Reviewerly/exHarmony huggingface dataset:
Banking on Stone Money: Ancient Antecedents to Bitcoin

Centuries ago in western Micronesia, Yapese islanders sailed to the Palauan archipelago 250 miles away to carve their famous stone money disks (rai) from limestone and then transported them back for use as exchange valuables in various social transactions. While rai were not strictly currency, their value is similar to other traditional and modern objects where worth is arbitrarily based on both real and perceived attributes. Recently, corollaries have been made between rai and newly established electronic cryptocurrencies that use blockchain technology—essentially, digital ledgers that track financial transactions in real time across a computer network to ensure that they are seamless and incorruptible. In this study, we examine the sociophysical similarities and differences between stone money and cryptocurrencies that rely on blockchain, both of which demand a unified and continuous chain of information to ensure that the value is known and ownership indisputable. This research suggests that stone money: (a) is an exemplary ancient analog to blockchain that used oral ledgers solidified through social networks to create accurate and unbroken lines of communication so that economic relationships involving rai could be established, maintained, exchanged, and rectified, and (b) may have been a progenitor that inspired the development of Bitcoin.
A data-driven robotic Chinese calligraphy system using convolutional auto-encoder and differential evolution

The Chinese stroke evaluation and generation systems required in an autonomous calligraphy robot play a crucial role in producing high-quality writing results with good diversity. These systems often suffer from inefficiency and non-optima despite of intensive research effort investment by the robotic community. This paper proposes a new learning system to allow a robot to automatically learn to write Chinese calligraphy effectively. In the proposed system, the writing quality evaluation subsystem assesses written strokes using a convolutional auto-encoder network (CAE), which enables the generation of aesthetic strokes with various writing styles. The trained CAE network effectively excludes poorly written strokes through stroke reconstruction, but guarantees the inheritance of information from well-written ones. With the support of the evaluation subsystem, the writing trajectory model generation subsystem is realized by multivariate normal distributions optimized by differential evolution (DE), a type of heuristic optimization search algorithm. The proposed approach was validated and evaluated using a dataset of nine stroke categories; high-quality written strokes have been resulted with good diversity which shows the robustness and efficacy of the proposed approach and its potential in autonomous action-state space exploration for other real-world applications.
Stochastic estimation of dynamical variables

Estimating the parameters governing the dynamics of a system is a prerequisite for its optimal control. We present a simple but powerful method that we call STEADY, for STochastic Estimation algorithm for DYnamical variables, to estimate the Hamiltonian (or Lindbladian) governing a quantum system of a few qubits. STEADY makes efficient use of all measurements and its performance scales as the information-theoretic limits for such an estimator. Importantly, it is inherently robust to state preparation and measurement errors. It is not limited to evaluating only a fixed set of possible gates, rather it estimates the complete Hamiltonian of the system. The estimator is applicable to any Hamiltonian that can be written as a piecewise-differentiable function and it can easily include estimators for the non-unitary parameters as well. At the heart of our approach is a stochastic gradient descent over the difference between experimental measurement and model prediction.
A Virtual Reality Interface for an Autonomous Spray Painting UAV

PaintCopter is an autonomous unmanned aerial vehicle (UAV) capable of spray painting on complex three-dimensional (3D) surfaces. This letter aims to make PaintCopter more user-friendly and to enable more intuitive human–robot interaction. We propose a virtual reality interface that allows the user to immerse in a virtual environment, navigate around the target surface, and paint at desired locations using a virtual spray gun. A realistic paint simulator provides a real-time previsualization of the painting activity that can either be processed right away or stored to a disk for later execution. An efficient optimization based planner uses this information to plan the painting task and execute it. The proposed planner maximizes the paint quality while respecting the spray nozzle constraints and platform dynamics. Our experiments show that the interface allows the user to make precise modifications to the target surface. Finally, we demonstrate the use of virtual reality interface to define a painting mission, and then the PaintCopter carrying out the mission to paint a desired multicolored pattern on a 3D surface.
Perch to Fly

Many studies have proposed different ways of supporting flying in embodied virtual reality (VR) interfaces with limited success. Our research explores the usage of a user's lower body to support flying locomotion control through a novel "flexible perching" (FlexPerch) stance that provides user with leg moving ability while sitting. We conducted an observational study exploring participants' preferred usage of the FlexPerch stance, and a mixed-method study comparing the same flying experience with existing sitting and standing stances. Our results show that FlexPerch markedly increased participants' feelings of flying. However, people may not like "flying" when they really can - the freedom, feeling of floating, and novelty contributing to this sensation can also mean more effort and feeling unsafe or unfamiliar. We suggest that researchers studying VR flying interfaces evaluate the feeling of flying, and raise design considerations to use stances like FlexPerch to elicit feelings of flying and stimulation.
Movie Recommender System Based on Percentage of View

With ever-increasing data on the internet, finding the desired content has become harder and that is why recommender systems' role is very important in business. As a specific example, media service providers, such as Netflix, can improve their service by recommending desirable content to each user. Most of the previous studies used explicit feedback of users, through likes and dislikes, to recommend items to their customers. However, in many cases, there is not much explicit feedback about items which cripples typical recommender systems to operate efficiently and provide accurate recommendation. In this paper, a percentage of view approach is proposed to find relevant movies for customers. To prove the effectiveness of the approach, first, it is shown that this feature can be a good indicator of users' like and dislike. Then the best approach is determined and used in a recommender system for Namava, a media service provider. Then the performance of this recommender system is compared to a random recommender system and the effectiveness of the approach is shown.
Multi-layer radial basis function neural network based on multi-scale kernel learning

The conversion functions in the hidden layer of radial basis function neural networks (RBFNN) are Gaussian functions. The Gaussian functions are local to the kernel centers. In most of the existing research, the spatial local response of the sample is inaccurately calculated because the kernels have the same shape as a hypersphere, and the kernel parameters in the network are determined by experience. The influence of the fine structure in the local space is not considered during feature extraction. In addition, it is difficult to obtain a better feature extraction ability with less computational complexity. Therefore, this paper develops a multi-scale RBF kernel learning algorithm and proposes a new multi-layer RBF neural network model. For the samples of each class, the expectation maximization (EM) algorithm is used to obtain multi-layer nested sub-distribution models with different local response ranges, which are called multi-scale kernels in the network. The prior information of each sub-distribution is used as the connection weight between the multi-scale kernels. Finally, feature extraction is implemented using multi-layer kernel subspace embedding. The multi-scale kernel learning model can efficiently and accurately describe the fine structure of the samples and is fault tolerant to setting the number of kernels to a certain extent. Considering the prior probability of each kernel as the weight makes the feature extraction process satisfy the Bayes rule, which can enhance the interpretability of feature extraction in the network. This paper also theoretically proves that the proposed neural network is a generalized version of the original RBFNN. The experimental results show that the proposed method has better performance compared with some state-of-the-art algorithms.
I Beg to Differ

The adoption of design fiction into design research has recently been expanding within the HCI community. Design fiction workshops have fruitfully facilitated users and researchers discussing and creating future technologies by exposing differing viewpoints. Yet, most scholarship focuses on the ostensibly successful outputs of these workshops. It remains unclear exactly what sort of interaction dynamics are instigated by design fiction in collaborative design. How might design fiction affect what we consider in design, and how is this reflected in the ensuing design? To fill this gap, our study examines design fictions across five workshops where diverse participants created futuristic autobiographies, a method to elicit values, and built diegetic prototypes both individually and collaboratively. We detail their design processes and unpack three kinds of soft conflicts that arose between participants and allowed them to bring up and discuss differing values regarding technology in society. Reflecting on our workshops, we discuss their implications on how one might employ design fiction in collaborative design.
Efficient Parallel Subgraph Enumeration on a Single Machine

Subgraph enumeration finds all subgraphs in an unlabeled graph that are isomorphic to another unlabeled graph. Existing depth-first search (DFS) based algorithms work on a single machine, but they are slow on large graphs due to the large search space. In contrast, distributed algorithms on clusters adopt a parallel breadth-first search (BFS) and improve the performance at the cost of large amounts of hardware resources, since the BFS approach incurs expensive data transfer and space cost due to the exponential number of intermediate results. In this paper, we develop an efficient parallel subgraph enumeration algorithm for a single machine, named LIGHT. Our algorithm reduces redundant computation in DFS by deferring the materialization of pattern vertices until necessary and converting the candidate set computation into finding a minimum set cover. Moreover, we parallelize our algorithm with both SIMD (Single-Instruction-Multiple-Data) instructions and SMT (Simultaneous Multi-Threading) technologies in modern CPUs. Our experimental results show that LIGHT running on a single machine outperforms existing single-machine DFS algorithms by more than three orders of magnitude, and is up to two orders of magnitude faster than the state-of-the-art distributed algorithms running on 12 machines. Additionally, LIGHT completed all test cases, whereas the existing algorithms fail in some cases due to either running out of time or running out of available hardware resources.
Construction of new substitution boxes using linear fractional transformation and enhanced chaos

Substitution boxes are used in different security techniques and cryptosystems to ensure the secure communication of data. To enhance the randomness and perplexity of data, chaos theory has utmost importance in encryption schemes and multimedia security. In this paper, Substitution boxes are developed by using linear fractional transformation and combination of chaotic systems with the increased chaotic range as compared to their seed maps. The Substitution boxes are assessed by using various analyses which include nonlinearity, strict Avalanche criterion, bit independence criterion, linear and differential approximation probabilities. Majority logic criterion is also performed to evaluate its application in various encryption systems.
Orientation-Aware Deep Neural Network for Real Image Super-Resolution

Recently, Convolutional Neural Network (CNN) based approaches have achieved impressive single image super-resolution (SISR) performance in terms of accuracy and visual effects. It is noted that most SISR methods assume that the low-resolution (LR) images are obtained through bicubic interpolation down-sampling, thus their performance on real-world LR images is limited. In this paper, we proposed a novel orientation-aware deep neural network (OA-DNN) model, which incorporate a number of orientation feature extraction and channel attention modules (OAMs), to achieve good SR performance on real-world LR images captured by a single-lens reflex (DSLR) camera. Orientation-aware features extracted in different directions are adaptively combined through a channel-wise attention mechanism to generate more distinctive features for high-fidelity recovery of image details. Moreover, we reshape the input image into smaller spatial size but deeper depth via an inverse pixel-shuffle operation to accelerate the training/testing speed without sacrificing restoration accuracy. Extensive experimental results indicate that our OA-DNN model achieves a good balance between accuracy and speed. The extended OA-DNN*+ model further increases PSNR index by 0.18 dB compared with our previously submitted version. Codes will be made public after publication.
Detecting Einstein-Podolsky-Rosen steering through entanglement detection

Quantum inseparabilities can be classified into three inequivalent forms: entanglement, Einstein-Podolsky-Rosen (EPR) steering, and Bell's nonlocality. Bell-nonlocal states form a strict subset of EPR steerable states which also form a strict subset of entangled states. Recently, EPR steerable states have been shown to be fundamental resources for one-sided device-independent quantum information processing tasks and, hence, identification of EPR steerable states becomes important from foundational as well as informational theoretic perspectives. In the present paper, we propose a criterion to detect whether a given two-qubit state is EPR steerable. From an arbitrary given two-qubit state, another two-qubit state is constructed in such a way that the given state is EPR steerable if the new constructed state is entangled. Hence, EPR steerability of an arbitrary two-qubit state can be detected by detecting entanglement of the newly constructed state. Apart from providing a distinctive way to detect EPR steering without using any steering inequality, the present paper also provides an alternative way to avoid locality loopholes in EPR steering tests and to reduce the ``complexity cost'' present in experimentally detecting EPR steering. We also generalize our criteria to detect EPR steering of higher dimensional quantum states. Finally, we illustrate our result by using our proposed technique to detect EPR steerability of various families of mixed states.
Cross-domain neural-kernel networks

This paper introduces a novel cross-domain neural-kernel networks architecture for semi-supervised domain adaption problem. The proposed model consists of two stream neural-kernel networks corresponding to the source and target domains which are enriched with a coupling term. Each stream neural-kernel networks follows a combination of neural network layer and an explicit feature map constructed by means of random Fourier features. The introduced coupling term aims at enforcing correlations among the output of the intermediate layers of the two stream networks as well as encouraging the two networks to learn shared representation of the data from both source and target domains. Experimental results are given to illustrate the effectiveness of the proposed approaches on synthetic and real-life datasets.
A New Steganography Method for Embedding Message in JPEG Images

Steganography is widely applied by information security systems. The aim of steganography is to create a secret and secure channel between the sender and receiver. Steganography is basically performed in the different data types, such as image and video. Given the rapid growth of digital systems and imaging tools, the number of images and transmission of secret information is also growing. JPEG images are very popular because of their smaller size that makes them suitable for the transmission. The steganography procedure in an image can usually be performed in two domain, space domain and transform domain such as Fourier. In this preliminary study, a text steganography technique in JPEG images is presented. The text steganography is carried out on the bits with the least significant value in the discrete matrix, thus the embedded message insertion has less impact on the image quality. In the proposed method, two less significant bits of pixels are used to hide the embedded message in the image. In JPEG compression procedure before encoding, the steganography operation is applied on the image after its transformation from time into the frequency space and exactly(or right away) after the discretization of transformed data. The experimental results suggest that our approach has high capacity performance in comparison with the conventional methods.
Diagnosing Sample Ratio Mismatch in Online Controlled Experiments

Accurately learning what delivers value to customers is difficult. Online Controlled Experiments (OCEs), aka A/B tests, are becoming a standard operating procedure in software companies to address this challenge as they can detect small causal changes in user behavior due to product modifications (e.g. new features). However, like any data analysis method, OCEs are sensitive to trustworthiness and data quality issues which, if go unaddressed or unnoticed, may result in making wrong decisions. One of the most useful indicators of a variety of data quality issues is a Sample Ratio Mismatch (SRM) ? the situation when the observed sample ratio in the experiment is different from the expected. Just like fever is a symptom for multiple types of illness, an SRM is a symptom for a variety of data quality issues. While a simple statistical check is used to detect an SRM, correctly identifying the root cause and preventing it from happening in the future is often extremely challenging and time consuming. Ignoring the SRM without knowing the root cause may result in a bad product modification appearing to be good and getting shipped to users, or vice versa. The goal of this paper is to make diagnosing, fixing, and preventing SRMs easier. Based on our experience of running OCEs in four different software companies in over 25 different products used by hundreds of millions of users worldwide, we have derived a taxonomy for different types of SRMs. We share examples, detection guidelines, and best practices for preventing SRMs of each type. We hope that the lessons and practical tips we describe in this paper will speed up SRM investigations and prevent some of them. Ultimately, this should lead to improved decision making based on trustworthy experiment analysis.
HGGC: A hybrid group recommendation model considering group cohesion

In the real world, people organize and participate in many group activities. Group recommendation that finds out preferable items for a group of users is a challenging problem since it is difficult to properly aggregate diverse preferences among the members. Group cohesion denotes proportion of group members who actively participate in various activities relevant to group’s objectives. In a strongly cohesive group, most members actively participate in group decision, while many of group members in a weakly cohesive group are bystanders who just follow the other member’s decision. Since group cohesion is an important factor in group recommendation, the concept of group cohesion needs to be properly reflected to the recommendation model. We present a new approach about group recommendation that appropriately captures group cohesion. A hybrid model that considers content information and rating data simultaneously is also proposed to alleviate a well-known data sparsity problem in group recommendation. We use a heterogeneous information network (HIN) that can contain rich information about entities and relationships from which additional content information can be extracted. Then, this information is properly incorporated to the group recommendation model. Experimental results on the real datasets show that our proposed method outperforms the existing state-of-the-art methods and improves recommendation performances significantly.
Improving the Quality of Explanations with Local Embedding Perturbations

Classifier explanations have been identified as a crucial component of knowledge discovery. Local explanations evaluate the behavior of a classifier in the vicinity of a given instance. A key step in this approach is to generate synthetic neighbors of the given instance. This neighbor generation process is challenging and it has considerable impact on the quality of explanations. To assess quality of generated neighborhoods, we propose a local intrinsic dimensionality (LID) based locality constraint. Based on this, we then propose a new neighborhood generation method. Our method first fits a local embedding/subspace around a given instance using the LID of the test instance as the target dimensionality, then generates neighbors in the local embedding and projects them back to the original space. Experimental results show that our method generates more realistic neighborhoods and consequently better explanations. It can be used in combination with existing local explanation algorithms.
The Stories People Tell About The Home Through IoT Toolkits

Stories on the home materialize in many different ways. Simple design scenarios of more efficient smart homes exist alongside more articulated design fictions narrating complex domestic futures. IoT toolkits can be used in co-design to narrate design stories together with people. However, there is little attention on the stories captured in the co-creation process. This paper presents a framework describing, comparing, and assessing design stories. We illustrate the framework through the comparison of the design stories captured from three divergent IoT toolkits in co-design workshops. Three dimensions characterize the design stories emerging from our inquiry: complexity (resolution and scope), likeliness (conceivability and feasibility), and implications (acceptability and consequentiality). This framework contributes towards understanding which properties of IoT toolkits support the emergence of what kind of design story. Our findings help designers to frame expectations when using IoT toolkits and to conceive IoT toolkits that support underexplored qualities of design stories.