Benjamin Hilprecht, Martin Härterich and Daniel Bernau
We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative model. Contrary to previous evaluation metrics for generative models, like Kernel Density Estimation, it only considers samples of the model which are close to training data records. The second attack specifically targets Variational Autoencoders, achieving high membership inference accuracy. Furthermore, previous work mostly considers membership inference adversaries who perform single record membership inference. We argue for considering regulatory actors who perform set membership inference to identify the use of specific datasets for training. The attacks are evaluated on two generative model architectures, Generative Adversarial Networks (GANs) and Variational Autoen-coders (VAEs), trained on standard image datasets. Our results show that the two attacks yield success rates superior to previous work on most data sets while at the same time having only very mild assumptions. We envision the two attacks in combination with the membership inference attack type formalization as especially useful. For example, to enforce data privacy standards and automatically assessing model quality in machine learning as a service setups. In practice, our work motivates the use of GANs since they prove less vulnerable against information leakage attacks while producing detailed samples.
Many scientific fields deal with the topic of multiculturalism which is gradually becoming a characteristic of the 21th century. When we examine culturally mixed societies, we compare different expectations that come as results of different habits. A goal in architecture is to respond to these different expectations, to adapt to new situations. In our research we examine the city of Pécs and its university in order to create a design concept for a new multi-belief sacred space.
Jonathan Rusert, Osama Khalid, Dat Hong, Zubair Shafiq and Padmini Srinivasan
There is a natural tension between the desire to share information and keep sensitive information private on online social media. Privacy seeking social media users may seek to keep their location private by avoiding the mentions of location revealing words such as points of interest (POIs), believing this to be enough. In this paper, we show that it is possible to uncover the location of a social media user’s post even when it is not geotagged and does not contain any POI information. Our proposed approach Jasoos achieves this by exploiting the shared vocabulary between users who reveal their location and those who do not. To this end, Jasoos uses a variant of the Naive Bayes algorithm to identify location revealing words or hashtags based on both temporal and atemporal perspectives. Our evaluation using tweets collected from four different states in the United States shows that Jasoos can accurately infer the locations of close to half a million tweets corresponding to more than 20,000 distinct users (i.e., more than 50% of the test users) from the four states. Our work demonstrates that location privacy leaks do occur despite due precautions by a privacy conscious user. We design and evaluate countermeasures based Jasoos to mitigate location privacy leaks.
In this article we discuss the interior-point algorithm for the general complementarity problems (LCP) introduced by Tibor Illés, Marianna Nagy and Tamás Terlaky. Moreover, we present a various set of numerical results with the help of a code implemented in the C++ programming language. These results support the efficiency of the algorithm for both monotone and sufficient LCPs.
This study describes 5G, the latest wireless technology that is currently under development. It will ensure increased bandwidth as well as newer and higher quality antennas. 5G is actually about further developing 4G/LTE. Due to the rapidly growing number of network devices, the current LTE technology will soon be unsatisfactory in terms of quality of service (QoS), therefore a new concept is needed.
The solution to this problem depends on the quality and complexity of the antennas, as well as traffic management. The planned Fifth Generation Network focuses on these issues to provide more accessible, faster, and more reliable services. The new technology will offer a lot of opportunities for IoT compatible devices such as self-driving vehicles or those used in healthcare. In our opinion we will soon achieve a world of unlimited Internet access.
Proper management of the realization of the general and special training objectives of technical higher education makes it necessary to organize the curriculum and the educational process according to didactic, methodological aspects. Selection of curriculum elements with deductive and inductive approach and their horizontal and vertical arrangement are required. In addition, the curriculum concentration of a given subject, i.e. its connection to other subjects, must be taken into account. This article intends to add to this by raising some points.
Sou Nobukawa, Haruhiko Nishimura and Teruya Yamanishi
Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopamine-modulated STDP (DA-STDP) for reward-related synaptic plasticity have also been gathering attention. Following these studies, we hypothesize that a network structure combining self-organized STDP and reward-related DA-STDP can solve the machine learning problem of pattern classification. Therefore, we studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised learning, with an output layer joined by DA-STDP for supervised learning, to perform pattern classification. We confirmed that this network could perform pattern classification using the STDP effect for emphasizing features of the input spike pattern and DA-STDP supervised learning. Therefore, our proposed spiking neural network may prove to be a useful approach for machine learning problems.
Apeksha Shewalkar, Deepika Nyavanandi and Simone A. Ludwig
Deep Neural Networks (DNN) are nothing but neural networks with many hidden layers. DNNs are becoming popular in automatic speech recognition tasks which combines a good acoustic with a language model. Standard feedforward neural networks cannot handle speech data well since they do not have a way to feed information from a later layer back to an earlier layer. Thus, Recurrent Neural Networks (RNNs) have been introduced to take temporal dependencies into account. However, the shortcoming of RNNs is that long-term dependencies due to the vanishing/exploding gradient problem cannot be handled. Therefore, Long Short-Term Memory (LSTM) networks were introduced, which are a special case of RNNs, that takes long-term dependencies in a speech in addition to short-term dependencies into account. Similarily, GRU (Gated Recurrent Unit) networks are an improvement of LSTM networks also taking long-term dependencies into consideration. Thus, in this paper, we evaluate RNN, LSTM, and GRU to compare their performances on a reduced TED-LIUM speech data set. The results show that LSTM achieves the best word error rates, however, the GRU optimization is faster while achieving word error rates close to LSTM.
In this research we will discuss the creation of the flower cart. It will be examined from an economical and environmental perspective. Additionally, the planning process regarding the carrying capacity and battery runtime will be explained. The cart is moved by three-phase electric motors which are controlled by Variable Frequency Drives (VFD). Electric power is supplied by the large battery pack. Overall, the purpose of this vehicle is to be able to participate in the carnival march while increasing the quality of the event.
Solutions based on Cisco firewall protection provide numerous possibilities for more efficient protection of the abundant quantity of data that is necessary for the operation of an educational institution. Firstly, data phishing can be complicated by the constitution of a virtual network. The IDPS-based access system enables the management center to identify a potential threat in a timely manner. Furthermore, the Cisco-type firewall of a new generation is able to verify the encrypted data in a way that avoids decoding and listening the communication itself. The AAA framework is also an imperative, as in case of a network, control of access is of the utmost importance.