Weighted Sum Coloring in Batch Scheduling of Conflicting Jobs

Motivated by applications in batch scheduling of jobs in manufacturing systems and distributed computing, we study two related problems. Given is a set of jobs {J ₁,…,J _n }, where J _j has a processing time p _j , and an undirected intersection graph G=({1,…,n},E), with an edge (i,j) whenever the pair of jobs J _i and J _j cannot be processed in the same batch. We are to schedule the jobs in batches, where each batch completes its processing when the last job in the batch completes execution. The goal is to minimize the sum of job completion times. Our two problems differ in the definition of completion time of a job within a given batch. In the first variant, a job completes its execution when its batch is completed, whereas in the second variant, a job completes execution when its own processing is completed. For the first variant, we show that an adaptation of the greedy set cover algorithm gives a 4-approximation for perfect graphs. For the second variant, we give new or improved approximations for a number of different classes of graphs. The algorithms are of widely different genres (LP, greedy, subgraph covering), yet they curiously share a common feature in their use of randomized geometric partitioning.     

Tomographic reconstruction of incompressible flow

In many experiments facilitating tomography the reconstruction problem is under-determined, meaning there are many possible solutions consistent with the measurements. If the sampling rate is fast relative to the typical evolution time, the known physical dynamics of the system can be used as additional reconstruction constraints. Here we demonstrate that incorporating the requirement of incompressible flow can improve significantly the fidelity of the reconstructed sequence. The incompressibility of the reconstruction is assured by requiring the conservation of the density moments. It is demonstrated that the "incompressible" reconstruction can be significantly more accurate than the reconstruction using standard methods. A consequence of the density moments' conservation is the conservation of the density histogram throughout the reconstructed sequence.     

Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

This research synthesizes a taxonomy for classifying detection methods of new malicious code by Machine Learning (ML) methods based on static features extracted from executables. The taxonomy is then operationalized to classify research on this topic and pinpoint critical open research issues in light of emerging threats. The article addresses various facets of the detection challenge, including: file representation and feature selection methods, classification algorithms, weighting ensembles, as well as the imbalance problem, active learning, and chronological evaluation. From the survey we conclude that a framework for detecting new malicious code in executable files can be designed to achieve very high accuracy while maintaining low false positives (i.e. misclassifying benign files as malicious). The framework should include training of multiple classifiers on various types of features (mainly OpCode and byte n-grams and Portable Executable Features), applying weighting algorithm on the classification results of the individual classifiers, as well as an active learning mechanism to maintain high detection accuracy. The training of classifiers should also consider the imbalance problem by generating classifiers that will perform accurately in a real-life situation where the percentage of malicious files among all files is estimated to be approximately 10%.     

Online-bounded analysis

Though competitive analysis is often a very good tool for the analysis of online algorithms, sometimes it does not give any insight and sometimes it gives counter-intuitive results. Much work has gone into exploring other performance measures, in particular targeted at what seems to be the core problem with competitive analysis: The comparison of the performance of an online algorithm is made with respect to a too powerful adversary. We consider a new approach to restricting the power of the adversary, by requiring that when judging a given online algorithm, the optimal offline algorithm must perform at least as well as the online algorithm, not just on the entire final request sequence, but also on any prefix of that sequence. This is limiting the adversary’s usual advantage of being able to exploit that it knows the sequence is continuing beyond the current request. Through a collection of online problems, including machine scheduling, bin packing, dual bin packing, and seat reservation, we investigate the significance of this particular offline advantage.     

Online Clustering with Variable Sized Clusters

Online clustering problems are problems where the classification of points into sets (called clusters) is performed in an online fashion. Points arrive at arbitrary locations, one by one, to be assigned to clusters at the time of arrival. A point can be either assigned to an existing cluster or a new cluster can be opened for it. Here, we study a one-dimensional variant on a line. Each cluster is a closed interval, and there is no restriction on the length of a cluster. The cost of a cluster is the sum of a fixed set-up cost and its diameter (or length). The goal is to minimize the sum of costs of the clusters used by the algorithm. We study several variants, each having the two essential properties that a point which has been assigned to a given cluster must remain assigned to that cluster and no pair of clusters can be merged. In the strict variant, the diameter and the exact location of the cluster must be fixed when it is initialized. In the flexible variant, the algorithm can shift the cluster or expand it, as long as it contains all points assigned to it. In an intermediate model, the diameter is fixed in advance but the exact location can be modified. Here we give tight bounds on the competitive ratio of any online algorithm in each of these variants. In addition, for each model we also consider the semi-online case where points are presented ordered by their location.     

SONET ADMs Minimization with Divisible Paths

We consider an optical routing problem. SONET add-drop multiplexers (ADMs) are the dominant cost factor in SONET/WDM rings. The number of SONET ADMs required by a set of traffic streams is determined by the routing and wavelength assignment of the traffic streams. In this paper we consider the version where a traffic stream may be divided into several parts and assigned different wavelengths. A specific division may increase or decrease the number of ADMs needed for a given input. Following previous work, we consider two versions. In the arc version the route of each traffic stream is given as input, and we need to decide on divisions of streams, and then to assign wavelengths so as to minimize the total number of used SONET ADMs. In the chord version the route is not prespecified, but is assigned by the algorithm, and only after this step are the divisions done and wavelengths assigned. The previously best known approximation algorithm for the arc version has a performance guarantee of 5/4 = 1.25 whereas the previously best known approximation algorithm for the chord version has a performance guarantee of 3/2 = 1.5. We improve both these results. We present a 36/29 ≈ 1.24138-approximation algorithm for the arc version and a 7/5 = 1.4-approximation algorithm for the chord version.     

Learning to bid in bridge

Bridge bidding is considered to be one of the most difficult problems for game-playing programs. It involves four agents rather than two, including a cooperative agent. In addition, the partial observability of the game makes it impossible to predict the outcome of each action. In this paper we present a new decision-making algorithm that is capable of overcoming these problems. The algorithm allows models to be used for both opponent agents and partners, while utilizing a novel model-based Monte Carlo sampling method to overcome the problem of hidden information. The paper also presents a learning framework that uses the above decision-making algorithm for co-training of partners. The agents refine their selection strategies during training and continuously exchange their refined strategies. The refinement is based on inductive learning applied to examples accumulated for classes of states with conflicting actions. The algorithm was empirically evaluated on a set of bridge deals. The pair of agents that co-trained significantly improved their bidding performance to a level surpassing that of the current state-of-the-art bidding algorithm. Editors: Michael Bowling, Johannes Fürnkranz, Thore Graepel, Ron Musick     

Free convection in porous media filled right-angle triangular enclosures

Steady-state free convection heat transfer in a right-angle triangular enclosure, whose vertical wall insulated and inclined and bottom walls are differentially heated, is performed in this study. The governing equations are obtained using Darcy model. In this study, the governing equations were solved by finite difference method and solution of algebraic equations was made via Successive Under Relaxation method. The effect of aspect ratios ranging from 0.25 to 1.0 and Rayleigh numbers 50 ≤ Ra ≤ 1000 is investigated as governing parameters on heat transfer and flow field. It is observed that heat transfer is increased with the decreasing of aspect ratio and multiple cells are formed at high Rayleigh numbers.     

Significance of leaf morphoanatomical markers for the authentication of adultered drugs sold in herbal markets of district Lahore,Pakistan

A number of herbal plants sold in herbal markets of Lahore are under adulteration threat which can pose harmful health effects to end‐user. This adulteration problem of medicinal plants can be resolved by the implication of some valuable taxonomic parameter such as leaf epidermal anatomical characteristics. Hence, this research was aimed to provide viable anatomical markers in order to resolve this adulteration issue persisted in some common marketed medicinal plants of district Lahore, that is, Cinnamomum verum Presl., Cinnamomum tamala (Buuch.‐Ham.) T.Nees&Eberm., Gymnema sylvestre (Retz.) R.Br.ex Sm., Sphaeranthus indicus Linn., Artemisia maritima Linn., Achillea millifolim L., Adhatoda vasica Nees, Butea monosperma (Lam.) Taub, and Morus nigra L. Overall multiple anatomical variations (epidermal cell shape, their length and width, type of stomata, length and width of guard cells along with presence or absence of trichomes) had been reported in the study that could be worthwhile for the correct identification of medicinal plants. Irregular shapes of epidermal cells were observed in Cinnamomum verum and Achillea millifolium while pentagonal and polygonal cells were found in their adultaerants, that is, Canella winterana and Adhatoda vasica, respectively. Types of stomata were also strikingly varied among genuine plant and its adulterant, for example, anisocytic stomata were observed in Artemisia maritima while in its adulterant (Artemisia absinthium) anomocytic stomata were found. Similarly, paracytic stomata were observed in Butea monosperma, whereas its adulterant plant (Averrhoa carambola) characteristically possessed anisocytic stomata. Hence, anatomical characteristics were proved to be a valuable taxonomic tool in resolving the adulteration issue of medicinal plants.