Binary Search Strategy Research Articles

Playing Guess Who with your kids: Code-word strategy against adversaries

Guess Who is a two-player search game in which each player chooses a character from a deck of 24 cards, and has to infer the other player's character by asking yes-no questions. A simple binary search strategy allows the starting player find the opponent's character by asking 5 questions only, when the opponent is honest.Real-life observations show that in more realistic scenarios, the game is played against adversaries that do not strictly follow the rules, e.g., kids. Such players might decide to answer all questions at once, answer only part of the questions as they do not know the answers to all, and even lie occasionally. We devise strategies for such scenarios using techniques from error-correcting and erasure codes. This connects to a recent line of work on search problems on graphs and trees with unreliable auxiliary information, and could be of independent interest.

Black-Box Boundary Attack Based on Gradient Optimization

Deep neural networks have gained extensive applications in computer vision, demonstrating significant success in fundamental research tasks such as image classification. However, the robustness of these networks faces severe challenges in the presence of adversarial attacks. In real-world scenarios, addressing hard-label attacks often requires the execution of tens of thousands of queries. To combat these challenges, the Black-Box Boundary Attack leveraging Gradient Optimization (GOBA) has been introduced. This method employs a binary search strategy to acquire an initial adversarial example with significant perturbation. The Monte Carlo algorithm is utilized to estimate the gradient of the sample, facilitating iterative movement along the estimated gradient and the direction of the malicious label. Moreover, query vectors positively correlated with the gradient are extracted to construct a sampling space with an optimal scale, thereby enhancing the efficiency of the Monte Carlo algorithm. Experimental evaluations were conducted using the HSJA, QEBA, and NLBA attack methodologies on the ImageNet, CelebA, and MNIST datasets, respectively. The results indicate that, under the constraint of 3 k query times, the GOBA, compared to other methods, can, on average, reduce perturbation (L2 distance) by 55.74% and simultaneously increase the attack success rate by an average of 13.78%.

An iteratively doubling binary search for the two-dimensional irregular multiple-size bin packing problem raised in the steel industry

This paper examines the two-dimensional irregular multiple-size bin packing problem, where the goal is to pack all the given irregular pieces into bins of various sizes such that the total area of the used bins is minimized. Meanwhile, the irregular pieces include holes, and the bins can be irregular, also. This problem is raised in the steel industry considering the reuse of leftover material. An iteratively doubling binary search is proposed to solve this problem. Moreover, a binary search strategy is introduced to search the bin combination with minimum area, and an iteratively doubling strategy is utilized to control the search effort on each bin combination. Once the bins are identified, a first-fit bottom-left method is utilized to generate the initial position for each piece. An overlap minimization approach, which includes a local search by exchanging the positions of two pieces, is adapted to minimize the overlap in the initial solution. Experiment results on existing instances show that our approach could find a better solution than existing methods. Several instances representing different application scenarios are generated, and the results show our approach’s effectiveness and generality.

Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2n potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead to instability. Because of this, we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process. For the selection of the best subset of features, we recommend using the binary ASSOA search strategy we developed before. According to the suggested approach, the number of features picked is reduced while maximizing classification accuracy. A ten-feature dataset from the University of California, Irvine (UCI) repository was used to test the proposed method’s performance vs. eleven other state-of-the-art approaches, including binary grey wolf optimization (bGWO), binary hybrid grey wolf and particle swarm optimization (bGWO-PSO), bPSO, binary stochastic fractal search (bSFS), binary whale optimization algorithm (bWOA), binary modified grey wolf optimization (bMGWO), binary multiverse optimization (bMVO), binary bowerbird optimization (bSBO), binary hybrid GWO and genetic algorithm (bGWO-GA), binary firefly algorithm (bFA), and bGA methods. Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

A binary individual search strategy-based bi-objective evolutionary algorithm for high-dimensional feature selection

Evolutionary computation is promising in tackling with the feature selection problem, but still has poor performance in obtaining good feature subset in high-dimensional problems. In order to efficiently obtain the optimal feature subset with higher classification accuracy and lower feature dimensions, a binary individual search strategy-based bi-objective evolutionary algorithm is proposed. The proposed algorithm has three advantages and contributions. Firstly, an improved fisher score is utilized to preprocess the feature space to remove the irrelevant and redundant features. It can decrease the feature dimensionality and compress the search space of feature subset effectively. Secondly, a binary individual search strategy is developed that contains a nearest neighbor binary individual crossover operator and an adaptive binary individual mutation operator, which can search the global optimal feature combination. Thirdly, enhanced population entropy and improved average convergence rate are adopted to monitor the correlation between the diversity of the population and the convergence of optimization objectives. Promising experimental results on twelve high-dimensional datasets reveal that the proposed algorithm can obtain competitive classification accuracy and effectively reduce the size of feature subset compared with ten state-of-the-art evolutionary algorithms.

A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy

In this study, a particle swarm optimization (PSO) algorithm with a negative gradient perturbation and binary tree depth‐first strategy (GB‐PSO) is proposed. The negative gradient term accelerates particle optimization in the direction of decreasing the objective function value. To calculate the step size of this gradient term more easily, a method based on the ratio was proposed. In addition, a new PSO strategy is also proposed. Each iteration of PSO yields not only the current optimal solution of the group, but also the solution based on the 2‐norm maximum. Under the current iteration solution of PSO, these two solutions are the children nodes. In the sense of the binary tree concept, the three solutions constitute the father‐son relationship, and the solution generated throughout the entire search process constitutes the binary tree. PSO uses a traceable depth‐first strategy to determine the optimal solution. Compared with the linear search strategy adopted by several algorithms, it can fully utilize the useful information obtained during the iterative process, construct a variety of particle swarm search paths, and prevent premature and enhance global optimization. The experimental results show that the algorithm outperforms some state‐of‐the‐art PSO algorithms in terms of search performance.

Novel strategy for applying hierarchical density-based spatial clustering of applications with noise towards spectroscopic analysis and detection of melanocytic lesions.

Advancements in dermoscopy techniques have elucidated identifiable characteristics of melanoma which revolve around the asymmetrical constitution of melanocytic lesions consequent of unfettered proliferative growth as a malignant lesion. This study explores the applications of hierarchical density-based spatial clustering of applications with noise (HDBSCAN) in terms of the direct diagnostic implications of applying agglomerative clustering in the spectroscopic analysis of malignant melanocytic lesions and benign dermatologic spots. 100 images of benign (n = 50) and malignant moles (n = 50) were sampled from the International Skin Imaging Collaboration Archive and processed through two separate Python algorithms. The first of which deconvolutes the three-digit tupled integer identifiers of pixel color in image composition into three separate matrices corresponding to the red, green and blue color channel. Statistical characterization of integer variance was utilized to determine the optimal channel for comparative analysis between malignant and benign image groups. The second applies HDBSCAN to the matrices, identifying agglomerative clustering in the dataset. The results indicate the potential diagnostic applications of HDBSCAN analysis in fast-processing dermoscopy, as optimization of clustering parameters according to a binary search strategy produced an accuracy of 85% in the classification of malignant and benign melanocytic lesions.

IBGSS: An Improved Binary Gravitational Search Algorithm based search strategy for QoS and ranking prediction in cloud environments

Quality of Service (QoS) value prediction and QoS ranking prediction have their significance in optimal service selection and service composition problems. QoS based service ranking prediction is an NP-Complete problem which examines the order of ranked service sequence with respect to the unique QoS requirements. To address the NP-Complete problem, greedy and optimization-based strategies such as CloudRank and PSO have been widely employed in service oriented environments. However, they pose several challenges with respect to the similarity measure based QoS prediction, trap at local optima, and near optimal solution. Hence, this paper presents Improved Binary Gravitational Search Strategy (IBGSS), an optimization based search strategy to address the challenges in the state-of-the-art QoS value prediction and service ranking prediction techniques. IBGSS employs improved cosine similarity measure, and Newton–Raphson inspired Binary Gravitational Search Algorithm (NR-BGSA) for accurate QoS value prediction and optimal service ranking prediction respectively. The effectiveness of IBGSS over the state-of-the-art QoS value prediction and ranking prediction techniques was validated using two real world QoS datasets, namely WSDream#1 and web service QoS dataset in terms of various statistical measures (Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Average Precision Correlation (APC)).

A fast and memory-efficient approach to NDN name lookup

For name-based routing/switching in NDN, the key challenges are to manage large-scale forwarding Tables, to lookup long names of variable lengths, and to deal with frequent updates. Hashing associated with proper length-detecting is a straightforward yet efficient solution. Binary search strategy can reduce the number of required hash detecting in the worst case. However, to assure the searching path correct in such a schema, either backtrack searching or redundantly storing some prefixes is required, leading to performance or memory issues as a result. In this paper, we make a deep study on the binary search, and propose a novel mechanism to ensure correct searching path without neither additional backtrack costs nor redundant memory consumptions. Along any binary search path, a bloom filter is employed at each branching point to verify whether a said prefix is present, instead of storing that prefix here. By this means, we can gain significantly optimization on memory efficiency, at the cost of bloom checking before each detecting. Our evaluation experiments on both real-world and randomly synthesized data sets demonstrate our superiorities clearly.

Multiconstraint Static Scheduling of Synchronous Dataflow Graphs Via Retiming and Unfolding

Synchronous dataflow graphs (SDFGs) are widely used to represent digital signal processing algorithms and streaming media applications. This paper presents several methods for binding and scheduling SDFGs on a multiprocessor platform. Exploring the state space generated by a self-timed execution (STE) of an SDFG, we present an exact method for static rate-optimal scheduling of SDFGs via implicit retiming and unfolding. By modeling a constraint as an extra enabling condition for the STE, we get a constrained STE which implies a schedule under the constraint. We present a general framework for scheduling SDFGs under constraints on the number of processors, buffer sizes, auto-concurrency, or combinations of them. Exploring the state space generated by the constrained STE, we can check whether a retiming, which leads to a rate-optimal schedule under the processor (or memory) constraint, exists. Combining this with a binary search strategy, we present heuristic methods to find a proper retiming and a static scheduling that schedules the retimed SDFG with optimal rate and with as few processors (or as little storage space) as possible. None of the methods explicitly converts an SDFG to its equivalent homogenous SDFG, the size of which may be tremendously larger than the original SDFG. We perform experiments on several models of real applications and hundreds of synthetic SDFGs. The results show that the exact method outperforms existing methods significantly; our heuristics reduce the resources used and are computationally efficient.

Learning user preferences by adaptive pairwise comparison

Users make choices among multi-attribute objects in a data set in a variety of domains including used car purchase, job search and hotel room booking. Individual users sometimes have strong preferences between objects, but these preferences may not be universally shared by all users. If we can cast these preferences as derived from a quantitative user-specific preference function, then we can predict user preferences by learning their preference function, even though the preference function itself is not directly observable, and may be hard to express. In this paper we study the problem of preference learning with pairwise comparisons on a set of entities with multiple attributes. We formalize the problem into two subproblems, namely preference estimation and comparison selection. We propose an innovative approach to estimate the preference, and introduce a binary search strategy to adaptively select the comparisons. We introduce the concept of an orthogonal query to support this adaptive selection, as well as a novel S-tree index to enable efficient evaluation of orthogonal queries. We integrate these components into a system for inferring user preference with adaptive pairwise comparisons. Our experiments and user study demonstrate that our adaptive system significantly outperforms the naïve random selection system on both real data and synthetic data, with either simulated or real user feedback. We also show our preference learning approach is much more effective than existing approaches, and our S-tree can be constructed efficiently and perform orthogonal query at interactive speeds.

Gaussian mixture modeling of histograms for contrast enhancement

The current major theme in contrast enhancement is to partition the input histogram into multiple sub-histograms before final equalization of each sub-histogram is performed. This paper presents a novel contrast enhancement method based on Gaussian mixture modeling of image histograms, which provides a sound theoretical underpinning of the partitioning process. Our method comprises five major steps. First, the number of Gaussian functions to be used in the model is determined using a cost function of input histogram partitioning. Then the parameters of a Gaussian mixture model are estimated to find the best fit to the input histogram under a threshold. A binary search strategy is then applied to find the intersection points between the Gaussian functions. The intersection points thus found are used to partition the input histogram into a new set of sub-histograms, on which the classical histogram equalization (HE) is performed. Finally, a brightness preservation operation is performed to adjust the histogram produced in the previous step into a final one. Based on three representative test images, the experimental results demonstrate the contrast enhancement advantage of the proposed method when compared to twelve state-of-the-art methods in the literature.

Economic-emission load dispatch using binary successive approximation-based evolutionary search

A novel binary successive approximation-based evolutionary search strategy has been proposed to solve the economic-emission load dispatch problem by searching the generation pattern of committed units. Inequality constraints are taken care of during the search of a generation pattern. To meet the demand, a slack generator is introduced to compensate the perturbation of unmet load during the search. To determine the trade-off relationship between conflicting objectives in the non-inferior domain, the weighting method is exploited. Once the trade-off has been obtained, fuzzy set theory helps the decision maker to choose the optimal operating point over the trade-off curve and adjust the generation schedule in the most preferred manner. Generally the weights are regulated in a systematic manner, which is a time-consuming process. To reduce the time to arrive at the best compromising solutions, the weight pattern assigned to objectives are searched for more significant digits in a fixed number of iterations. Performance of the algorithm is investigated on economic load dispatch problems of different sizes and complexity having non-convex cost curves where conventional gradient-based methods are inapplicable. This technique has emerged as the useful optimisation tool for handling network losses, ramp rate limits, valve point loading and prohibited zone avoidance into account to determine the global optimal dispatch solution as well as optimal operating point in the non-inferior domain for any number of the goals. The method is applied on various test systems and better results are achieved with reference to emission (Kg/h), however when compared with other techniques, the proposed method has lower performance with reference to losses (MW) and cost ($/h).

A Portable Sensing System for Electronic Tongue Operations

A portable, low cost sensing system is described which interfaces to an electronictongue sensor. The sensor used is a voltammetric sensor which monitors electrochemicalreactions that occur in solutions. The sensor is able to test a range of liquids with differentelectrochemical properties without any hardware adjustments to the system. The system canautomatically adjust for the change in solution properties by performing a routine whichuses an auto-ranging feature to determine a current-to-voltage conversion of the sensor databy using a binary search strategy. This eliminates the intervention of the user to modify thesystem each time a new solution is tested. The effectiveness of the calibration routine wastested by carrying out cyclic voltammetry in two different solutions, 0.1M sulfuric acidsolution and the phosphate buffered solution of pH3. The sensor system was able toaccurately acquire the sensor data for each solution.

A real-time tracker for hadronic collider experiments

In this paper we propose highly parallel dedicated processors, able to provide precise on-line track reconstruction for future hadronic collider experiments. The processors, organized in a 2-level pipelined architecture, execute very fast algorithms based on the use of a large bank of pre-stored patterns of trajectory points. An associative memory implements the first stage by recognizing track candidates at low resolution to match the demanding task of tracking at the detector readout rate. Alternative technological implementations for the associative memory are compared. The second stage receives track candidates and high resolution hits to refine pattern recognition at the associative memory output rate. A parallel and pipelined hardware implements a binary search strategy inside a hierarchically structured pattern bank, stored in high density commercial RAMs.

Eye Movements Reveal the Spatiotemporal Dynamics of Visual Search

Given that attention precedes an eye movement to a target it becomes possible to use fixation sequences to probe the spatiotemporal dynamics of search Applying this method to a realistic search task, we found eye movements directed to the geometric centers of progressively smaller groups of objects rather than accurate fixations to individual objects in a display Such a binary search strategy is consistent with zoom-lens models positing an initially broad distribution of search, followed by a narrowing of this search region until only the target is selected We also interpret this oculomotor averaging behavior as evidence for an initially parallel search analysis that becomes increasingly serial as the search process converges on the target

Binary search in flow titration employing photometric end-point detection

A binary search strategy is proposed and implemented in a continuous flow system to find the end-point titration by employing spectrophotometric detection. It takes advantage of the binary sampling process under a constant flow-rate. For this task an automated flow set-up based on solenoid valves was designed. A 386 microcomputer was employed to control the valves, and to perform data acquisition and data processing. The accuracy level can be previously settled as a software parameter. Samples with concentrations ranging within two orders of magnitude could be titrated by making use of the same flow set-up. Titration of hydrochloric acid with sodium hydroxide was performed to demonstrate the feasibility of the proposal. Titrations in triplicate, with 99.99% precision were carried out in 3 min with a solution consumption of 2 ml.

An Efficient Algorithm for the Time-Domain Simulation of Regulated Energy-Storage DC-to-DC Converters

An algorithm is presented for the efficient and accurate simulation of switched-mode piecewise-linear systems, a subclass of which includes regulated energy-storage dc-to-dc converters. Three key features of the new algorithm that lead to significant improvements in computational efficiency and accuracy are 1) a state-transition-matrix table-lookup scheme to solve differential equations describing the system, 2) a modified binary-search strategy to obtain an initial estimate of the instant at which the system topology switches, and 3) a quadratic extrapolation for the accurate final determination of the switching instant. The computational efficiency of a program using the new algorithm is demonstrated by comparing its execution time with that of a program employing a standard fixed-step-size integration algorithm. The accuracy of the new algorithm is verified by comparing results obtained from the simulations with results obtained analytically, and by comparing data from the simulation of two different regulated converters with data measured experimentally.