OpenRaiser/Intern-Atlas huggingface dataset:
Exascale Deep Learning for Climate Analytics
Venue: arXiv cs.DC 2018
Year: 2018

We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipeline, and the network training algorithms necessary to efficiently scale deep learning on the Piz Daint and Summit systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor Cores, a half-precision version of the DeepLabv3+ network achieves a peak and sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.
Design and Evaluation of A Data Partitioning-Based Intrusion Management Architecture for Database Systems
Venue: arXiv cs.CR 2018
Year: 2018

Data-intensive applications exhibit increasing reliance on Database Management Systems (DBMSs, for short). With the growing cyber-security threats to government and commercial infrastructures, the need to develop high resilient cyber systems is becoming increasingly important. Cyber-attacks on DBMSs include intrusion attacks that may result in severe degradation in performance. Several efforts have been directed towards designing an integrated management system to detect, respond, and recover from malicious attacks. In this paper, we propose a data Partitioning-based Intrusion Management System (PIMS, for short) that can endure intense malicious intrusion attacks on DBMS. The novelty in PIMS is the ability to contain the damage into data partitions, termed Intrusion Boundaries (IBs, for short). The IB Demarcation Problem (IBDP, for short) is formulated as a mixed integer nonlinear programming. We prove that IBDP is NP-hard. Accordingly, two heuristic solutions for IBDP are introduced. The proposed architecture for PIMS includes novel IB-centric response and recovery mechanisms, which executes compensating transactions. PIMS is prototyped within PostgreSQL, an open-source DBMS. Finally, empirical and experimental performance evaluation of PIMS are conducted to demonstrate that intelligent partitioning of data tuples improves the overall availability of the DBMS under intrusion attacks.
Image Manipulation Specifications on Social Networking Services for Encryption-then-Compression Systems
Venue: arXiv cs.CR 2018
Year: 2018

Encryption-then-Compression (EtC) systems have been proposed to securely transmit images through an untrusted channel provider. In this study, EtC systems were applied to social media like Twitter that carry out image manipulations. The block scrambling-based encryption schemes used in EtC systems were evaluated in terms of their robustness against image manipulation on social media. The aim was to investigate how five social networking service (SNS) providers, Facebook, Twitter, Google+, Tumblr and Flickr, manipulate images and to determine whether the encrypted images uploaded to SNS providers can avoid being distorted by such manipulations. In an experiment, encrypted and non-encrypted JPEG images were uploaded to various SNS providers. The results show that EtC systems are applicable to the five SNS providers.
Longest Property-Preserved Common Factor
Venue: arXiv cs.DS 2018
Year: 2018

In this paper we introduce a new family of string processing problems. We are given two or more strings and we are asked to compute a factor common to all strings that preserves a specific property and has maximal length. Here we consider three fundamental string properties: square-free factors, periodic factors, and palindromic factors under three different settings, one per property. In the first setting, we are given a string $x$ and we are asked to construct a data structure over $x$ answering the following type of on-line queries: given string $y$, find a longest square-free factor common to $x$ and $y$. In the second setting, we are given $k$ strings and an integer $1 < k'\leq k$ and we are asked to find a longest periodic factor common to at least $k'$ strings. In the third setting, we are given two strings and we are asked to find a longest palindromic factor common to the two strings. We present linear-time solutions for all settings. We anticipate that our paradigm can be extended to other string properties or settings.
Exploring the role and nature of interactions between Institutes in a Local Affiliation Network
Venue: arXiv cs.DL 2018
Year: 2018

In this work, we have studied the collaboration and citation network between Indian Institutes from publications in American Physical Society(APS) journals between 1970-2013. We investigate the role of geographic proximity on the network structure and find that it is the characteristics of the Institution, rather than the geographic distance, that plays a dominant role in collaboration networks. We find that Institutions with better federal funding dominate the network topology and play a crucial role in overall research output. We find that the citation flow across different categories of institutions is strongly linked to the collaborations between them. We have estimated the knowledge flow in and out of Institutions and identified the top knowledge source and sinks.
Propositional logic with short-circuit evaluation: a non-commutative and a commutative variant
Venue: arXiv cs.LO 2018
Year: 2018

Short-circuit evaluation denotes the semantics of propositional connectives in which the second argument is evaluated only if the first argument does not suffice to determine the value of the expression. Short-circuit evaluation is widely used in programming, with sequential conjunction and disjunction as primitive connectives. We study the question which logical laws axiomatize short-circuit evaluation under the following assumptions: compound statements are evaluated from left to right, each atom (propositional variable) evaluates to either true or false, and atomic evaluations can cause a side effect. The answer to this question depends on the kind of atomic side effects that can occur and leads to different "short-circuit logics". The basic case is FSCL (free short-circuit logic), which characterizes the setting in which each atomic evaluation can cause a side effect. We recall some main results and then relate FSCL to MSCL (memorizing short-circuit logic), where in the evaluation of a compound statement, the first evaluation result of each atom is memorized. MSCL can be seen as a sequential variant of propositional logic: atomic evaluations cannot cause a side effect and the sequential connectives are not commutative. Then we relate MSCL to SSCL (static short-circuit logic), the variant of propositional logic that prescribes short-circuit evaluation with commutative sequential connectives. We present evaluation trees as an intuitive semantics for short-circuit evaluation, and simple equational axiomatizations for the short-circuit logics mentioned that use negation and the sequential connectives only.
Neural Networks for Cross-lingual Negation Scope Detection
Venue: arXiv cs.CL 2018
Year: 2018

Negation scope has been annotated in several English and Chinese corpora, and highly accurate models for this task in these languages have been learned from these annotations. Unfortunately, annotations are not available in other languages. Could a model that detects negation scope be applied to a language that it hasn't been trained on? We develop neural models that learn from cross-lingual word embeddings or universal dependencies in English, and test them on Chinese, showing that they work surprisingly well. We find that modelling syntax is helpful even in monolingual settings and that cross-lingual word embeddings help relatively little, and we analyse cases that are still difficult for this task.
Memristor-based Deep Convolution Neural Network: A Case Study
Venue: arXiv cs.NE 2018
Year: 2018

In this paper, we firstly introduce a method to efficiently implement large-scale high-dimensional convolution with realistic memristor-based circuit components. An experiment verified simulator is adapted for accurate prediction of analog crossbar behavior. An improved conversion algorithm is developed to convert convolution kernels to memristor-based circuits, which minimizes the error with consideration of the data and kernel patterns in CNNs. With circuit simulation for all convolution layers in ResNet-20, we found that 8-bit ADC/DAC is necessary to preserve software level classification accuracy.
Italian Event Detection Goes Deep Learning
Venue: arXiv cs.CL 2018
Year: 2018

This paper reports on a set of experiments with different word embeddings to initialize a state-of-the-art Bi-LSTM-CRF network for event detection and classification in Italian, following the EVENTI evaluation exercise. The net- work obtains a new state-of-the-art result by improving the F1 score for detection of 1.3 points, and of 6.5 points for classification, by using a single step approach. The results also provide further evidence that embeddings have a major impact on the performance of such architectures.
A Novel Heat Exchanger Design Method Using a Delayed Rejection Adaptive Metropolis Hasting Algorithm
Venue: arXiv cs.CE 2018
Year: 2018

In this study, a shell-and-tube heat exchanger (STHX) design based on seven continuous independent design variables is proposed. Delayed Rejection Adaptive Metropolis hasting (DRAM) was utilized as a powerful tool in the Markov chain Monte Carlo (MCMC) sampling method. This Reverse Sampling (RS) method was used to find the probability distribution of design variables of the shell and tube heat exchanger. Thanks to this probability distribution, an uncertainty analysis was also performed to find the quality of these variables. In addition, a decision-making strategy based on confidence intervals of design variables and on the Total Annual Cost (TAC) provides the final selection of design variables. Results indicated high accuracies for the estimation of design variables which leads to marginally improved performance compared to commonly used optimization methods. In order to verify the capability of the proposed method, a case of study is also presented, it shows that a significant cost reduction is feasible with respect to multi-objective and single-objective optimization methods. Furthermore, the selected variables have good quality (in terms of probability distribution) and a lower TAC was also achieved. Results show that the costs of the proposed design are lower than those obtained from optimization method reported in previous studies. The algorithm was also used to determine the impact of using probability values for the design variables rather than single values to obtain the best heat transfer area and pumping power. In particular, a reduction of the TAC up to 3.5% was achieved in the case considered.
Deriving sorting algorithms via abductive logic program transformation
Venue: arXiv cs.PL 2018
Year: 2018

Logic program transformation by the unfold/fold method ad- vocates the writing of correct logic programs via the application of some rules to a naive program. This work focuses on how to overcome subgoal- introduction difficulties in synthesizing efficient sorting algorithms from an naive sorting algorithm, through logic program transformation and abductive reasoning.
MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning
Venue: arXiv cs.NE 2018
Year: 2018

Over the past few years Caffe, from Berkeley AI Research, has gained a strong following in the deep learning community with over 15K forks on the github.com/BLVC/Caffe site. With its well organized, very modular C++ design it is easy to work with and very fast. However, in the world of Windows development, C# has helped accelerate development with many of the enhancements that it offers over C++, such as garbage collection, a very rich .NET programming framework and easy database access via Entity Frameworks. So how can a C# developer use the advances of C# to take full advantage of the benefits offered by the Berkeley Caffe deep learning system? The answer is the fully open source, 'MyCaffe' for Windows .NET programmers. MyCaffe is an open source, complete C# language re-write of Berkeley's Caffe. This article describes the general architecture of MyCaffe including the newly added MyCaffeTrainerRL for Reinforcement Learning. In addition, this article discusses how MyCaffe closely follows the C++ Caffe, while talking efficiently to the low level NVIDIA CUDA hardware to offer a high performance, highly programmable deep learning system for Windows .NET programmers.
Global Software Development: Practices for Cultural Differences
Venue: arXiv cs.SE 2018
Year: 2018

Drivers for globalization are significant where today's organizations look for cheaper and faster ways to develop software as well as ways to satisfy quality and investment requirements imposed by customers, shareholders, and governments. Given these needs, Global Software Development (GSD) has become a "normal" way of doing business. Working in GSD often require teams of different cultures to work together. A poor understanding of cultural differences can create barriers to trust or missed opportunities. The literature on culture in GSD is either outdated or disparate, requiring practitioners to read many papers to get an overview of how to manage multi-cultural teams. In this study, we aim to highlight how to increase cultural awareness within teams, avoid potential conflict and harness differences for improved team spirit. To answer our research question, "How should cultural differences be managed, identified and communicated to a GSD team?", we conducted a systematic literature review of the GSD literature. A synthesis of solutions found in nineteen studies provided 12 distinct practices that organizations can implement, to include, "provide a cultural knowledge base", "understand and make team members aware of cultural differences" and "plan responses to mitigate occurrences of cultural misunderstandings". These implementable cultural practices go some way to providing solutions to managing multi-cultural development teams, and thus to support one of the problem dimensions in GSD and embrace cultural differences.
On the Inner Product Predicate and a Generalization of Matching Vector Families
Venue: arXiv cs.CC 2018
Year: 2018

Motivated by cryptographic applications such as predicate encryption, we consider the problem of representing an arbitrary predicate as the inner product predicate on two vectors. Concretely, fix a Boolean function $P$ and some modulus $q$. We are interested in encoding $x$ to $\vec x$ and $y$ to $\vec y$ so that $$P(x,y) = 1 \Longleftrightarrow \langle\vec x,\vec y\rangle= 0 \bmod q,$$ where the vectors should be as short as possible. This problem can also be viewed as a generalization of matching vector families, which corresponds to the equality predicate. Matching vector families have been used in the constructions of Ramsey graphs, private information retrieval (PIR) protocols, and more recently, secret sharing. Our main result is a simple lower bound that allows us to show that known encodings for many predicates considered in the cryptographic literature such as greater than and threshold are essentially optimal for prime modulus $q$. Using this approach, we also prove lower bounds on encodings for composite $q$, and then show tight upper bounds for such predicates as greater than, index and disjointness.
FashionNet: Personalized Outfit Recommendation with Deep Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

With the rapid growth of fashion-focused social networks and online shopping, intelligent fashion recommendation is now in great need. We design algorithms which automatically suggest users outfits (e.g. a shirt, together with a skirt and a pair of high-heel shoes), that fit their personal fashion preferences. Recommending sets, each of which is composed of multiple interacted items, is relatively new to recommender systems, which usually recommend individual items to users. We explore the use of deep networks for this challenging task. Our system, dubbed FashionNet, consists of two components, a feature network for feature extraction and a matching network for compatibility computation. The former is achieved through a deep convolutional network. And for the latter, we adopt a multi-layer fully-connected network structure. We design and compare three alternative architectures for FashionNet. To achieve personalized recommendation, we develop a two-stage training strategy, which uses the fine-tuning technique to transfer a general compatibility model to a model that embeds personal preference. Experiments on a large scale data set collected from a popular fashion-focused social network validate the effectiveness of the proposed networks.
Realisability of Pomsets via Communicating Automata
Venue: arXiv cs.LO 2018
Year: 2018

Pomsets are a model of concurrent computations introduced by Pratt. They can provide a syntax-oblivious description of semantics of coordination models based on asynchronous message-passing, such as Message Sequence Charts (MSCs). In this paper, we study conditions that ensure a specification expressed as a set of pomsets can be faithfully realised via communicating automata. Our main contributions are (i) the definition of a realisability condition accounting for termination soundness, (ii) conditions for global specifications with "multi-threaded" participants, and (iii) the definition of realisability conditions that can be decided directly over pomsets. A positive by-product of our approach is the efficiency gain in the verification of the realisability conditions obtained when restricting to specific classes of choreographies characterisable in term of behavioural types.
Interference Mitigation Using Dynamic Frequency Re-use for Dense Femtocell Network Architectures
Venue: arXiv cs.NI 2018
Year: 2018

The next generation network aims to efficiently deploy low cost and low power cellular base station in the subscriber's home environment. For the femtocell deployment, frequency allocation among femtocells and macrocell is big concern to mitigate the interference, and to ensure the best use of the expensive spectrum. There are many sources of interference in integrated femtocell/macrocell networks. Lagging in proper management of interference reduces the system capacity, increases the outage probability, and finally users feel bad quality of experience (QoE). The cost effective interference management technique depends on the size of femtocells deployment. In this paper, firstly we present deployable various possible femtocell network scenarios. We propose the dynamic frequency re-use scheme to mitigate interference for femtocell deployment. For highly dense femtocells, we propose the functionalities of self organizing network (SON) based femtocell network architecture. The outage probability of a femtocell user is analyzed in details. The performances of the proposed schemes for various femtocell deployments are performed using numerical analysis.
The role of memory in transition from direct to indirect reciprocity
Venue: arXiv cs.GT 2018
Year: 2018

It has been suggested that direct reciprocity operates well within small groups of people where it would be hard to get away with cheating one another but no research has been done yet to show how exactly the mechanism of direct reciprocity fails to operate as the group size increases. Unlike previous models that have neglected the role of memory, our model takes into account the memory capacity of the agents as well as the cost of having such memory. We have shown that the optimal memory capacity for handling the exploiters grows with the group size in a similar way as the relative size of the neocortex grows with the group size of the primates as it was found by Robin Dunbar. The time required for reaching the relative fitness of the defectors increases rapidly with the group size which points to the conclusion that there is an upper group size limit over which the mechanism of direct reciprocity is insufficient to maintain the cooperation.
Optimal Denial-of-Service Attack Energy Management over an SINR-Based Network
Venue: arXiv cs.SY 2018
Year: 2018

We consider a scenario in which a DoS attacker with the limited power resource jams a wireless network through which the packet from a sensor is sent to a remote estimator to estimate the system state. To degrade the estimation quality with power constraint, the attacker aims to solve how much power to obstruct the channel each time, which is the recently proposed optimal attack energy management problem. The existing works are built on an ideal link model in which the packet dropout never occurs without attack. To encompass wireless transmission losses, we introduce the SINR-based link. First, we focus on the case when the attacker employs the constant power level. To maximize the terminal error at the remote estimator, we provide some sufficient conditions for the existence of an explicit solution to the optimal static attack energy management problem and the solution is constructed. Compared with the existing result in which corresponding sufficient conditions work only when the system matrix is normal, the obtained conditions in this paper are viable for a general system and shown to be more relaxed. For the other system index, the average error, the associated sufficient conditions are also derived based on different analysis with the existing work. And a feasible method is presented for both indexes when the system cannot meet the sufficient conditions. Then when the real-time ACK information can be acquired, an MDP based algorithm is designed to solve the optimal dynamic attack energy management problem. We further study the optimal tradeoff between attack power and system degradation. By moving power constraint into the objective function to maximize system index and minimize energy consumption, the other MDP based algorithm is proposed to find the optimal attack policy which is further shown to have a monotone structure. The theoretical results are illustrated by simulations.
An Implementation Approach and Performance Analysis of Image Sensor Based Multilateral Indoor Localization and Navigation System
Venue: arXiv cs.NI 2018
Year: 2018

Optical camera communication (OCC) exhibits considerable importance nowadays in various indoor camera based services such as smart home and robot-based automation. An android smart phone camera that is mounted on a mobile robot (MR) offers a uniform communication distance when the camera remains at the same level that can reduce the communication error rate. Indoor mobile robot navigation (MRN) is considered to be a promising OCC application in which the white light emitting diodes (LEDs) and an MR camera are used as transmitters and receiver respectively. Positioning is a key issue in MRN systems in terms of accuracy, data rate, and distance. We propose an indoor navigation and positioning combined algorithm and further evaluate its performance. An android application is developed to support data acquisition from multiple simultaneous transmitter links. Experimentally, we received data from four links which are required to ensure a higher positioning accuracy.