Binary Ranking Research Articles

High Throughput Scheduling Algorithms for Input Queued Packet Switches

The high-performance computing paradigm needs high-speed switching fabrics to meet the heavy traffic generated by their applications. These switching fabrics are efficiently driven by the deployed scheduling algorithms. In this paper, we proposed two scheduling algorithms for input queued switches whose operations are based on ranking procedures. At first, we proposed a Simple 2-Bit (S2B) scheme which uses binary ranking procedure and queue size for scheduling the packets. Here, the Virtual Output Queue (VOQ) set with maximum number of empty queues receives higher rank than other VOQ’s. Through simulation, we showed S2B has better throughput performance than Highest Ranking First (HRF) arbitration under uniform, and non-uniform traffic patterns. To further improve the throughput-delay performance, an Enhanced 2-Bit (E2B) approach is proposed. This approach adopts an integer representation for rank, which is the number of empty queues in a VOQ set. The simulation result shows E2B outperforms S2B and HRF scheduling algorithms with maximum throughput-delay performance. Furthermore, the algorithms are simulated under hotspot traffic and E2B proves to be more efficient.

Facial Age and Expression Synthesis Using Ordinal Ranking Adversarial Networks

Facial image synthesis has been extensively studied, for a long time, in both computer graphics and computer vision. Particularly, the synthesis of face images with varying ages, expressions and poses has received an increasing attention owing to several real-world applications. In this paper, facial age and expression synthesis are addressed. While previous and current research papers on facial age synthesis mostly adopt an age span of 10 years, this paper investigates face aging with a shorter time span. For expression synthesis, given a neutral face, we work on synthesizing faces with varying expression intensities (e.g., from zero to high). Note that both human ages and expression intensities are inherently ordinal. To fully exploit this ordinal nature, we devise ordinal ranking generative adversarial networks (ranking GAN). For each face, a one-hot label is assigned to define its age range/expression intensity. By exploiting the relative order information among age ranges/expression intensities, a binary ranking vector is further computed for each face. In ranking GAN, one-hot labels are used as the condition of the generator for synthesizing faces with target age groups/expression intensities. Moreover, we add a sequence of cost-sensitive ordinal rankers on top of several multi-scale discriminators, with the aim of minimizing age/intensity rank estimation loss when optimizing both the generator and discriminators. In order to evaluate the proposed ranking GAN, extensive experiments are carried out on several public face databases. As demonstrated by the experimental testing, this ranking scheme performs well even when the amount of available labeled training data is limited. The reported experimental results well demonstrate the effectiveness of ranking GAN on synthesizing face aging sequences and faces with varying expression intensities.

Binary ranking for ordinal class imbalance

Imbalanced classification has been extensively researched in the last years due to its prevalence in real-world datasets, ranging from very different topics such as health care or fraud detection. This literature has long been dominated by variations of the same family of solutions (e.g. mainly resampling and cost-sensitive learning). Recently, a new and promising way of tackling this problem has been introduced: learning with scoring pairwise ranking so that each pair of classes contribute in tandem to the decision boundary. In this sense, the paper addresses the problem of class imbalance in the context of ordinal regression, proposing two novel contributions: (a) approaching the imbalance by binary pairwise ranking using a well-known label decomposition ensemble, and (b) introducing a regularization into this ensemble so that parallel decision boundaries are favored. These are two independent contributions that synergize well. Our model is tested using linear Support Vector Machines and our results are compared against state-of-the-art models. Both approaches show promising performance in ordinal class imbalance, with an overall 15% improvement relative to the state-of-the-art, as evaluated by a balanced metric.

Multi-Scale Difference Map Fusion for Tamper Localization Using Binary Ranking Hashing

The block-based analysis for tamper localization is a prevailing mechanism in hash-based forgery detection algorithm. One of the main problems with a block-based analysis is its rough localization stemming from the demand to use relatively large blocks to reduce hash length. While decreasing the block size can improve the localization resolution, the hash length tends to become too long to be practical. In this paper, we propose a binary ranking hashing approach that satisfies both the requirements of compact hash length and small block size, to obtain a binary map combined with spatial information. Meanwhile, we investigate a multiscale difference map fusion approach that fuses multiple candidate difference maps, resulting from the analysis of the subtraction between two binary maps with different sliding windows, to obtain a single, more reliable tampering map with better localization resolution. We use manifold ranking to model this multiscale difference map fusion problem and propose a two-stage scheme, namely, ranking with tampering queries and nontampering queries. Our results indicate that the proposed tamper detection method can improve the tamper localization resolution compared with state-of-the-art methods.

Essays in Behavioral Economics

This thesis contains two theoretical essays on reciprocity and one that analyzes the effects of perception biases on learning and decision-making. In the first chapter, I propose a new theory of intention-based reciprocity that addresses the question of when a mutually beneficial action is kind. When both benefit from the action, a player’s motive is unclear: he may be perceived as kind for improving the other player’s payoff, or as self-interested and not-kind for improving his own. I use trust as an intuitive mechanism to solve this ambiguity. Whenever a player puts himself in a vulnerable position by taking such an action, he can be perceived as kind. In contrast, if this action makes him better off than his alternative actions do, even if it is met by the most selfish response, he cannot be kind. My model explains why papers in the literature fail to find (much) positive reciprocity when players can reward and punish. The second chapter extends my theory of reciprocity to incomplete information. I outline how reciprocity can give rise to pay-what-you-want pricing schemes. In the classic bilateral trade setting, I show that sequential interactions can be more efficient than normal form mechanisms when some people are motivated by reciprocity. Reciprocity creates incentives for information sharing. The last chapter is co-authored with Manuel Staab. We study the effects of perception biases and incorrect priors on learning behavior, and the welfare ranking of information experiments. We find that both types of biases by themselves reduce expected utility in a model where payoff relevant actions also generate informative signals, i.e. when actions constitute information experiments. However, experiments can be affected to different degrees by these biases. We provide necessary and sufficient conditions for when any binary ranking of action profiles can be reversed. Building on these findings, we show that an agent can be better off suffering from both biases rather than just one.

An efficient and scalable ranking technique for mashups involving RSS data sources

Mashups are key category of Web 2.0 personalized applications. Due to personalization property of Web 2.0 applications, number of mashups hosted by a mashup platform is increasing. End-users are overwhelmed by the increasing number of mashups. Therefore, they cannot easily find mashups of their interest. In this paper, we propose a novel mashup ranking technique based on the popular Vector Space Model (VSM) for mashups that use RSS feeds as data sources. Mashups that are ranked higher would be more interesting to end-users. In order to evaluate our mashup ranking technique, we implement it in a prototype where end-users select mashups that they consider interesting. We implicitly collect the end-user mashup selections and record the outcome of our ranking technique, and then we analyze them. Recorded R-Precision value in our technique is on an average 30% higher than R-Precision value in binary ranking technique which shows an improvement in capturing mashups that resemble end-user interest. In our design, we make sure our mashup ranking technique scales well to increasing number of mashups.

Impact of SPECT and integrated low-dose CT after radioiodine therapy on the management of patients with thyroid carcinoma

To determine the value of single photon emission computed tomography (SPECT) with integrated low-dose computed tomography (CT) for the interpretation of inconclusive foci in planar I whole-body scans after radioiodine therapy. Twenty-five patients with inconclusive findings in planar scanning after ablative radioiodine therapy (3.7 GBq I) due to differentiated thyroid cancer were included. SPECT/CT of the region in question was performed with the Millennium VG Hawkeye (GE Medical Systems). SPECT with and without CT fusion were evaluated by two blinded independent nuclear medicine physicians in a consensus reading (including visual plausibility control). Each focus was judged according to its topographical assignment and clinical interpretation. With regard to therapeutic relevance, this information was evaluated in a focus based and patient based analysis. All evaluations used a binary ranking system. Focus assignments were compared to clinical and imaging follow-up. Forty-one lesions were observed in 25 patients. According to follow-up, 17/41 (41%) foci were caused by thyroid residue, 13/41 (32%) were caused by metastases, and 11/41 (27%) were not malignant. Of these foci, a SPECT/CT consensus reading assigned 39 (95%) correctly, as fused images of two foci did not pass the visual plausibility control (excluding one patient due to misregistration). For the remaining 39 foci, improved anatomical assignment by SPECT/CT was seen in 17/39 (44%) cases. The changed interpretation of 15/39 (38%) foci would have been relevant for therapy in the focus based analysis. In the patient based analysis the information was still therapeutically relevant in 6/24 (25%) patients. Furthermore, plausibility control is also crucial in SPECT/CT image fusion in order to rule out artifacts.

Binary ranking for the signature file method

A new method for ranking the output of the superimposed variation of the signature file method (SC/SF) is presented. The method is termed ‘Binary Ranking’ and assigns a value (‘B-rank value’) to each block candidacy of the signature file output in a way which reflects the credibility of the signature to stand in place of the corresponding real text. In order to measure the performance of the proposed technique, a simulation environment based on a Relational Database Management System is established. Binary Ranking is found to be promising for a category of real-life applications and allows for future enhancements.

Signal and image processing using three-dimensional binary ranking transforms

Because binary mathematical morphology permits fast local neighborhood operations by flash conversion, it is used extensively in high-speed pattern-recognition computer systems. Further, since anyN-dimensional integer function may be represented by an (N + 1)-dimensional binary (bilevel) function, ordinary two-dimensional graylevel images become three-dimensional binary images. Thus these images may be processed by high-speed flash-conversion computers assuming that a sufficiently compact three-dimensional kernel can be devised. The tetradekahedron of the face-centered-cubic tessellation forms a perfect kernel in three-dimensions. Its neighborhood is compact. It has total symmetry with all 12 neighbors equidistant from the central element. Using this kernel a variety of useful three-dimensional morphological operations may be performed for target track detection, shaded graphics, data clustering, automated focusing, and spatial filtering.

The Borda method is most likely to respect the Condorcet principle

We prove that in the class of weighted voting systems the Borda Count maximizes the probability that a Condorcet candidate is ranked first in a group election. A direct result is that the Borda Count maximizes the probability that a transitive, binary ranking of the candidates is preserved in a group election. A preliminary result, but one of independent interest, is that the Borda Count maximizes the probability that a majority outcome betweenany two candidates is reflected by the group election. All theorems are valid when there is a uniform probability distribution on the voter profiles and can be generalized to other “uniform-like” probability distributions. This work extends previous results of Fishburn and Gehrlein from three candidates to any number of candidates.

Detection of weak, subpixel targets using mesh-connected cellular automata

Effective use of military cellular automata such as military data array processor (MilDAP) and geometric arithmetic parallel processor (GAPP), in weak, subpixel target detection is shown to be possible by using new signal processing regimes based on binary ranking filter theory. By using binary ranking filters, the MilDAP can furnish 6 dB of processing gain against white Gaussian noise while monitoring from one to four million potential target tracks at 10-40 frames/s. GAPP is shown to be capable of monitoring 3.7 million tracks over 216*384 detectors at 14000 frames/s and, in a time sharing mode, 15 million tracks over 432*768 detectors at 24 frames/s. The special case of threatening targets is discussed, as well as alternate cellular architectures which use multidimensional binary ranking filters in multidimensional coordinate systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Response Threshold Models for Binary Ranking and Sorting

The present paper provides two response models: one for binary ranking and the other for sorting. The former is a behavior of choosing, in a random order, only those comparison stimuli which are judged to be very similar to a standard stimulus, and the latter is that of selecting stimuli which are judged very similar to each other to form them into clusters. The key assumption of these models is that the subject perceives any two stimuli as very similar to each other when their dissimilarity, which varies over time, is below response thresholds that are associated with those stimuli. Maximum likelihood estimation procedures are used for the estimation of parameters of these models. The proposed models are applied, for illustrative purposes, to the similarity data collected by the binary ranking and sorting methods. We discuss some advantages of the binary ranking method to be used for collecting similarity data and a practical limitation of our response model for sorting.