Reviewerly/exHarmony huggingface dataset:
A flexible multi-view calibration and 3D measurement method based on digital fringe projection

Multi-view systems are an effective scheme for solving the problem of a small field-of-view and self-occlusion in optical 3D measurements. However, due to the dispersed distribution of views and the lack of a common field-of-view, the problem of global calibration is difficult and essential. A new method is proposed, by which digital fringe projection and a phase map are used to establish global calibration information. Firstly, fringes are projected to the field of the system. By using a phase unwrapping algorithm, an absolute phase map is obtained. The projector is deemed as the reverse of a camera, which can be calibrated in a similar way to that of the camera. Therefore, the parameters of the cameras, projector and the location of each calibration plane are calculated, and the transformation relationship of adjacent cameras is obtained. Secondly, the phase is used in 3D measurement as well as the global registration of views. Sub-pixel point matching and 3D reconstruction is performed according to the phase map. Global system parameters are optimized by minimizing the reconstruction errors, and precise calibration results can be achieved. With this method, a large-sized calibration target and other auxiliary calibration devices are not required. It is reliable and easy to implement multi-view calibration and reconstruction. The calibration model and calculation processing are simple. The experiment is carried out on a multi-view 3D measurement system. It is shown that high precision multi-view calibration and reconstruction results are obtained.
Alongside, not against: balancing man with machine in the HR function

Purpose In a world of mass technological advancement in our daily lives and in business, the HR function is facing an uphill battle. How can HR professionals ensure they are digitally transforming at the right pace for their business without losing the all-important human touch? Design/methodology/approach This paper outlines the thinking behind integrating artificial intelligence (AI) and automation technologies into HR, and it explores in depth each of the key ways in which we are beginning to see these technologies change HR as we know it. From operations to recruitment and interviewing, to on-boarding employees and maintaining performance, the opportunities are numerous – and they are right on the horizon. Findings AI and automation are already being integrated into HR in many organisations around the world. However, we can in the near future expect to see technology not only automating back-office functions, but increasingly taking on the more “human” elements of HR roles. There is a fine balance between man and machine, and while these technologies will increase efficiency, decrease bias and improve the value of HR in businesses, the human touch will always be the key to success. Originality/value This paper assesses not only how technology is impacting HR but also the interplay between man and machine, and it offers insights into how HR professionals can balance the need for digital transformation with the core human element of human resources. As such, it ties the human and the technology together inextricably, concluding that AI and humans work better together.
Features Combined From Hundreds of Midlayers: Hierarchical Networks With Subnetwork Nodes

In this paper, we believe that the mixed selectivity of neuron in the top layer encodes distributed information produced from other neurons to offer a significant computational advantage over recognition accuracy. Thus, this paper proposes a hierarchical network framework that the learning behaviors of features combined from hundreds of midlayers. First, a subnetwork neuron, which itself could be constructed by other nodes, is functional as a subspace features extractor. The top layer of a hierarchical network needs subspace features produced by the subnetwork neurons to get rid of factors that are not relevant, but at the same time, to recast the subspace features into a mapping space so that the hierarchical network can be processed to generate more reliable cognition. Second, this paper shows that with noniterative learning strategy, the proposed method has a wider and shallower structure, providing a significant role in generalization performance improvements. Hence, compared with other state-of-the-art methods, multiple channel features with the proposed method could provide a comparable or even better performance, which dramatically boosts the learning speed. Our experimental results show that our platform can provide a much better generalization performance than 55 other state-of-the-art methods.
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the `long tail' is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized diversification re-ranking approach to increase the representation of less popular items in recommendations while maintaining acceptable recommendation accuracy. Our approach is a post-processing step that can be applied to the output of any recommender system. We show that our approach is capable of managing popularity bias more effectively, compared with an existing method based on regularization. We also examine both new and existing metrics to measure the coverage of long-tail items in the recommendation.
Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome

Main adverse cardiac events (MACE) are essentially composite endpoints for assessing safety and efficacy of treatment processes of acute coronary syndrome (ACS) patients. Timely prediction of MACE is highly valuable for improving the effects of ACS treatments. Most existing tools are specific to predict MACE by mainly using static patient features and neglecting dynamic treatment information during learning.We address this challenge by developing a deep learning-based approach to utilize a large volume of heterogeneous electronic health record (EHR) for predicting MACE after ACS. Specifically, we obtain the deep representation of dynamic treatment features from EHR data, using the bidirectional recurrent neural network. And then, the extracted latent representation of treatment features can be utilized to predict whether a patient occurs MACE in his or her hospitalization.We validate the effectiveness of our approach on a clinical dataset containing 2930 ACS patient samples with 232 static feature types and 2194 dynamic feature types. The performance of our best model for predicting MACE after ACS remains robust and reaches 0.713 and 0.764 in terms of AUC and Accuracy, respectively, and has over 11.9% (1.2%) and 1.9% (7.5%) performance gain of AUC (Accuracy) in comparison with both logistic regression and a boosted resampling model presented in our previous work, respectively. The results are statistically significant.We hypothesize that our proposed model adapted to leverage dynamic treatment information in EHR data appears to boost the performance of MACE prediction for ACS, and can readily meet the demand clinical prediction of other diseases, from a large volume of EHR in an open-ended fashion.
Reward Shaping via Meta-Learning

Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment in Reinforcement Learning (RL). However, designing shaping functions usually requires much expert knowledge and hand-engineering, and the difficulties are further exacerbated given multiple similar tasks to solve. In this paper, we consider reward shaping on a distribution of tasks, and propose a general meta-learning framework to automatically learn the efficient reward shaping on newly sampled tasks, assuming only shared state space but not necessarily action space. We first derive the theoretically optimal reward shaping in terms of credit assignment in model-free RL. We then propose a value-based meta-learning algorithm to extract an effective prior over the optimal reward shaping. The prior can be applied directly to new tasks, or provably adapted to the task-posterior while solving the task within few gradient updates. We demonstrate the effectiveness of our shaping through significantly improved learning efficiency and interpretable visualizations across various settings, including notably a successful transfer from DQN to DDPG.
Region based multi-stream convolutional neural networks for collective activity recognition

Collective activity recognition, which analyses the behavior of groups of people in videos, is an important goal of video surveillance systems. In this paper, we focus on collective activity recognition problem and propose a new multi-stream convolutional neural network architecture that utilizes information extracted from multiple regions. The proposed method is the first work that uses a multi-stream network and multiple regions in this problem. Various strategies to fuse multiple spatial and temporal streams are explored. We evaluate the proposed method on two benchmark datasets, the Collective Activity Dataset and the Volleyball Dataset. Our experimental results show that the proposed method improves collective activity recognition performance when compared to the state-of-the-art approaches.
Coloring Big Graphs with AlphaGoZero

We show that recent innovations in deep reinforcement learning can effectively color very large graphs -- a well-known NP-hard problem with clear commercial applications. Because the Monte Carlo Tree Search with Upper Confidence Bound algorithm used in AlphaGoZero can improve the performance of a given heuristic, our approach allows deep neural networks trained using high performance computing (HPC) technologies to transform computation into improved heuristics with zero prior knowledge. Key to our approach is the introduction of a novel deep neural network architecture (FastColorNet) that has access to the full graph context and requires $O(V)$ time and space to color a graph with $V$ vertices, which enables scaling to very large graphs that arise in real applications like parallel computing, compilers, numerical solvers, and design automation, among others. As a result, we are able to learn new state of the art heuristics for graph coloring.
SPOILER: Speculative Load Hazards Boost Rowhammer and Cache Attacks

Modern microarchitectures incorporate optimization techniques such as speculative loads and store forwarding to improve the memory bottleneck. The processor executes the load speculatively before the stores, and forwards the data of a preceding store to the load if there is a potential dependency. This enhances performance since the load does not have to wait for preceding stores to complete. However, the dependency prediction relies on partial address information, which may lead to false dependencies and stall hazards. In this work, we are the first to show that the dependency resolution logic that serves the speculative load can be exploited to gain information about the physical page mappings. Microarchitectural side-channel attacks such as Rowhammer and cache attacks like Prime+Probe rely on the reverse engineering of the virtual-to-physical address mapping. We propose the SPOILER attack which exploits this leakage to speed up this reverse engineering by a factor of 256. Then, we show how this can improve the Prime+Probe attack by a 4096 factor speed up of the eviction set search, even from sandboxed environments like JavaScript. Finally, we improve the Rowhammer attack by showing how SPOILER helps to conduct DRAM row conflicts deterministically with up to 100% chance, and by demonstrating a double-sided Rowhammer attack with normal user's privilege. The later is due to the possibility of detecting contiguous memory pages using the SPOILER leakage.
Algorithms for Verifying Deep Neural Networks

Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control. Although these networks involve the composition of simple arithmetic operations, it can be very challenging to verify whether a particular network satisfies certain input-output properties. This article surveys methods that have emerged recently for soundly verifying such properties. These methods borrow insights from reachability analysis, optimization, and search. We discuss fundamental differences and connections between existing algorithms. In addition, we provide pedagogical implementations of existing methods and compare them on a set of benchmark problems.
Predictive lithological mapping through machine learning methods: a case study in the Cinzento Lineament, Carajás Province, Brazil

The Cinzento Lineament (Carajas Mineral Province) represents a complex deformational system with great associated mineral potential, mainly for IOCG deposits. However, the tropical vegetation of the Amazon rainforest considerably limits the number of outcrops available for systematic geological mapping. Therefore, the use of remote data such as airborne geophysics and remote sensing is essential to provide a reliable geological map. The airborne magnetometric data to define lithological units and its boundaries is a challenge, especially in regions with low magnetic latitude and/or remanent magnetization. In this work, we proposed an approach using Magnetization Vector Inversion (MVI) to map the distribution of the magnetic susceptibility, in order to replace techniques such as pole reduction and total gradient. We applied the Random Forest algorithm (supervised Machine Learning algorithm) to recognize patterns in remote data and improve the current mapped lithological units. With 1400 training samples (2.5% of the total samples), we produced two Predictive lithological maps: a first with remote data only and a second with remote data and spatial coordinates. We evaluate the advantages and disadvantages of each Predictive map, and we conclude that both maps need to be analyzed together for the refinement of the current geological map. These predictive maps represent a powerful tool to combine remote data to improve current geological maps, or even generate the first-pass geological map for regions with scarce geological knowledge.
Work motivation and its effects on organizational performance: the case of nurses in Hawassa public and private hospitals: Mixed method study approach

The main objective of the study was to assess level of motivation, how nurses perceived work motivation and its effects on organizational performance among nurses working in Hawassa public and private hospitals.It was found that majority (64.1%) of the nurses perceived motivation as motivators. Getting prospective encouragement, recognition and financial incentives were the main descriptions the nurses gave to motivation. Increased work performance, job satisfaction, good team spirit, patient satisfaction and job attachment were the reported effects of nurses' motivation.
Techniques for fault-tolerant decomposition of a multicontrolled Toffoli gate

Physical implementation of scalable quantum architectures faces an immense challenge in the form of fragile quantum states. To overcome it, quantum architectures with fault tolerance are desirable. These are achieved currently by using a surface code along with a transversal gate set. This indicates the need for decomposition of universal $n$-qubit multicontrolled Toffoli ($n$-MCT) gates using a transversal gate set. Additionally, the transversal non-Clifford phase gate incurs high latency, which makes it an important factor to consider during decomposition. Additionally, the decomposition of a large MCT gate without ancillas presents an additional hurdle. In this paper, we address both of these issues by introducing the $\mathrm{Clifford}+{Z}_{N}$ gate library. We present an ancilla-free decomposition of MCT gates with linear phase depth and quadratic phase count. Furthermore, we provide a technique for decomposition of MCT gates in unit phase depth using the $\mathrm{Clifford}+{Z}_{N}$ library, albeit at the cost of ancillary lines and exponential phase count.
A new attack on RSA and Demytko’s elliptic curve cryptosystem

Let N = p q be an RSA modulus and e be a public exponent. Numerous attacks on RSA exploit the arithmetical properties of the key equation ed – k (p – 1)(q – 1) = 1. In this paper, we study the more general equation eu – (p – s)(p - r)v = w . We show that when the unknown integers u, v, w, r and s are suitably small and p - s or p - s is factorable using the Elliptic Curve Method for factorization ECM, then one can break the RSA system. As an application, we propose an attack on Demytko’s elliptic curve cryptosystem. Our method is based on Coppersmith’s technique for solving multivariate polynomial modular equations.
Investigating the Successes and Failures of BERT for Passage Re-Ranking

The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking. In this paper, we analyze the results produced by a fine-tuned BERT model to better understand the reasons behind such substantial improvements. To this aim, we focus on the MS MARCO passage re-ranking dataset and provide potential reasons for the successes and failures of BERT for retrieval. In more detail, we empirically study a set of hypotheses and provide additional analysis to explain the successful performance of BERT.
Spectral composition of low-coherence interferograms at high numerical apertures

Interference signals in coherence scanning interferometry at high numerical apertures and narrow bandwidth illumination are spectrally broadened. This enables phase analysis within a spectral range much wider than the spectral distribution of the light emitted by the light source. Consequently, different surface features can be resolved depending on the wavelength used for phase analysis of the interference signals. In addition, the surface topography itself affects the spectral composition of interference signals in different ways. Signals related to tilted surfaces or step height structures show special spectral characteristics. Thus, spectral amplitude and phase analysis enables a better understanding of the underlying physical mechanisms and gives hints how to improve the measurement accuracy.
Efficient Multiple Kernel k-Means Clustering With Late Fusion

The recently proposed multiple-kernel clustering algorithms have demonstrated promising performance in various applications.However, most of the existing methods suffer from high computational complexity and intensive time cost.To address this issue, we propose to fulfill multiple kernel k-means clustering via a late fusion manner.In specific, we design two multiple kernel k-means algorithms with late fusion, whose computational complexities linearly grow with the number of samples.The proposed algorithms integrally optimize the various clustering matrices into the optimal consensus clustering results iteratively.Furthermore, we analyze the computational complexities of the proposed algorithms and theoretically prove their convergence.As demonstrated by the experiments on six benchmark datasets, our algorithms achieve comparable or better clustering performance to state-of-the-art ones with less time cost, which demonstrates the advantages of the late fusion in multiple kernel k-means.
Pedagogical models of blended learning: On the experience of Russian and foreign practice of design and implementation

Introduction. One of the strategic components of modern education modernisation is its technological diversification by means of traditional and emedia blend in the educational process aimed at acquiring knowledge, skills and competences. This blend is considered a trigger for innovative development of higher education worldwide due to its adequate adaptive personalised professional training for students. Thereby, to interpret and evaluate the pedagogical potential for blended learning models implementation in higher education environment is regarded as foremost. The aim of the article is to theoretically and practically provide rationale for the efficiency of structural and functional content of the existing blended learning models that designate specific patterns for education management in Russian and foreign universities. Methodology and research methods . System approach underlies this research. Retrospective and comparative analysis methods, synthesis and classification have been employed to conduct the research. A survey is a supplementary method used to collect empirical data. Results and scientific novelty. The existing definitions of blended learning are reviewed. Pedagogical models of blended learning are compared according to their dominant functional feature, design principles and basic components. The models are classified into institutional, technological, didactic and synergistic on the basis of synthesis and systematisation of the theoretical and practical experience of an integrative didactic teaching system design in the university electronic educational environment. Each type of these models is described; their features, peculiarities and advantages are shown. It is concluded that there is no unified approach in understanding the essence of blended learning. Thus, the authors’ interpretation of the blended learning concept is developed in the research. Practical significance . The results of the research may be applied in design, implementation and assessment performance of blended learning models or their components in practice of particular universities.
Identifiers for MRD-codes

For any admissible value of the parameters $n$ and $k$ there exist $[n,k]$-Maximum Rank distance ${\mathbb F}_q$-linear codes. Indeed, it can be shown that if field extensions large enough are considered, almost all rank distance codes are MRD. On the other hand, very few families up to equivalence of such codes are currently known. In the present paper we study some invariants of MRD codes and evaluate their value for the known families, providing a new characterization of generalized twisted Gabidulin codes.
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese

In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics.In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentencelevel and topic-level representations.In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations.Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topiclevel relevance using multi-slice tensors.Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.