OpenRaiser/Intern-Atlas huggingface dataset:
Performance-oriented DevOps: A Research Agenda
Venue: arXiv cs.SE 2015
Year: 2015

DevOps is a trend towards a tighter integration between development (Dev) and operations (Ops) teams. The need for such an integration is driven by the requirement to continuously adapt enterprise applications (EAs) to changes in the business environment. As of today, DevOps concepts have been primarily introduced to ensure a constant flow of features and bug fixes into new releases from a functional perspective. In order to integrate a non-functional perspective into these DevOps concepts this report focuses on tools, activities, and processes to ensure one of the most important quality attributes of a software system, namely performance. Performance describes system properties concerning its timeliness and use of resources. Common metrics are response time, throughput, and resource utilization. Performance goals for EAs are typically defined by setting upper and/or lower bounds for these metrics and specific business transactions. In order to ensure that such performance goals can be met, several activities are required during development and operation of these systems as well as during the transition from Dev to Ops. Activities during development are typically summarized by the term Software Performance Engineering (SPE), whereas activities during operations are called Application Performance Management (APM). SPE and APM were historically tackled independently from each other, but the newly emerging DevOps concepts require and enable a tighter integration between both activity streams. This report presents existing solutions to support this integration as well as open research challenges in this area.
Alignment of protein-coding sequences with frameshift extension penalties
Venue: arXiv cs.DS 2015
Year: 2015

We introduce an algorithm for the alignment of protein- coding sequences accounting for frameshifts. The main specificity of this algorithm as compared to previously published protein-coding sequence alignment methods is the introduction of a penalty cost for frameshift ex- tensions. Previous algorithms have only used constant frameshift penal- ties. This is similar to the use of scoring schemes with affine gap penalties in classical sequence alignment algorithms. However, the overall penalty of a frameshift portion in an alignment cannot be formulated as an affine function, because it should also incorporate varying codon substitution scores. The second specificity of the algorithm is its search space being the set of all possible alignments between two coding sequences, under the classical definition of an alignment between two DNA sequences. Previous algorithms have introduced constraints on the length of the alignments, and additional symbols for the representation of frameshift openings in an alignment. The algorithm has the same asymptotic space and time complexity as the classical Needleman-Wunsch algorithm.
Dither is Better than Dropout for Regularising Deep Neural Networks
Venue: arXiv cs.LG 2015
Year: 2015

Regularisation of deep neural networks (DNN) during training is critical to performance. By far the most popular method is known as dropout. Here, cast through the prism of signal processing theory, we compare and contrast the regularisation effects of dropout with those of dither. We illustrate some serious inherent limitations of dropout and demonstrate that dither provides a more effective regulariser.
Relating BIP and Reo
Venue: arXiv cs.PL 2015
Year: 2015

Coordination languages simplify design and development of concurrent systems. Particularly, exogenous coordination languages, like BIP and Reo, enable system designers to express the interactions among components in a system explicitly. In this paper we establish a formal relation between BI(P) (i.e., BIP without the priority layer) and Reo, by defining transformations between their semantic models. We show that these transformations preserve all properties expressible in a common semantics. This formal relation comprises the basis for a solid comparison and consolidation of the fundamental coordination concepts behind these two languages. Moreover, this basis offers translations that enable users of either language to benefit from the toolchains of the other.
Orchestrated Session Compliance
Venue: arXiv cs.LO 2015
Year: 2015

We investigate the notion of orchestrated compliance for client/server interactions in the context of session contracts. Devising the notion of orchestrator in such a context makes it possible to have orchestrators with unbounded buffering capabilities and at the same time to guarantee any message from the client to be eventually delivered by the orchestrator to the server, while preventing the server from sending messages which are kept indefinitely inside the orchestrator. The compliance relation is shown to be decidable by means of 1) a procedure synthesising the orchestrators, if any, making a client compliant with a server, and 2) a procedure for deciding whether an orchestrator behaves in a proper way as mentioned before.
On the Expressiveness of Joining
Venue: arXiv cs.LO 2015
Year: 2015

The expressiveness of communication primitives has been explored in a common framework based on the pi-calculus by considering four features: synchronism (asynchronous vs synchronous), arity (monadic vs polyadic data), communication medium (shared dataspaces vs channel-based), and pattern-matching (binding to a name vs testing name equality vs intensionality). Here another dimension coordination is considered that accounts for the number of processes required for an interaction to occur. Coordination generalises binary languages such as pi-calculus to joining languages that combine inputs such as the Join Calculus and general rendezvous calculus. By means of possibility/impossibility of encodings, this paper shows coordination is unrelated to the other features. That is, joining languages are more expressive than binary languages, and no combination of the other features can encode a joining language into a binary language. Further, joining is not able to encode any of the other features unless they could be encoded otherwise.
Multi-criteria Similarity-based Anomaly Detection using Pareto Depth Analysis
Venue: arXiv cs.CV 2015
Year: 2015

We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g.~as measured by nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including non-metric measures, and one can test for anomalies by scalarizing using a non-negative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based anomaly detection using a novel multi-criteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) anomaly detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria and shows superior performance on experiments with synthetic and real data sets.
Translation into any natural language of the error messages generated by any computer program
Venue: arXiv cs.CY 2015
Year: 2015

Since the introduction of the Fortran programming language some 60 years ago, there has been little progress in making error messages more user-friendly. A first step in this direction is to translate them into the natural language of the students. In this paper we propose a simple script for Linux systems which gives word by word translations of error messages. It works for most programming languages and for all natural languages. Understanding the error messages generated by compilers is a major hurdle for students who are learning programming, particularly for non-native English speakers. Not only may they never become "fluent" in programming but many give up programming altogether. Whereas programming is a tool which can be useful in many human activities, e.g. history, genealogy, astronomy, entomology, in many countries the skill of programming remains confined to a narrow fringe of professional programmers. In all societies, besides professional violinists there are also amateurs. It should be the same for programming. It is our hope that once translated and explained the error messages will be seen by the students as an aid rather than as an obstacle and that in this way more students will enjoy learning and practising programming. They should see it as a funny game.
Lifted Relational Neural Networks
Venue: arXiv cs.AI 2015
Year: 2015

We propose a method combining relational-logic representations with neural network learning. A general lifted architecture, possibly reflecting some background domain knowledge, is described through relational rules which may be handcrafted or learned. The relational rule-set serves as a template for unfolding possibly deep neural networks whose structures also reflect the structures of given training or testing relational examples. Different networks corresponding to different examples share their weights, which co-evolve during training by stochastic gradient descent algorithm. The framework allows for hierarchical relational modeling constructs and learning of latent relational concepts through shared hidden layers weights corresponding to the rules. Discovery of notable relational concepts and experiments on 78 relational learning benchmarks demonstrate favorable performance of the method.
Spatial Traffic Shaping in Heterogeneous Cellular Networks with Energy Harvesting
Venue: arXiv cs.NI 2015
Year: 2015

Energy harvesting (EH), which explores renewable energy as a supplementary power source, is a promising 5G technology to support the huge energy demand of heterogeneous cellular networks (HCN). However, the random arrival of renewable energy brings great challenges to network management. By adjusting the distribution of traffic load in spatial domain, traffic shaping helps to balance the cell-level power demand and supply, and thus improves the utilization of renewable energy. In this paper, we investigate the power saving performance of traffic shaping in an analytical way, based on the statistic information of energy arrival and traffic load. Specifically, an energy-optimal traffic shaping scheme (EOTS) is devised for HCNs with EH, whereby the on-off state of the off-grid small cell and the amount of offloading traffic are adjusted dynamically with the energy variation, to minimize the on-grid power consumption. Numerical results are given to demonstrate that for the daily traffic and solar energy profiles, EOTS scheme can significantly reduce the energy consumption, compared with the greedy method where users are always offloaded to the off-grid small cell with priority.
Cooperative Binning for Semideterministic Channels
Venue: arXiv cs.IT 2015
Year: 2015

The capacity regions of semideterministic multiuser channels, such as the semideterministic relay channel and the multiple access channel with partially cribbing encoders, have been characterized using the idea of partial-decode-forward. However, the requirement to explicitly decode part of the message at intermediate nodes can be restrictive in some settings; for example, when nodes have different side information regarding the state of the channel. In this paper, we generalize this scheme to $\textit{cooperative-bin-forward}$ by building on the observation that explicit recovering of part of the message is not needed to induce cooperation. Instead, encoders can bin their received signals and cooperatively forward the bin index to the decoder. The main advantage of this new scheme is illustrated by considering state-dependent extensions of the aforementioned semideterministic setups. While partial-decode-forward is not applicable in these new setups, cooperative-bin-forward continues to achieve capacity.
Correlation in Hard Distributions in Communication Complexity
Venue: arXiv cs.CC 2015
Year: 2015

We study the effect that the amount of correlation in a bipartite distribution has on the communication complexity of a problem under that distribution. We introduce a new family of complexity measures that interpolates between the two previously studied extreme cases: the (standard) randomised communication complexity and the case of distributional complexity under product distributions. We give a tight characterisation of the randomised complexity of Disjointness under distributions with mutual information $k$, showing that it is $Θ(\sqrt{n(k+1)})$ for all $0\leq k\leq n$. This smoothly interpolates between the lower bounds of Babai, Frankl and Simon for the product distribution case ($k=0$), and the bound of Razborov for the randomised case. The upper bounds improve and generalise what was known for product distributions, and imply that any tight bound for Disjointness needs $Ω(n)$ bits of mutual information in the corresponding distribution. We study the same question in the distributional quantum setting, and show a lower bound of $Ω((n(k+1))^{1/4})$, and an upper bound, matching up to a logarithmic factor. We show that there are total Boolean functions $f_d$ on $2n$ inputs that have distributional communication complexity $O(\log n)$ under all distributions of information up to $o(n)$, while the (interactive) distributional complexity maximised over all distributions is $Θ(\log d)$ for $6n\leq d\leq 2^{n/100}$. We show that in the setting of one-way communication under product distributions, the dependence of communication cost on the allowed error $ε$ is multiplicative in $\log(1/ε)$ -- the previous upper bounds had the dependence of more than $1/ε$.
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification
Venue: arXiv cs.CV 2015
Year: 2015

In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, shareable filters are jointly learned for classes which share the similar patterns. Discriminative power of the filters is achieved by enforcing the features from the same category to be close, while features from different categories to be far away from each other. Furthermore, we also propose two exemplar selection methods to iteratively select training data for more efficient and effective learning. Based on the experimental results, DDSFL can achieve very promising performance, and it also shows great complementary effect to the state-of-the-art Caffe features.
A Multi-Scale Spatiotemporal Perspective of Connected and Automated Vehicles: Applications and Wireless Networking
Venue: arXiv cs.NI 2015
Year: 2015

Wireless communication is a basis of the vision of connected and automated vehicles (CAVs). Given the heterogeneity of both wireless communication technologies and CAV applications, one question that is critical to technology road-mapping and policy making is which communication technology is more suitable for a specific CAV application. Focusing on the technical aspect of this question, we present a multi-scale spatiotemporal perspective of wireless communication technologies as well as canonical CAV applications in active safety, fuel economy and emission control, vehicle automation, and vehicular infotainment. Our analysis shows that CAV applications in the regime of small spatiotemporal scale communication requirements are best supported by V2V communications, applications in the regime of large spatiotemporal scale communication requirements are better supported by cellular communications, and applications in the regime of small spatial scale but medium-to-large temporal scale can be supported by both V2V and cellular communications and provide the opportunity of leveraging heterogeneous communication resources.
On Social and Economic Spheres: An Observation of the 'gantangan' Indonesian tradition
Venue: arXiv cs.CY 2015
Year: 2015

Indonesian traditional villagers have a tradition for the sake of their own social and economic security named 'nyumbang'. There are wide variations of the traditions across the archipelago, and we revisit an observation to one in Subang, West Java, Indonesia. The paper discusses and employs the evolutionary game theoretic insights to see the process of 'gantangan', of the intertwining social cohesion and economic expectation of the participation within the traditional activities. The current development of the gantangan tradition is approached and generalized to propose a view between the economic and social sphere surrounding modern people. While some explanations due to the current development of gantangan is drawn, some aspects related to traditional views complying the modern life with social and economic expectations is outlined.
Renewable Energy-Aware Inter-datacenter Virtual Machine Migration over Elastic Optical Networks
Venue: arXiv cs.NI 2015
Year: 2015

Datacenters (DCs) are deployed in a large scale to support the ever increasing demand for data processing to support various applications. The energy consumption of DCs becomes a critical issue. Powering DCs with renewable energy can effectively reduce the brown energy consumption and thus alleviates the energy consumption problem. Owing to geographical deployments of DCs, the renewable energy generation and the data processing demands usually vary in different DCs. Migrating virtual machines (VMs) among DCs according to the availability of renewable energy helps match the energy demands and the renewable energy generation in DCs, and thus maximizes the utilization of renewable energy. Since migrating VMs incurs additional traffic in the network, the VM migration is constrained by the network capacity. The inter-datacenter (inter-DC) VM migration with network capacity constraints is an NP-hard problem. In this paper, we propose two heuristic algorithms that approximate the optimal VM migration solution. Through extensive simulations, we show that the proposed algorithms, by migrating VM among DCs, can reduce up to 31% of brown energy consumption.
On the use of homomorphic encryption to secure cloud computing, services, and routing protocols
Venue: arXiv cs.CR 2015
Year: 2015

The trend towards delegating data processing to a remote party raises major concerns related to privacy violations for both end-users and service providers. These concerns have attracted the attention of the research community, and several techniques have been proposed to protect against malicious parties by providing secure communication protocols. Most of the proposed techniques, however, require the involvement of a third party, and this by itself can be viewed as another security concern. These security breaches can be avoided by following a new approach that depends on data sorted, managed, and stored in encrypted form at the remote servers. To realize such an approach, the encryption cryptosystem must support algebraic operations over encrypted data. This cryptosystem can be effective in protecting data and supporting the construction of programs that can process encrypted input and produce encrypted output. In fact, the latter programs do not decrypt the input, and therefore, they can be run by an un-trusted party without revealing their data and internal states. Furthermore, such programs prove to be practical in situations where we need to outsource private computations, especially in the context of cloud computing. Homomorphic cryptosystems are perfectly aligned with these objectives as they are a strong foundation for schemes that allow a blind processing of encrypted data without the need to decrypt them. In this dissertation we rely on homomorphic encryption schemes to secure cloud computing, services and routing protocols. We design several circuits that allow for the blind processing and management of data such that malicious parties are denied access to sensitive information. We select five areas to apply our models to. These models are easily customized for many other areas. We also provide prototypes that we use to study the performance and robustness of our models.
StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity
Venue: arXiv cs.CV 2015
Year: 2015

Deep neural networks is a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data. One area in deep neural networks that is ripe for exploration is neural connectivity formation. A pivotal study on the brain tissue of rats found that synaptic formation for specific functional connectivity in neocortical neural microcircuits can be surprisingly well modeled and predicted as a random formation. Motivated by this intriguing finding, we introduce the concept of StochasticNet, where deep neural networks are formed via stochastic connectivity between neurons. As a result, any type of deep neural networks can be formed as a StochasticNet by allowing the neuron connectivity to be stochastic. Stochastic synaptic formations, in a deep neural network architecture, can allow for efficient utilization of neurons for performing specific tasks. To evaluate the feasibility of such a deep neural network architecture, we train a StochasticNet using four different image datasets (CIFAR-10, MNIST, SVHN, and STL-10). Experimental results show that a StochasticNet, using less than half the number of neural connections as a conventional deep neural network, achieves comparable accuracy and reduces overfitting on the CIFAR-10, MNIST and SVHN dataset. Interestingly, StochasticNet with less than half the number of neural connections, achieved a higher accuracy (relative improvement in test error rate of ~6% compared to ConvNet) on the STL-10 dataset than a conventional deep neural network. Finally, StochasticNets have faster operational speeds while achieving better or similar accuracy performances.