the suitable encoding methods for DNN watermarking remains an open problem. Herein, we presented a novel encoding approach using constant weight codes to protect the DNN watermarking against pruning attacks. The experimental results confirmed that the robustness against pruning attacks could be controlled by carefully setting two thresholds for binary symbols in the codeword