Reviewerly/exHarmony huggingface dataset:
Hierarchical Reinforcement Learning With Monte Carlo Tree Search in Computer Fighting Game

Fighting games are complex environments where challenging action-selection problems arise, mainly due to a diversity of opponents and possible actions. In this paper, we present the design and evaluation of a lighting player on top of the FightingICE platform that is used in the Fighting Game Artilicial Intelligence (FTGAI) competition. Our proposal is based on hierarchical reinforcement learning (HRL) in combination with Monte Carlo tree search (MCTS) designed as options. By using the FightingICE framework, we evaluate our player against state-of-the-art FTGAIs. We train our player against the current FTGAI champion (GigaThunder). The resulting learned policy is comparable with the champion in direct confront in regard to the number of victories, with the advantage of having less need for expert knowledge. We also evaluate the proposed player against the runners-up and show that adaptation to the strategies of each opponent is necessary for building stronger lighting players.
Area-Time Efficient Streaming Architecture for FAST and BRIEF Detector

The combination of features from an accelerated segment test (FAST) corners and binary robust independent elementary feature (BRIEF) descriptors provide highly robust image features. We present a novel detector for computing the FAST-BRIEF features from streaming images. To reduce the complexity of the BRIEF descriptor, we employ an optimized adder tree to perform summation by accumulation on streaming pixels for the smoothing operation. Since the window buffer used in existing designs for computing the BRIEF point-pairs are often poorly utilized, we propose an efficient sampling scheme that exploits register reuse to minimize the number of registers. Synthesis results based on 65-nm CMOS technology show that the proposed FAST-BRIEF core achieves over 40% reduction in area-delay product compared to the baseline design. In addition, we show that the proposed architecture can achieve 1.4× higher throughput than the baseline architecture with slightly lower energy consumption.
Modeling and simulation of improved artificial bee colony algorithm with data-driven optimization

To balance the exploration and exploitation and to enhance the convergence rate of an artificial bee colony (ABC) algorithm, the driving force of using additional data during searching process is studied in this paper, and an improved ABC algorithm with data-driven optimization (DDABC) is proposed. First, to speed up convergence rate, the searching process is driven by directional guiding data. Therefore, a bee colony would learn from the directional guiding data, instead of picking up a random direction. Second, to enhance the exploitation capability of the onlooker bees, the searching process is driven by local data of onlooker bees. Every onlooker bee would search independently for multiple times to generate local data applied into optimization. Comparisons are made with a number of other ABC-based and nature-inspired algorithms. The results show that the proposed DDABC achieves improvements in both exploitation capability and convergence rates.
Disaggregated Cloud Memory with Elastic Block Management

With the growing importance of in-memory data processing, cloud service providers have launched large memory virtual machine services to accommodate memory intensive workloads. Such large memory services using low volume scaled-up machines are far less cost-efficient than scaled-out services consisting of high volume commodity servers. By exploiting memory usage imbalance across cloud nodes, disaggregated memory can scale up the memory capacity for a virtual machine in a cost-effective way. Disaggregated memory allows available memory in remote nodes to be used for the virtual machine requiring more memory than its locally available memory. It supports high performance with the faster direct memory while satisfying the memory capacity demand with the slower remote memory. This paper proposes a new hypervisor-integrated disaggregated memory system for cloud computing. The hypervisor-integrated design has several new contributions in its disaggregated memory design and implementation. First, with the tight hypervisor integration, it investigates a new page management mechanism and policy tuned for disaggregated memory in virtualized systems. Second, it restructures the memory management procedures and relieves the scalability concern for supporting large virtual machines. Third, exploiting page access records available to the hypervisor, it supports application-aware elastic block sizes for fetching remote memory pages with different granularities. Depending on the degrees of spatial locality for different regions of memory in a virtual machine, the optimal block size for each memory region is dynamically selected. The experimental results with the implementation integrated to the KVM hypervisor, show that the disaggregated memory can provide on average 6 percent performance degradation compared to the ideal local-memory only machine, even though the direct memory capacity is only 50 percent of the total memory footprint.
Recommender System for Items in <i>Dota 2</i>

Dota 2 is one of several multiplayer online battle arena games that have recently become extremely popular. A central feature of Dota 2 is that players select and purchase items that are used in the game and the selections strongly affect the outcome of the game. Item recommendations and purchase predictions in Dota 2 turn out to be interesting problems due to the many factors affecting the choice of items and due to the frequency by which items are purchased throughout the game. We present three recommender systems for recommending which items a player might buy throughout a match, based on commonly used purchasing strategies. These systems are a rule-based system, a logistic regression based system, and a logistic regression based system enhanced with clustering. Knowing only the player's hero and inventory items, the first two systems are able to predict the purchases by highly skilled players with accuracies ranging from 66.5% to 87.1%, depending on the hero where accuracy is defined as recommended items that were actually purchased in the next 5 min of the game. For the third system, the players are clustered by purchasing strategy which in turn group players by role and play style. Adding the clustering feature to the second version of the recommender system only improve the results slightly. The reason for this could be that the items in the player's inventory are already a strong indicator of a player's role and play style both of which were used to develop the first two recommender systems. An important question for future work is how the recommendations could be useful in practice for human or AI players.
Error Detection in Monotone Span Programs with Application to Communication-Efficient Multi-party Computation

Recent improvements in the state-of-the-art of MPC for non-full-threshold access structures introduced the idea of using a collision-resistant hash functions and redundancy in the secret-sharing scheme to construct a communication-efficient MPC protocol which is computationally-secure against malicious adversaries, with abort. The prior work is based on replicated secret-sharing; in this work we extend this methodology to any LSSS implementing a $$\mathcal {Q}_{2}$$ access structure. To do so we need to establish a folklore property of error detection for such LSSS and their associated Monotone Span Programs. In doing so we obtain communication-efficient online and offline protocols for MPC in the pre-processing model.
Hyper-Clique Graph Matching and Applications

This paper proposes a method for hyper-clique graph (HCG) generation, which can be considered an extension of classical graphs and hyper-graphs in which the node is replaced with the clique (a set of neighboring nodes in a specific feature space) and the hyper-edge linking multiple nodes is replaced with the hyper-edge linking multiple cliques. In addition, we propose the HCG matching method by preserving global and local structures. Specifically, we embed the clique relations of arbitrary orders in a high-order similarity tensor in a recursive manner. Then, we formulate the objective function of HCG matching with respect to two latent variables: the latent clique structure information in the original graph and the similarity measure of clique sets from pairwise HCGs. Since the objective function is not jointly convex with respect to both latent variables, we decompose it into two consecutive measurements for optimization: 1) a clique-to-clique similarity measurement by preserving local unary and pairwise correspondences and 2) a graph-to-graph similarity measurement by preserving global clique-to-clique correspondence. We suitably adopt the affinity-preserving reweighted random walks to optimize the objective function. We extensively evaluate the HCG matching performance on multiple applications: 1) we evaluate the robustness of HCG with respect to the deformation noise, the number of outliers, and the edge density on synthetic data and explore the effects of both the clique order and hyper-edge order on performance; 2) we explore HCG matching for feature point matching on multiple image data sets (CMU house sequence, Caltech+MSRC, and Car+Motor); and 3) we explore HCG matching for multi-view object retrieval, which is a much more challenging task since multi-view objects contain significant variations of illumination, viewpoint, and so on, using popular data sets (MV-RED and NTU). A comparison against the state-of-the-art methods demonstrates the superior performance of the proposed method.
Mosaic-Based Color-Transform Optimization for Lossy and Lossy-to-Lossless Compression of Pathology Whole-Slide Images

The use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though the state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called mosaic optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than the other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen-Loève Transform in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB), and the accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). In addition, reversible optimized transforms achieve PSNR, HDR-VDP-2, and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB, and 0.025, respectively, when compared with the reversible color transform in lossy-to-lossless compression regimes.
Face-Cap: Image Captioning Using Facial Expression Analysis

Image captioning is the process of generating a natural language description of an image. Most current image captioning models, however, do not take into account the emotional aspect of an image, which is very relevant to activities and interpersonal relationships represented therein. Towards developing a model that can produce human-like captions incorporating these, we use facial expression features extracted from images including human faces, with the aim of improving the descriptive ability of the model. In this work, we present two variants of our Face-Cap model, which embed facial expression features in different ways, to generate image captions. Using all standard evaluation metrics, our Face-Cap models outperform a state-of-the-art baseline model for generating image captions when applied to an image caption dataset extracted from the standard Flickr 30 K dataset, consisting of around 11 K images containing faces. An analysis of the captions finds that, perhaps surprisingly, the improvement in caption quality appears to come not from the addition of adjectives linked to emotional aspects of the images, but from more variety in the actions described in the captions. Code related to this paper is available at: https://github.com/omidmn/Face-Cap .
Sequence in sequence for video captioning

For video captioning, the words in the caption are closely related to an overall understanding of the video. Thus, a suitable representation for the video is rather important for the description. For more precise words in the task of video captioning, we aim to encode the video feature for current word at each time-stamp of the generation process. This paper proposes a new framework of ‘Sequence in Sequence’ to encode the sequential frames into a spatio-temporal representation at each time-stamp to utter a word and further distill most related visual content by an extra semantic loss. First, we aggregate the sequential frames to extract related visual content guided by last word, and get a representation with rich spatio-temporal information. Then, to decode the aggregated representation for a precise word, we leverage two layers of GRU structure, where the first layer further distills useful visual content based on an extra semantic loss and the second layer selects the correct word according to the distilled features. Experiments on two benchmark datasets demonstrate that our method outperforms the current state-of-the-art methods on [email protected], METEOR and CIDEr metrics.
Gaussian process methods for nonparametric functional regression with mixed predictors

Gaussian process methods are proposed for nonparametric functional regression for both scalar and functional responses with mixed multidimensional functional and scalar predictors. The proposed models allow the response variables to depend on the entire trajectories of the functional predictors. They inherit the desirable properties of Gaussian process regression, and can naturally accommodate both scalar and functional variables as the predictors, as well as easy to obtain and express uncertainty in predictions. The numerical experiments show that the proposed methods significantly outperform the competing models, and their usefulness is also demonstrated by the application to two real datasets.
Effect of Region of Interest Size on the Repeatability of Quantitative Brain Imaging Biomarkers

In the repeatability analysis, when the measurement is the mean value of a parametric map within a region of interest (ROI), the ROI size becomes important as by increasing the size, the measurement will have a smaller variance. This is important in decision-making in prospective clinical studies of brain when the ROI size is variable, e.g., in monitoring the effect of treatment on lesions by quantitative MRI, and in particular when the ROI is small, e.g., in the case of brain lesions in multiple sclerosis. Thus, methods to estimate repeatability measures for arbitrary sizes of ROI are desired. We propose a statistical model of the values of parametric map within the ROI and a method to approximate the model parameters, based on which we estimate a number of repeatability measures including repeatability coefficient, coefficient of variation, and intraclass correlation coefficient for an ROI with an arbitrary size. We also show how this gives an insight into related problems such as spatial smoothing in voxel-wise analysis. Experiments are conducted on simulated data as well as on scan-rescan brain MRI of healthy subjects. The main application of this study is the adjustment of the decision threshold based on the lesion size in treatment monitoring.
Elastic nonnegative matrix factorization

Nonnegative matrix factorization (NMF) plays a vital role in data mining and machine learning fields. Standard NMF utilizes the Frobenius norm while robust NMF uses the robust ℓ2,1-norm to measure the quality of factorization, given the assumption of i.i.d Gaussian noise model and i.i.d Laplacian noise model, respectively. In this paper, we propose a novel elastic loss which is intercalated and adapted between Frobenius norm and ℓ2,1-norm. Inspired by this, we derive an elastic NMF model guided by the elastic loss with incorporating geometry manifold information while enforcing sparsity of coefficients at intra-cluster level via ℓ1,2-norm. The new formulation is more robust to noises while preserving the stronger capability of clustering. We propose an EM-like algorithm (using an auxiliary function) to solve the resultant optimization problem, whose convergence can be rigorously proved. The extensive experiments demonstrate the effectiveness of the novel elastic NMF model on benchmarks.
Multiview Fuzzy Logic System With the Cooperation Between Visible and Hidden Views

Multiview datasets are frequently encountered in learning tasks, such as web data mining and multimedia information analysis. Given a multiview dataset, traditional learning algorithms usually decompose it into several single-view datasets, from each of which a single-view model is learned. In contrast, a multiview learning algorithm can achieve better performance by cooperative learning on the multiview data. However, existing multiview approaches mainly focus on the views that are visible and ignore the hidden information behind the visible views, which usually contains some intrinsic information of the multiview data, or vice versa. To address this problem, this paper proposes a multiview fuzzy logic system which utilizes both the hidden information shared by the multiple visible views and the information of each visible view. Extensive experiments were conducted to validate its effectiveness.
Two Round Information-Theoretic MPC with Malicious Security

We provide the first constructions of two round information-theoretic (IT) secure multiparty computation (MPC) protocols in the plain model that tolerate any $$t<n/2$$ malicious corruptions. Our protocols satisfy the strongest achievable standard notions of security in two rounds in different communication models. Previously, IT-MPC protocols in the plain model either required a larger number of rounds, or a smaller minority of corruptions.
Fuzzy keywords enabled ranked searchable encryption scheme for a public Cloud environment

Searchable Encryption allows a user or organization to outsource their encrypted documents to a Cloud-based storage service, while maintaining the ability to perform keyword searches over the encrypted text. However, most of the existing search schemes do not take the almost certain presence of typographical errors in the documents under consideration, when trying to obtain meaningful and accurate results. This paper presents a novel ranked searchable encryption scheme that addresses this issue by supporting fuzzy keywords. The proposed construction is based on probabilistic trapdoors that help resist distinguishability attacks. This paper for the first time proposes Searchable Encryption as a Service (SEaaS). The proposed construction is deployed on the British Telecommunication’s public Cloud architecture and evaluated over a real-life speech corpus. Our security analysis yields that the construction satisfies strong security guarantees and is also quiet lightweight, by analyzing its performance over the speech corpus.
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search

Exact Maximum Inner Product Search (MIPS) is an important task that is widely pertinent to recommender systems and high-dimensional similarity search. The brute-force approach to solving exact MIPS is computationally expensive, thus spurring recent development of novel indexes and pruning techniques for this task. In this paper, we show that a hardware-efficient brute-force approach, blocked matrix multiply (BMM), can outperform the state-of-the-art MIPS solvers by over an order of magnitude, for some-but not all-inputs. In this paper we also present a novel MIPS solution, MAX-IMUS, that takes advantage of hardware efficiency and pruning of the search space. Like BMM, MAXIMUS is faster than other solvers by up to an order of magnitude, but again only for some inputs. Since no single solution offers the best runtime performance for all inputs, we introduce a new data-dependent optimizer, OPTIMUS, that selects online with minimal overhead the best MIPS solver for a given input. Together, OPTIMUS and MAXIMUS outperform state-of-the-art MIPS solvers by 3.2× on average, and up to 10.9×, on widely studied MIPS datasets.
On the Structure and Distances of Repeated-Root Constacyclic Codes of Prime Power Lengths Over Finite Commutative Chain Rings

Let p be a prime, s be a positive integer, and R be a finite commutative chain ring with the characteristic as a power of p. For a unit λ ε R, λ-constacyclic codes of length ps over R are ideals of the quotient ring R[x]/(x(p) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> -λ). In this paper, we derive necessary and sufficient conditions under which the quotient ring R[x]/(x(p) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> - λ) is a chain ring. When R[x]/(x(p) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> - λ) is a chain ring, all λ-constacyclic codes of length ps over R are known. In this paper, we establish the algebraic structures of all λ-constacyclic codes of length ps over R when R[x]/(x(p) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> - λ) is a non-chain ring. We also determine the number of codewords in each of these codes. Using their algebraic structures, we obtain symbol-pair distances, Rosenbloom-Tsfasman (RT) distances, and RT weight distributions of all constacyclic codes of length ps over R. Apart from this, we derive necessary and sufficient conditions under which a constacyclic code of length ps over R is maximumdistance separable with respect to the: 1) Hamming metric; 2) symbol-pair metric; and 3) RT metric. We also provide an algorithm to decode the constacyclic codes of length ps over R using the known decoding algorithms of linear codes over finite fields with respect to the Hamming, symbol-pair, and RT metrics.
New LCD MDS codes constructed from generalized Reed–Solomon codes

Maximum distance separable codes with complementary duals (LCD MDS codes) are very important in coding theory and practice, and have attracted a lot of attention. In this paper, we focus on LCD MDS codes constructed from generalized Reed–Solomon (GRS) codes over a finite field with odd characteristic. We detail two constructions of new LCD MDS codes, using invertible matrices and the roots of three classes of polynomials, respectively.
Optimal non-projective linear codes constructed from down-sets

By an optimal linear code we mean that it has the highest minimum distance with a prescribed length and dimension. We construct several families of optimal linear codes over the finite field F p by making use of down-sets generated by one maximal element of F p n . Moreover, we show that these families of optimal linear codes are minimal and contain relative two-weight linear codes, and have applications to secret sharing schemes and wire-tap channel of type II with the coset coding scheme, respectively.