OpenRaiser/Intern-Atlas huggingface dataset:
Electricity markets regarding the operational flexibility of power plants
Venue: arXiv cs.CY 2015
Year: 2015

Electricity market mechanisms designed to steer sustainable generation of electricity play an important role for the energy transition intended to mitigate climate change. One of the major problems is to complement volatile renewable energy sources by operationally flexible capacity reserves. In this paper a proposal is given to determine prices on electricity markets taking into account the operational flexibility of power plants, such that the costs of long-term capacity reserves can be paid by short-term electricity spot markets. For this purpose, a measure of operational flexibility is introduced enabling to compute an inflexibility fee charging each individual power plant on a wholesale electricity spot market. The total sum of inflexibility fees accumulated on the spot markets then can be used to finance a capacity market keeping the necessary reserves to warrant grid reliability. Here each reserve power plant then gets a reliability payment depending on its operational flexibility. The proposal is applied to a small exemplary grid, illustrating its main idea and also revealing the caveat that too high fees paradoxically could create incentives to employ highly flexible power plants on the spot market rather than to run them as backup capacity.
The Search for Computational Intelligence
Venue: arXiv cs.NE 2015
Year: 2015

We define and explore in simulation several rules for the local evolution of generative rules for 1D and 2D cellular automata. Our implementation uses strategies from conceptual blending. We discuss potential applications to modelling social dynamics.
Hybrid model for LTE Network-Assisted D2D communications
Venue: arXiv cs.NI 2015
Year: 2015

New Architecture to support D2D communications, where discovery is made directly between devices while communications occur with the help of the E-Node B
Minimizing Regret in Dynamic Decision Problems
Venue: arXiv cs.AI 2015
Year: 2015

The menu-dependent nature of regret-minimization creates subtleties when it is applied to dynamic decision problems. Firstly, it is not clear whether \emph{forgone opportunities} should be included in the \emph{menu}, with respect to which regrets are computed, at different points of the decision problem. If forgone opportunities are included, however, we can characterize when a form of dynamic consistency is guaranteed. Secondly, more subtleties arise when sophistication is used to deal with dynamic inconsistency. In the full version of this paper, we examine, axiomatically and by common examples, the implications of different menu definitions for sophisticated, regret-minimizing agents.
Spectral Detection in the Censored Block Model
Venue: arXiv cs.SI 2015
Year: 2015

We consider the problem of partially recovering hidden binary variables from the observation of (few) censored edge weights, a problem with applications in community detection, correlation clustering and synchronization. We describe two spectral algorithms for this task based on the non-backtracking and the Bethe Hessian operators. These algorithms are shown to be asymptotically optimal for the partial recovery problem, in that they detect the hidden assignment as soon as it is information theoretically possible to do so.
Sensor Deployment for Air Pollution Monitoring Using Public Transportation System
Venue: arXiv cs.NE 2015
Year: 2015

Air pollution monitoring is a very popular research topic and many monitoring systems have been developed. In this paper, we formulate the Bus Sensor Deployment Problem (BSDP) to select the bus routes on which sensors are deployed, and we use Chemical Reaction Optimization (CRO) to solve BSDP. CRO is a recently proposed metaheuristic designed to solve a wide range of optimization problems. Using the real world data, namely Hong Kong Island bus route data, we perform a series of simulations and the results show that CRO is capable of solving this optimization problem efficiently.
Optimal V2G Scheduling of Electric Vehicles and Unit Commitment using Chemical Reaction Optimization
Venue: arXiv cs.NE 2015
Year: 2015

An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid. In order to flatten the load profile of the electricity system, EV scheduling has become a hot research topic in recent years. In this paper, we propose a new formulation of the joint scheduling of EV and Unit Commitment (UC), called EVUC. Our formulation considers the characteristics of EVs while optimizing the system total running cost. We employ Chemical Reaction Optimization (CRO), a general-purpose optimization algorithm to solve this problem and the simulation results on a widely used set of instances indicate that CRO can effectively optimize this problem.
A New Parameter Estimation Algorithm Based on Sub-band Dual Frequency Conjugate LVT
Venue: arXiv cs.IT 2015
Year: 2015

A new parameter estimation algorithm, known as Sub-band Dual Frequency Conjugate LVT (SDFC-LVT), is proposed for the ground moving targets. This algorithm first constructs two sub-band signals with different central frequencies. After that, the two signals are shifted by different values in frequency domain and a new signal is constructed by multiplying one with the conjugate of the other. Finally, Keystone transform and LVT operation are performed on the constructed signal to attain the estimates. The cross-term and the performance of the proposed method are analyzed in detail. Since the equivalent carrier frequency is reduced greatly, the proposed method is capable of obtaining the accurate parameter estimates and resolving the problem of ambiguity which invalidates Keystone transform. It is search-free and can compensate the range walk of multiple targets simultaneously, thereby reducing the computational burden. The effectiveness of the proposed method is demonstrated by both simulated and real data.
Modified Fast Fractal Image Compression Algorithm in spatial domain
Venue: arXiv cs.CV 2015
Year: 2015

In this paper a new fractal image compression algorithm is proposed in which the time of encoding process is considerably reduced. The algorithm exploits a domain pool reduction approach, along with using innovative predefined values for contrast scaling factor, S, instead of searching it across [0,1]. Only the domain blocks with entropy greater than a threshold are considered as domain pool. As a novel point, it is assumed that in each step of the encoding process, the domain block with small enough distance shall be found only for the range blocks with low activity (equivalently low entropy). This novel point is used to find reasonable estimations of S, and use them in the encoding process as predefined values, mentioned above, the remaining range blocks are split into four new smaller range blocks and the algorithm must be iterated for them, considered as the other step of encoding process. The algorithm has been examined for some of the well-known images and the results have been compared with the state-of-the-art algorithms. The experiments show that our proposed algorithm has considerably lower encoding time than the other where the encoded images are approximately the same in quality.
Iterated Support Vector Machines for Distance Metric Learning
Venue: arXiv cs.LG 2015
Year: 2015

Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while many existing metric learning algorithms become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem, and solve it by iterated training of support vector machines (SVM). The new formulation is easy to implement, efficient in training, and tractable for large-scale problems. Two novel metric learning models, namely Positive-semidefinite Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experimental results on UCI dataset classification, handwritten digit recognition, face verification and person re-identification demonstrate that the proposed metric learning methods achieve higher classification accuracy than state-of-the-art methods and they are significantly more efficient in training.
On the Performance of Single- and Multi-carrier Modulation Schemes for Indoor Visible Light Communication Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we investigate and compare the performance of single- and multi-carrier modulation schemes for indoor visible light communication (VLC). Particularly, the performances of single carrier frequency domain equalization (SCFDE), orthogonal frequency division multiplexing (OFDM) and on-off keying (OOK) with minimum mean square error equalization (MMSE) are analyzed in order to mitigate the effect of multipath distortion of the indoor optical channel where nonlinearity distortion of light emitting diode (LED) transfer function is taken into account. Our results indicate that SCFDE system, in contrast to OFDM system, does not suffer from high peak to average power ratio (PAPR) and can outperform OFDM and OOK systems. We further investigate the impact of LED bias point on the performance of OFDM systems and show that biasing LED with the optimum value can significantly enhance the performance of the system. Bit-interleaved coded modulation (BICM) is also considered for OFDM and SCFDE systems to further compensate signal degradation due to inter-symbol interference (ISI) and LED nonlinearity.
Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance
Venue: arXiv cs.CV 2015
Year: 2015

In order to track the moving objects in long range against occlusion, interruption, and background clutter, this paper proposes a unified approach for global trajectory analysis. Instead of the traditional frame-by-frame tracking, our method recovers target trajectories based on a short sequence of video frames, e.g. $15$ frames. We initially calculate a foreground map at each frame, as obtained from a state-of-the-art background model. An attribute graph is then extracted from the foreground map, where the graph vertices are image primitives represented by the composite features. With this graph representation, we pose trajectory analysis as a joint task of spatial graph partitioning and temporal graph matching. The task can be formulated by maximizing a posteriori under the Bayesian framework, in which we integrate the spatio-temporal contexts and the appearance models. The probabilistic inference is achieved by a data-driven Markov Chain Monte Carlo (MCMC) algorithm. Given a peroid of observed frames, the algorithm simulates a ergodic and aperiodic Markov Chain, and it visits a sequence of solution states in the joint space of spatial graph partitioning and temporal graph matching. In the experiments, our method is tested on several challenging videos from the public datasets of visual surveillance, and it outperforms the state-of-the-art methods.
Multi-sources Randomness Extraction over Finite Fields and Elliptic Curve
Venue: arXiv cs.CR 2015
Year: 2015

This work is based on the proposal of a deterministic randomness extractor of a random Diffie-Hellman element defined over two prime order multiplicative subgroups of a finite fields $\mathbb{F}_{p^n}$, $G_1$ and $G_2$. We show that the least significant bits of a random element in $G_1*G_2$, are indistinguishable from a uniform bit-string of the same length. One of the main application of this extractor is to replace the use of hash functions in pairing by the use of a good deterministic randomness extractor.
Assessing the Fault Proneness Degree (DFP) by Estimating the Impact of Change Request Artifacts Correlation
Venue: arXiv cs.SE 2015
Year: 2015

Exploring the impact of change requests applied to a software maintenance project helps to assess the fault-proneness of the change request to be handled further, which is perhaps a bug fix or even a new feature demand. In practice, the major development community stores change requests and related data using bug tracking systems such as Bugzilla. These data, together with the data stored in a versioning system, such as Concurrent Versioning Systems, are a valuable source of information to create descriptions and also can perform useful analyzes. In our earlier work, we proposed a novel statistical bipartite weighted graph-based approach to assessing the degree of fault-proneness of the change request and Change Request artifacts. With the motivation gained from this model, here we propose a novel strategy that estimates the degree of fault-proneness of a change request by assessing the impact of a change request artifact towards fault-proneness that considers the correlation between change requests artifact as another factor, which is in addition to our earlier strategy. The proposed model can be titled as Assessing the Fault Proneness Degree of Change Request Artifacts by estimating the impact of change requests correlation (DFP-CRC). As stated in our earlier model, the method DFP-CRC also makes use of information retrieval methods to identify the change request artifacts of the devised change request. And further evaluates the degree of fault-proneness of the Change Requests by estimating the correlation between change requests. The proposed method is evaluated by applying on concurrent versioning and Change request logs of the production level maintenance project.
Deep Boosting: Layered Feature Mining for General Image Classification
Venue: arXiv cs.CV 2015
Year: 2015

Constructing effective representations is a critical but challenging problem in multimedia understanding. The traditional handcraft features often rely on domain knowledge, limiting the performances of exiting methods. This paper discusses a novel computational architecture for general image feature mining, which assembles the primitive filters (i.e. Gabor wavelets) into compositional features in a layer-wise manner. In each layer, we produce a number of base classifiers (i.e. regression stumps) associated with the generated features, and discover informative compositions by using the boosting algorithm. The output compositional features of each layer are treated as the base components to build up the next layer. Our framework is able to generate expressive image representations while inducing very discriminate functions for image classification. The experiments are conducted on several public datasets, and we demonstrate superior performances over state-of-the-art approaches.
Exploiting the Preferred Domain of FDD Massive MIMO Systems with Uniform Planar Arrays
Venue: arXiv cs.IT 2015
Year: 2015

Massive multiple-input multiple-output (MIMO) systems hold the potential to be an enabling technology for 5G cellular. Uniform planar array (UPA) antenna structures are a focus of much commercial discussion because of their ability to enable a large number of antennas in a relatively small area. With UPA antenna structures, the base station can control the beam direction in both the horizontal and vertical domains simultaneously. However, channel conditions may dictate that one dimension requires higher channel state information (CSI) accuracy than the other. We propose the use of an additional one bit of feedback information sent from the user to the base station to indicate the preferred domain on top of the feedback overhead of CSI quantization in frequency division duplexing (FDD) massive MIMO systems. Combined with variable-rate CSI quantization schemes, the numerical studies show that the additional one bit of feedback can increase the quality of CSI significantly for UPA antenna structures.
Optimal dynamic program for r-domination problems over tree decompositions
Venue: arXiv cs.DS 2015
Year: 2015

There has been recent progress in showing that the exponential dependence on treewidth in dynamic programming algorithms for solving NP-hard problems are optimal under the Strong Exponential Time Hypothesis (SETH). We extend this work to $r$-domination problems. In $r$-dominating set, one wished to find a minimum subset $S$ of vertices such that every vertex of $G$ is within $r$ hops of some vertex in $S$. In connected $r$-dominating set, one additionally requires that the set induces a connected subgraph of $G$. We give a $O((2r+1)^{\mathrm{tw}} n)$ time algorithm for $r$-dominating set and a $O((2r+2)^{\mathrm{tw}} n^{O(1)})$ time algorithm for connected $r$-dominating set in $n$-vertex graphs of treewidth $\mathrm{tw}$. We show that the running time dependence on $r$ and $\mathrm{tw}$ is the best possible under SETH. This adds to earlier observations that a "+1" in the denominator is required for connectivity constraints.
Review of Considerations for Mobile Device based Secure Access to Financial Services and Risk Handling Strategy for CIOs, CISOs and CTOs
Venue: arXiv cs.CR 2015
Year: 2015

The information technology and security stakeholders like CIOs, CISOs and CTOs in financial services organization are often asked to identify the risks with mobile computing channel for financial services that they support. They are also asked to come up with approaches for handling risks, define risk acceptance level and mitigate them. This requires them to articulate strategy for supporting a huge variety of mobile devices from various vendors with different operating systems and hardware platforms and at the same time stay within the accepted risk level. These articulations should be captured in information security policy document or other suitable document of financial services organization like banks, payment service provider, etc. While risks and mitigation approaches are available from multiple sources, the senior stakeholders may find it challenging to articulate the issues in a comprehensive manner for sharing with business owners and other technology stakeholders. This paper reviews the current research that addresses the issues mentioned above and articulates a strategy that the senior stakeholders may use in their organization. It is assumed that this type of comprehensive strategy guide for senior stakeholders is not readily available and CIOs, CISOs and CTOs would find this paper to be very useful.
Clothing Co-Parsing by Joint Image Segmentation and Labeling
Venue: arXiv cs.CV 2015
Year: 2015

This paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. The first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (E-SVM) technique [23]. In the second phase (i.e. "region co-labeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89% recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods.
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection
Venue: arXiv cs.CV 2015
Year: 2015

This paper studies a novel discriminative part-based model to represent and recognize object shapes with an "And-Or graph". We define this model consisting of three layers: the leaf-nodes with collaborative edges for localizing local parts, the or-nodes specifying the switch of leaf-nodes, and the root-node encoding the global verification. A discriminative learning algorithm, extended from the CCCP [23], is proposed to train the model in a dynamical manner: the model structure (e.g., the configuration of the leaf-nodes associated with the or-nodes) is automatically determined with optimizing the multi-layer parameters during the iteration. The advantages of our method are two-fold. (i) The And-Or graph model enables us to handle well large intra-class variance and background clutters for object shape detection from images. (ii) The proposed learning algorithm is able to obtain the And-Or graph representation without requiring elaborate supervision and initialization. We validate the proposed method on several challenging databases (e.g., INRIA-Horse, ETHZ-Shape, and UIUC-People), and it outperforms the state-of-the-arts approaches.
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars
Venue: arXiv cs.CV 2015
Year: 2015

This article investigates a data-driven approach for semantically scene understanding, without pixelwise annotation and classifier training. Our framework parses a target image with two steps: (i) retrieving its exemplars (i.e. references) from an image database, where all images are unsegmented but annotated with tags; (ii) recovering its pixel labels by propagating semantics from the references. We present a novel framework making the two steps mutually conditional and bootstrapped under the probabilistic Expectation-Maximization (EM) formulation. In the first step, the references are selected by jointly matching their appearances with the target as well as the semantics (i.e. the assigned labels of the target and the references). We process the second step via a combinatorial graphical representation, in which the vertices are superpixels extracted from the target and its selected references. Then we derive the potentials of assigning labels to one vertex of the target, which depend upon the graph edges that connect the vertex to its spatial neighbors of the target and to its similar vertices of the references. Besides, the proposed framework can be naturally applied to perform image annotation on new test images. In the experiments, we validate our approach on two public databases, and demonstrate superior performances over the state-of-the-art methods in both semantic segmentation and image annotation tasks.
Algorithm for Achieving Consensus Over Conflicting Rumors: Convergence Analysis and Applications
Venue: arXiv cs.DC 2015
Year: 2015

Motivated by the large expansion in the study of social networks, this paper deals with the problem of multiple messages spreading over the same network using gossip algorithms. Given two messages distributed over some nodes of the graph, we first investigate the final distribution of the messages given an initial state. Then, an algorithm is presented to achieve consensus over one of the messages. Finally, a game theoretical application and an analogy with word-of-mouth marketing are outlined.
Software that Learns from its Own Failures
Venue: arXiv cs.SE 2015
Year: 2015

All non-trivial software systems suffer from unanticipated production failures. However, those systems are passive with respect to failures and do not take advantage of them in order to improve their future behavior: they simply wait for them to happen and trigger hard-coded failure recovery strategies. Instead, I propose a new paradigm in which software systems learn from their own failures. By using an advanced monitoring system they have a constant awareness of their own state and health. They are designed in order to automatically explore alternative recovery strategies inferred from past successful and failed executions. Their recovery capabilities are assessed by self-injection of controlled failures; this process produces knowledge in prevision of future unanticipated failures.