Reviewerly/exHarmony huggingface dataset:
Mapping internal deformation fields in 3D printed porous structure with digital volume correlation

As a new manufacturing technology, 3D printing is gradually attracting more interest in both the academic community and the industrial field. To realize its full potential in engineering applications, it is necessary to characterize the mechanical behaviors and properties of 3D printed materials and structures under external loading. Although surface deformation of 3D printed materials has been extensively investigated, the 3D internal deformation measurement and further mechanical analysis using digital volume correlation (DVC) gain less attention in the field of 3D printing. In this work, step-wise uniaxial compression tests of a 3D printed porous structure were first performed in combination with in-situ X-ray computed tomography (CT) imaging. Then, the global and local evolutions of internal full-field deformation at different compressive stages were evaluated via DVC technique. Finally, a multiscale experimental framework was presented to correlate mechanical properties with geometric structure of the 3D printed porous structure. On this basis, the failure patterns along with the possible damage mechanism were predicted and discussed.
Whale Optimization Algorithm with Chaos Strategy and Weight Factor

Abstract Whale optimization algorithm (WOA) is a novel optimization algorithm inspired by humpback whale hunting behavior. Due to the defect of unbalanced exploration and exploitation by using control parameter with linear change, WOA has slow convergence and is easy to fall into local optimum. Thus, a novel whale optimization algorithm with chaos strategy and weight factor (WOACW) is proposed to improve the convergence speed and accuracy. In this work, the chaos strategy is executed to initialize the population to enhance the diversity of the initial population. The weight factor is introduced to adjust the influence degree of the current optimal solution on the generation of new individuals in order to improve the convergence speed and accuracy. At the same time, the convergence factor is adjusted by cosine function to better balance the relationship between exploration and exploitation. In addition, using greedy strategy to fully retain the dominant individuals from the parents and the generated candidates to generate offspring, improves the convergence speed of the algorithm. To verify the performance of our approach, WOACW is benchmarked on 13 classical benchmark functions, and the statistical results are compared with the original WOA and three other WOA variants, IWOA, WOAWC, and two state-of-the-art algorithms, SSA, GWO. The experimental results and Wilcoxon signed ranks test show that WOACW has a higher convergence speed and precision than compared algorithms, which verifies the effectiveness of WOACW in this work.
Loopy Residual Hashing: Filling the Quantization Gap for Image Retrieval

Hashing has been widely used in large-scale image retrieval based on approximate nearest neighbor search. Most learning-to-hashing methods adopt a two-stage algorithm to generate binary codes. First, original images are mapped into continuous visual features. Then, binary codes are generated by quantization step or separate projection. Nevertheless, these methods are sensitive to quantization operation, i.e., thresholding. To explicitly address this issue, this study proposes a novel feature quantization scheme with a loopy recurrent neural network, called loopy residual hashing, for the purpose of high accuracy in image retrieval. Instead of one-off thresholding-based feature binarization, the proposed approach performs an iterative threshold-then-approximate operation, which calculates the quantization residual after each thresholding step and then imitates another round of binarization to further approximate the coding residual. The resulting sequences of binary codes possess higher representation accuracy and extensive experiments on image retrieval demonstrate its superior discriminative capability over the prior art. In the meantime, theoretical approximation error analysis is given.
Focused Context Balancing for Robust Offline Policy Evaluation

Precisely evaluating the effect of new policies (e.g. ad-placement models, recommendation functions, ranking functions) is one of the most important problems for improving interactive systems. The conventional policy evaluation methods rely on online A/B tests, but they are usually extremely expensive and may have undesirable impacts. Recently, Inverse Propensity Score (IPS) estimators are proposed as alternatives to evaluate the effect of new policy with offline logged data that was collected from a different policy in the past. They tend to remove the distribution shift induced by past policy. However, they ignore the distribution shift that would be induced by the new policy, which results in imprecise evaluation. Moreover, their performances rely on accurate estimation of propensity score, which can not be guaranteed or validated in practice. In this paper, we propose a non-parametric method, named Focused Context Balancing (FCB) algorithm, to learn sample weights for context balancing, so that the distribution shift induced by the past policy and new policy can be eliminated respectively. To validate the effectiveness of our FCB algorithm, we conduct extensive experiments on both synthetic and real world datasets. The experimental results clearly demonstrate that our FCB algorithm outperforms existing estimators by achieving more precise and robust results for offline policy evaluation.
Deep convolutional tree networks

In this work, we present a new residual network family based on a tree structure. We present three types of tree modules that can be employed in different kinds of convolutional networks as a replacement for one or more convolutional layers. The new architecture exposes two new hyper-parameters – tree height and branching factor – that grant more control on the model size and codependency between the maps. Tree modules provide the flexibility to merge two important techniques: branching technique, which provides better feature representation, and group convolution, which reduces the number of parameters. Most previous studies focused on accuracy regardless of the model complexity. However, we focus on information density metric to design a model that effectively utilizes its parametric space. We conducted numerous experiments on the dataset of the Canadian Institute for Advanced Research-10 (CIFAR-10) and demonstrated that the proposed networks are superior to many famous networks in terms of information density and accuracy.
Assessing the security implication of Bitcoin exchange rates

Cryptographic assets such as Bitcoin and Ethereum provide distributed consensus with a Proof-of-Work protocol and incentive-based engineering. The consensus is inherently dependent on the value of the asset due to the incentives. The value of these assets frequently fluctuates, which in turn influences the incentive component of the consensus mechanism. For a proof-of-work consensus to be secure, the participation reward must have a perceived real-world value. The future of this perception is not at all clear. The recent 70% drop in the value of Bitcoin versus the US Dollar may be precipitating a circle of declining security of the platform which we explore in depth in this paper. In this paper, we analyze the impact of fluctuations on the security of Bitcoin now, and in the future. We introduce a novel method to examine the rationale of a miner based on the price fluctuations. We integrate our method with an existing security evaluation framework and simulator. Using our approach, we determine and report on the impact of the value of the cryptographic asset on the security of the blockchain given the miner’s rationale. Our method allows us to evaluate the impact of different methods of incentive manipulation such as reduced block-reward and transaction fees, by simulation.
On the Quantum and Classical Complexity of Solving Subtraction Games

We study algorithms for solving Subtraction games, which are sometimes referred as one-heap Nim games. We describe a quantum algorithm which is applicable to any game on DAG, and show that its query complexity for solving an arbitrary Subtraction game of n stones is $$O\left( n^{3/2}\log n\right) $$ . The best known deterministic algorithms for solving such games are based on the dynamic programming approach [8]. We show that this approach is asymptotically optimal and that classical query complexity for solving a Subtraction game $$\varTheta \left( n^2\right) $$ in general. Of course, this difference between classical and quantum algorithms is far from the best known examples, but, up to our knowledge, this paper is the first constructive "quantum" contribution to the algorithmic game theory.
Learning APT chains from cyber threat intelligence

With the rapidly evolving cyber attacks, cybersecurity specialists are actively using cyber threat intelligence to identify and respond to cyber attacks in a timely manner. However, this information will be highly useful for attack detection and mitigation if we can construct structured cyber threat intelligence and accurately generate TTP Chains to understand the steps of cyber attacks. In this poster, we present our preliminary Natural Language Processing (NLP) analysis to characterize the temporal relationship of attack actions of an APT attack to extract and construct the reported TTP chains using the popular standard, MITRE ATT&CK [1], and the Structured Sharing Language, STIX 2 [2], a machine-readable language that will help automate the process of understanding and responding to the cyber attacks shared in unstructured text via blogs, emails, and social media.
Design considerations for the processing system of a CNN-based automated surveillance system

Modern surveillance systems combine tens or even hundreds of cameras to ensure a wide area of coverage. With such a large amount of incoming video data, it is difficult for humans to effectively monitor each camera for potential threats. Recent advances in machine learning have brought forth great improvements in computer vision, which forms the key to automating surveillance systems. Previous works on the topic of automated surveillance systems have not been updated to leverage the emerging wave of convolutional neural network (CNN) based algorithms, nor considered the implications of these rapidly evolving frameworks. This work investigates the design of the processing system for a CNN-based automated surveillance system, including off-the-shelf algorithms and hardware components. Performance benchmarks are then used to evaluate several design options that impact processing speed of CNN-based surveillance algorithms. Finally, a set of design considerations are presented for the processing system of a modern CNN-based automated surveillance system.
State-Sharing Sparse Hidden Markov Models for Personalized Sequences

Hidden Markov Model (HMM) is a powerful tool that has been widely adopted in sequence modeling tasks, such as mobility analysis, healthcare informatics, and online recommendation. However, using HMM for modeling personalized sequences remains a challenging problem: training a unified HMM with all the sequences often fails to uncover interesting personalized patterns; yet training one HMM for each individual inevitably suffers from data scarcity. We address this challenge by proposing a state-sharing sparse hidden Markov model (S3HMM) that can uncover personalized sequential patterns without suffering from data scarcity. This is achieved by two design principles: (1) all the HMMs in the ensemble share the same set of latent states; and (2) each HMM has its own transition matrix to model the personalized transitions. The result optimization problem for S3HMM becomes nontrivial, because of its two-layer hidden state design and the non-convexity in parameter estimation. We design a new Expectation-Maximization algorithm based, which treats the difference of convex programming as a sub-solver to optimize the non-convex function in the M-step with convergence guarantee. Our experimental results show that, S3HMM can successfully uncover personalized sequential patterns in various applications and outperforms baselines significantly in downstream prediction tasks.
Measurement of three dimensional volcanic plume properties using multiple ground based infrared cameras

This study presents a method and a proof of principle system for the direct measurement of volcanic plume 3-D spatial properties. The shape of a plume is reconstructed in three dimensions using multi-view imagery collected from static ground-based cameras. The method was developed using data collected during an expedition to Volcán de Fuego in Guatemala, where four thermal infrared cameras were deployed to capture simultaneous images of the regular ash-rich eruptions. A space carving method was applied to the problem to estimate the volume of the plume at any moment in time. By successively applying the method to sequential sets of images, other quantitative measurements such as the drift direction, ascent rate, and dispersion rate can be deduced. The complete method work-flow is presented including data capture, calibration processes, image processing, the space-carving method, and practical implementation issues. The method is sensitive to the camera alignment, hence a novel technique for estimating the camera orientation angles, making use if a high-accuracy terrain model, is described. Other sources of error relating to the number, synchronisation and resolution of the cameras are also discussed. Preliminary results are presented using data collected at Volcán de Fuego in November 2017 over a period of 1.25 h including three distinct eruptions.
Creative Data Work in the Design Process

The current work investigates how creativity manifests when designers use data work in the early phase of design. Designers are increasingly interested in utilizing the massive amounts of data surrounding our everyday lives. However, data work is still challenging to incorporate into the design process. In this paper, we present a case study with three novice design teams who were tasked to integrate data work into their design process. During the study, we observed how creativity took place in framing a design problem. We present and discuss their actions from a creativity process perspective, highlighting how they used and rationalized data-inspired inquiries creatively in the early phase of design. The current results inform the development of a design framework to structure data work methodologically and coherently into design processes. We coin this design framework Exploratory Data Inquiry.
Improved MPC Algorithms for Edit Distance and Ulam Distance

Edit distance is one of the most fundamental problems in combinatorial optimization. Ulam distance is a special case of edit distance where no character is allowed to appear more than once in a string. Recent developments have been very fruitful for obtaining fast and parallel algorithms for both edit distance and Ulam distance. In this work, we present an almost optimal MPC algorithm for Ulam distance and improve MPC algorithms for edit distance. Our algorithm for Ulam distance is optimal in the sense that (1) the approximation factor of our algorithm is 1+ε, (2) the round complexity of our algorithm is constant, (3) the total memory of our algorithm is almost linear (~O(n)), and (4) the overall running time of our algorithm is almost linear which is the best known for Ulam distance. Similar to edit distance and longest common subsequence (LCS) which are considered as dual problems, Ulam distance and longest increasing subsequence (LIS) are also seen as dual problems. LIS is equivalent to a special case of LCS where each string can contain each character at most once. In that sense, our result for Ulam distance complements the work of Im et al., wherein a similar result is presented for łis. We also improve the work of Hajiaghayi et al. for edit distance in terms of total memory. The best previously known MPC algorithm for edit distance requires ~O(n2x) machines when the memory of each machine is bounded by ~O(n1-x). In this work, we improve the number of machines to ~O(n1.75x) while keeping the memory limit intact. Moreover, the round complexity of our algorithm is constant and the total running time of our algorithm is truly subquadratic. However, our improvement comes at the expense of a constant factor in the approximation guarantee of the algorithm. This improvement is inspired by the recent techniques of Boroujeni et al. and Chakraborty et al. for obtaining truly subquadratic time algorithms for edit distance.
An intuitionistic fuzzy set based hybrid similarity model for recommender system

In general, a practical online recommendation system does not rely on only one algorithm but adopts different types of algorithms to predict user preferences. Although most of similarity measures can rapidly calculate the similarity on the basis of co-rated items, their prediction accuracy is not satisfactory in the case of sparse datasets. Making full use of all the rating information can effectively improve the recommendation quality, but it reduces the system efficiency because all the ratings need to be calculated. To recommend items for target users rapidly and accurately, this paper designs a hybrid item similarity model that achieves a trade-off between prediction accuracy and efficiency by combining the advantages of the two above-mentioned methods. First, we introduce an adjusted Google similarity to rapidly and precisely calculate the item similarity in the condition of enough co-rated items. Subsequently, an intuitionistic fuzzy set (IFS) based Kullback–Leibler (KL) similarity is presented from the perspective of user preference probability to effectively compute the item similarity in the condition of rare co-rated items. Finally, the two proposed schemes are integrated by an adjusted variable to comprehensively evaluate the similarity values when the number of co-rated items lies in a certain range of value. The proposed model is implemented and tested on some benchmark datasets with different thresholds of co-rated items. The experimental results indication that the proposed system has a favorable efficiency and guarantees the quality of recommendations.
Hidden Markov Contour Tree

Spatial structured models are predictive models that capture dependency structure between samples based on their locations in the space. Learning such models plays an important role in many geoscience applications such as water surface mapping, but it also poses significant challenges due to implicit dependency structure in continuous space and high computational costs. Existing models often assume that the dependency structure is based on either spatial proximity or network topology, and thus cannot incorporate complex dependency structure such as contour and flow direction on a 3D potential surface. To fill the gap, this paper proposes a novel spatial structured model called hidden Markov contour tree (HMCT), which generalizes the traditional hidden Markov model from a total order sequence to a partial order polytree. HMCT also advances existing work on hidden Markov trees through capturing complex contour structures on a 3D surface. We propose efficient model construction and learning algorithms. Evaluations on real world hydrological datasets show that our HMCT outperforms multiple baseline methods in classification performance and that HMCT is scalable to large data sizes (e.g., classifying millions of samples in seconds).
Combine Topic Modeling with Semantic Embedding: Embedding Enhanced Topic Model

Topic model and word embedding reflect two perspectives of text semantics. Topic model maps documents into topic distribution space by utilizing word collocation patterns within and across documents, while word embedding represents words within a continuous embedding space by exploiting the local word collocation patterns in context windows. Clearly, these two types of patterns are complementary. In this paper, we propose a novel integration framework to combine the two representation methods, where topic information can be transmitted into corresponding semantic embedding structure. Based on this framework, we construct a Embedding Enhanced Topic Model (EETM), which can improve topic modeling and generate topic embeddings by leveraging the word embedding. Extensive experimental results show that EETM can learn high-quality document representations for common text analysis tasks across multiple data sets, indicating it is very effective for merging topic models with word embeddings.
The construction of high order convergent look-ahead finite difference formulas for Zhang Neural Networks

Zhang Neural Networks rely on convergent 1-step ahead finite difference formulas of which very few are known. Those which are known have been constructed in ad-hoc ways and suffer from low truncation error orders. This paper develops a constructive method to find convergent look-ahead finite difference schemes of higher truncation error orders. The method consists of seeding the free variables of a linear system comprised of Taylor expansion coefficients followed by a minimization algorithm for the maximal magnitude root of the formula's characteristic polynomial. This helps us find new convergent 1-step ahead finite difference formulas of any truncation error order. Once a polynomial has been found with roots inside the complex unit circle and no repeated roots on it, the associated look-ahead ZNN discretization formula is convergent and can be used for solving any discretized ZNN based model. Our method recreates and validates the few known convergent formulas, all of which have truncation error orders at most 4. It also creates new convergent 1-step ahead difference formulas with truncation error orders 5 through 8.
The Tilting Bowl: Electronic Design for a Research Product

The Tilting Bowl is a ceramic bowl that unpredictably but gently tilts multiple times daily. This pictorial reports on the crafting of the electronics of the Tilting Bowl within the concept of a research product [10]. From this perspective, the seemingly simple task of making a bowl tilt holds unique challenges and demands - especially as a research product that is deployed in everyday settings for lengthy periods of time. We highlight electronic design challenges that came up in three processes of making the Tilting Bowl: the tilting mechanism, hardware integration of electronics and power management. Lastly, we offer three suggestions for designing electronics for research products.