Reviewerly/exHarmony huggingface dataset:
Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes

Selection hyper-heuristics (HHs) are randomised search methodologies which choose and execute heuristics during the optimisation process from a set of low-level heuristics. A machine learning mechanism is generally used to decide which low-level heuristic should be applied in each decision step. In this article, we analyse whether sophisticated learning mechanisms are always necessary for HHs to perform well. To this end we consider the most simple HHs from the literature and rigorously analyse their performance for the LeadingOnes benchmark function. Our analysis shows that the standard Simple Random, Permutation, Greedy, and Random Gradient HHs show no signs of learning. While the former HHs do not attempt to learn from the past performance of low-level heuristics, the idea behind the Random Gradient HH is to continue to exploit the currently selected heuristic as long as it is successful. Hence, it is embedded with a reinforcement learning mechanism with the shortest possible memory. However, the probability that a promising heuristic is successful in the next step is relatively low when perturbing a reasonable solution to a combinatorial optimisation problem. We generalise the “simple” Random Gradient HH so success can be measured over a fixed period of time [Formula: see text], instead of a single iteration. For LeadingOnes we prove that the Generalised Random Gradient (GRG) HH can learn to adapt the neighbourhood size of Randomised Local Search to optimality during the run. As a result, we prove it has the best possible performance achievable with the low-level heuristics (Randomised Local Search with different neighbourhood sizes), up to lower-order terms. We also prove that the performance of the HH improves as the number of low-level local search heuristics to choose from increases. In particular, with access to [Formula: see text] low-level local search heuristics, it outperforms the best-possible algorithm using any subset of the [Formula: see text] heuristics. Finally, we show that the advantages of GRG over Randomised Local Search and Evolutionary Algorithms using standard bit mutation increase if the anytime performance is considered (i.e., the performance gap is larger if approximate solutions are sought rather than exact ones). Experimental analyses confirm these results for different problem sizes (up to [Formula: see text]) and shed some light on the best choices for the parameter [Formula: see text] in various situations.
Multiscale Fourier descriptor based on triangular features for shape retrieval

Shape information plays an important role in the human visual system, and how to represent the shape accurately is a very challenging issue in shape recognition. In recent years, many shape description approaches have been proposed. However, most of the existing methods are still facing challenges on accuracy and computational efficiency. Fourier descriptor is regarded as a promising shape description method because it has a solid theoretical foundation and at the same time possesses the strength of appealing invariance properties and high computational efficiency. We introduce a novel multiscale Fourier descriptor based on triangular features which is used to identify shapes. The local and global characteristics of a shape are effectively captured by the proposed shape descriptor. Meanwhile, the proposed shape descriptor has good properties, such as the invariant of the geometric transformation and the starting point of an object. We tested this descriptor on four popular benchmarking datasets, including MPEG-7, Swedish leaf, ETH-80, and Flavia leaf. The results confirm that our method is better than comparable-complexity approaches based on Fourier descriptors and does not perform unfavorably with respect to more complex, state-of-the-art, shape descriptors. In particular, our method is far superior to the complex shape description methods in terms of retrieval efficiency and computational complexity.
Multi-modal semantic autoencoder for cross-modal retrieval

Cross-modal retrieval has gained much attention in recent years. As the research mainstream, most of existing approaches learn projections for data from different modalities into a common space where data can be compared directly. However, they neglect the preservation of feature and semantic information, so they are unable to obtain satisfactory results as expected. In this paper, we propose a two-stage learning method to learn multi-modal mappings that project multi-modal data to low dimensional embeddings that preserve both feature and semantic information. In the first stage, we combine both low-level feature and high-level semantic information to learn feature-aware semantic code vectors. In the second stage, we use encoder–decoder paradigm to learn projections. The encoder projects feature vectors to code vectors, and the decoder projects code vectors back to feature vectors. The encoder-decoder paradigm guarantees the embeddings to preserve both feature and semantic information. An alternating minimization procedure is developed to solve the multi-modal semantic autoencoder optimization problem. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art cross-modal retrieval methods.
Trigonometric comparison measure: A feature selection method for text categorization

Text data represented using vector space model is high dimensional data since the number of words can easily grow to tens of thousands for a moderate sized dataset. It may contain lots of redundant or irrelevant features that degrade the performance of a classifier for text categorization. To address this problem, feature selection can be applied for dimensionality reduction and it aims to find a set of highly distinguishing features. Most of filter feature selection methods for text categorization are based on document frequencies in positive and negative classes. Considering only document frequencies favors terms frequently used in a larger class and ignores relative document frequencies in the classes. In this paper, we present a new filter feature selection method, named Trigonometric Comparison Measure (TCM) considering relative document frequencies. The proposed method utilizes true positive rate and false positive rate to determine a better subset of features for text categorization and prefers terms that appear only in documents of one class with high probability. In order to assign a higher rank to terms that are frequently used in one class and rarely appears in another class, TCM calculates off-axis angles of a vector represented as (tpr,fpr) and gives a larger score to terms with a small angle using sin andcos functions. The proposed method is compared with eight well-known filter feature selection methods including balanced accuracy measure (ACC2), information gain (IG), chi-squared (CHI), odds ratio (OR), Gini index (Gini), Deviation from a Poisson distribution (DP), distinguishing feature selector (DFS) and normalized difference measure (NDM) on ten datasets using the multinomial naïve Bayes and support vector machines. The experimental results show that TCM achieves significantly better performance for text categorization.
Bitcoin price growth and Indonesia's monetary system

Concerned by the volatility of Bitcoin price growth (BPG), Bank Indonesia—Indonesia's central bank—discourages trading cryptocurrencies. We examine the relationship between Bitcoin price growth (BPG) and Indonesia's monetary aggregates (inflation, real exchange rate, and money velocity). In doing so, we develop the conceptual link between Bitcoin and monetary aggregates. We find strong and robust evidence that BPG leads to inflation growth, currency appreciation, and a reduction in money velocity. Our results have policy implications for other central banks in terms of achieving stability of the monetary system if BPG is indeed a concern for those countries.
FSNet: An Identity-Aware Generative Model for Image-Based Face Swapping

This paper presents FSNet, a deep generative model for image-based face swapping. Traditionally, face-swapping methods are based on three-dimensional morphable models (3DMMs), and facial textures are replaced between the estimated three-dimensional (3D) geometries in two images of different individuals. However, the estimation of 3D geometries along with different lighting conditions using 3DMMs is still a difficult task. We herein represent the face region with a latent variable that is assigned with the proposed deep neural network (DNN) instead of facial textures. The proposed DNN synthesizes a face-swapped image using the latent variable of the face region and another image of the non-face region. The proposed method is not required to fit to the 3DMM; additionally, it performs face swapping only by feeding two face images to the proposed network. Consequently, our DNN-based face swapping performs better than previous approaches for challenging inputs with different face orientations and lighting conditions. Through several experiments, we demonstrated that the proposed method performs face swapping in a more stable manner than the state-of-the-art method, and that its results are compatible with the method thereof.
Personalized recommendation based on hierarchical interest overlapping community

Ontology user profiles describe users’ structural semantic interests. Studying similar relationships between user profiles is crucial to detecting interest overlapping communities. The novel view assumes that hierarchical interests of user profiles can generate multiple similarity relations, which is conducive to forming interest clusters. In this research, we develop a hierarchical interest overlapping community (HIOC) detection method and present a personalized recommendation model. First, content interest closeness and semantic interest closeness between user profiles are computed to measure multi-granularity subject similarity of users. Then, using the multi-granularity subject similarity and follow similarity of users, a heterogeneous hypergraph is constructed to represent an interest network. By application of the interest density peaks mechanism, the HIOC detection method is adopted for identifying communities of interest. Further, personalized interest prediction is implemented by consideration of the memberships of a user in a community and a subject distributed in a community. Finally, we verify the performance of the HIOC detection algorithm on several real networks and validate the effectiveness of the proposed recommendation approach. The experimental results illustrate that the proposed approach outperforms classical recommendation methods in precision and recall.
Watermarking PRFs from Lattices: Stronger Security via Extractable PRFs

A software watermarking scheme enables one to embed a "mark" (i.e., a message) within a program while preserving the program's functionality. Moreover, there is an extraction algorithm that recovers an embedded message from a program. The main security goal is that it should be difficult to remove the watermark without destroying the functionality of the program. Existing constructions of watermarking focus on watermarking cryptographic functions like pseudorandom functions (PRFs); even in this setting, realizing watermarking from standard assumptions remains difficult. The first lattice-based construction of secret-key watermarking due to Kim and Wu (CRYPTO 2017) only ensures mark-unremovability against an adversary who does not have access to the mark-extraction oracle. The construction of Quach et al. (TCC 2018) achieves the stronger notion of mark-unremovability even if the adversary can make extraction queries, but has the drawback that the watermarking authority (who holds the watermarking secret key) can break pseudorandomness of all PRF keys in the family (including unmarked keys). In this work, we construct new lattice-based secret-key watermarking schemes for PRFs that both provide unremovability against adversaries that have access to the mark-extraction oracle and offer a strong and meaningful notion of pseudorandomness even against the watermarking authority (i.e., the outputs of unmarked keys are pseudorandom almost everywhere). Moreover, security of several of our schemes can be based on the hardness of computing nearly polynomial approximations to worst-case lattice problems. This is a qualitatively weaker assumption than that needed for existing lattice-based constructions of watermarking (that support message-embedding), all of which require quasi-polynomial approximation factors. Our constructions rely on a new cryptographic primitive called an extractable PRF, which may be of independent interest.
Spark-GHSOM: Growing Hierarchical Self-Organizing Map for large scale mixed attribute datasets

The Growing Hierarchical Self-Organizing Map (GHSOM) algorithm has shown its potential for performing several tasks such as exploratory analysis, anomaly detection and forecasting on a variety of domains including the financial and cyber-security domains. GHSOM is a dynamic variant of the SOM algorithm which generates a multi-level hierarchy of SOM maps based solely on input data. However, in order to generate this multi-level structure, GHSOM requires multiple iterations over the input dataset, thus making it intractable on large datasets. Moreover, the conventional GHSOM algorithm is designed to handle datasets with numeric attributes only. This represents an important limitation as most modern real-world datasets are characterized by mixed attributes - numerical and categorical. In this work, we propose an extension of the conventional GHSOM algorithm called Spark-GHSOM, which exploits the Spark platform to process massive datasets in a distributed manner. Moreover, we leverage a method known as the distance hierarchy approach to modify the optimization function of GHSOM so that it can (also) coherently handle mixed-attribute datasets. We test our new method with respect to accuracy, scalability and descriptive power. The results obtained using different datasets demonstrate the superior predictive and descriptive capabilities of Spark-GHSOM, as well as its applicability to large-scale datasets which could not be analyzed before.
Behavioural Digital Forensics Model: Embedding Behavioural Evidence Analysis into the Investigation of Digital Crimes

The state-of-the-art and practice show an increased recognition, but limited adoption, of Behavioural Evidence Analysis (BEA) within the Digital Forensics (DF) investigation process. Yet, there is currently no BEA-driven process model and guidelines for DF investigators to follow in order to take advantage of such an approach. This paper proposes the Behavioural Digital Forensics Model to fill this gap. It takes a multidisciplinary approach which incorporates BEA into in-lab investigation of seized devices related to interpersonal cases (i.e., digital crimes involving human interactions between offender(s) and victim(s)). The model was designed based on the application of traditional BEA phases to 35 real cases, and evaluated using 5 real digital crime cases - all from Dubai Police archive. This paper, however, provides details of only one case from this evaluation pool. Compared to the outcome of these cases using a traditional DF investigation process, the new model showed a number of benefits. It allowed a more effective focusing of the investigation, and provided logical directions for identifying the location of further relevant evidence. It also enabled a better understanding and interpretation of victim/offender behaviours (e.g., probable offenders' motivations and modus operandi), which facilitated a more in depth understanding of the dynamics of the specific crime. Finally, in some cases, it enabled the identification of suspect's collaborators, something which was not identified via the traditional investigative process.
Minority oversampling for imbalanced ordinal regression

Ordinal regression naturally presents class imbalance distribution, because the samples of the boundary classes tend to have lower appearing probability than that of the other classes. As the most common solutions for class imbalance problems, the traditional oversampling algorithms can improve the classification of minority classes, but they result in the problem of over generalization at the same time, where the synthetic samples are created in incorrect regions. In the context of ordinal regression, over generalization can damage the ordering of space distribution of samples, thereby hamper the ordinal regression models to benefit from ordering information. In this paper, we propose a generation direction-aware Synthetic Minority oversampling technique to deal exclusively with imbalanced Ordinal Regression (SMOR). SMOR for each candidate generation direction computes a selection weight of being used to yield synthetic samples. By considering the ordering of the classes, the candidate generation directions, which may potentially distort ordinal sample structure, will tend to be assigned low selection weights. In this way, SMOR improves the ordering of minority classes without severely damaging the existing ordering of the problem. Extensive experiments with three ordinal regression classifiers show that our proposed method outperforms existing typical oversampling algorithms in terms of the Average of Mean Absolute Error (AMAE)and the Maximum Mean Absolute Error (MMAE).
Weather-Classification-MARS-Based Photovoltaic Power Forecasting for Energy Imbalance Market

Energy imbalance market (EIM) provides an opportunity that allows larger shares of variable renewable energy sources in the grid. Under highly volatile weather conditions, an accurate forecasting of photovoltaic (PV) power is necessary for grid stability and market operation. Most of existing forecasting methods strongly rely on the accuracy of measurements, and the adaptability of these methods to complex weather conditions is rarely discussed. In this paper, a weather classification multivariate adaptive regression spline (MARS) forecasting model is introduced for complex weather conditions in all seasons. It can be updated incrementally and its high computational efficiency satisfies EIM operations. A data set that consists of the historical power and meteorological parameters produced by a small-scale PV platform is classified and used to train MARS models with forecast horizons ranging from 15 min to 24 h in different seasons. The tests and analyses results indicate higher accuracy, adaptability, and efficiency of the novel model.
FD-ZKF: A Zonotopic Kalman Filter optimizing fault detection rather than state estimation

Enhancing the sensitivity to faults with respect to disturbances, rather than optimizing the precision of the state estimation using Kalman Filters (KF) is the subject of this paper. The stochastic paradigm (KF) is based on minimizing the trace of the state estimation error covariance. In the context of the bounded-error paradigm using Zonotopic Kalman Filters (ZKF), this is analog to minimize the covariation trace. From this analogy and keeping a similar observer-based structure as in ZKF, a criterion jointly inspired by set-membership approaches and approximate decoupling techniques coming from parity-space residual generation is proposed. Its on-line maximization provides an optimal time-varying observer gain leading to the so-called FD-ZKF filter that allows enhancing the fault detection properties. The characterization of minimum detectable fault magnitude is done based on a sensitivity analysis. The effect of the uncertainty is addressed using a set-membership approach and a zonotopic representation reducing set operations to simple matrix calculations. A case study based on a quadruple-tank system is used both to illustrate and compare the effectiveness of the results obtained from the FD-ZKF approach compared to a pure ZKF approach.
On the Utility of Model Learning in HRI

Fundamental to robotics is the debate between model-based and model-free learning: should the robot build an explicit model of the world, or learn a policy directly? In the context of HRI, part of the world to be modeled is the human. One option is for the robot to treat the human as a black box and learn a policy for how they act directly. But it can also model the human as an agent, and rely on a “theory of mind” to guide or bias the learning (grey box). We contribute a characterization of the performance of these methods under the optimistic case of having an ideal theory of mind, as well as under different scenarios in which the assumptions behind the robot's theory of mind for the human are wrong, as they inevitably will be in practice. We find that there is a significant sample complexity advantage to theory of mind methods and that they are more robust to covariate shift, but that when enough interaction data is available, black box approaches eventually dominate.
Proof-of-Stake Protocols for Privacy-Aware Blockchains

Proof-of-stake (PoS) protocols are emerging as one of the most promising alternative to the wasteful proof-of-work (PoW) protocols for consensus in Blockchains (or distributed ledgers). However, current PoS protocols inherently disclose both the identity and the wealth of the stakeholders, and thus seem incompatible with privacy-preserving cryptocurrencies (such as ZCash, Monero, etc.). In this paper we initiate the formal study for PoS protocols with privacy properties. Our results include: Towards our result, we define the notion of anonymous verifiable random function, which we believe is of independent interest.
Joint Optimization of Cascade Ranking Models

Reducing excessive costs in feature acquisition and model evaluation has been a long-standing challenge in learning-to-rank systems. A cascaded ranking architecture turns ranking into a pipeline of multiple stages, and has been shown to be a powerful approach to balancing efficiency and effectiveness trade-offs in large-scale search systems. However, learning a cascade model is often complex, and usually performed stagewise independently across the entire ranking pipeline. In this work we show that learning a cascade ranking model in this manner is often suboptimal in terms of both effectiveness and efficiency. We present a new general framework for learning an end-to-end cascade of rankers using backpropagation. We show that stagewise objectives can be chained together and optimized jointly to achieve significantly better trade-offs globally. This novel approach is generalizable to not only differentiable models but also state-of-the-art tree-based algorithms such as LambdaMART and cost-efficient gradient boosted trees, and it opens up new opportunities for exploring additional efficiency-effectiveness trade-offs in large-scale search systems.
Extreme learning machine Cox model for high‐dimensional survival analysis

Some interesting recent studies have shown that neural network models are useful alternatives in modeling survival data when the assumptions of a classical parametric or semiparametric survival model such as the Cox (1972) model are seriously violated. However, to the best of our knowledge, the plausibility of adapting the emerging extreme learning machine (ELM) algorithm for single-hidden-layer feedforward neural networks to survival analysis has not been explored. In this paper, we present a kernel ELM Cox model regularized by an L0 -based broken adaptive ridge (BAR) penalization method. Then, we demonstrate that the resulting method, referred to as ELMCoxBAR, can outperform some other state-of-art survival prediction methods such as L1 - or L2 -regularized Cox regression, random survival forest with various splitting rules, and boosted Cox model, in terms of its predictive performance using both simulated and real world datasets. In addition to its good predictive performance, we illustrate that the proposed method has a key computational advantage over the above competing methods in terms of computation time efficiency using an a real-world ultra-high-dimensional survival data.