Reviewerly/exHarmony huggingface dataset:
Algorithm Selection in Adversarial Settings: From Experiments to Tournaments in <i>StarCraft</i>

Algorithm selection-the mapping of problem instances to algorithms-has been successfully applied to a variety of complex theoretical and practical problems, including computer games. In this paper, we extend the traditional framework, which considers a single decision maker, to adversarial settings, by modeling algorithm selection as a normal-form game. In this “game of algorithm selection,” agents select algorithms to play a computer game on their behalf. The game's payoff matrix stores the relative performance among algorithms. We also consider nonstationary scenarios, where algorithms can learn from previous matches. We apply this approach to real-time strategy game StarCraft, using bots developed for the game as our algorithms. Our experiments suggest that minimax-Q is a suitable method for algorithm selection in both stationary and nonstationary conditions. We proceed by implementing our approach in MegaBot, a fully capable StarCraft bot. MegaBot showed robustness to nonstationarity, competing in the difficult scenario of StarCraft AI tournaments. MegaBot successfully learns how to select algorithms, exhibiting increasing win rates with tournament progress. In 2016, its debut year, it left 60% of opponents behind in two out of three tournaments. In 2017, however, MegaBot faced difficulties as its algorithm portfolio got outdated compared to newer entries.
Deep neural network with FGL for small dataset classification

In certain applications, classification models have to be trained with small datasets. This study proposes a new deep neural network with a feature generalisation layer (FGL). First, instead of using a generative network for data augmentation, the FGL is modelled using a latent variable model to diversify features directly by sharing other layers. Then, dual-objective functions are defined to optimise the parameters of the network: one minimises the generation error and the other minimises the classification error. Finally, a parallel multibranch structure is used in the FGL to improve the convergence of model training. The classification accuracy obtained using various quantities of training samples increased up to 4.63% on the MNIST dataset, up to 3.00% on the CIFAR10 nature image dataset, over the reference model. These experimental results illustrate the effectiveness of the authors’ method for training classification models with small datasets.
Adaptive and service-oriented embedded system for information security applications

To provide a complete information protection mechanism, we propose an adaptive and service-oriented embedded system (ASOS) design. The access control policies are implemented as protection matrices and designed in hardware; thus, the information access method is specific and not generic, so that the risks of illegal access of information can be reduced. A layered and virtualizable design method is also introduced in the ASOS to enhance its scalability. Furthermore, the ASOS contains a system adaptation manager that can dynamically adapt its hardware functions to support various application requirements. Experiments show that compared to a pure software-based design, the ASOS can accelerate the access time by a factor of 8.75. Moreover, compared to a conventional embedded system design containing 13 hardware functions, the ASOS can reduce 26.42% of slice registers and 25.81% of slice LUTs in a Xilinx Virtex 6 XC6VLX240T FPGA.
Duvet woman versus action man: the gendered aetiology of Chronic Fatigue Syndrome according to English newspapers

Media portrayals of conditions such as Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME) demand exploration as the media is a potent source of information and meaning, and as such has the potential to inform public and professional understandings. To date, there is little systematic exploration of print media representations of CFS/ME. In this study, we address that gap by exploring the voices of CFS/ME sufferers in the English print media (1998–2015) from a constructionist feminist perspective. We found that portrayals of CFS/ME differ meaningfully, depending on gender. The psychological and emotional tended to be foregrounded where women were concerned and the scepticism surrounding CFS/ME as a “non disease” was much more evident. On some occasions this was dealt with directly, whilst on others it was “leaked in” or hinted at. This serves to delegitimise the illness in women. In contrast, the physical was usually foregrounded in the case of men suffering from the condition and their experiences were accredited greater legitimacy. We problematise these representations and discuss the potential impact upon public and professional sympathy, treatment options and long-standing, gendered constructions of illness.
Gaussian multipartite quantum discord from classical mutual information

Quantum discord is a measure of non-classical correlations, which are excess correlations inherent in quantum states that cannot be accessed by classical measurements. For multipartite states, the classically accessible correlations can be defined by the mutual information of the multipartite measurement outcomes. In general the quantum discord of an arbitrary quantum state involves an optimisation of over the classical measurements which is hard to compute. In this paper, we examine the quantum discord in the experimentally relevant case when the quantum states are Gaussian and the measurements are restricted to Gaussian measurements. We perform the optimisation over the measurements to find the Gaussian discord of the bipartite EPR state and tripartite GHZ state in the presence of different types of noise: uncorrelated noise, multiplicative noise and correlated noise. We find that by adding uncorrelated noise and multiplicative noise, the quantum discord always decreases. However, correlated noise can either increase or decrease the quantum discord. We also find that for low noise, the optimal classical measurements are single quadrature measurements. As the noise increases, a dual quadrature measurement becomes optimal.
Algorithmic Governance by Online Intermediaries

Abstract In recent years, there is a growing use of algorithmic law enforcement by online intermediaries. Algorithmic enforcement by private intermediaries is located at the interface between public law and private ordering. It often reflects risk management and commercial interests of online intermediaries, effectively converging law enforcement and adjudication powers, at the hands of a small number of mega platforms. At the same time, algorithmic governance also plays a critical role in shaping access to online content and facilitating public discourse. Yet, online intermediaries are hardly held accountable for algorithmic enforcement, even though they may reach erroneous decisions. Developing proper accountability mechanisms is hence vital to create a check on algorithmic enforcement. Accordingly, relying on lessons drawn from algorithmic copyright enforcement by online intermediaries, this chapter demonstrates the accountability deficiencies in algorithmic copyright enforcement; maps the barriers for algorithmic accountability and discusses various strategies for enhancing accountability in algorithmic governance.
One-layer neural network for solving least absolute deviation problem with box and equality constraints

This paper presents a neural network for solving least absolute deviation problems with equality and box constraints. Compared with some existing models, the proposed neural network has fewer state variables and only one-layer structure. The proposed model is proved to be Lyapunov stable and converge to an exact optimal solution of the original problem. Some simulation results show the validity and transient behavior of the proposed neural network.
A multilinear collaborative representation preserving projections method for feature extraction

In this paper, a multilinear collaborative representation preserving projections (MCRPP) is developed for feature extraction and recognition on tensor objects. MCRPP is an unsupervised method built on collaborative representation and multilinear algebra. MCRPP calculates the projection matrix by simultaneously considering both the distribution information of all the samples and the spatial structure information of the sample. MCRPP aims to preserve the sparse reconstruction relations of data in the reduced subspace via collaborative representation reconstruction. Redefining the local multilinear scatter and the total multilinear scatter, MCRPP seeks the projection matrix by maximizing the ratio between the total multilinear scatter and local multilinear scatter using generalized eigenvalue decomposition. The final experimental results on FERET, AR and Yale face databases demonstrate that MCRPP is an efficient and effective method for feature extraction and dimensionality reduction.
NP-Hardness of minimum expected coverage

In multi-label learning a single object can be associated with multiple labels simultaneously. In a context where labels follow a random distribution, every labelling has a probability of occurrence. Thus, any prediction is associated with an expected error measured by a predefined loss function. From an exponential number of possible labellings, an algorithm should choose the prediction that minimizes the expected error. This is known as loss minimization. This work shows a proof of the NP-completeness, with respect to the number of labels, of a specific case of the loss minimization of the Coverage loss function, which allows to conclude that the general case is NP-hard.
A Conflict Detection Method for Ellipsoidal Safety Regions

A novel pairwise comparison algorithm that determines whether a pair of ellipsoids is overlapping, touching, or separated by exploiting two new Mahalanobis distance conditions is presented. These conditions are filtering steps prior to a previously proposed method that transforms the intersection query problem into a least squares minimization over a sphere. The algorithm introduced here is shown to be more computationally efficient than previous methods, which either solve the least squares minimization over a sphere or check the signs of the roots of a fourth order polynomial. The proposed method may be applied in cases when an ellipsoid is an appropriate safety region. The resulting algorithm is useful in applications including, but not limited to, collision detection and avoidance.
Estimating the mean and variance of a high-dimensional normal distribution using a mixture prior

This paper provides a framework for estimating the mean and variance of a high-dimensional normal density. The main setting considered is a fixed number of vector following a high-dimensional normal distribution with unknown mean and diagonal covariance matrix. The diagonal covariance matrix can be known or unknown. If the covariance matrix is unknown, the sample size can be as small as $2$. The proposed estimator is based on the idea that the unobserved pairs of mean and variance for each dimension are drawn from an unknown bivariate distribution, which we model as a mixture of normal-inverse gammas. The mixture of normal-inverse gamma distributions provides advantages over more traditional empirical Bayes methods, which are based on a normal-normal model. When fitting a mixture model, we are essentially clustering the unobserved mean and variance pairs for each dimension into different groups, with each group having a different normal-inverse gamma distribution. The proposed estimator of each mean is the posterior mean of shrinkage estimates, each of which shrinks a sample mean towards a different component of the mixture distribution. Similarly, the proposed estimator of variance has an analogous interpretation in terms of sample variances and components of the mixture distribution. If diagonal covariance matrix is known, then the sample size can be as small as $1$, and we treat the pairs of known variance and unknown mean for each dimension as random observations coming from a flexible mixture of normal-inverse gamma distributions.
A completed implementation for Xiangqi rules

Even both Xiangqi (Chinese chess) and chess are considered to have the same origin and similar playing style, Xiangqi has a very difficult rule set for both understanding and implementing fully to judge games when repetitions happen. This paper introduces an algorithm to implement completely the ru le set of Asian Xiangqi Federation (AXF Rules) and some discussions about a few controversial cases of illustrational games.
An Intuitive Interface for Interactively Pairing Multiple Mobile Devices

We designed an intuitive user interface to connect multiple mobile devices over a network and relate the applications running on them. We proposed a pinching gesture for making a connection between two devices, which is realized by swiping the touch screens of the two annexed mobile devices as if to stitch them together. We believe that this user interface can create new user experiences for multiple-screen usage, especially by designing the application content to react instantly to the connection and disconnection triggered by the gesture. We expect this interface to fulfill a great potential in inspiring application designers to conceive various ideas, particularly suited for visually fascinating content that takes advantage of the dynamic reconfigurable multi-display feature. To demonstrate the potential, we produced some prototype applications. In this article, we explain the idea and details of the interface mechanism, and introduce the design of the sample applications.
Multi-task learning network for handwritten numeral recognition

Handwritten numeral recognition is a challenging problem in the character recognition field due to the large variation in the writing styles of different persons and high similarity in the contour of different numerals. To address this problem, an effective multi-task learning network (MTLN) for ha ndwritten numeral recognition is presented in this paper. Based on the observation that the writing style could play an effective complementary role to the learned feature extracted from numerals, the proposed MTLN simultaneously performs the handwritten numeral learning module and the writing style learning module. Consequently, the determination of scratchy/non-scratchy in the writing style learning module can effectively assist the handwritten numeral learning module to obtain a more robust and distinguishable feature so as to improve the recognition performance. Extensive experiments on multiple existing handwritten numeral datasets have demonstrated that the proposed MTLN can effectively improve the recognition accuracy, and outperform multiple state-of-the-art methods.
UX Evaluation Methods and Techniques

In this chapter, the first of the chapters about the Evaluate UX lifecycle activity, we begin by introducing UX evaluation methods and techniques and associated terminology. Types of evaluation and evaluation data are discussed, including formative evaluation (helps form the design), summative evaluation (helps sum up the design), and informal summative evaluation (used in UX practice). Empirical methods depend on data observed in the performance of real user participants and data coming directly from user participants, including critical incident data observed and comments from users while “thinking aloud” and/or in their questionnaire responses. Analytic methods, which include design reviews, design walk-throughs, and inspection methods, such as heuristic evaluation, are based on examining inherent attributes of the design rather than seeing the design in use. Except for numerical ratings and similar data, analytic methods yield qualitative, subjective data. Although analytic UX evaluation methods can be applied rigorously and correspondingly more slowly, they were developed as faster and less expensive methods to produce approximations to or predictors of empirical results. UX practitioners are urged to adapt and appropriate evaluation methods and techniques to achieve their own approach or a given design situation.
Law, discipline and governance based on cognitive process simulation – analysis of legal forms in the modern rights relationship

People are becoming more and more humanized in the process of understanding the law. According to the right to discipline, the law has its own core setting factors, while some limits can't reach people's desire. Therefore, the legal and illegal mode of transcending rights is very important. In order to analyze the legal form of modern rights, in this paper, the cognitive learning and memory process of human brain were simulated through the artificial neural network and the understanding of human brain structure, and the role of law, discipline and governance was reflected. In the study, the structure and algorithm of the model neural network were optimized, the memory forgetting curve mechanism that can simulate the human brain was introduced, and thus the network recognition rate was improved. And in the algorithm, the calculation of matching degree was avoided, and the computational complexity was reduced to the sample. Then the sample was compared with the SOM, ART1, and PNN algorithms. The experimental simulation results show that the recognition speed of this sample is 1.9 times faster than that of ART1, 58 times than that of SOM, and 1.5 times than that of the PNN network.
The Role of Customer Commercial Knowledge Management in Improving the Performance of Employees of Insurance Firms in Iran

The purpose of this article is to determine the effect of customer commercial knowledge management on the performance of employees of insurance firms. The population comprises the employees of all insurance companies of Ardabil province in Iran. Due to the small size of the population, no sampling was carried out and the population was examined through a census procedure. Data collection was carried out using Newman and Conrad's knowledge management questionnaire and Patterson's job performance questionnaire. A data analysis was conducted with Pearson correlation and regression analysis using SPSS software. The results showed that customer commercial knowledge management can predict staff performance; hence, it is one of the effective factors in improving staff performance.
XINA: Explainable Instance Alignment Using Dominance Relationship

Over the past few years, knowledge bases (KBs) like DBPedia, Freebase, and YAGO have accumulated a massive amount of knowledge from web data. Despite their seemingly large size, however, individual KBs often lack comprehensive information on any given domain. For example, over 70 percent of people on Freebase lack information on place of birth. For this reason, the complementary nature across different KBs motivates their integration through a process of aligning instances. Meanwhile, since application-level machine systems, such as medical diagnosis, have heavily relied on KBs, it is necessary to provide users with trustworthy reasons why the alignment decisions are made. To address this problem, we propose a new paradigm, explainable instance alignment (XINA), which provides user-understandable explanations for alignment decisions. Specifically, given an alignment candidate, XINA replaces existing scalar representation of an aggregated score, by decision and explanation-vector spaces for machine decision and user understanding, respectively. To validate XINA, we perform extensive experiments on real-world KBs and show that XINA achieves comparable performance with state-of-the-arts, even with far less human effort.
Monotonicity Under Local Operations: Linear Entropic Formulas

All correlation measures, classical and quantum, must be monotonic under local operations. In this paper, we characterize monotonic formulas that are linear combinations of the von Neumann entropies associated with the quantum state of a physical system that has n parts. We show that these formulas form a polyhedral convex cone, which we call the monotonicity cone, and enumerate its facets. We illustrate its structure and prove that it is equivalent to the cone of monotonic formulas implied by strong subadditivity. We explicitly compute its extremal rays for n ≤ 5. We also consider the symmetric monotonicity cone, in which the formulas are required to be invariant under subsystem permutations. We describe this cone fully for all n.
Variable-based missing mechanism for an incomplete contingency table with unit missingness

For an incomplete two-way contingency table including cases of missing units (i.e., unobserved data for both variables), we show that every pair of missing mechanisms posited for bivariate categorical variables is uniquely reproduced by an extended missing at random (EMAR) model with exactly equal fit. A condition is thus proposed to identify EMAR from the other missing mechanisms.
Video‐based person re‐identification based on regularised hull distance learning

Existing person re‐identification (re‐id) models mainly focus on still‐image‐based module, namely matching person images across non‐overlapping camera views. Since video sequence contains much more information than still images and can be easily achieved by tracking algorithms in practical applications, the video re‐id has attracted increasing attention in recent years. Distance learning is crucial for a re‐id system. However, the computed distances in traditional video‐based methods are easily distracted by the randomness of data distribution, especially with small sample size for training. To preferably distinguish different people, a novel regularised hull distance learning video‐based person re‐id method is proposed. It is advantageous in two aspects: robustness is guaranteed due to expanded video samples by regularised affine hull with limited ones, discriminability is ensured due to penalised hard negative samples more severely. Hence, the discriminability and robustness of the learnt metric are strengthened. Comparisons with the state‐of‐the‐art video‐based methods as well as related methods on PRID 2011, iLIDS‐VID and MARS datasets demonstrate the superiority of the authors’ method.
Securing large datasets involving fast-performing key bunch matrix block cipher

The steep rise in the variety and volume of data is promoting new data storage techniques, in almost all sectors. In addition, the seamless connectivity of networks spread across multiple platforms and locations has added a new dimension in the secure storage and access of information. Governments and laws also stress the secrecy and privacy of data stored at their behest and prevention of its misuse. For this, numerous block ciphers are used. One of the prominent features in the development of any cipher is the number of bits in the keys chosen. The larger the length of key, the higher the security it offers. However, increasing the number of bits in the key chosen would result in an exponential increase in the response time required for executing cryptographic procedures in legacy systems. Most of the available block ciphers are impractically time-consuming, with longer keys, and raise serious concerns when highly confidential data in huge volumes are to be transferred across computer networks, with improved security. Addressing this issue, to provide security and privacy for large datasets at rest as well as during transmission, a novel algorithm is developed which uses a large key that offers higher security against attacks on data confidentiality with lower computational costs than the conventional ones. In this chapter, as an illustration, a large key bunch matrix is chosen with 2048 bits and the block cipher is developed, although the size of the key is further expandable with very trivial changes in the computational costs that would be incurred, in spite of using a lower configured computing system. The relaxation of the key-size constraint is realized, thus paving the way for using the keys with a large number of bits in the cryptosystem and enhancing security, without overburdening the system with complex and time-consuming computations involved in the encryption and decryption procedures.