Reviewerly/exHarmony huggingface dataset:
Computer loses in king-size blunder

This article is based on my personal reminiscences about the early days of computer chess tournaments, describing not only how different the technology was, but also that progress was steady and continues today in the broader field of Artificial Intelligence. The author was a participant in the 1st ACM computer chess championship (1970) and continued to compete well into the 1980s. Speaking for myself, I learned how to play chess in Junior High School (actually King Charles 1 Grammar School in Kidderminster, UK), but now only remember losing in a simultaneous game with C.H.O’D. Alexander (the UK Chess Champion) in 1950. In High School (Preston Grammar School) I played for the school’s chess team, who were undefeated in the 1954–55 school year. Naturally I played for the University of Nottingham (where I was studying Mathematics), and later for the Bedfordshire County team, before leaving to join Boeing, Seattle, in 1962. That said, I don’t think I was ever better than a Class A player. Basically, I have played chess all my life, and it has helped develop my problem-solving skills.
Conducting research with school children and data in line with “ethical principles” lawyers at work in the ethics management of the H2020 mathisis project

Recent advancements in human-computer interaction, machine learning and in artificial intelligence hold the potential to influence both the curriculum and the pedagogy of school children. While the impacts of new technologies remain uncertain, ongoing research and innovation projects are already developing and testing such technologies in schools. This article builds on the experience of the authors as advisors for a Horizon 2020 (H2020) project conducting research with schoolchildren in twenty schools across the United Kingdom, Italy and Spain (the project MaTHiSiS). This contribution presents and discusses how the authors lived up to the obligation of conducting research in line with "ethical principles".
A Reinforcement Learning Framework for Space Missions in Unknown Environments

A land-and-traverse mission to icy worlds such as Europa and Enceladus is challenging due to lack of prior knowledge regarding the terrain conditions. Previous work [1] showed that rovers with high degrees of freedom (DoF) can achieve robust traversal by leveraging redundant modes for mobility to counter terrain uncertainty (e.g. walking, driving, or inch-worming). This paper presents a generic and scalable reinforcement learning scheme for enabling on-board decision making on rovers to automatically switch between modes of traversal based on online performance feedback. The objective is to maximize energy efficiency, minimize operator input and successfully negotiate unstructured terrain conditions without relying on exhaustive prior knowledge. The proposed methodology is well grounded in the literature on reinforcement learning and has been adapted to address conformance to validation and verification requirements and JPL flight operations history of using per-sol prescribed sequences for a space mission.
Inferring Authors’ Relative Contributions to Publications from the Order of Their Names When Default Order Is Alphabetical

In attributing individual credit for co-authored academic publications, one issue is how to apportion (unequal) credit, based on the order of authorship. Apportioning credit for completed joint undertakings has always been a challenge. Academic promotion committees are faced with such tasks regularly, when trying to infer a candidate's contribution to an article they coauthored with others. We propose a method for achieving this goal in disciplines (such as the author's) where the default order is alphabetical. The credits are those maximizing Shannon entropy subject to order constraints.
Automated Surveillance Systems of Smart Cameras in Trains

Last year's we can observe an enormous grow of surveillance systems in the public domain. In many countries surveillance systems are under discussion because of the privacy aspects of people. In the nineties many multimodal systems were developed for surveillance in Dutch trains. The use of camera systems was complicated because of bad lighting conditions, occlusion and posture. Only recently it is possible to use smart cameras to localize and track faces and displayed facial expressions by smart cameras installed in the public domain. Most systems are based on the Active Appearance model. In this paper we present a real version of a surveillance systems called Image Processing System developed in the vision lab of TUDelft. In an experiment we recorded the behavior of stand-up comedians in trains acting in several scenarios. The facial expressions in the video footage were analyzed.
Second-order asymptotics of quantum data compression from partially-smoothed conditional entropy

Anshu et al. recently introduced "partially" smoothed information measures and used them to derive tighter bounds for several information-processing tasks, including quantum state merging and privacy amplification against quantum adversaries [arXiv:1807.05630 [quant-ph]]. Yet, a tight second- order asymptotic expansion of the partially smoothed conditional min-entropy in the i.i.d. setting remains an open question. Here we establish the second-order term in the expansion for pure states, and find that it differs from that of the original "globally" smoothed conditional min-entropy. Remarkably, this reveals that the second-order term is not uniform across states, since for other classes of states the second-order term for partially and globally smoothed quantities coincides. By relating the task of quantum compression to that of quantum state merging, our derived expansion allows us to determine the second-order asymptotic expansion of the optimal rate of quantum data compression. This closes a gap in the bounds determined by Datta and Leditzky [IEEE Trans. Inf. Theory 61, 582 (2015)], and shows that the straightforward compression protocol of cutting off the eigenspace of least weight is indeed asymptotically optimal at second order.
Heidegger: Interpretable Temporal Causal Discovery

Temporal causal discovery aims to find cause-effect relationships between time-series. However, none of the existing techniques is able to identify the causal profile, the temporal pattern that the causal variable needs to follow in order to trigger the most significant change in the outcome. Toward a new horizon, this study introduces the novel problem of Causal Profile Discovery, which is crucial for many applications such as adverse drug reaction and cyber-attack detection. This work correspondingly proposes Heidegger to discover causal profiles, comprised of a flexible randomized block design for hypothesis evaluation and an efficient profile search via on-the-fly graph construction and entropy-based pruning. Heidegger's performance is demonstrated/evaluated extensively on both synthetic and real-world data. The experimental results show the proposed method is robust to noise and flexible at detecting complex patterns.
ECycleGAN

Unsupervised image-to-image translation, which aims in translating two irrelevant domains of images, has increased substantially in recent years with the success of Generative Adversarial Networks (GANs) based on the cycle-consistency assumption. Especially, the Cycle-Consistent Generative Adversarial Network (CycleGAN) has shown remarkable success for two domains translation. However, the details about texture and style are often accompanied with unpleasant artifacts. To further enhance the translational quality, we thoroughly study the key components of CycleGAN - network architecture and adversarial loss, and improve each of them to derive an Enhanced CycleGAN (ECycleGAN). In particular, we propose a perceptual loss function which motivated by perceptual similarity instead of similarity in pixel space. Moreover, we introduce the Residual Dense Normalization Block (RDNB) to replace the residual blocks as the basic network building unit. Finally, we borrow the idea from WGAN-GP as the adversarial loss functions. The ECycleGAN, thanks to these changes, demonstrates appealing visual quality with more realistic and natural textures than any state-of-the-art methods.
Instantaneous Frequency Estimation of HFM Signal Based on Interpolated STFRFT-Variable Weight Least Square Fitting

To solve the problem of instantaneous frequency estimation of hyperbolic frequency modulation (HFM) signal under low signal to noise ratio (SNR), a Rife interpolation short-time fractional Fourier transform (STFRFT)-variable weight least square fitting algorithm is proposed. In a certain range of curvature, the fractional Fourier transform (FRFT) is used to better the energy aggregation of the HFM signal. The Rife interpolation is used to improve the estimation accuracy of the instantaneous frequency of the HFM signal. The variable weight least square fitting is used to eliminate the influence of the Rife interpolation abnormal point on the instantaneous frequency estimation and the accuracy is further improved. Finally, the effectiveness of the algorithm is verified by Monte Carlo simulation. The simulation results show that the performance of the algorithm is better than the STFT method.
Phase retrieval by pattern classification and circular mean for robust optical testing

For the optical testing of a large mirror with a long radius of curvature, it is generally necessary to use a single-shot phase-shifting interferometer to take several measurements because the influence of air turbulence on air stratification prevention should be reduced for an accurate measurement. In this paper, a new technique that applies hierarchical clustering, which classifies a few clusters according to the pattern similarity of acquired wrapped phases, is proposed. As the minority patterns of the wrapped phases are excluded, the effects of unknown noises can be reduced. For each cluster, the circular mean is used to calculate the denoised wrapped phases. The surface figure is obtained from the unwrapped phases. Because the running number of the phase-unwrapping algorithm becomes the same as the number of chosen clusters, the proposed technique is much faster than conventional procedures. In 500 measurements using a spherical mirror with a diameter of 1.5 m, the measurement time and standard deviation of the surface figure's root-mean-square error are reduced by up to approximately 23% and 15%, respectively, of the results obtained via the conventional procedure.
A novel statistical mechanics-based metric for characterization of text documents

Machine learning (ML) uses intelligence-based statistical models to resolve characterization problems. However, the accuracy of ML models decreases as the volume of data increases since they are not able to capture the diversity and important regularities accompanying the huge volume. In this regard, the statistical mechanics’ (SM) paradigm for ML models must be investigated. SM like Big Data also deals with a large volume of instances. A correlation principle is proposed between data represented as words in a text corpus and a large volume of particles within a system. Based on it a new similarity measure, namely degeneracy-based statistical similarity measure (DSSM) is proposed. The DSSM is directly proportional to the ratio of the total energy of two documents with maximally similar words and the total number of distinct words in the document. The DSSM showed higher performance with regards to existing measures such as Euclidean, Cosine, SMTP, and MBSM.
Cyber-Security of Neural Networks in Medical Devices

European and national initiatives, as well as changes in the standards, have led to a dramatic increase in the demand for interconnected medical devices during the last few years. In addition to the increased connectivity of devices in a larger network, economic reasons demand for AI (artificial intelligence) enhanced support functions even in the medical devices domain. Both connectivity and the usage of AI in safety-relevant devices are the basis for new and additional challenges for the manufacturers. The more devices become networked, the more they become targets of cyber-attacks. This means that safety of medical devices can no longer be guaranteed without adequate cyber-security measures. These cyber-security measures must cover the whole design of the devices including their internal neural networks to ensure that the inferences are free from influences from attackers. Modern, advanced attack vectors demand for very advanced countermeasures to ensure or even increase cyber-security of medical devices in this environment. This industrial experience paper will show the required concert of measures that is accompanied by a security informed safety analysis from the earliest steps of development to increase general safety and security in the design of a modern intensive care ventilator.
Generalizing the phishing principle

Capturing behavioral data to assess users' IT security awareness is state of the art. However, recording the click rate on a company wide phishing test for IT security awareness measurement does not suffice. Perceivable artifacts, that the user might be exposed to during an attack, are manifold. We introduce a framework that allows capturing user's responses to such artifacts similar to phishing tests. A field study among 259 users shows, that the expected effect of a well-established IT security awareness intervention can be demonstrated using arbitrary artifacts. It also shows that this intervention may impair the probability of a user reporting the sighting of an artifact and therefore impair an organization's capability to detect such events and possibly decrease overall security.
Do commodity price shocks impact central bank governors' removals? Evidence from Africa

Abstract Using data from 1980 to 2018 for 32 African countries (13 CFA countries and 19 non‐CFA countries) and a conditional fixed effects logit model, this paper examines the effect of commodity price shocks on the likelihood of a central bank governor removal. Governor removals are decomposed into premature removals and ally replacements. Our results show that commodity price shocks lead to a statistically significant increase in the probability of a central bank governor's premature removal and ally replacement. This trend holds when commodities price shocks are separated into oil/mineral shocks and agricultural commodities shocks. We also find that this probability significantly jumps for countries who have a lower degree of export diversification, that is, countries whose are primarily dependent on a single commodity.
Spatial Representation in Sequential Action Organization of Weakly Constrained Everyday Activities

The ability to organize and control routine sequential action is of major importance for successfully mastering everyday life and activities. Although previous research has started to uncover mechanisms and principles underlying human control of such activities, the possible influence of spatial aspects has not received sufficient attention. In this contribution we argue that human behavior organization relies heavily on spatial aspects in everyday activities such as setting the table. Employing a modeling approach, we examine the influence of distance, topology (containment), relational dependencies (strong spatial cognition), and dimensionality on action organization. The application of our model to one laboratory and one real world dataset reveals that all four aspects notably influence action organization in human table setting. Additional exploration of the model’s performance sheds further light on regionalization and dimensionality of human spatial representations.
Transaction Dependency Model for Block Minimization in Arbitrary Blockchains

Blockchains are distributed replicated state machines with a continuously increasing data storage underneath. The size of the storage can cause problems especially in limited IoT devices. In order to address that, this paper is based on the following ideas: While two state transitions could be replaced by a single one to represent the same state, this is not commonly done to reduce the blockchains storage size. To facilitate squashing of transactions independent of the application semantics a blockchain frameworks needs to know the interdependencies of transactions.
Discrete Classification Optimization Hashing For Image Retrieval

Deep supervised hashing is popular for large scale image retrieval in recent years. Most existing deep hashing methods which preserved the relationship between Hamming distance and inner product to implement the distance metric of the binary codes. And they usually used sign function made the back propagation impractical. In this paper, we propose a novel deep supervised hashing method called discrete classification optimization hashing (DCOH). A new loss function is designed by introducing the constraints of cosine similarity and classification label. It can increase the similarity between the original image and the corresponding binary codes. Furthermore, the sigmoid function, which can iteratively approach the sign function, is used to resolve the problem of the back propagation impractical. The experimental results confirm the effectiveness of the proposed algorithm.
Tree-Structured Channel-Fuse Network for Scene Parsing

Scene parsing requires effectively discovering context information and the structure of the network plays a critical role in the task. To handle the problem of segmentation objects at multiple scales, we proposed a tree-structured channel-fuse network (TCFNet) to obtain more representative information. In detail, we create a tree structure to merge the multiple-level feature maps, which are fused by the channel-fuse module, with multi-scale context information in a hierarchical way. And the module refines the feature maps during the process of propagating context between channels. The proposed TCFNet achieves impressive results on Cityscapes validation set and test set, verify the effectiveness of our proposed approach.
Attention-based Joint Representation Learning Network for Short text Classification

Deep neural networks have gained success recently in learning distributed representations for text classification. However, due to the sparsity of information in user-generated comments, existing approaches still suffer from the problem of exploiting the semantic information by halves to classify current sentence. In this paper, we propose a novel attention-based joint representation learning network (AJRLN). The proposed model provides two attention-based subnets to extract different attentive features of the sentence embedding. Then, these features are combined by the representation combination layer to get the joint representation of the whole sentence for classification. We conduct extensive experiments on SST, TREC and SUBJ datasets. The experimental results demonstrate that our model achieved comparable or better performance than other state-of-the-art methods.
Polynomial-time solution for the #P problem based on classical electronic circuits

The #P problem is believed to be intractable using classical computers due to difficulties presented by exponential time. It is generally assumed that the best way to solve this problem is to use an algorithm based on quantum mechanics. Here, we propose and demonstrate experimentally an alternative way to solve the #P problem by implementing classical electronic circuits. The typical #P problem to calculate the permanent of a matrix and the related boson sampling problem are solved in polynomial time with exponential frequency bandwidth which limits the scalability of the scheme. The running time of our scheme to solve these problems is equivalent to those based on quantum mechanics. It is also important that our method has good stability of classical circuits. Thus, our findings are advantageous for information processing in the era of big data.
Auxiliary Network: Scalable and Agile Online Learning for Dynamic System with Inconsistently Available Inputs

Streaming classification methods assume the number of input features is fixed and always received. But in many real-world scenarios, some features are reliable while others are unreliable or inconsistent. We propose a novel online deep learning-based model called Auxiliary Network (Aux-Net), which is scalable and agile and can handle any number of inputs at each time instance. The Aux-Net model is based on the hedging algorithm and online gradient descent. It employs a model of varying depth in an online setting using single pass learning. Aux-Net is a foundational work towards scalable neural network for a dynamic complex environment dealing ad hoc or inconsistent inputs. The efficacy of Aux-Net is shown on the Italy Power Demand dataset.
Binary Hypothesis Testing with Deterministic Finite-Memory Decision Rules

In this paper we consider the problem of binary hypothesis testing with finite memory systems. Let X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , .. . be a sequence of independent identically distributed Bernoulli random variables, with expectation p under H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and q under H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> . Consider a finite-memory deterministic machine with S states that updates its state M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ∈ {1, 2, ... , S} at each time according to the rule M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> = f(M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-1</sub> , X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ), where f is a deterministic time-invariant function. Assume that we let the process run for a very long time (n → ∞), and then make our decision according to some mapping from the state space to the hypothesis space. The main contribution of this paper is a lower bound on the Bayes error probability P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> of any such machine. In particular, our findings show that the ratio between the maximal exponential decay rate of P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> with S for a deterministic machine and for a randomized one, can become unbounded, complementing a result by Hellman.
Aircraft Motion State Control Based on Radar Cooperative Detection

Aircraft motion state control or path planning is an important research problem in sensor management of multi- aircraft cooperative detection. By controlling the motion state or moving path of sensor platform, the radar detection performance is optimal. Firstly, the Aircraft motion control problem is regarded as a kind of multi-step control problem. Then, the dynamic control method is used to describe the optimal control principle of the aircraft motion, and the relationship between aircraft motion control and the radar detection performance is esTablelished. Finally, the dynamic programming method is adopted to achieve the optimal solution of radar detection performance by controlling the movement of the aircraft in the target tracking task. Simulation results show that the radar detection performance is effectively improved by controlling aircraft movement, and radar working time is shortened for completing detection task.